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import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
# See all BART models at https://huggingface.co/models?filter=bart
_lowerCAmelCase = {
"vocab_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
},
"merges_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
},
}
_lowerCAmelCase = {
"facebook/bart-base": 1_024,
"facebook/bart-large": 1_024,
"facebook/bart-large-mnli": 1_024,
"facebook/bart-large-cnn": 1_024,
"facebook/bart-large-xsum": 1_024,
"yjernite/bart_eli5": 1_024,
}
@lru_cache()
def _snake_case ( ):
_UpperCamelCase = (
list(range(ord('''!''' ) , ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' ) , ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' ) , ord('''ÿ''' ) + 1 ) )
)
_UpperCamelCase = bs[:]
_UpperCamelCase = 0
for b in range(2**8 ):
if b not in bs:
bs.append(__snake_case )
cs.append(2**8 + n )
n += 1
_UpperCamelCase = [chr(__snake_case ) for n in cs]
return dict(zip(__snake_case , __snake_case ) )
def _snake_case ( __snake_case ):
_UpperCamelCase = set()
_UpperCamelCase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_UpperCamelCase = char
return pairs
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = VOCAB_FILES_NAMES
UpperCAmelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase = ["input_ids", "attention_mask"]
def __init__( self : Dict , _A : Optional[int] , _A : List[Any] , _A : List[str]="replace" , _A : Optional[int]="<s>" , _A : Optional[int]="</s>" , _A : str="</s>" , _A : Any="<s>" , _A : Tuple="<unk>" , _A : Dict="<pad>" , _A : List[Any]="<mask>" , _A : List[Any]=False , **_A : Tuple , ):
_UpperCamelCase = AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else bos_token
_UpperCamelCase = AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else eos_token
_UpperCamelCase = AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else sep_token
_UpperCamelCase = AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else cls_token
_UpperCamelCase = AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else unk_token
_UpperCamelCase = AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
_UpperCamelCase = AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else mask_token
super().__init__(
errors=_A , bos_token=_A , eos_token=_A , unk_token=_A , sep_token=_A , cls_token=_A , pad_token=_A , mask_token=_A , add_prefix_space=_A , **_A , )
with open(_A , encoding='''utf-8''' ) as vocab_handle:
_UpperCamelCase = json.load(_A )
_UpperCamelCase = {v: k for k, v in self.encoder.items()}
_UpperCamelCase = errors # how to handle errors in decoding
_UpperCamelCase = bytes_to_unicode()
_UpperCamelCase = {v: k for k, v in self.byte_encoder.items()}
with open(_A , encoding='''utf-8''' ) as merges_handle:
_UpperCamelCase = merges_handle.read().split('''\n''' )[1:-1]
_UpperCamelCase = [tuple(merge.split() ) for merge in bpe_merges]
_UpperCamelCase = dict(zip(_A , range(len(_A ) ) ) )
_UpperCamelCase = {}
_UpperCamelCase = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
_UpperCamelCase = re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )
@property
def UpperCamelCase_ ( self : Optional[Any] ):
return len(self.encoder )
def UpperCamelCase_ ( self : List[Any] ):
return dict(self.encoder , **self.added_tokens_encoder )
def UpperCamelCase_ ( self : Tuple , _A : Dict ):
if token in self.cache:
return self.cache[token]
_UpperCamelCase = tuple(_A )
_UpperCamelCase = get_pairs(_A )
if not pairs:
return token
while True:
_UpperCamelCase = min(_A , key=lambda _A : self.bpe_ranks.get(_A , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
_UpperCamelCase , _UpperCamelCase = bigram
_UpperCamelCase = []
_UpperCamelCase = 0
while i < len(_A ):
try:
_UpperCamelCase = word.index(_A , _A )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_UpperCamelCase = j
if word[i] == first and i < len(_A ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_UpperCamelCase = tuple(_A )
_UpperCamelCase = new_word
if len(_A ) == 1:
break
else:
_UpperCamelCase = get_pairs(_A )
_UpperCamelCase = ''' '''.join(_A )
_UpperCamelCase = word
return word
def UpperCamelCase_ ( self : Optional[int] , _A : Tuple ):
_UpperCamelCase = []
for token in re.findall(self.pat , _A ):
_UpperCamelCase = ''''''.join(
self.byte_encoder[b] for b in token.encode('''utf-8''' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_A ).split(''' ''' ) )
return bpe_tokens
def UpperCamelCase_ ( self : Any , _A : List[str] ):
return self.encoder.get(_A , self.encoder.get(self.unk_token ) )
def UpperCamelCase_ ( self : int , _A : Tuple ):
return self.decoder.get(_A )
def UpperCamelCase_ ( self : Dict , _A : List[Any] ):
_UpperCamelCase = ''''''.join(_A )
_UpperCamelCase = bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8''' , errors=self.errors )
return text
def UpperCamelCase_ ( self : Optional[Any] , _A : str , _A : Optional[str] = None ):
if not os.path.isdir(_A ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
_UpperCamelCase = os.path.join(
_A , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
_UpperCamelCase = os.path.join(
_A , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(_A , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=_A , ensure_ascii=_A ) + '''\n''' )
_UpperCamelCase = 0
with open(_A , '''w''' , encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda _A : kv[1] ):
if index != token_index:
logger.warning(
F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
''' Please check that the tokenizer is not corrupted!''' )
_UpperCamelCase = token_index
writer.write(''' '''.join(_A ) + '''\n''' )
index += 1
return vocab_file, merge_file
def UpperCamelCase_ ( self : Optional[int] , _A : List[int] , _A : Optional[List[int]] = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_UpperCamelCase = [self.cls_token_id]
_UpperCamelCase = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCamelCase_ ( self : Dict , _A : List[int] , _A : Optional[List[int]] = None , _A : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_A , token_ids_a=_A , already_has_special_tokens=_A )
if token_ids_a is None:
return [1] + ([0] * len(_A )) + [1]
return [1] + ([0] * len(_A )) + [1, 1] + ([0] * len(_A )) + [1]
def UpperCamelCase_ ( self : Union[str, Any] , _A : List[int] , _A : Optional[List[int]] = None ):
_UpperCamelCase = [self.sep_token_id]
_UpperCamelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def UpperCamelCase_ ( self : Dict , _A : int , _A : List[Any]=False , **_A : List[Any] ):
_UpperCamelCase = kwargs.pop('''add_prefix_space''' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(_A ) > 0 and not text[0].isspace()):
_UpperCamelCase = ''' ''' + text
return (text, kwargs)
| 10 | import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
_lowerCAmelCase = HfApi()
_lowerCAmelCase = {}
# fmt: off
_lowerCAmelCase = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189,
-1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839,
0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557
])
_lowerCAmelCase = torch.tensor([
-2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436,
1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208,
-2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948,
2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365
])
_lowerCAmelCase = torch.tensor([
-0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,
-0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304,
-0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925,
0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943
])
_lowerCAmelCase = torch.tensor([
0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172,
-0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309,
0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805,
-0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505
])
_lowerCAmelCase = torch.tensor([
0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133,
-0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395,
0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559,
-0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386
])
_lowerCAmelCase = torch.tensor([
0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078,
-0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330,
0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683,
-0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431
])
_lowerCAmelCase = torch.tensor([
0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042,
-0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398,
0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574,
-0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390
])
_lowerCAmelCase = torch.tensor([
0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042,
-0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290,
0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746,
-0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473
])
_lowerCAmelCase = torch.tensor([
-1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330,
1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243,
-2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810,
1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251])
_lowerCAmelCase = torch.tensor([
-1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324,
0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181,
-2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259,
1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266
])
_lowerCAmelCase = torch.tensor([
-1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212,
0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027,
-2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131,
1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355
])
_lowerCAmelCase = torch.tensor([
-2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959,
1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351,
-3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341,
3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066
])
_lowerCAmelCase = torch.tensor([
-2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740,
1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398,
-2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395,
2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243
])
_lowerCAmelCase = torch.tensor([
-2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336,
1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908,
-3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560,
3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343
])
_lowerCAmelCase = torch.tensor([
-1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344,
1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391,
-2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439,
1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219
])
# fmt: on
_lowerCAmelCase = api.list_models(filter="diffusers")
for mod in models:
if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
_lowerCAmelCase = "/home/patrick/google_checkpoints/" + mod.modelId.split("/")[-1]
print(f'Started running {mod.modelId}!!!')
if mod.modelId.startswith("CompVis"):
_lowerCAmelCase = UNetaDModel.from_pretrained(local_checkpoint, subfolder="unet")
else:
_lowerCAmelCase = UNetaDModel.from_pretrained(local_checkpoint)
torch.manual_seed(0)
random.seed(0)
_lowerCAmelCase = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
_lowerCAmelCase = torch.tensor([10] * noise.shape[0])
with torch.no_grad():
_lowerCAmelCase = model(noise, time_step).sample
assert torch.allclose(
logits[0, 0, 0, :30], results["_".join("_".join(mod.modelId.split("/")).split("-"))], atol=1E-3
)
print(f'{mod.modelId} has passed successfully!!!')
| 10 | 1 |
import os
def _snake_case ( ):
_UpperCamelCase = os.path.join(os.path.dirname(__snake_case ) , '''num.txt''' )
with open(__snake_case ) as file_hand:
return str(sum(int(__snake_case ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 10 | from typing import List
from .keymap import KEYMAP, get_character
def _snake_case ( __snake_case ):
def decorator(__snake_case ):
_UpperCamelCase = getattr(__snake_case , '''handle_key''' , [] )
handle += [key]
setattr(__snake_case , '''handle_key''' , __snake_case )
return func
return decorator
def _snake_case ( *__snake_case ):
def decorator(__snake_case ):
_UpperCamelCase = getattr(__snake_case , '''handle_key''' , [] )
handle += keys
setattr(__snake_case , '''handle_key''' , __snake_case )
return func
return decorator
class lowerCAmelCase_ ( __lowercase ):
def __new__( cls : Optional[Any] , _A : Optional[Any] , _A : Optional[int] , _A : Union[str, Any] ):
_UpperCamelCase = super().__new__(cls , _A , _A , _A )
if not hasattr(_A , '''key_handler''' ):
setattr(_A , '''key_handler''' , {} )
setattr(_A , '''handle_input''' , KeyHandler.handle_input )
for value in attrs.values():
_UpperCamelCase = getattr(_A , '''handle_key''' , [] )
for key in handled_keys:
_UpperCamelCase = value
return new_cls
@staticmethod
def UpperCamelCase_ ( cls : str ):
_UpperCamelCase = get_character()
if char != KEYMAP["undefined"]:
_UpperCamelCase = ord(_A )
_UpperCamelCase = cls.key_handler.get(_A )
if handler:
_UpperCamelCase = char
return handler(cls )
else:
return None
def _snake_case ( cls ):
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 10 | 1 |
import heapq as hq
import math
from collections.abc import Iterator
class lowerCAmelCase_ :
def __init__( self : Union[str, Any] , _A : Optional[int] ):
_UpperCamelCase = str(id_ )
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = []
_UpperCamelCase = {} # {vertex:distance}
def __lt__( self : Optional[Any] , _A : Optional[Any] ):
return self.key < other.key
def __repr__( self : Optional[int] ):
return self.id
def UpperCamelCase_ ( self : int , _A : List[str] ):
self.neighbors.append(_A )
def UpperCamelCase_ ( self : List[str] , _A : Tuple , _A : int ):
_UpperCamelCase = weight
def _snake_case ( __snake_case , __snake_case , __snake_case , __snake_case ):
# add the neighbors:
graph[a - 1].add_neighbor(graph[b - 1] )
graph[b - 1].add_neighbor(graph[a - 1] )
# add the edges:
graph[a - 1].add_edge(graph[b - 1] , __snake_case )
graph[b - 1].add_edge(graph[a - 1] , __snake_case )
def _snake_case ( __snake_case , __snake_case ):
_UpperCamelCase = []
for u in graph:
_UpperCamelCase = math.inf
_UpperCamelCase = None
_UpperCamelCase = 0
_UpperCamelCase = graph[:]
while q:
_UpperCamelCase = min(__snake_case )
q.remove(__snake_case )
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
_UpperCamelCase = u
_UpperCamelCase = u.edges[v.id]
for i in range(1 , len(__snake_case ) ):
a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
return a
def _snake_case ( __snake_case , __snake_case ):
for u in graph:
_UpperCamelCase = math.inf
_UpperCamelCase = None
_UpperCamelCase = 0
_UpperCamelCase = list(__snake_case )
hq.heapify(__snake_case )
while h:
_UpperCamelCase = hq.heappop(__snake_case )
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
_UpperCamelCase = u
_UpperCamelCase = u.edges[v.id]
hq.heapify(__snake_case )
for i in range(1 , len(__snake_case ) ):
yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)
def _snake_case ( ):
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 10 | import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
require_torch_gpu,
require_torch_or_tf,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
class lowerCAmelCase_ ( unittest.TestCase ):
UpperCAmelCase = MODEL_FOR_CAUSAL_LM_MAPPING
UpperCAmelCase = TF_MODEL_FOR_CAUSAL_LM_MAPPING
@require_torch
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = pipeline(task='''text-generation''' , model='''sshleifer/tiny-ctrl''' , framework='''pt''' )
# Using `do_sample=False` to force deterministic output
_UpperCamelCase = text_generator('''This is a test''' , do_sample=_A )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
''' oscope. FiliFili@@'''
)
}
] , )
_UpperCamelCase = text_generator(['''This is a test''', '''This is a second test'''] )
self.assertEqual(
_A , [
[
{
'''generated_text''': (
'''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
''' oscope. FiliFili@@'''
)
}
],
[
{
'''generated_text''': (
'''This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'''
''' oscope. oscope. FiliFili@@'''
)
}
],
] , )
_UpperCamelCase = text_generator('''This is a test''' , do_sample=_A , num_return_sequences=2 , return_tensors=_A )
self.assertEqual(
_A , [
{'''generated_token_ids''': ANY(_A )},
{'''generated_token_ids''': ANY(_A )},
] , )
_UpperCamelCase = text_generator.model.config.eos_token_id
_UpperCamelCase = '''<pad>'''
_UpperCamelCase = text_generator(
['''This is a test''', '''This is a second test'''] , do_sample=_A , num_return_sequences=2 , batch_size=2 , return_tensors=_A , )
self.assertEqual(
_A , [
[
{'''generated_token_ids''': ANY(_A )},
{'''generated_token_ids''': ANY(_A )},
],
[
{'''generated_token_ids''': ANY(_A )},
{'''generated_token_ids''': ANY(_A )},
],
] , )
@require_tf
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = pipeline(task='''text-generation''' , model='''sshleifer/tiny-ctrl''' , framework='''tf''' )
# Using `do_sample=False` to force deterministic output
_UpperCamelCase = text_generator('''This is a test''' , do_sample=_A )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
''' please,'''
)
}
] , )
_UpperCamelCase = text_generator(['''This is a test''', '''This is a second test'''] , do_sample=_A )
self.assertEqual(
_A , [
[
{
'''generated_text''': (
'''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
''' please,'''
)
}
],
[
{
'''generated_text''': (
'''This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'''
''' Cannes 閲閲Cannes Cannes Cannes 攵 please,'''
)
}
],
] , )
def UpperCamelCase_ ( self : int , _A : str , _A : Union[str, Any] , _A : Any ):
_UpperCamelCase = TextGenerationPipeline(model=_A , tokenizer=_A )
return text_generator, ["This is a test", "Another test"]
def UpperCamelCase_ ( self : Union[str, Any] ):
_UpperCamelCase = '''Hello I believe in'''
_UpperCamelCase = pipeline('''text-generation''' , model='''hf-internal-testing/tiny-random-gpt2''' )
_UpperCamelCase = text_generator(_A )
self.assertEqual(
_A , [{'''generated_text''': '''Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'''}] , )
_UpperCamelCase = text_generator(_A , stop_sequence=''' fe''' )
self.assertEqual(_A , [{'''generated_text''': '''Hello I believe in fe'''}] )
def UpperCamelCase_ ( self : Any , _A : List[Any] , _A : Union[str, Any] ):
_UpperCamelCase = text_generator.model
_UpperCamelCase = text_generator.tokenizer
_UpperCamelCase = text_generator('''This is a test''' )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )
_UpperCamelCase = text_generator('''This is a test''' , return_full_text=_A )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
self.assertNotIn('''This is a test''' , outputs[0]['''generated_text'''] )
_UpperCamelCase = pipeline(task='''text-generation''' , model=_A , tokenizer=_A , return_full_text=_A )
_UpperCamelCase = text_generator('''This is a test''' )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
self.assertNotIn('''This is a test''' , outputs[0]['''generated_text'''] )
_UpperCamelCase = text_generator('''This is a test''' , return_full_text=_A )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )
_UpperCamelCase = text_generator(['''This is great !''', '''Something else'''] , num_return_sequences=2 , do_sample=_A )
self.assertEqual(
_A , [
[{'''generated_text''': ANY(_A )}, {'''generated_text''': ANY(_A )}],
[{'''generated_text''': ANY(_A )}, {'''generated_text''': ANY(_A )}],
] , )
if text_generator.tokenizer.pad_token is not None:
_UpperCamelCase = text_generator(
['''This is great !''', '''Something else'''] , num_return_sequences=2 , batch_size=2 , do_sample=_A )
self.assertEqual(
_A , [
[{'''generated_text''': ANY(_A )}, {'''generated_text''': ANY(_A )}],
[{'''generated_text''': ANY(_A )}, {'''generated_text''': ANY(_A )}],
] , )
with self.assertRaises(_A ):
_UpperCamelCase = text_generator('''test''' , return_full_text=_A , return_text=_A )
with self.assertRaises(_A ):
_UpperCamelCase = text_generator('''test''' , return_full_text=_A , return_tensors=_A )
with self.assertRaises(_A ):
_UpperCamelCase = text_generator('''test''' , return_text=_A , return_tensors=_A )
# Empty prompt is slighly special
# it requires BOS token to exist.
# Special case for Pegasus which will always append EOS so will
# work even without BOS.
if (
text_generator.tokenizer.bos_token_id is not None
or "Pegasus" in tokenizer.__class__.__name__
or "Git" in model.__class__.__name__
):
_UpperCamelCase = text_generator('''''' )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
else:
with self.assertRaises((ValueError, AssertionError) ):
_UpperCamelCase = text_generator('''''' )
if text_generator.framework == "tf":
# TF generation does not support max_new_tokens, and it's impossible
# to control long generation with only max_length without
# fancy calculation, dismissing tests for now.
return
# We don't care about infinite range models.
# They already work.
# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
_UpperCamelCase = ['''RwkvForCausalLM''', '''XGLMForCausalLM''', '''GPTNeoXForCausalLM''']
if (
tokenizer.model_max_length < 1_0000
and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
):
# Handling of large generations
with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError) ):
text_generator('''This is a test''' * 500 , max_new_tokens=20 )
_UpperCamelCase = text_generator('''This is a test''' * 500 , handle_long_generation='''hole''' , max_new_tokens=20 )
# Hole strategy cannot work
with self.assertRaises(_A ):
text_generator(
'''This is a test''' * 500 , handle_long_generation='''hole''' , max_new_tokens=tokenizer.model_max_length + 10 , )
@require_torch
@require_accelerate
@require_torch_gpu
def UpperCamelCase_ ( self : Optional[int] ):
import torch
# Classic `model_kwargs`
_UpperCamelCase = pipeline(
model='''hf-internal-testing/tiny-random-bloom''' , model_kwargs={'''device_map''': '''auto''', '''torch_dtype''': torch.bfloataa} , )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
_UpperCamelCase = pipe('''This is a test''' )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
_UpperCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' , torch_dtype=torch.bfloataa )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
_UpperCamelCase = pipe('''This is a test''' )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
_UpperCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.floataa )
_UpperCamelCase = pipe('''This is a test''' )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
@require_torch
@require_torch_gpu
def UpperCamelCase_ ( self : Union[str, Any] ):
import torch
_UpperCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device=0 , torch_dtype=torch.floataa )
pipe('''This is a test''' )
@require_torch
@require_accelerate
@require_torch_gpu
def UpperCamelCase_ ( self : Optional[int] ):
import torch
_UpperCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' , torch_dtype=torch.floataa )
pipe('''This is a test''' , do_sample=_A , top_p=0.5 )
def UpperCamelCase_ ( self : Tuple ):
_UpperCamelCase = '''Hello world'''
_UpperCamelCase = pipeline('''text-generation''' , model='''hf-internal-testing/tiny-random-gpt2''' )
if text_generator.model.framework == "tf":
_UpperCamelCase = logging.get_logger('''transformers.generation.tf_utils''' )
else:
_UpperCamelCase = logging.get_logger('''transformers.generation.utils''' )
_UpperCamelCase = '''Both `max_new_tokens`''' # The beggining of the message to be checked in this test
# Both are set by the user -> log warning
with CaptureLogger(_A ) as cl:
_UpperCamelCase = text_generator(_A , max_length=10 , max_new_tokens=1 )
self.assertIn(_A , cl.out )
# The user only sets one -> no warning
with CaptureLogger(_A ) as cl:
_UpperCamelCase = text_generator(_A , max_new_tokens=1 )
self.assertNotIn(_A , cl.out )
with CaptureLogger(_A ) as cl:
_UpperCamelCase = text_generator(_A , max_length=10 )
self.assertNotIn(_A , cl.out )
| 10 | 1 |
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
_lowerCAmelCase = "__DUMMY_TRANSFORMERS_USER__"
_lowerCAmelCase = "Dummy User"
_lowerCAmelCase = "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"
_lowerCAmelCase = "https://hub-ci.huggingface.co"
_lowerCAmelCase = CI_HUB_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
_lowerCAmelCase = CI_HUB_ENDPOINT + "/{repo_id}/resolve/{revision}/{filename}"
_lowerCAmelCase = Path("~/.huggingface/hub_ci_token").expanduser()
@pytest.fixture
def _snake_case ( __snake_case ):
monkeypatch.setattr(
'''huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE''' , __snake_case )
@pytest.fixture
def _snake_case ( __snake_case ):
monkeypatch.setattr('''datasets.config.HF_ENDPOINT''' , __snake_case )
monkeypatch.setattr('''datasets.config.HUB_DATASETS_URL''' , __snake_case )
@pytest.fixture
def _snake_case ( __snake_case ):
monkeypatch.setattr('''huggingface_hub.hf_api.HfFolder.path_token''' , __snake_case )
@pytest.fixture
def _snake_case ( __snake_case , __snake_case ):
HfFolder.save_token(__snake_case )
yield
HfFolder.delete_token()
@pytest.fixture(scope='''session''' )
def _snake_case ( ):
return HfApi(endpoint=__snake_case )
@pytest.fixture(scope='''session''' )
def _snake_case ( __snake_case ):
_UpperCamelCase = HfFolder.get_token()
HfFolder.save_token(__snake_case )
yield CI_HUB_USER_TOKEN
if previous_token is not None:
HfFolder.save_token(__snake_case )
@pytest.fixture
def _snake_case ( __snake_case ):
def _cleanup_repo(__snake_case ):
hf_api.delete_repo(__snake_case , token=__snake_case , repo_type='''dataset''' )
return _cleanup_repo
@pytest.fixture
def _snake_case ( __snake_case ):
@contextmanager
def _temporary_repo(__snake_case ):
try:
yield repo_id
finally:
cleanup_repo(__snake_case )
return _temporary_repo
@pytest.fixture(scope='''session''' )
def _snake_case ( __snake_case , __snake_case , __snake_case ):
_UpperCamelCase = f"""repo_txt_data-{int(time.time() * 10E3 )}"""
_UpperCamelCase = f"""{CI_HUB_USER}/{repo_name}"""
hf_api.create_repo(__snake_case , token=__snake_case , repo_type='''dataset''' , private=__snake_case )
hf_api.upload_file(
token=__snake_case , path_or_fileobj=str(__snake_case ) , path_in_repo='''data/text_data.txt''' , repo_id=__snake_case , repo_type='''dataset''' , )
yield repo_id
try:
hf_api.delete_repo(__snake_case , token=__snake_case , repo_type='''dataset''' )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def _snake_case ( __snake_case , __snake_case , __snake_case ):
return hf_private_dataset_repo_txt_data_
@pytest.fixture(scope='''session''' )
def _snake_case ( __snake_case , __snake_case , __snake_case ):
_UpperCamelCase = f"""repo_zipped_txt_data-{int(time.time() * 10E3 )}"""
_UpperCamelCase = f"""{CI_HUB_USER}/{repo_name}"""
hf_api.create_repo(__snake_case , token=__snake_case , repo_type='''dataset''' , private=__snake_case )
hf_api.upload_file(
token=__snake_case , path_or_fileobj=str(__snake_case ) , path_in_repo='''data.zip''' , repo_id=__snake_case , repo_type='''dataset''' , )
yield repo_id
try:
hf_api.delete_repo(__snake_case , token=__snake_case , repo_type='''dataset''' )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def _snake_case ( __snake_case , __snake_case , __snake_case ):
return hf_private_dataset_repo_zipped_txt_data_
@pytest.fixture(scope='''session''' )
def _snake_case ( __snake_case , __snake_case , __snake_case ):
_UpperCamelCase = f"""repo_zipped_img_data-{int(time.time() * 10E3 )}"""
_UpperCamelCase = f"""{CI_HUB_USER}/{repo_name}"""
hf_api.create_repo(__snake_case , token=__snake_case , repo_type='''dataset''' , private=__snake_case )
hf_api.upload_file(
token=__snake_case , path_or_fileobj=str(__snake_case ) , path_in_repo='''data.zip''' , repo_id=__snake_case , repo_type='''dataset''' , )
yield repo_id
try:
hf_api.delete_repo(__snake_case , token=__snake_case , repo_type='''dataset''' )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def _snake_case ( __snake_case , __snake_case , __snake_case ):
return hf_private_dataset_repo_zipped_img_data_
| 10 | def _snake_case ( __snake_case = 100 ):
_UpperCamelCase = (n * (n + 1) // 2) ** 2
_UpperCamelCase = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f'{solution() = }')
| 10 | 1 |
def _snake_case ( __snake_case , __snake_case ):
return number | (1 << position)
def _snake_case ( __snake_case , __snake_case ):
return number & ~(1 << position)
def _snake_case ( __snake_case , __snake_case ):
return number ^ (1 << position)
def _snake_case ( __snake_case , __snake_case ):
return ((number >> position) & 1) == 1
def _snake_case ( __snake_case , __snake_case ):
return int((number & (1 << position)) != 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 10 | import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
_lowerCAmelCase = logging.get_logger(__name__)
def _snake_case ( __snake_case , __snake_case , __snake_case , __snake_case ):
def constraint_to_multiple_of(__snake_case , __snake_case , __snake_case=0 , __snake_case=None ):
_UpperCamelCase = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
_UpperCamelCase = math.floor(val / multiple ) * multiple
if x < min_val:
_UpperCamelCase = math.ceil(val / multiple ) * multiple
return x
_UpperCamelCase = (output_size, output_size) if isinstance(__snake_case , __snake_case ) else output_size
_UpperCamelCase , _UpperCamelCase = get_image_size(__snake_case )
_UpperCamelCase , _UpperCamelCase = output_size
# determine new height and width
_UpperCamelCase = output_height / input_height
_UpperCamelCase = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
_UpperCamelCase = scale_width
else:
# fit height
_UpperCamelCase = scale_height
_UpperCamelCase = constraint_to_multiple_of(scale_height * input_height , multiple=__snake_case )
_UpperCamelCase = constraint_to_multiple_of(scale_width * input_width , multiple=__snake_case )
return (new_height, new_width)
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = ["pixel_values"]
def __init__( self : List[Any] , _A : bool = True , _A : Dict[str, int] = None , _A : PILImageResampling = PILImageResampling.BILINEAR , _A : bool = False , _A : int = 1 , _A : bool = True , _A : Union[int, float] = 1 / 255 , _A : bool = True , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[float, List[float]]] = None , **_A : List[str] , ):
super().__init__(**_A )
_UpperCamelCase = size if size is not None else {'''height''': 384, '''width''': 384}
_UpperCamelCase = get_size_dict(_A )
_UpperCamelCase = do_resize
_UpperCamelCase = size
_UpperCamelCase = keep_aspect_ratio
_UpperCamelCase = ensure_multiple_of
_UpperCamelCase = resample
_UpperCamelCase = do_rescale
_UpperCamelCase = rescale_factor
_UpperCamelCase = do_normalize
_UpperCamelCase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_UpperCamelCase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def UpperCamelCase_ ( self : List[str] , _A : np.ndarray , _A : Dict[str, int] , _A : bool = False , _A : int = 1 , _A : PILImageResampling = PILImageResampling.BICUBIC , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Any , ):
_UpperCamelCase = get_size_dict(_A )
if "height" not in size or "width" not in size:
raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
_UpperCamelCase = get_resize_output_image_size(
_A , output_size=(size['''height'''], size['''width''']) , keep_aspect_ratio=_A , multiple=_A , )
return resize(_A , size=_A , resample=_A , data_format=_A , **_A )
def UpperCamelCase_ ( self : str , _A : np.ndarray , _A : Union[int, float] , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Any , ):
return rescale(_A , scale=_A , data_format=_A , **_A )
def UpperCamelCase_ ( self : int , _A : np.ndarray , _A : Union[float, List[float]] , _A : Union[float, List[float]] , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Any , ):
return normalize(_A , mean=_A , std=_A , data_format=_A , **_A )
def UpperCamelCase_ ( self : Optional[int] , _A : ImageInput , _A : bool = None , _A : int = None , _A : bool = None , _A : int = None , _A : PILImageResampling = None , _A : bool = None , _A : float = None , _A : bool = None , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[str, TensorType]] = None , _A : ChannelDimension = ChannelDimension.FIRST , **_A : str , ):
_UpperCamelCase = do_resize if do_resize is not None else self.do_resize
_UpperCamelCase = size if size is not None else self.size
_UpperCamelCase = get_size_dict(_A )
_UpperCamelCase = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
_UpperCamelCase = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
_UpperCamelCase = resample if resample is not None else self.resample
_UpperCamelCase = do_rescale if do_rescale is not None else self.do_rescale
_UpperCamelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
_UpperCamelCase = do_normalize if do_normalize is not None else self.do_normalize
_UpperCamelCase = image_mean if image_mean is not None else self.image_mean
_UpperCamelCase = image_std if image_std is not None else self.image_std
_UpperCamelCase = make_list_of_images(_A )
if not valid_images(_A ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None or resample is None:
raise ValueError('''Size and resample must be specified if do_resize is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
_UpperCamelCase = [to_numpy_array(_A ) for image in images]
if do_resize:
_UpperCamelCase = [self.resize(image=_A , size=_A , resample=_A ) for image in images]
if do_rescale:
_UpperCamelCase = [self.rescale(image=_A , scale=_A ) for image in images]
if do_normalize:
_UpperCamelCase = [self.normalize(image=_A , mean=_A , std=_A ) for image in images]
_UpperCamelCase = [to_channel_dimension_format(_A , _A ) for image in images]
_UpperCamelCase = {'''pixel_values''': images}
return BatchFeature(data=_A , tensor_type=_A )
def UpperCamelCase_ ( self : Any , _A : Any , _A : List[Tuple] = None ):
_UpperCamelCase = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(_A ) != len(_A ):
raise ValueError(
'''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )
if is_torch_tensor(_A ):
_UpperCamelCase = target_sizes.numpy()
_UpperCamelCase = []
for idx in range(len(_A ) ):
_UpperCamelCase = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='''bilinear''' , align_corners=_A )
_UpperCamelCase = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(_A )
else:
_UpperCamelCase = logits.argmax(dim=1 )
_UpperCamelCase = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 10 | 1 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class lowerCAmelCase_ ( TensorFormatter[Mapping, "torch.Tensor", Mapping] ):
def __init__( self : Any , _A : List[str]=None , **_A : Optional[int] ):
super().__init__(features=_A )
_UpperCamelCase = torch_tensor_kwargs
import torch # noqa import torch at initialization
def UpperCamelCase_ ( self : List[Any] , _A : int ):
import torch
if isinstance(_A , _A ) and column:
if all(
isinstance(_A , torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
for x in column ):
return torch.stack(_A )
return column
def UpperCamelCase_ ( self : Optional[int] , _A : str ):
import torch
if isinstance(_A , (str, bytes, type(_A )) ):
return value
elif isinstance(_A , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
_UpperCamelCase = {}
if isinstance(_A , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
_UpperCamelCase = {'''dtype''': torch.intaa}
elif isinstance(_A , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
_UpperCamelCase = {'''dtype''': torch.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(_A , PIL.Image.Image ):
_UpperCamelCase = np.asarray(_A )
return torch.tensor(_A , **{**default_dtype, **self.torch_tensor_kwargs} )
def UpperCamelCase_ ( self : List[Any] , _A : List[str] ):
import torch
# support for torch, tf, jax etc.
if hasattr(_A , '''__array__''' ) and not isinstance(_A , torch.Tensor ):
_UpperCamelCase = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(_A , np.ndarray ):
if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(_A ) for substruct in data_struct] )
elif isinstance(_A , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(_A ) for substruct in data_struct] )
return self._tensorize(_A )
def UpperCamelCase_ ( self : Optional[int] , _A : dict ):
return map_nested(self._recursive_tensorize , _A , map_list=_A )
def UpperCamelCase_ ( self : Optional[int] , _A : pa.Table ):
_UpperCamelCase = self.numpy_arrow_extractor().extract_row(_A )
_UpperCamelCase = self.python_features_decoder.decode_row(_A )
return self.recursive_tensorize(_A )
def UpperCamelCase_ ( self : str , _A : pa.Table ):
_UpperCamelCase = self.numpy_arrow_extractor().extract_column(_A )
_UpperCamelCase = self.python_features_decoder.decode_column(_A , pa_table.column_names[0] )
_UpperCamelCase = self.recursive_tensorize(_A )
_UpperCamelCase = self._consolidate(_A )
return column
def UpperCamelCase_ ( self : Optional[int] , _A : pa.Table ):
_UpperCamelCase = self.numpy_arrow_extractor().extract_batch(_A )
_UpperCamelCase = self.python_features_decoder.decode_batch(_A )
_UpperCamelCase = self.recursive_tensorize(_A )
for column_name in batch:
_UpperCamelCase = self._consolidate(batch[column_name] )
return batch
| 10 | import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_lowerCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_lowerCAmelCase = " \"\"\"\n Output class for the scheduler's step function output.\n\n Args:\n prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.FloatTensor\n pred_original_sample: Optional[torch.FloatTensor] = None\n"
class lowerCAmelCase_ ( unittest.TestCase ):
def UpperCamelCase_ ( self : List[Any] ):
_UpperCamelCase = tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir , '''schedulers/''' ) )
_UpperCamelCase = self.diffusers_dir
shutil.copy(
os.path.join(_A , '''src/diffusers/schedulers/scheduling_ddpm.py''' ) , os.path.join(self.diffusers_dir , '''schedulers/scheduling_ddpm.py''' ) , )
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = '''src/diffusers'''
shutil.rmtree(self.diffusers_dir )
def UpperCamelCase_ ( self : str , _A : List[str] , _A : Optional[Any] , _A : List[str] , _A : Optional[int]=None ):
_UpperCamelCase = comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
if overwrite_result is not None:
_UpperCamelCase = comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
_UpperCamelCase = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 )
_UpperCamelCase = black.format_str(_A , mode=_A )
_UpperCamelCase = os.path.join(self.diffusers_dir , '''new_code.py''' )
with open(_A , '''w''' , newline='''\n''' ) as f:
f.write(_A )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(_A ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=_A )
with open(_A , '''r''' ) as f:
self.assertTrue(f.read() , _A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput''' )
self.assertEqual(_A , _A )
def UpperCamelCase_ ( self : List[str] ):
# Base copy consistency
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' , '''DDPMSchedulerOutput''' , REFERENCE_CODE + '''\n''' , )
# With no empty line at the end
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' , '''DDPMSchedulerOutput''' , _A , )
# Copy consistency with rename
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' , '''TestSchedulerOutput''' , re.sub('''DDPM''' , '''Test''' , _A ) , )
# Copy consistency with a really long name
_UpperCamelCase = '''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
self.check_copy_consistency(
F"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}""" , F"""{long_class_name}SchedulerOutput""" , re.sub('''Bert''' , _A , _A ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' , '''TestSchedulerOutput''' , _A , overwrite_result=re.sub('''DDPM''' , '''Test''' , _A ) , )
| 10 | 1 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case ( __snake_case , __snake_case , __snake_case , __snake_case="attention" ):
_UpperCamelCase = params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
_UpperCamelCase = params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
_UpperCamelCase = params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
_UpperCamelCase = params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
return k, o, q, v
def _snake_case ( __snake_case , __snake_case , __snake_case , __snake_case=False ):
if split_mlp_wi:
_UpperCamelCase = params[f"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
_UpperCamelCase = params[f"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
_UpperCamelCase = (wi_a, wi_a)
else:
_UpperCamelCase = params[f"""{prefix}/layers_{i}/mlp/wi/kernel"""]
_UpperCamelCase = params[f"""{prefix}/layers_{i}/mlp/wo/kernel"""]
return wi, wo
def _snake_case ( __snake_case , __snake_case , __snake_case , __snake_case ):
return params[f"""{prefix}/layers_{i}/{layer_name}/scale"""]
def _snake_case ( __snake_case , *, __snake_case , __snake_case ):
_UpperCamelCase = traverse_util.flatten_dict(variables['''target'''] )
_UpperCamelCase = {'''/'''.join(__snake_case ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
_UpperCamelCase = '''encoder/layers_0/mlp/wi_0/kernel''' in old
print('''Split MLP:''' , __snake_case )
_UpperCamelCase = collections.OrderedDict()
# Shared embeddings.
_UpperCamelCase = old['''token_embedder/embedding''']
# Encoder.
for i in range(__snake_case ):
# Block i, layer 0 (Self Attention).
_UpperCamelCase = tax_layer_norm_lookup(__snake_case , __snake_case , '''encoder''' , '''pre_attention_layer_norm''' )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = tax_attention_lookup(__snake_case , __snake_case , '''encoder''' , '''attention''' )
_UpperCamelCase = layer_norm
_UpperCamelCase = k.T
_UpperCamelCase = o.T
_UpperCamelCase = q.T
_UpperCamelCase = v.T
# Block i, layer 1 (MLP).
_UpperCamelCase = tax_layer_norm_lookup(__snake_case , __snake_case , '''encoder''' , '''pre_mlp_layer_norm''' )
_UpperCamelCase , _UpperCamelCase = tax_mlp_lookup(__snake_case , __snake_case , '''encoder''' , __snake_case )
_UpperCamelCase = layer_norm
if split_mlp_wi:
_UpperCamelCase = wi[0].T
_UpperCamelCase = wi[1].T
else:
_UpperCamelCase = wi.T
_UpperCamelCase = wo.T
_UpperCamelCase = old[
'''encoder/relpos_bias/rel_embedding'''
].T
_UpperCamelCase = old['''encoder/encoder_norm/scale''']
if not is_encoder_only:
# Decoder.
for i in range(__snake_case ):
# Block i, layer 0 (Self Attention).
_UpperCamelCase = tax_layer_norm_lookup(__snake_case , __snake_case , '''decoder''' , '''pre_self_attention_layer_norm''' )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = tax_attention_lookup(__snake_case , __snake_case , '''decoder''' , '''self_attention''' )
_UpperCamelCase = layer_norm
_UpperCamelCase = k.T
_UpperCamelCase = o.T
_UpperCamelCase = q.T
_UpperCamelCase = v.T
# Block i, layer 1 (Cross Attention).
_UpperCamelCase = tax_layer_norm_lookup(__snake_case , __snake_case , '''decoder''' , '''pre_cross_attention_layer_norm''' )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = tax_attention_lookup(__snake_case , __snake_case , '''decoder''' , '''encoder_decoder_attention''' )
_UpperCamelCase = layer_norm
_UpperCamelCase = k.T
_UpperCamelCase = o.T
_UpperCamelCase = q.T
_UpperCamelCase = v.T
# Block i, layer 2 (MLP).
_UpperCamelCase = tax_layer_norm_lookup(__snake_case , __snake_case , '''decoder''' , '''pre_mlp_layer_norm''' )
_UpperCamelCase , _UpperCamelCase = tax_mlp_lookup(__snake_case , __snake_case , '''decoder''' , __snake_case )
_UpperCamelCase = layer_norm
if split_mlp_wi:
_UpperCamelCase = wi[0].T
_UpperCamelCase = wi[1].T
else:
_UpperCamelCase = wi.T
_UpperCamelCase = wo.T
_UpperCamelCase = old['''decoder/decoder_norm/scale''']
_UpperCamelCase = old[
'''decoder/relpos_bias/rel_embedding'''
].T
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
_UpperCamelCase = old['''decoder/logits_dense/kernel'''].T
return new
def _snake_case ( __snake_case , __snake_case ):
_UpperCamelCase = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
_UpperCamelCase = state_dict['''shared.weight''']
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
_UpperCamelCase = state_dict['''shared.weight''']
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print('''Using shared word embeddings as lm_head.''' )
_UpperCamelCase = state_dict['''shared.weight''']
return state_dict
def _snake_case ( __snake_case , __snake_case , __snake_case , __snake_case ):
_UpperCamelCase = checkpoints.load_tax_checkpoint(__snake_case )
_UpperCamelCase = convert_tax_to_pytorch(__snake_case , num_layers=config.num_layers , is_encoder_only=__snake_case )
_UpperCamelCase = make_state_dict(__snake_case , __snake_case )
model.load_state_dict(__snake_case , strict=__snake_case )
def _snake_case ( __snake_case , __snake_case , __snake_case , __snake_case = False ):
_UpperCamelCase = TaConfig.from_json_file(__snake_case )
print(f"""Building PyTorch model from configuration: {config}""" )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
_UpperCamelCase = TaEncoderModel(__snake_case )
else:
_UpperCamelCase = TaForConditionalGeneration(__snake_case )
# Load weights from tf checkpoint
load_tax_weights_in_ta(__snake_case , __snake_case , __snake_case , __snake_case )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(__snake_case )
# Verify that we can load the checkpoint.
model.from_pretrained(__snake_case )
print('''Done''' )
if __name__ == "__main__":
_lowerCAmelCase = argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
# Required parameters
parser.add_argument(
"--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
)
_lowerCAmelCase = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
)
| 10 | import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaProcessor,
is_apex_available,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
_lowerCAmelCase = True
from torch.cuda.amp import autocast
_lowerCAmelCase = logging.getLogger(__name__)
def _snake_case ( __snake_case=None , __snake_case=None ):
return field(default_factory=lambda: default , metadata=__snake_case )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
UpperCAmelCase = field(
default=0.1, metadata={"help": "The dropout ratio for the attention probabilities."} )
UpperCAmelCase = field(
default=0.1, metadata={"help": "The dropout ratio for activations inside the fully connected layer."} )
UpperCAmelCase = field(
default=0.1, metadata={
"help": "The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."
}, )
UpperCAmelCase = field(
default=0.1, metadata={"help": "The dropout probabilitiy for all 1D convolutional layers in feature extractor."}, )
UpperCAmelCase = field(
default=0.0_5, metadata={
"help": (
"Propability of each feature vector along the time axis to be chosen as the start of the vector"
"span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"
"vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."
)
}, )
UpperCAmelCase = field(default=0.0, metadata={"help": "The LayerDrop probability."} )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
UpperCAmelCase = field(
default="train+validation", metadata={
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "The number of processes to use for the preprocessing."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
}, )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of validation examples to this "
"value if set."
)
}, )
UpperCAmelCase = list_field(
default=[",", "?", ".", "!", "-", ";", ":", "\"\"", "%", "'", "\"", "�"], metadata={"help": "A list of characters to remove from the transcripts."}, )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = 42
UpperCAmelCase = True
UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = None
def __call__( self : Union[str, Any] , _A : List[Dict[str, Union[List[int], torch.Tensor]]] ):
# split inputs and labels since they have to be of different lenghts and need
# different padding methods
_UpperCamelCase = [{'''input_values''': feature['''input_values''']} for feature in features]
_UpperCamelCase = [{'''input_ids''': feature['''labels''']} for feature in features]
_UpperCamelCase = self.processor.pad(
_A , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' , )
_UpperCamelCase = self.processor.pad(
labels=_A , padding=self.padding , max_length=self.max_length_labels , pad_to_multiple_of=self.pad_to_multiple_of_labels , return_tensors='''pt''' , )
# replace padding with -100 to ignore loss correctly
_UpperCamelCase = labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1 ) , -100 )
_UpperCamelCase = labels
return batch
class lowerCAmelCase_ ( __lowercase ):
def UpperCamelCase_ ( self : Dict , _A : nn.Module , _A : Dict[str, Union[torch.Tensor, Any]] ):
model.train()
_UpperCamelCase = self._prepare_inputs(_A )
if self.use_amp:
with autocast():
_UpperCamelCase = self.compute_loss(_A , _A )
else:
_UpperCamelCase = self.compute_loss(_A , _A )
if self.args.n_gpu > 1:
if model.module.config.ctc_loss_reduction == "mean":
_UpperCamelCase = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
_UpperCamelCase = loss.sum() / (inputs['''labels'''] >= 0).sum()
else:
raise ValueError(F"""{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']""" )
if self.args.gradient_accumulation_steps > 1:
_UpperCamelCase = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(_A ).backward()
elif self.use_apex:
with amp.scale_loss(_A , self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(_A )
else:
loss.backward()
return loss.detach()
def _snake_case ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_UpperCamelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
_UpperCamelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCamelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'''Use --overwrite_output_dir to overcome.''' )
elif last_checkpoint is not None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info('''Training/evaluation parameters %s''' , __snake_case )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets:
_UpperCamelCase = datasets.load_dataset(
'''common_voice''' , data_args.dataset_config_name , split=data_args.train_split_name )
_UpperCamelCase = datasets.load_dataset('''common_voice''' , data_args.dataset_config_name , split='''test''' )
# Create and save tokenizer
_UpperCamelCase = f"""[{"".join(data_args.chars_to_ignore )}]"""
def remove_special_characters(__snake_case ):
_UpperCamelCase = re.sub(__snake_case , '''''' , batch['''sentence'''] ).lower() + ''' '''
return batch
_UpperCamelCase = train_dataset.map(__snake_case , remove_columns=['''sentence'''] )
_UpperCamelCase = eval_dataset.map(__snake_case , remove_columns=['''sentence'''] )
def extract_all_chars(__snake_case ):
_UpperCamelCase = ''' '''.join(batch['''text'''] )
_UpperCamelCase = list(set(__snake_case ) )
return {"vocab": [vocab], "all_text": [all_text]}
_UpperCamelCase = train_dataset.map(
__snake_case , batched=__snake_case , batch_size=-1 , keep_in_memory=__snake_case , remove_columns=train_dataset.column_names , )
_UpperCamelCase = train_dataset.map(
__snake_case , batched=__snake_case , batch_size=-1 , keep_in_memory=__snake_case , remove_columns=eval_dataset.column_names , )
_UpperCamelCase = list(set(vocab_train['''vocab'''][0] ) | set(vocab_test['''vocab'''][0] ) )
_UpperCamelCase = {v: k for k, v in enumerate(__snake_case )}
_UpperCamelCase = vocab_dict[''' ''']
del vocab_dict[" "]
_UpperCamelCase = len(__snake_case )
_UpperCamelCase = len(__snake_case )
with open('''vocab.json''' , '''w''' ) as vocab_file:
json.dump(__snake_case , __snake_case )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCamelCase = WavaVecaCTCTokenizer(
'''vocab.json''' , unk_token='''[UNK]''' , pad_token='''[PAD]''' , word_delimiter_token='''|''' , )
_UpperCamelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0.0 , do_normalize=__snake_case , return_attention_mask=__snake_case )
_UpperCamelCase = WavaVecaProcessor(feature_extractor=__snake_case , tokenizer=__snake_case )
_UpperCamelCase = WavaVecaForCTC.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , activation_dropout=model_args.activation_dropout , attention_dropout=model_args.attention_dropout , hidden_dropout=model_args.hidden_dropout , feat_proj_dropout=model_args.feat_proj_dropout , mask_time_prob=model_args.mask_time_prob , gradient_checkpointing=training_args.gradient_checkpointing , layerdrop=model_args.layerdrop , ctc_loss_reduction='''mean''' , pad_token_id=processor.tokenizer.pad_token_id , vocab_size=len(processor.tokenizer ) , )
if data_args.max_train_samples is not None:
_UpperCamelCase = min(len(__snake_case ) , data_args.max_train_samples )
_UpperCamelCase = train_dataset.select(range(__snake_case ) )
if data_args.max_val_samples is not None:
_UpperCamelCase = eval_dataset.select(range(data_args.max_val_samples ) )
_UpperCamelCase = torchaudio.transforms.Resample(48000 , 16000 )
# Preprocessing the datasets.
# We need to read the aduio files as arrays and tokenize the targets.
def speech_file_to_array_fn(__snake_case ):
_UpperCamelCase , _UpperCamelCase = torchaudio.load(batch['''path'''] )
_UpperCamelCase = resampler(__snake_case ).squeeze().numpy()
_UpperCamelCase = 16000
_UpperCamelCase = batch['''text''']
return batch
_UpperCamelCase = train_dataset.map(
__snake_case , remove_columns=train_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
_UpperCamelCase = eval_dataset.map(
__snake_case , remove_columns=eval_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
def prepare_dataset(__snake_case ):
# check that all files have the correct sampling rate
assert (
len(set(batch['''sampling_rate'''] ) ) == 1
), f"""Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."""
_UpperCamelCase = processor(
audio=batch['''speech'''] , text=batch['''target_text'''] , sampling_rate=batch['''sampling_rate'''][0] )
batch.update(__snake_case )
return batch
_UpperCamelCase = train_dataset.map(
__snake_case , remove_columns=train_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=__snake_case , num_proc=data_args.preprocessing_num_workers , )
_UpperCamelCase = eval_dataset.map(
__snake_case , remove_columns=eval_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=__snake_case , num_proc=data_args.preprocessing_num_workers , )
# Metric
_UpperCamelCase = datasets.load_metric('''wer''' )
def compute_metrics(__snake_case ):
_UpperCamelCase = pred.predictions
_UpperCamelCase = np.argmax(__snake_case , axis=-1 )
_UpperCamelCase = processor.tokenizer.pad_token_id
_UpperCamelCase = processor.batch_decode(__snake_case )
# we do not want to group tokens when computing the metrics
_UpperCamelCase = processor.batch_decode(pred.label_ids , group_tokens=__snake_case )
_UpperCamelCase = wer_metric.compute(predictions=__snake_case , references=__snake_case )
return {"wer": wer}
if model_args.freeze_feature_extractor:
model.freeze_feature_extractor()
# Data collator
_UpperCamelCase = DataCollatorCTCWithPadding(processor=__snake_case , padding=__snake_case )
# Initialize our Trainer
_UpperCamelCase = CTCTrainer(
model=__snake_case , data_collator=__snake_case , args=__snake_case , compute_metrics=__snake_case , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=processor.feature_extractor , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
_UpperCamelCase = last_checkpoint
elif os.path.isdir(model_args.model_name_or_path ):
_UpperCamelCase = model_args.model_name_or_path
else:
_UpperCamelCase = None
# Save the feature_extractor and the tokenizer
if is_main_process(training_args.local_rank ):
processor.save_pretrained(training_args.output_dir )
_UpperCamelCase = trainer.train(resume_from_checkpoint=__snake_case )
trainer.save_model()
_UpperCamelCase = train_result.metrics
_UpperCamelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__snake_case )
)
_UpperCamelCase = min(__snake_case , len(__snake_case ) )
trainer.log_metrics('''train''' , __snake_case )
trainer.save_metrics('''train''' , __snake_case )
trainer.save_state()
# Evaluation
_UpperCamelCase = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
_UpperCamelCase = trainer.evaluate()
_UpperCamelCase = data_args.max_val_samples if data_args.max_val_samples is not None else len(__snake_case )
_UpperCamelCase = min(__snake_case , len(__snake_case ) )
trainer.log_metrics('''eval''' , __snake_case )
trainer.save_metrics('''eval''' , __snake_case )
return results
if __name__ == "__main__":
main()
| 10 | 1 |
import math
class lowerCAmelCase_ :
def UpperCamelCase_ ( self : Dict , _A : list[list[float]] , _A : list[int] ):
_UpperCamelCase = 0.0
_UpperCamelCase = 0.0
for i in range(len(_A ) ):
da += math.pow((sample[i] - weights[0][i]) , 2 )
da += math.pow((sample[i] - weights[1][i]) , 2 )
return 0 if da > da else 1
return 0
def UpperCamelCase_ ( self : Optional[Any] , _A : list[list[int | float]] , _A : list[int] , _A : int , _A : float ):
for i in range(len(_A ) ):
weights[j][i] += alpha * (sample[i] - weights[j][i])
return weights
def _snake_case ( ):
# Training Examples ( m, n )
_UpperCamelCase = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]
# weight initialization ( n, C )
_UpperCamelCase = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]
# training
_UpperCamelCase = SelfOrganizingMap()
_UpperCamelCase = 3
_UpperCamelCase = 0.5
for _ in range(__snake_case ):
for j in range(len(__snake_case ) ):
# training sample
_UpperCamelCase = training_samples[j]
# Compute the winning vector
_UpperCamelCase = self_organizing_map.get_winner(__snake_case , __snake_case )
# Update the winning vector
_UpperCamelCase = self_organizing_map.update(__snake_case , __snake_case , __snake_case , __snake_case )
# classify test sample
_UpperCamelCase = [0, 0, 0, 1]
_UpperCamelCase = self_organizing_map.get_winner(__snake_case , __snake_case )
# results
print(f"""Clusters that the test sample belongs to : {winner}""" )
print(f"""Weights that have been trained : {weights}""" )
# running the main() function
if __name__ == "__main__":
main()
| 10 | import math
class lowerCAmelCase_ :
def __init__( self : Tuple , _A : int=0 ): # a graph with Node 0,1,...,N-1
_UpperCamelCase = n
_UpperCamelCase = [
[math.inf for j in range(0 , _A )] for i in range(0 , _A )
] # adjacency matrix for weight
_UpperCamelCase = [
[math.inf for j in range(0 , _A )] for i in range(0 , _A )
] # dp[i][j] stores minimum distance from i to j
def UpperCamelCase_ ( self : Dict , _A : str , _A : List[str] , _A : Optional[Any] ):
_UpperCamelCase = w
def UpperCamelCase_ ( self : Optional[int] ):
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
_UpperCamelCase = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def UpperCamelCase_ ( self : List[str] , _A : Optional[int] , _A : Optional[int] ):
return self.dp[u][v]
if __name__ == "__main__":
_lowerCAmelCase = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 10 | 1 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class lowerCAmelCase_ ( unittest.TestCase ):
@require_torch
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = pipeline(
task='''zero-shot-audio-classification''' , model='''hf-internal-testing/tiny-clap-htsat-unfused''' )
_UpperCamelCase = load_dataset('''ashraq/esc50''' )
_UpperCamelCase = dataset['''train''']['''audio'''][-1]['''array''']
_UpperCamelCase = audio_classifier(_A , candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''] )
self.assertEqual(
nested_simplify(_A ) , [{'''score''': 0.501, '''label''': '''Sound of a dog'''}, {'''score''': 0.499, '''label''': '''Sound of vaccum cleaner'''}] , )
@unittest.skip('''No models are available in TF''' )
def UpperCamelCase_ ( self : Tuple ):
pass
@slow
@require_torch
def UpperCamelCase_ ( self : Tuple ):
_UpperCamelCase = pipeline(
task='''zero-shot-audio-classification''' , model='''laion/clap-htsat-unfused''' , )
# This is an audio of a dog
_UpperCamelCase = load_dataset('''ashraq/esc50''' )
_UpperCamelCase = dataset['''train''']['''audio'''][-1]['''array''']
_UpperCamelCase = audio_classifier(_A , candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''] )
self.assertEqual(
nested_simplify(_A ) , [
{'''score''': 0.999, '''label''': '''Sound of a dog'''},
{'''score''': 0.001, '''label''': '''Sound of vaccum cleaner'''},
] , )
_UpperCamelCase = audio_classifier([audio] * 5 , candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''] )
self.assertEqual(
nested_simplify(_A ) , [
[
{'''score''': 0.999, '''label''': '''Sound of a dog'''},
{'''score''': 0.001, '''label''': '''Sound of vaccum cleaner'''},
],
]
* 5 , )
_UpperCamelCase = audio_classifier(
[audio] * 5 , candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''] , batch_size=5 )
self.assertEqual(
nested_simplify(_A ) , [
[
{'''score''': 0.999, '''label''': '''Sound of a dog'''},
{'''score''': 0.001, '''label''': '''Sound of vaccum cleaner'''},
],
]
* 5 , )
@unittest.skip('''No models are available in TF''' )
def UpperCamelCase_ ( self : List[str] ):
pass
| 10 | import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
def _snake_case ( __snake_case=None , __snake_case=None ):
return field(default_factory=lambda: default , metadata=__snake_case )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = list_field(
default=[], metadata={
"help": (
"Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
" of all available models"
)
}, )
UpperCAmelCase = list_field(
default=[8], metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"} )
UpperCAmelCase = list_field(
default=[8, 32, 128, 512], metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Use FP16 to accelerate inference."} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Benchmark training of model"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Verbose memory tracing"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
}, )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Trace memory line by line"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Save result to a CSV file"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Save all print statements in a log file"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Whether to print environment information"} )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
" multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
" for debugging / testing and on TPU."
)
}, )
UpperCAmelCase = field(
default=F"""inference_time_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving time results to csv."}, )
UpperCAmelCase = field(
default=F"""inference_memory_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving memory results to csv."}, )
UpperCAmelCase = field(
default=F"""train_time_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving time results to csv for training."}, )
UpperCAmelCase = field(
default=F"""train_memory_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving memory results to csv for training."}, )
UpperCAmelCase = field(
default=F"""env_info_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving environment information."}, )
UpperCAmelCase = field(
default=F"""log_{round(time() )}.csv""", metadata={"help": "Log filename used if print statements are saved in log."}, )
UpperCAmelCase = field(default=3, metadata={"help": "Times an experiment will be run."} )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
" model weights."
)
}, )
def UpperCamelCase_ ( self : Union[str, Any] ):
warnings.warn(
F"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
''' are deprecated in general and it is advised to use external Benchmarking libraries '''
''' to benchmark Transformer models.''' , _A , )
def UpperCamelCase_ ( self : str ):
return json.dumps(dataclasses.asdict(self ) , indent=2 )
@property
def UpperCamelCase_ ( self : List[Any] ):
if len(self.models ) <= 0:
raise ValueError(
'''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
''' bert-base-cased` or `args.models = [\'bert-base-cased\'].''' )
return self.models
@property
def UpperCamelCase_ ( self : Optional[int] ):
if not self.multi_process:
return False
elif self.is_tpu:
logger.info('''Multiprocessing is currently not possible on TPU.''' )
return False
else:
return True
| 10 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
# See all SEW models at https://huggingface.co/models?filter=sew
}
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = "sew"
def __init__( self : List[str] , _A : Union[str, Any]=32 , _A : str=768 , _A : List[str]=12 , _A : List[str]=12 , _A : Optional[int]=3072 , _A : Optional[int]=2 , _A : str="gelu" , _A : Dict=0.1 , _A : List[Any]=0.1 , _A : Any=0.1 , _A : Dict=0.0 , _A : str=0.1 , _A : Optional[int]=0.1 , _A : Dict=0.02 , _A : Optional[int]=1e-5 , _A : Dict="group" , _A : Optional[Any]="gelu" , _A : str=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , _A : Union[str, Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , _A : List[Any]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , _A : Optional[int]=False , _A : str=128 , _A : Any=16 , _A : List[Any]=True , _A : Optional[int]=0.05 , _A : Tuple=10 , _A : List[str]=2 , _A : List[str]=0.0 , _A : Optional[int]=10 , _A : int=0 , _A : Optional[int]="mean" , _A : Dict=False , _A : List[Any]=False , _A : List[Any]=256 , _A : List[Any]=0 , _A : List[str]=1 , _A : List[str]=2 , **_A : int , ):
super().__init__(**_A , pad_token_id=_A , bos_token_id=_A , eos_token_id=_A )
_UpperCamelCase = hidden_size
_UpperCamelCase = feat_extract_norm
_UpperCamelCase = feat_extract_activation
_UpperCamelCase = list(_A )
_UpperCamelCase = list(_A )
_UpperCamelCase = list(_A )
_UpperCamelCase = conv_bias
_UpperCamelCase = num_conv_pos_embeddings
_UpperCamelCase = num_conv_pos_embedding_groups
_UpperCamelCase = len(self.conv_dim )
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = intermediate_size
_UpperCamelCase = squeeze_factor
_UpperCamelCase = hidden_act
_UpperCamelCase = num_attention_heads
_UpperCamelCase = hidden_dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = activation_dropout
_UpperCamelCase = feat_proj_dropout
_UpperCamelCase = final_dropout
_UpperCamelCase = layerdrop
_UpperCamelCase = layer_norm_eps
_UpperCamelCase = initializer_range
_UpperCamelCase = vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect.'''
'''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
F"""but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)"""
F"""= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_UpperCamelCase = apply_spec_augment
_UpperCamelCase = mask_time_prob
_UpperCamelCase = mask_time_length
_UpperCamelCase = mask_time_min_masks
_UpperCamelCase = mask_feature_prob
_UpperCamelCase = mask_feature_length
_UpperCamelCase = mask_feature_min_masks
# ctc loss
_UpperCamelCase = ctc_loss_reduction
_UpperCamelCase = ctc_zero_infinity
# sequence classification
_UpperCamelCase = use_weighted_layer_sum
_UpperCamelCase = classifier_proj_size
@property
def UpperCamelCase_ ( self : Union[str, Any] ):
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 10 | import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _snake_case ( *__snake_case , __snake_case = None , __snake_case=True , __snake_case=2 ):
from .. import __version__
_UpperCamelCase = take_from
_UpperCamelCase = ()
if not isinstance(args[0] , __snake_case ):
_UpperCamelCase = (args,)
for attribute, version_name, message in args:
if version.parse(version.parse(__snake_case ).base_version ) >= version.parse(__snake_case ):
raise ValueError(
f"""The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"""
f""" version {__version__} is >= {version_name}""" )
_UpperCamelCase = None
if isinstance(__snake_case , __snake_case ) and attribute in deprecated_kwargs:
values += (deprecated_kwargs.pop(__snake_case ),)
_UpperCamelCase = f"""The `{attribute}` argument is deprecated and will be removed in version {version_name}."""
elif hasattr(__snake_case , __snake_case ):
values += (getattr(__snake_case , __snake_case ),)
_UpperCamelCase = f"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
elif deprecated_kwargs is None:
_UpperCamelCase = f"""`{attribute}` is deprecated and will be removed in version {version_name}."""
if warning is not None:
_UpperCamelCase = warning + ''' ''' if standard_warn else ''''''
warnings.warn(warning + message , __snake_case , stacklevel=__snake_case )
if isinstance(__snake_case , __snake_case ) and len(__snake_case ) > 0:
_UpperCamelCase = inspect.getouterframes(inspect.currentframe() )[1]
_UpperCamelCase = call_frame.filename
_UpperCamelCase = call_frame.lineno
_UpperCamelCase = call_frame.function
_UpperCamelCase , _UpperCamelCase = next(iter(deprecated_kwargs.items() ) )
raise TypeError(f"""{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`""" )
if len(__snake_case ) == 0:
return
elif len(__snake_case ) == 1:
return values[0]
return values
| 10 | 1 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _snake_case ( __snake_case , __snake_case ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(__snake_case , __snake_case ) ) )
def _snake_case ( __snake_case , __snake_case ):
if dataset.ndim != value_array.ndim:
_UpperCamelCase = (
'''Wrong input data\'s dimensions... '''
f"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
)
raise ValueError(__snake_case )
try:
if dataset.shape[1] != value_array.shape[1]:
_UpperCamelCase = (
'''Wrong input data\'s shape... '''
f"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
)
raise ValueError(__snake_case )
except IndexError:
if dataset.ndim != value_array.ndim:
raise TypeError('''Wrong shape''' )
if dataset.dtype != value_array.dtype:
_UpperCamelCase = (
'''Input data have different datatype... '''
f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
)
raise TypeError(__snake_case )
_UpperCamelCase = []
for value in value_array:
_UpperCamelCase = euclidean(__snake_case , dataset[0] )
_UpperCamelCase = dataset[0].tolist()
for dataset_value in dataset[1:]:
_UpperCamelCase = euclidean(__snake_case , __snake_case )
if dist > temp_dist:
_UpperCamelCase = temp_dist
_UpperCamelCase = dataset_value.tolist()
answer.append([vector, dist] )
return answer
def _snake_case ( __snake_case , __snake_case ):
return np.dot(__snake_case , __snake_case ) / (norm(__snake_case ) * norm(__snake_case ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 10 | import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
_lowerCAmelCase = logging.getLogger(__name__)
def _snake_case ( __snake_case , __snake_case ):
return (preds == labels).mean()
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Pretrained config name or path if not the same as model_name"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys() )} )
UpperCAmelCase = field(metadata={"help": "Should contain the data files for the task."} )
UpperCAmelCase = field(
default=128, metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Overwrite the cached training and evaluation sets"} )
def _snake_case ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_UpperCamelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
''' --overwrite_output_dir to overcome.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('''Training/evaluation parameters %s''' , __snake_case )
# Set seed
set_seed(training_args.seed )
try:
_UpperCamelCase = processors[data_args.task_name]()
_UpperCamelCase = processor.get_labels()
_UpperCamelCase = len(__snake_case )
except KeyError:
raise ValueError('''Task not found: %s''' % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCamelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__snake_case , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
_UpperCamelCase = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
_UpperCamelCase = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=__snake_case , cache_dir=model_args.cache_dir , )
# Get datasets
_UpperCamelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__snake_case , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
_UpperCamelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__snake_case , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def compute_metrics(__snake_case ) -> Dict:
_UpperCamelCase = np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(__snake_case , p.label_ids )}
# Data collator
_UpperCamelCase = DataCollatorWithPadding(__snake_case , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
_UpperCamelCase = Trainer(
model=__snake_case , args=__snake_case , train_dataset=__snake_case , eval_dataset=__snake_case , compute_metrics=__snake_case , data_collator=__snake_case , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
_UpperCamelCase = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
_UpperCamelCase = trainer.evaluate()
_UpperCamelCase = os.path.join(training_args.output_dir , '''eval_results.txt''' )
if trainer.is_world_master():
with open(__snake_case , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''' , __snake_case , __snake_case )
writer.write('''%s = %s\n''' % (key, value) )
results.update(__snake_case )
return results
def _snake_case ( __snake_case ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 10 | 1 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class lowerCAmelCase_ ( unittest.TestCase ):
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = torch.nn.Linear(10 , 10 )
_UpperCamelCase = torch.optim.SGD(model.parameters() , 0.1 )
_UpperCamelCase = Accelerator()
_UpperCamelCase = accelerator.prepare(_A )
try:
pickle.loads(pickle.dumps(_A ) )
except Exception as e:
self.fail(F"""Accelerated optimizer pickling failed with {e}""" )
AcceleratorState._reset_state()
| 10 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
),
# See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = "trocr"
UpperCAmelCase = ["past_key_values"]
UpperCAmelCase = {
"num_attention_heads": "decoder_attention_heads",
"hidden_size": "d_model",
"num_hidden_layers": "decoder_layers",
}
def __init__( self : List[str] , _A : Optional[Any]=5_0265 , _A : Optional[Any]=1024 , _A : Optional[Any]=12 , _A : Any=16 , _A : Any=4096 , _A : Optional[Any]="gelu" , _A : Union[str, Any]=512 , _A : Dict=0.1 , _A : List[str]=0.0 , _A : Optional[Any]=0.0 , _A : Union[str, Any]=2 , _A : Any=0.02 , _A : List[str]=0.0 , _A : List[str]=True , _A : str=False , _A : List[str]=True , _A : Optional[Any]=True , _A : Optional[int]=1 , _A : int=0 , _A : Any=2 , **_A : Optional[int] , ):
_UpperCamelCase = vocab_size
_UpperCamelCase = d_model
_UpperCamelCase = decoder_layers
_UpperCamelCase = decoder_attention_heads
_UpperCamelCase = decoder_ffn_dim
_UpperCamelCase = activation_function
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = activation_dropout
_UpperCamelCase = init_std
_UpperCamelCase = decoder_layerdrop
_UpperCamelCase = use_cache
_UpperCamelCase = scale_embedding
_UpperCamelCase = use_learned_position_embeddings
_UpperCamelCase = layernorm_embedding
super().__init__(
pad_token_id=_A , bos_token_id=_A , eos_token_id=_A , decoder_start_token_id=_A , **_A , )
| 10 | 1 |
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
_lowerCAmelCase = {
"vocab_file": {
"Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json",
},
"merges_file": {
"Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt",
},
"tokenizer_file": {
"Salesforce/codegen-350M-mono": (
"https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"
),
},
}
_lowerCAmelCase = {
"Salesforce/codegen-350M-mono": 2_048,
}
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = VOCAB_FILES_NAMES
UpperCAmelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase = ["input_ids", "attention_mask"]
UpperCAmelCase = CodeGenTokenizer
def __init__( self : Optional[Any] , _A : List[Any]=None , _A : str=None , _A : str=None , _A : List[str]="<|endoftext|>" , _A : Any="<|endoftext|>" , _A : Optional[Any]="<|endoftext|>" , _A : List[Any]=False , **_A : Optional[int] , ):
super().__init__(
_A , _A , tokenizer_file=_A , unk_token=_A , bos_token=_A , eos_token=_A , add_prefix_space=_A , **_A , )
if kwargs.pop('''add_bos_token''' , _A ):
_UpperCamelCase = kwargs.pop('''name_or_path''' , '''''' )
raise ValueError(
'''Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'''
'''Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'''
F"""`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"""
F"""`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"""
'''This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'''
''' so that the fast tokenizer works correctly.''' )
_UpperCamelCase = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , _A ) != add_prefix_space:
_UpperCamelCase = getattr(_A , pre_tok_state.pop('''type''' ) )
_UpperCamelCase = add_prefix_space
_UpperCamelCase = pre_tok_class(**_A )
_UpperCamelCase = add_prefix_space
def UpperCamelCase_ ( self : int , *_A : int , **_A : Any ):
_UpperCamelCase = kwargs.get('''is_split_into_words''' , _A )
assert self.add_prefix_space or not is_split_into_words, (
F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*_A , **_A )
def UpperCamelCase_ ( self : Optional[int] , *_A : Dict , **_A : int ):
_UpperCamelCase = kwargs.get('''is_split_into_words''' , _A )
assert self.add_prefix_space or not is_split_into_words, (
F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._encode_plus(*_A , **_A )
def UpperCamelCase_ ( self : Any , _A : str , _A : Optional[str] = None ):
_UpperCamelCase = self._tokenizer.model.save(_A , name=_A )
return tuple(_A )
def UpperCamelCase_ ( self : Tuple , _A : Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"] , _A : bool = False , _A : bool = None , _A : Optional[List[str]] = None , **_A : Optional[int] , ):
_UpperCamelCase = super().decode(
token_ids=_A , skip_special_tokens=_A , clean_up_tokenization_spaces=_A , **_A , )
if truncate_before_pattern is not None and len(_A ) > 0:
_UpperCamelCase = self.truncate(_A , _A )
return decoded_text
def UpperCamelCase_ ( self : Optional[int] , _A : str , _A : Dict ):
def find_re(_A : Optional[Any] , _A : Optional[int] , _A : Tuple ):
_UpperCamelCase = pattern.search(_A , _A )
return m.start() if m else -1
_UpperCamelCase = [re.compile(_A , re.MULTILINE ) for pattern in truncate_before_pattern]
_UpperCamelCase = list(re.finditer('''^print''' , _A , re.MULTILINE ) )
if len(_A ) > 1:
_UpperCamelCase = completion[: prints[1].start()]
_UpperCamelCase = list(re.finditer('''^def''' , _A , re.MULTILINE ) )
if len(_A ) > 1:
_UpperCamelCase = completion[: defs[1].start()]
_UpperCamelCase = 0
_UpperCamelCase = [
pos for pos in [find_re(_A , _A , _A ) for terminal in terminals] if pos != -1
]
if len(_A ) > 0:
return completion[: min(_A )]
else:
return completion
| 10 | import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
FlaubertForMultipleChoice,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertModel,
FlaubertWithLMHeadModel,
)
from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase_ ( __lowercase ):
def __init__( self : Union[str, Any] , _A : Optional[Any] , _A : Any=13 , _A : Union[str, Any]=7 , _A : List[str]=True , _A : List[str]=True , _A : List[str]=True , _A : List[str]=True , _A : List[Any]=True , _A : Optional[int]=False , _A : Any=False , _A : int=False , _A : Optional[Any]=2 , _A : Any=99 , _A : str=0 , _A : Union[str, Any]=32 , _A : List[Any]=5 , _A : Tuple=4 , _A : List[str]=0.1 , _A : Union[str, Any]=0.1 , _A : int=512 , _A : Union[str, Any]=12 , _A : List[str]=2 , _A : int=0.02 , _A : Optional[Any]=3 , _A : Any=4 , _A : Optional[int]="last" , _A : Any=None , _A : Dict=None , ):
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = seq_length
_UpperCamelCase = is_training
_UpperCamelCase = use_input_lengths
_UpperCamelCase = use_token_type_ids
_UpperCamelCase = use_labels
_UpperCamelCase = gelu_activation
_UpperCamelCase = sinusoidal_embeddings
_UpperCamelCase = causal
_UpperCamelCase = asm
_UpperCamelCase = n_langs
_UpperCamelCase = vocab_size
_UpperCamelCase = n_special
_UpperCamelCase = hidden_size
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = hidden_dropout_prob
_UpperCamelCase = attention_probs_dropout_prob
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = type_vocab_size
_UpperCamelCase = type_sequence_label_size
_UpperCamelCase = initializer_range
_UpperCamelCase = num_labels
_UpperCamelCase = num_choices
_UpperCamelCase = summary_type
_UpperCamelCase = use_proj
_UpperCamelCase = scope
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCamelCase = None
if self.use_input_lengths:
_UpperCamelCase = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
_UpperCamelCase = None
if self.use_token_type_ids:
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = None
if self.use_labels:
_UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCamelCase = ids_tensor([self.batch_size] , 2 ).float()
_UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices )
_UpperCamelCase = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def UpperCamelCase_ ( self : str ):
return FlaubertConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , )
def UpperCamelCase_ ( self : str , _A : Union[str, Any] , _A : Optional[Any] , _A : str , _A : Tuple , _A : List[str] , _A : List[Any] , _A : Any , _A : str , _A : Optional[int] , ):
_UpperCamelCase = FlaubertModel(config=_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A , lengths=_A , langs=_A )
_UpperCamelCase = model(_A , langs=_A )
_UpperCamelCase = model(_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase_ ( self : Tuple , _A : List[Any] , _A : str , _A : Optional[int] , _A : Optional[Any] , _A : List[str] , _A : int , _A : str , _A : List[Any] , _A : Any , ):
_UpperCamelCase = FlaubertWithLMHeadModel(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A , token_type_ids=_A , labels=_A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase_ ( self : Tuple , _A : List[str] , _A : List[str] , _A : Optional[Any] , _A : Union[str, Any] , _A : str , _A : List[str] , _A : Tuple , _A : Optional[int] , _A : Dict , ):
_UpperCamelCase = FlaubertForQuestionAnsweringSimple(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A )
_UpperCamelCase = model(_A , start_positions=_A , end_positions=_A )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase_ ( self : Tuple , _A : str , _A : Tuple , _A : Tuple , _A : Union[str, Any] , _A : List[str] , _A : int , _A : str , _A : Dict , _A : List[Any] , ):
_UpperCamelCase = FlaubertForQuestionAnswering(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A )
_UpperCamelCase = model(
_A , start_positions=_A , end_positions=_A , cls_index=_A , is_impossible=_A , p_mask=_A , )
_UpperCamelCase = model(
_A , start_positions=_A , end_positions=_A , cls_index=_A , is_impossible=_A , )
((_UpperCamelCase) , ) = result_with_labels.to_tuple()
_UpperCamelCase = model(_A , start_positions=_A , end_positions=_A )
((_UpperCamelCase) , ) = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def UpperCamelCase_ ( self : List[Any] , _A : Union[str, Any] , _A : Tuple , _A : str , _A : int , _A : int , _A : Optional[int] , _A : Optional[int] , _A : int , _A : List[str] , ):
_UpperCamelCase = FlaubertForSequenceClassification(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A )
_UpperCamelCase = model(_A , labels=_A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCamelCase_ ( self : Optional[int] , _A : List[str] , _A : Optional[Any] , _A : str , _A : Union[str, Any] , _A : List[Any] , _A : int , _A : List[Any] , _A : str , _A : List[str] , ):
_UpperCamelCase = self.num_labels
_UpperCamelCase = FlaubertForTokenClassification(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A , attention_mask=_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase_ ( self : Tuple , _A : Dict , _A : str , _A : Optional[Any] , _A : List[str] , _A : Any , _A : Optional[int] , _A : Optional[Any] , _A : List[Any] , _A : List[str] , ):
_UpperCamelCase = self.num_choices
_UpperCamelCase = FlaubertForMultipleChoice(config=_A )
model.to(_A )
model.eval()
_UpperCamelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCamelCase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCamelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCamelCase = model(
_A , attention_mask=_A , token_type_ids=_A , labels=_A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCamelCase_ ( self : Tuple ):
_UpperCamelCase = self.prepare_config_and_inputs()
(
(
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) ,
) = config_and_inputs
_UpperCamelCase = {
'''input_ids''': input_ids,
'''token_type_ids''': token_type_ids,
'''lengths''': input_lengths,
'''attention_mask''': input_mask,
}
return config, inputs_dict
@require_torch
class lowerCAmelCase_ ( __lowercase, __lowercase, unittest.TestCase ):
UpperCAmelCase = (
(
FlaubertModel,
FlaubertWithLMHeadModel,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertForMultipleChoice,
)
if is_torch_available()
else ()
)
UpperCAmelCase = (
{
"feature-extraction": FlaubertModel,
"fill-mask": FlaubertWithLMHeadModel,
"question-answering": FlaubertForQuestionAnsweringSimple,
"text-classification": FlaubertForSequenceClassification,
"token-classification": FlaubertForTokenClassification,
"zero-shot": FlaubertForSequenceClassification,
}
if is_torch_available()
else {}
)
def UpperCamelCase_ ( self : Union[str, Any] , _A : Dict , _A : Dict , _A : Tuple , _A : int , _A : Any ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('''Fast''' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def UpperCamelCase_ ( self : str , _A : Any , _A : List[str] , _A : Optional[int]=False ):
_UpperCamelCase = super()._prepare_for_class(_A , _A , return_labels=_A )
if return_labels:
if model_class.__name__ == "FlaubertForQuestionAnswering":
_UpperCamelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_A )
_UpperCamelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_A )
return inputs_dict
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = FlaubertModelTester(self )
_UpperCamelCase = ConfigTester(self , config_class=_A , emb_dim=37 )
def UpperCamelCase_ ( self : Optional[Any] ):
self.config_tester.run_common_tests()
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_model(*_A )
def UpperCamelCase_ ( self : Optional[Any] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_lm_head(*_A )
def UpperCamelCase_ ( self : Optional[Any] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_simple_qa(*_A )
def UpperCamelCase_ ( self : Union[str, Any] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_qa(*_A )
def UpperCamelCase_ ( self : Optional[int] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_sequence_classif(*_A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_token_classif(*_A )
def UpperCamelCase_ ( self : Optional[int] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_multiple_choice(*_A )
@slow
def UpperCamelCase_ ( self : str ):
for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = FlaubertModel.from_pretrained(_A )
self.assertIsNotNone(_A )
@slow
@require_torch_gpu
def UpperCamelCase_ ( self : List[Any] ):
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == FlaubertForMultipleChoice:
return
_UpperCamelCase = True
_UpperCamelCase = model_class(config=_A )
_UpperCamelCase = self._prepare_for_class(_A , _A )
_UpperCamelCase = torch.jit.trace(
_A , (inputs_dict['''input_ids'''].to('''cpu''' ), inputs_dict['''attention_mask'''].to('''cpu''' )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(_A , os.path.join(_A , '''traced_model.pt''' ) )
_UpperCamelCase = torch.jit.load(os.path.join(_A , '''traced_model.pt''' ) , map_location=_A )
loaded(inputs_dict['''input_ids'''].to(_A ) , inputs_dict['''attention_mask'''].to(_A ) )
@require_torch
class lowerCAmelCase_ ( unittest.TestCase ):
@slow
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = FlaubertModel.from_pretrained('''flaubert/flaubert_base_cased''' )
_UpperCamelCase = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
with torch.no_grad():
_UpperCamelCase = model(_A )[0]
_UpperCamelCase = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , _A )
_UpperCamelCase = torch.tensor(
[[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _A , atol=1e-4 ) )
| 10 | 1 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class lowerCAmelCase_ :
def __init__( self : Union[str, Any] , _A : List[Any] , _A : List[Any]=2 , _A : Tuple=True , _A : Any=False , _A : Dict=10 , _A : Optional[int]=3 , _A : Union[str, Any]=32 * 8 , _A : Optional[Any]=32 * 8 , _A : List[str]=4 , _A : Dict=64 , ):
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = is_training
_UpperCamelCase = use_auxiliary_loss
_UpperCamelCase = num_queries
_UpperCamelCase = num_channels
_UpperCamelCase = min_size
_UpperCamelCase = max_size
_UpperCamelCase = num_labels
_UpperCamelCase = hidden_dim
_UpperCamelCase = hidden_dim
def UpperCamelCase_ ( self : List[Any] ):
_UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
_A )
_UpperCamelCase = torch.ones([self.batch_size, self.min_size, self.max_size] , device=_A )
_UpperCamelCase = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=_A ) > 0.5
).float()
_UpperCamelCase = (torch.rand((self.batch_size, self.num_labels) , device=_A ) > 0.5).long()
_UpperCamelCase = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = MaskaFormerConfig(
hidden_size=self.hidden_dim , )
_UpperCamelCase = self.num_queries
_UpperCamelCase = self.num_labels
_UpperCamelCase = [1, 1, 1, 1]
_UpperCamelCase = self.num_channels
_UpperCamelCase = 64
_UpperCamelCase = 128
_UpperCamelCase = self.hidden_dim
_UpperCamelCase = self.hidden_dim
_UpperCamelCase = self.hidden_dim
return config
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = self.prepare_config_and_inputs()
_UpperCamelCase = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def UpperCamelCase_ ( self : List[str] , _A : Optional[int] , _A : Union[str, Any] ):
_UpperCamelCase = output.encoder_hidden_states
_UpperCamelCase = output.pixel_decoder_hidden_states
_UpperCamelCase = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(_A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_A ) , config.decoder_layers )
def UpperCamelCase_ ( self : Union[str, Any] , _A : List[Any] , _A : Dict , _A : int , _A : List[Any]=False ):
with torch.no_grad():
_UpperCamelCase = MaskaFormerModel(config=_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(pixel_values=_A , pixel_mask=_A )
_UpperCamelCase = model(_A , output_hidden_states=_A )
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(_A , _A )
def UpperCamelCase_ ( self : Union[str, Any] , _A : Optional[Any] , _A : Any , _A : Any , _A : Any , _A : Tuple ):
_UpperCamelCase = MaskaFormerForUniversalSegmentation(config=_A )
model.to(_A )
model.eval()
def comm_check_on_output(_A : List[Any] ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
_UpperCamelCase = model(pixel_values=_A , pixel_mask=_A )
_UpperCamelCase = model(_A )
comm_check_on_output(_A )
_UpperCamelCase = model(
pixel_values=_A , pixel_mask=_A , mask_labels=_A , class_labels=_A )
comm_check_on_output(_A )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class lowerCAmelCase_ ( __lowercase, __lowercase, unittest.TestCase ):
UpperCAmelCase = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
UpperCAmelCase = {"feature-extraction": MaskaFormerModel} if is_torch_available() else {}
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = False
def UpperCamelCase_ ( self : Union[str, Any] ):
_UpperCamelCase = MaskaFormerModelTester(self )
_UpperCamelCase = ConfigTester(self , config_class=_A , has_text_modality=_A )
def UpperCamelCase_ ( self : Optional[Any] ):
self.config_tester.run_common_tests()
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(_A , **_A , output_hidden_states=_A )
def UpperCamelCase_ ( self : Tuple ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*_A )
@unittest.skip(reason='''Mask2Former does not use inputs_embeds''' )
def UpperCamelCase_ ( self : Dict ):
pass
@unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method''' )
def UpperCamelCase_ ( self : str ):
pass
@unittest.skip(reason='''Mask2Former is not a generative model''' )
def UpperCamelCase_ ( self : Union[str, Any] ):
pass
@unittest.skip(reason='''Mask2Former does not use token embeddings''' )
def UpperCamelCase_ ( self : List[str] ):
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def UpperCamelCase_ ( self : Tuple ):
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def UpperCamelCase_ ( self : Union[str, Any] ):
pass
def UpperCamelCase_ ( self : Optional[int] ):
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase = model_class(_A )
_UpperCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCamelCase = [*signature.parameters.keys()]
_UpperCamelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _A )
@slow
def UpperCamelCase_ ( self : int ):
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
_UpperCamelCase = MaskaFormerModel.from_pretrained(_A )
self.assertIsNotNone(_A )
def UpperCamelCase_ ( self : Union[str, Any] ):
_UpperCamelCase = (self.model_tester.min_size,) * 2
_UpperCamelCase = {
'''pixel_values''': torch.randn((2, 3, *size) , device=_A ),
'''mask_labels''': torch.randn((2, 10, *size) , device=_A ),
'''class_labels''': torch.zeros(2 , 10 , device=_A ).long(),
}
_UpperCamelCase = self.model_tester.get_config()
_UpperCamelCase = MaskaFormerForUniversalSegmentation(_A ).to(_A )
_UpperCamelCase = model(**_A )
self.assertTrue(outputs.loss is not None )
def UpperCamelCase_ ( self : Tuple ):
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(_A , **_A , output_hidden_states=_A )
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase = model_class(_A ).to(_A )
_UpperCamelCase = model(**_A , output_attentions=_A )
self.assertTrue(outputs.attentions is not None )
def UpperCamelCase_ ( self : Optional[Any] ):
if not self.model_tester.is_training:
return
_UpperCamelCase = self.all_model_classes[1]
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs()
_UpperCamelCase = model_class(_A )
model.to(_A )
model.train()
_UpperCamelCase = model(_A , mask_labels=_A , class_labels=_A ).loss
loss.backward()
def UpperCamelCase_ ( self : Tuple ):
_UpperCamelCase = self.all_model_classes[1]
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs()
_UpperCamelCase = True
_UpperCamelCase = True
_UpperCamelCase = model_class(_A ).to(_A )
model.train()
_UpperCamelCase = model(_A , mask_labels=_A , class_labels=_A )
_UpperCamelCase = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
_UpperCamelCase = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
_UpperCamelCase = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
_UpperCamelCase = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=_A )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
_lowerCAmelCase = 1E-4
def _snake_case ( ):
_UpperCamelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class lowerCAmelCase_ ( unittest.TestCase ):
@cached_property
def UpperCamelCase_ ( self : Any ):
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def UpperCamelCase_ ( self : Union[str, Any] ):
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None
def UpperCamelCase_ ( self : Optional[Any] ):
_UpperCamelCase = MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(_A )
_UpperCamelCase = self.default_image_processor
_UpperCamelCase = prepare_img()
_UpperCamelCase = image_processor(_A , return_tensors='''pt''' ).to(_A )
_UpperCamelCase = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(_A , (1, 3, 384, 384) )
with torch.no_grad():
_UpperCamelCase = model(**_A )
_UpperCamelCase = torch.tensor(
[[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]] ).to(_A )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , _A , atol=_A ) )
_UpperCamelCase = torch.tensor(
[[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]] ).to(_A )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , _A , atol=_A ) )
_UpperCamelCase = torch.tensor(
[[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]] ).to(_A )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , _A , atol=_A ) )
def UpperCamelCase_ ( self : List[Any] ):
_UpperCamelCase = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(_A ).eval()
_UpperCamelCase = self.default_image_processor
_UpperCamelCase = prepare_img()
_UpperCamelCase = image_processor(_A , return_tensors='''pt''' ).to(_A )
_UpperCamelCase = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(_A , (1, 3, 384, 384) )
with torch.no_grad():
_UpperCamelCase = model(**_A )
# masks_queries_logits
_UpperCamelCase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
_UpperCamelCase = [
[-8.7839, -9.0056, -8.8121],
[-7.4104, -7.0313, -6.5401],
[-6.6105, -6.3427, -6.4675],
]
_UpperCamelCase = torch.tensor(_A ).to(_A )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _A , atol=_A ) )
# class_queries_logits
_UpperCamelCase = outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) )
_UpperCamelCase = torch.tensor(
[
[1.8324, -8.0835, -4.1922],
[0.8450, -9.0050, -3.6053],
[0.3045, -7.7293, -3.0275],
] ).to(_A )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _A , atol=_A ) )
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(_A ).eval()
_UpperCamelCase = self.default_image_processor
_UpperCamelCase = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors='''pt''' , )
_UpperCamelCase = inputs['''pixel_values'''].to(_A )
_UpperCamelCase = [el.to(_A ) for el in inputs['''mask_labels''']]
_UpperCamelCase = [el.to(_A ) for el in inputs['''class_labels''']]
with torch.no_grad():
_UpperCamelCase = model(**_A )
self.assertTrue(outputs.loss is not None )
| 10 | from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase_ :
def __init__( self : Any , _A : int , _A : int=12 , _A : int=7 , _A : Tuple=True , _A : Optional[int]=True , _A : Union[str, Any]=True , _A : str=99 , _A : str=32 , _A : int=32 , _A : Optional[Any]=2 , _A : Dict=4 , _A : int=37 , _A : List[Any]=0.1 , _A : str=0.1 , _A : Any=512 , _A : int=0.02 , _A : Optional[Any]=0 , _A : Dict=None , ):
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = seq_length
_UpperCamelCase = is_training
_UpperCamelCase = use_input_mask
_UpperCamelCase = use_labels
_UpperCamelCase = vocab_size
_UpperCamelCase = hidden_size
_UpperCamelCase = projection_dim
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = intermediate_size
_UpperCamelCase = dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = initializer_range
_UpperCamelCase = scope
_UpperCamelCase = bos_token_id
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCamelCase = None
if self.use_input_mask:
_UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
_UpperCamelCase = input_mask.numpy()
_UpperCamelCase , _UpperCamelCase = input_mask.shape
_UpperCamelCase = np.random.randint(1 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(_A ):
_UpperCamelCase = 1
_UpperCamelCase = 0
_UpperCamelCase = self.get_config()
return config, input_ids, tf.convert_to_tensor(_A )
def UpperCamelCase_ ( self : str ):
return BlipTextConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , projection_dim=self.projection_dim , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , dropout=self.dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , bos_token_id=self.bos_token_id , )
def UpperCamelCase_ ( self : List[str] , _A : Tuple , _A : str , _A : Optional[Any] ):
_UpperCamelCase = TFBlipTextModel(config=_A )
_UpperCamelCase = model(_A , attention_mask=_A , training=_A )
_UpperCamelCase = model(_A , training=_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def UpperCamelCase_ ( self : Tuple ):
_UpperCamelCase = self.prepare_config_and_inputs()
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = config_and_inputs
_UpperCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class lowerCAmelCase_ ( __lowercase, unittest.TestCase ):
UpperCAmelCase = (TFBlipTextModel,) if is_tf_available() else ()
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = False
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = BlipTextModelTester(self )
_UpperCamelCase = ConfigTester(self , config_class=_A , hidden_size=37 )
def UpperCamelCase_ ( self : Dict ):
self.config_tester.run_common_tests()
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_A )
def UpperCamelCase_ ( self : List[Any] ):
pass
def UpperCamelCase_ ( self : Tuple ):
pass
@unittest.skip(reason='''Blip does not use inputs_embeds''' )
def UpperCamelCase_ ( self : Dict ):
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def UpperCamelCase_ ( self : Dict ):
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def UpperCamelCase_ ( self : List[str] ):
pass
@slow
def UpperCamelCase_ ( self : Optional[int] ):
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = TFBlipTextModel.from_pretrained(_A )
self.assertIsNotNone(_A )
def UpperCamelCase_ ( self : int , _A : Optional[int]=True ):
super().test_pt_tf_model_equivalence(allow_missing_keys=_A )
| 10 | 1 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _snake_case ( ):
_UpperCamelCase = HfArgumentParser(__snake_case )
_UpperCamelCase = parser.parse_args_into_dataclasses()[0]
_UpperCamelCase = TensorFlowBenchmark(args=__snake_case )
try:
_UpperCamelCase = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
_UpperCamelCase = '''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
_UpperCamelCase = ''' '''.join(str(__snake_case ).split(''' ''' )[:-1] )
_UpperCamelCase = ''''''
_UpperCamelCase = eval(str(__snake_case ).split(''' ''' )[-1] )
_UpperCamelCase = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:] )
else:
wrong_args.append(__snake_case )
if len(__snake_case ) > 0:
_UpperCamelCase = full_error_msg + begin_error_msg + str(__snake_case )
raise ValueError(__snake_case )
benchmark.run()
if __name__ == "__main__":
main()
| 10 | from __future__ import annotations
_lowerCAmelCase = [True] * 1_000_001
_lowerCAmelCase = 2
while i * i <= 1_000_000:
if seive[i]:
for j in range(i * i, 1_000_001, i):
_lowerCAmelCase = False
i += 1
def _snake_case ( __snake_case ):
return seive[n]
def _snake_case ( __snake_case ):
return any(digit in '''02468''' for digit in str(__snake_case ) )
def _snake_case ( __snake_case = 1000000 ):
_UpperCamelCase = [2] # result already includes the number 2.
for num in range(3 , limit + 1 , 2 ):
if is_prime(__snake_case ) and not contains_an_even_digit(__snake_case ):
_UpperCamelCase = str(__snake_case )
_UpperCamelCase = [int(str_num[j:] + str_num[:j] ) for j in range(len(__snake_case ) )]
if all(is_prime(__snake_case ) for i in list_nums ):
result.append(__snake_case )
return result
def _snake_case ( ):
return len(find_circular_primes() )
if __name__ == "__main__":
print(f'{len(find_circular_primes()) = }')
| 10 | 1 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def _snake_case ( ):
_UpperCamelCase = {
'''repo_name''': ['''test_repo1''', '''test_repo2''', '''test_repo3'''],
'''path''': ['''test_1.py''', '''test_2.py''', '''unit_test.py'''],
'''content''': ['''a ''' * 20, '''a ''' * 30, '''b ''' * 7],
}
_UpperCamelCase = Dataset.from_dict(__snake_case )
return dataset
class lowerCAmelCase_ ( __lowercase ):
def UpperCamelCase_ ( self : List[Any] ):
_UpperCamelCase = get_dataset()
_UpperCamelCase = make_duplicate_clusters(_A , 0.85 )
self.assertEqual(len(duplicate_clusters[0] ) , 2 )
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = get_dataset()
_UpperCamelCase , _UpperCamelCase = deduplicate_dataset(_A )
self.assertEqual(len(_A ) , 2 )
print(_A )
self.assertEqual(duplicate_clusters[0][0]['''copies'''] , 2 )
self.assertEqual(duplicate_clusters[0][0]['''is_extreme'''] , _A )
| 10 | import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCAmelCase = get_tests_dir("fixtures/spiece.model")
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_ ( __lowercase, unittest.TestCase ):
UpperCAmelCase = DebertaVaTokenizer
UpperCAmelCase = DebertaVaTokenizerFast
UpperCAmelCase = True
UpperCAmelCase = True
def UpperCamelCase_ ( self : List[Any] ):
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCamelCase = DebertaVaTokenizer(_A , unk_token='''<unk>''' )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase_ ( self : Dict , _A : Union[str, Any] ):
_UpperCamelCase = '''this is a test'''
_UpperCamelCase = '''this is a test'''
return input_text, output_text
def UpperCamelCase_ ( self : Optional[Any] ):
_UpperCamelCase = '''<pad>'''
_UpperCamelCase = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A ) , _A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A ) , _A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<pad>''' )
self.assertEqual(vocab_keys[1] , '''<unk>''' )
self.assertEqual(vocab_keys[-1] , '''[PAD]''' )
self.assertEqual(len(_A ) , 3_0001 )
def UpperCamelCase_ ( self : List[Any] ):
self.assertEqual(self.get_tokenizer().vocab_size , 3_0000 )
def UpperCamelCase_ ( self : List[str] ):
# fmt: off
_UpperCamelCase = ''' \tHeLLo!how \n Are yoU? '''
_UpperCamelCase = ['''▁hello''', '''!''', '''how''', '''▁are''', '''▁you''', '''?''']
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
@unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.''' )
def UpperCamelCase_ ( self : Dict ):
pass
@unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.''' )
def UpperCamelCase_ ( self : Optional[Any] ):
pass
def UpperCamelCase_ ( self : Dict ):
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : List[Any] ):
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : Dict ):
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : int ):
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : Tuple ):
# fmt: off
_UpperCamelCase = ''' \tHeLLo!how \n Are yoU? '''
_UpperCamelCase = ['''▁''', '''<unk>''', '''e''', '''<unk>''', '''o''', '''!''', '''how''', '''▁''', '''<unk>''', '''re''', '''▁yo''', '''<unk>''', '''?''']
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = self.get_tokenizer()
_UpperCamelCase = self.get_rust_tokenizer()
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.encode(_A , add_special_tokens=_A )
_UpperCamelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = self.get_rust_tokenizer()
_UpperCamelCase = tokenizer.encode(_A )
_UpperCamelCase = rust_tokenizer.encode(_A )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = '''This is a test'''
_UpperCamelCase = [13, 1, 4398, 25, 21, 1289]
_UpperCamelCase = ['''▁''', '''T''', '''his''', '''▁is''', '''▁a''', '''▁test''']
_UpperCamelCase = ['''▁''', '''<unk>''', '''his''', '''▁is''', '''▁a''', '''▁test''']
_UpperCamelCase = DebertaVaTokenizer(_A , keep_accents=_A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , keep_accents=_A )
_UpperCamelCase = tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = [13, 1, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]
_UpperCamelCase = ['''▁''', '''I''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''', ]
_UpperCamelCase = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
# fmt: on
_UpperCamelCase = tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = DebertaVaTokenizer(_A )
_UpperCamelCase = tokenizer.encode('''sequence builders''' )
_UpperCamelCase = tokenizer.encode('''multi-sequence build''' )
_UpperCamelCase = tokenizer.build_inputs_with_special_tokens(_A )
_UpperCamelCase = tokenizer.build_inputs_with_special_tokens(_A , _A )
self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] , _A )
self.assertEqual(
[tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id] , _A , )
@slow
def UpperCamelCase_ ( self : Optional[Any] ):
# fmt: off
_UpperCamelCase = {'''input_ids''': [[1, 3_9867, 36, 1_9390, 486, 27, 3_5052, 8_1436, 18, 6_0685, 1225, 7, 3_5052, 8_1436, 18, 9367, 1_6899, 18, 1_5937, 53, 594, 773, 18, 1_6287, 3_0465, 36, 1_5937, 6, 4_1139, 38, 3_6979, 6_0763, 191, 6, 3_4132, 99, 6, 5_0538, 390, 4_3230, 6, 3_4132, 2779, 2_0850, 14, 699, 1072, 1194, 36, 382, 1_0901, 53, 7, 699, 1072, 2084, 36, 2_0422, 630, 53, 19, 105, 3049, 1896, 1053, 1_6899, 1506, 11, 3_7978, 4243, 7, 1237, 3_1869, 200, 1_6566, 654, 6, 3_5052, 8_1436, 7, 5_5630, 1_3593, 4, 2], [1, 26, 1_5011, 13, 667, 8, 1053, 18, 2_3611, 1237, 7_2356, 1_2820, 34, 10_4134, 1209, 35, 1_3313, 6627, 21, 202, 347, 7, 164, 2399, 11, 46, 4485, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 1232, 2864, 1_5785, 1_4951, 105, 5, 8581, 1250, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_A , model_name='''microsoft/deberta-v2-xlarge''' , revision='''ad6e42c1532ddf3a15c39246b63f5559d558b670''' , )
| 10 | 1 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
_lowerCAmelCase = logging.getLogger(__name__)
def _snake_case ( __snake_case , __snake_case ):
return (preds == labels).mean()
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Pretrained config name or path if not the same as model_name"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys() )} )
UpperCAmelCase = field(metadata={"help": "Should contain the data files for the task."} )
UpperCAmelCase = field(
default=128, metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Overwrite the cached training and evaluation sets"} )
def _snake_case ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_UpperCamelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
''' --overwrite_output_dir to overcome.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('''Training/evaluation parameters %s''' , __snake_case )
# Set seed
set_seed(training_args.seed )
try:
_UpperCamelCase = processors[data_args.task_name]()
_UpperCamelCase = processor.get_labels()
_UpperCamelCase = len(__snake_case )
except KeyError:
raise ValueError('''Task not found: %s''' % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCamelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__snake_case , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
_UpperCamelCase = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
_UpperCamelCase = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=__snake_case , cache_dir=model_args.cache_dir , )
# Get datasets
_UpperCamelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__snake_case , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
_UpperCamelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__snake_case , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def compute_metrics(__snake_case ) -> Dict:
_UpperCamelCase = np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(__snake_case , p.label_ids )}
# Data collator
_UpperCamelCase = DataCollatorWithPadding(__snake_case , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
_UpperCamelCase = Trainer(
model=__snake_case , args=__snake_case , train_dataset=__snake_case , eval_dataset=__snake_case , compute_metrics=__snake_case , data_collator=__snake_case , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
_UpperCamelCase = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
_UpperCamelCase = trainer.evaluate()
_UpperCamelCase = os.path.join(training_args.output_dir , '''eval_results.txt''' )
if trainer.is_world_master():
with open(__snake_case , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''' , __snake_case , __snake_case )
writer.write('''%s = %s\n''' % (key, value) )
results.update(__snake_case )
return results
def _snake_case ( __snake_case ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 10 | import sys
from collections import defaultdict
class lowerCAmelCase_ :
def __init__( self : Optional[int] ):
_UpperCamelCase = []
def UpperCamelCase_ ( self : Any , _A : str ):
return self.node_position[vertex]
def UpperCamelCase_ ( self : Optional[Any] , _A : List[str] , _A : Union[str, Any] ):
_UpperCamelCase = pos
def UpperCamelCase_ ( self : Any , _A : List[str] , _A : int , _A : Optional[Any] , _A : Union[str, Any] ):
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
_UpperCamelCase = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
_UpperCamelCase = 2 * start + 1
else:
_UpperCamelCase = 2 * start + 2
if heap[smallest_child] < heap[start]:
_UpperCamelCase , _UpperCamelCase = heap[smallest_child], positions[smallest_child]
_UpperCamelCase , _UpperCamelCase = (
heap[start],
positions[start],
)
_UpperCamelCase , _UpperCamelCase = temp, tempa
_UpperCamelCase = self.get_position(positions[smallest_child] )
self.set_position(
positions[smallest_child] , self.get_position(positions[start] ) )
self.set_position(positions[start] , _A )
self.top_to_bottom(_A , _A , _A , _A )
def UpperCamelCase_ ( self : List[str] , _A : Tuple , _A : Optional[Any] , _A : int , _A : Optional[int] ):
_UpperCamelCase = position[index]
while index != 0:
_UpperCamelCase = int((index - 2) / 2 ) if index % 2 == 0 else int((index - 1) / 2 )
if val < heap[parent]:
_UpperCamelCase = heap[parent]
_UpperCamelCase = position[parent]
self.set_position(position[parent] , _A )
else:
_UpperCamelCase = val
_UpperCamelCase = temp
self.set_position(_A , _A )
break
_UpperCamelCase = parent
else:
_UpperCamelCase = val
_UpperCamelCase = temp
self.set_position(_A , 0 )
def UpperCamelCase_ ( self : int , _A : Tuple , _A : int ):
_UpperCamelCase = len(_A ) // 2 - 1
for i in range(_A , -1 , -1 ):
self.top_to_bottom(_A , _A , len(_A ) , _A )
def UpperCamelCase_ ( self : Any , _A : int , _A : List[str] ):
_UpperCamelCase = positions[0]
_UpperCamelCase = sys.maxsize
self.top_to_bottom(_A , 0 , len(_A ) , _A )
return temp
def _snake_case ( __snake_case ):
_UpperCamelCase = Heap()
_UpperCamelCase = [0] * len(__snake_case )
_UpperCamelCase = [-1] * len(__snake_case ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
_UpperCamelCase = [] # Heap of Distance of vertices from their neighboring vertex
_UpperCamelCase = []
for vertex in range(len(__snake_case ) ):
distance_tv.append(sys.maxsize )
positions.append(__snake_case )
heap.node_position.append(__snake_case )
_UpperCamelCase = []
_UpperCamelCase = 1
_UpperCamelCase = sys.maxsize
for neighbor, distance in adjacency_list[0]:
_UpperCamelCase = 0
_UpperCamelCase = distance
heap.heapify(__snake_case , __snake_case )
for _ in range(1 , len(__snake_case ) ):
_UpperCamelCase = heap.delete_minimum(__snake_case , __snake_case )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
_UpperCamelCase = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(__snake_case )]
):
_UpperCamelCase = distance
heap.bottom_to_top(
__snake_case , heap.get_position(__snake_case ) , __snake_case , __snake_case )
_UpperCamelCase = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
_lowerCAmelCase = int(input("Enter number of edges: ").strip())
_lowerCAmelCase = defaultdict(list)
for _ in range(edges_number):
_lowerCAmelCase = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 10 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
# See all Cvt models at https://huggingface.co/models?filter=cvt
}
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = "cvt"
def __init__( self : Optional[int] , _A : List[Any]=3 , _A : Any=[7, 3, 3] , _A : Optional[int]=[4, 2, 2] , _A : Union[str, Any]=[2, 1, 1] , _A : str=[64, 192, 384] , _A : Tuple=[1, 3, 6] , _A : Optional[Any]=[1, 2, 10] , _A : Optional[int]=[4.0, 4.0, 4.0] , _A : Optional[Any]=[0.0, 0.0, 0.0] , _A : Optional[int]=[0.0, 0.0, 0.0] , _A : int=[0.0, 0.0, 0.1] , _A : Tuple=[True, True, True] , _A : Any=[False, False, True] , _A : Union[str, Any]=["dw_bn", "dw_bn", "dw_bn"] , _A : Union[str, Any]=[3, 3, 3] , _A : List[str]=[1, 1, 1] , _A : str=[2, 2, 2] , _A : Union[str, Any]=[1, 1, 1] , _A : Dict=[1, 1, 1] , _A : List[str]=0.02 , _A : int=1e-12 , **_A : Any , ):
super().__init__(**_A )
_UpperCamelCase = num_channels
_UpperCamelCase = patch_sizes
_UpperCamelCase = patch_stride
_UpperCamelCase = patch_padding
_UpperCamelCase = embed_dim
_UpperCamelCase = num_heads
_UpperCamelCase = depth
_UpperCamelCase = mlp_ratio
_UpperCamelCase = attention_drop_rate
_UpperCamelCase = drop_rate
_UpperCamelCase = drop_path_rate
_UpperCamelCase = qkv_bias
_UpperCamelCase = cls_token
_UpperCamelCase = qkv_projection_method
_UpperCamelCase = kernel_qkv
_UpperCamelCase = padding_kv
_UpperCamelCase = stride_kv
_UpperCamelCase = padding_q
_UpperCamelCase = stride_q
_UpperCamelCase = initializer_range
_UpperCamelCase = layer_norm_eps
| 10 | import logging
import os
from .state import PartialState
class lowerCAmelCase_ ( logging.LoggerAdapter ):
@staticmethod
def UpperCamelCase_ ( _A : Any ):
_UpperCamelCase = PartialState()
return not main_process_only or (main_process_only and state.is_main_process)
def UpperCamelCase_ ( self : Union[str, Any] , _A : Optional[Any] , _A : str , *_A : int , **_A : List[Any] ):
if PartialState._shared_state == {}:
raise RuntimeError(
'''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.''' )
_UpperCamelCase = kwargs.pop('''main_process_only''' , _A )
_UpperCamelCase = kwargs.pop('''in_order''' , _A )
if self.isEnabledFor(_A ):
if self._should_log(_A ):
_UpperCamelCase , _UpperCamelCase = self.process(_A , _A )
self.logger.log(_A , _A , *_A , **_A )
elif in_order:
_UpperCamelCase = PartialState()
for i in range(state.num_processes ):
if i == state.process_index:
_UpperCamelCase , _UpperCamelCase = self.process(_A , _A )
self.logger.log(_A , _A , *_A , **_A )
state.wait_for_everyone()
def _snake_case ( __snake_case , __snake_case = None ):
if log_level is None:
_UpperCamelCase = os.environ.get('''ACCELERATE_LOG_LEVEL''' , __snake_case )
_UpperCamelCase = logging.getLogger(__snake_case )
if log_level is not None:
logger.setLevel(log_level.upper() )
logger.root.setLevel(log_level.upper() )
return MultiProcessAdapter(__snake_case , {} )
| 10 | 1 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_lowerCAmelCase = abspath(join(dirname(dirname(dirname(__file__))), "src"))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="ignore", category=FutureWarning)
def _snake_case ( __snake_case ):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(__snake_case )
def _snake_case ( __snake_case ):
from transformers.testing_utils import pytest_terminal_summary_main
_UpperCamelCase = terminalreporter.config.getoption('''--make-reports''' )
if make_reports:
pytest_terminal_summary_main(__snake_case , id=__snake_case )
| 10 | import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCAmelCase = "▁"
_lowerCAmelCase = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
class lowerCAmelCase_ ( __lowercase, unittest.TestCase ):
UpperCAmelCase = BertGenerationTokenizer
UpperCAmelCase = False
UpperCAmelCase = True
def UpperCamelCase_ ( self : List[str] ):
super().setUp()
_UpperCamelCase = BertGenerationTokenizer(_A , keep_accents=_A )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = '''<s>'''
_UpperCamelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A ) , _A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A ) , _A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<unk>''' )
self.assertEqual(vocab_keys[1] , '''<s>''' )
self.assertEqual(vocab_keys[-1] , '''<pad>''' )
self.assertEqual(len(_A ) , 1002 )
def UpperCamelCase_ ( self : Dict ):
self.assertEqual(self.get_tokenizer().vocab_size , 1000 )
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = BertGenerationTokenizer(_A , keep_accents=_A )
_UpperCamelCase = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(_A , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_A ) , [285, 46, 10, 170, 382] , )
_UpperCamelCase = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
_A , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
_UpperCamelCase = tokenizer.convert_tokens_to_ids(_A )
self.assertListEqual(
_A , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(
_A , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
@cached_property
def UpperCamelCase_ ( self : Union[str, Any] ):
return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''' )
@slow
def UpperCamelCase_ ( self : Optional[Any] ):
_UpperCamelCase = '''Hello World!'''
_UpperCamelCase = [1_8536, 2260, 101]
self.assertListEqual(_A , self.big_tokenizer.encode(_A ) )
@slow
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
_UpperCamelCase = [
871,
419,
358,
946,
991,
2521,
452,
358,
1357,
387,
7751,
3536,
112,
985,
456,
126,
865,
938,
5400,
5734,
458,
1368,
467,
786,
2462,
5246,
1159,
633,
865,
4519,
457,
582,
852,
2557,
427,
916,
508,
405,
3_4324,
497,
391,
408,
1_1342,
1244,
385,
100,
938,
985,
456,
574,
362,
1_2597,
3200,
3129,
1172,
]
self.assertListEqual(_A , self.big_tokenizer.encode(_A ) )
@require_torch
@slow
def UpperCamelCase_ ( self : Dict ):
import torch
from transformers import BertGenerationConfig, BertGenerationEncoder
# Build sequence
_UpperCamelCase = list(self.big_tokenizer.get_vocab().keys() )[:10]
_UpperCamelCase = ''' '''.join(_A )
_UpperCamelCase = self.big_tokenizer.encode_plus(_A , return_tensors='''pt''' , return_token_type_ids=_A )
_UpperCamelCase = self.big_tokenizer.batch_encode_plus(
[sequence + ''' ''' + sequence] , return_tensors='''pt''' , return_token_type_ids=_A )
_UpperCamelCase = BertGenerationConfig()
_UpperCamelCase = BertGenerationEncoder(_A )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**_A )
model(**_A )
@slow
def UpperCamelCase_ ( self : Dict ):
# fmt: off
_UpperCamelCase = {'''input_ids''': [[3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114], [448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_A , model_name='''google/bert_for_seq_generation_L-24_bbc_encoder''' , revision='''c817d1fd1be2ffa69431227a1fe320544943d4db''' , )
| 10 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class lowerCAmelCase_ ( __lowercase, __lowercase ):
@register_to_config
def __init__( self : Any , _A : int = 768 , ):
super().__init__()
_UpperCamelCase = nn.Parameter(torch.zeros(1 , _A ) )
_UpperCamelCase = nn.Parameter(torch.ones(1 , _A ) )
def UpperCamelCase_ ( self : List[str] , _A : Optional[Union[str, torch.device]] = None , _A : Optional[torch.dtype] = None , ):
_UpperCamelCase = nn.Parameter(self.mean.to(_A ).to(_A ) )
_UpperCamelCase = nn.Parameter(self.std.to(_A ).to(_A ) )
return self
def UpperCamelCase_ ( self : Optional[int] , _A : List[Any] ):
_UpperCamelCase = (embeds - self.mean) * 1.0 / self.std
return embeds
def UpperCamelCase_ ( self : str , _A : List[str] ):
_UpperCamelCase = (embeds * self.std) + self.mean
return embeds
| 10 | import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class lowerCAmelCase_ ( __lowercase, __lowercase, __lowercase, unittest.TestCase ):
UpperCAmelCase = StableUnCLIPPipeline
UpperCAmelCase = TEXT_TO_IMAGE_PARAMS
UpperCAmelCase = TEXT_TO_IMAGE_BATCH_PARAMS
UpperCAmelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
UpperCAmelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
UpperCAmelCase = False
def UpperCamelCase_ ( self : Optional[int] ):
_UpperCamelCase = 32
_UpperCamelCase = embedder_hidden_size
# prior components
torch.manual_seed(0 )
_UpperCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
torch.manual_seed(0 )
_UpperCamelCase = CLIPTextModelWithProjection(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=_A , projection_dim=_A , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , ) )
torch.manual_seed(0 )
_UpperCamelCase = PriorTransformer(
num_attention_heads=2 , attention_head_dim=12 , embedding_dim=_A , num_layers=1 , )
torch.manual_seed(0 )
_UpperCamelCase = DDPMScheduler(
variance_type='''fixed_small_log''' , prediction_type='''sample''' , num_train_timesteps=1000 , clip_sample=_A , clip_sample_range=5.0 , beta_schedule='''squaredcos_cap_v2''' , )
# regular denoising components
torch.manual_seed(0 )
_UpperCamelCase = StableUnCLIPImageNormalizer(embedding_dim=_A )
_UpperCamelCase = DDPMScheduler(beta_schedule='''squaredcos_cap_v2''' )
torch.manual_seed(0 )
_UpperCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
torch.manual_seed(0 )
_UpperCamelCase = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=_A , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , ) )
torch.manual_seed(0 )
_UpperCamelCase = UNetaDConditionModel(
sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''') , up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''') , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type='''projection''' , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=_A , layers_per_block=1 , upcast_attention=_A , use_linear_projection=_A , )
torch.manual_seed(0 )
_UpperCamelCase = DDIMScheduler(
beta_schedule='''scaled_linear''' , beta_start=0.0_0085 , beta_end=0.012 , prediction_type='''v_prediction''' , set_alpha_to_one=_A , steps_offset=1 , )
torch.manual_seed(0 )
_UpperCamelCase = AutoencoderKL()
_UpperCamelCase = {
# prior components
'''prior_tokenizer''': prior_tokenizer,
'''prior_text_encoder''': prior_text_encoder,
'''prior''': prior,
'''prior_scheduler''': prior_scheduler,
# image noising components
'''image_normalizer''': image_normalizer,
'''image_noising_scheduler''': image_noising_scheduler,
# regular denoising components
'''tokenizer''': tokenizer,
'''text_encoder''': text_encoder,
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
}
return components
def UpperCamelCase_ ( self : Dict , _A : Tuple , _A : Dict=0 ):
if str(_A ).startswith('''mps''' ):
_UpperCamelCase = torch.manual_seed(_A )
else:
_UpperCamelCase = torch.Generator(device=_A ).manual_seed(_A )
_UpperCamelCase = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''prior_num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = torch_device == '''cpu'''
self._test_attention_slicing_forward_pass(test_max_difference=_A )
def UpperCamelCase_ ( self : List[Any] ):
_UpperCamelCase = torch_device in ['''cpu''', '''mps''']
self._test_inference_batch_single_identical(test_max_difference=_A )
@slow
@require_torch_gpu
class lowerCAmelCase_ ( unittest.TestCase ):
def UpperCamelCase_ ( self : Optional[Any] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy''' )
_UpperCamelCase = StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l''' , torch_dtype=torch.floataa )
pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
_UpperCamelCase = torch.Generator(device='''cpu''' ).manual_seed(0 )
_UpperCamelCase = pipe('''anime turle''' , generator=_A , output_type='''np''' )
_UpperCamelCase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_A , _A )
def UpperCamelCase_ ( self : Optional[Any] ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
_UpperCamelCase = StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l''' , torch_dtype=torch.floataa )
_UpperCamelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
_UpperCamelCase = pipe(
'''anime turtle''' , prior_num_inference_steps=2 , num_inference_steps=2 , output_type='''np''' , )
_UpperCamelCase = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 10 | 1 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
SCREAMING_SNAKE_CASE__ : List[str] = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class lowerCamelCase_ ( lowerCamelCase ):
def __init__( self , *__lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , **__lowerCAmelCase ):
"""simple docstring"""
super().__init__(*__lowerCAmelCase , **__lowerCAmelCase )
__magic_name__ :Any = eval_examples
__magic_name__ :str = post_process_function
__magic_name__ :int = quant_trainer_args
__magic_name__ :List[str] = 1_2_8 # default number of calibration samples
def A ( self , __lowerCAmelCase=None ):
"""simple docstring"""
if calib_dataset is None and self.calib_dataset is None:
raise ValueError('''Trainer: calibration requires an calib_dataset.''' )
__magic_name__ :Optional[Any] = calib_dataset if calib_dataset is not None else self.calib_dataset
__magic_name__ :Optional[int] = self._remove_unused_columns(__lowerCAmelCase , description='''Calibration''' )
return DataLoader(
__lowerCAmelCase , batch_size=self.args.eval_batch_size , collate_fn=self.data_collator , drop_last=self.args.dataloader_drop_last , num_workers=self.args.dataloader_num_workers , pin_memory=self.args.dataloader_pin_memory , shuffle=__lowerCAmelCase , )
def A ( self , __lowerCAmelCase=None ):
"""simple docstring"""
__magic_name__ :Dict = self.train_dataset if calib_dataset is None else calib_dataset
__magic_name__ :Any = self.get_calib_dataloader(__lowerCAmelCase )
__magic_name__ :List[str] = self.model
quant_trainer.configure_model(__lowerCAmelCase , self.quant_trainer_args , calib=__lowerCAmelCase )
model.eval()
quant_trainer.enable_calibration(__lowerCAmelCase )
logger.info('''***** Running calibration *****''' )
logger.info(F''' Num examples = {self.calib_num}''' )
logger.info(F''' Batch size = {calib_dataloader.batch_size}''' )
for step, inputs in enumerate(__lowerCAmelCase ):
# Prediction step
__magic_name__ , __magic_name__ , __magic_name__ :str = self.prediction_step(__lowerCAmelCase , __lowerCAmelCase , prediction_loss_only=__lowerCAmelCase )
if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
break
quant_trainer.finish_calibration(__lowerCAmelCase , self.quant_trainer_args )
__magic_name__ :Any = model
def A ( self , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase = "eval" ):
"""simple docstring"""
__magic_name__ :Tuple = self.eval_dataset if eval_dataset is None else eval_dataset
__magic_name__ :Optional[Any] = self.get_eval_dataloader(__lowerCAmelCase )
__magic_name__ :str = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
__magic_name__ :Any = self.compute_metrics
__magic_name__ :List[Any] = None
__magic_name__ :List[str] = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
__magic_name__ :Optional[Any] = eval_loop(
__lowerCAmelCase , description='''Evaluation''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__lowerCAmelCase , )
finally:
__magic_name__ :Union[str, Any] = compute_metrics
if self.post_process_function is not None and self.compute_metrics is not None:
__magic_name__ :Union[str, Any] = self.post_process_function(__lowerCAmelCase , __lowerCAmelCase , output.predictions )
__magic_name__ :int = self.compute_metrics(__lowerCAmelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'''{metric_key_prefix}_''' ):
__magic_name__ :Dict = metrics.pop(__lowerCAmelCase )
self.log(__lowerCAmelCase )
else:
__magic_name__ :List[str] = {}
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
__magic_name__ :Optional[Any] = self.callback_handler.on_evaluate(self.args , self.state , self.control , __lowerCAmelCase )
return metrics
def A ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase = "test" ):
"""simple docstring"""
__magic_name__ :int = self.get_test_dataloader(__lowerCAmelCase )
# Temporarily disable metric computation, we will do it in the loop here.
__magic_name__ :Dict = self.compute_metrics
__magic_name__ :str = None
__magic_name__ :Optional[int] = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
__magic_name__ :int = eval_loop(
__lowerCAmelCase , description='''Prediction''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__lowerCAmelCase , )
finally:
__magic_name__ :List[Any] = compute_metrics
if self.post_process_function is None or self.compute_metrics is None:
return output
__magic_name__ :Optional[Any] = self.post_process_function(__lowerCAmelCase , __lowerCAmelCase , output.predictions , '''predict''' )
__magic_name__ :Dict = self.compute_metrics(__lowerCAmelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'''{metric_key_prefix}_''' ):
__magic_name__ :List[str] = metrics.pop(__lowerCAmelCase )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=__lowerCAmelCase )
def A ( self , __lowerCAmelCase="./" ):
"""simple docstring"""
__magic_name__ :List[Any] = self.eval_dataset
__magic_name__ :Any = self.get_eval_dataloader(__lowerCAmelCase )
__magic_name__ :int = next(iter(__lowerCAmelCase ) )
# saving device - to make it consistent
__magic_name__ :str = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
# convert to tuple
__magic_name__ :int = tuple(v.to(__lowerCAmelCase ) for k, v in batch.items() )
logger.info('''Converting model to be onnx compatible''' )
from pytorch_quantization.nn import TensorQuantizer
__magic_name__ :Any = True
__magic_name__ :Optional[int] = self.model.to(__lowerCAmelCase )
model.eval()
model.float()
__magic_name__ :Any = model.module if hasattr(__lowerCAmelCase , '''module''' ) else model
quant_trainer.configure_model(__lowerCAmelCase , self.quant_trainer_args )
__magic_name__ :int = os.path.join(__lowerCAmelCase , '''model.onnx''' )
logger.info(F'''exporting model to {output_model_file}''' )
__magic_name__ :Dict = {0: '''batch_size''', 1: '''seq_len'''}
torch.onnx.export(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , export_params=__lowerCAmelCase , opset_version=1_3 , do_constant_folding=__lowerCAmelCase , input_names=['''input_ids''', '''attention_mask''', '''token_type_ids'''] , output_names=['''output_start_logits''', '''output_end_logits'''] , dynamic_axes={
'''input_ids''': axes,
'''attention_mask''': axes,
'''token_type_ids''': axes,
'''output_start_logits''': axes,
'''output_end_logits''': axes,
} , verbose=__lowerCAmelCase , )
logger.info('''onnx export finished''' )
| 0 | from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _snake_case ( __snake_case , __snake_case ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(__snake_case , __snake_case ) ) )
def _snake_case ( __snake_case , __snake_case ):
if dataset.ndim != value_array.ndim:
_UpperCamelCase = (
'''Wrong input data\'s dimensions... '''
f"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
)
raise ValueError(__snake_case )
try:
if dataset.shape[1] != value_array.shape[1]:
_UpperCamelCase = (
'''Wrong input data\'s shape... '''
f"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
)
raise ValueError(__snake_case )
except IndexError:
if dataset.ndim != value_array.ndim:
raise TypeError('''Wrong shape''' )
if dataset.dtype != value_array.dtype:
_UpperCamelCase = (
'''Input data have different datatype... '''
f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
)
raise TypeError(__snake_case )
_UpperCamelCase = []
for value in value_array:
_UpperCamelCase = euclidean(__snake_case , dataset[0] )
_UpperCamelCase = dataset[0].tolist()
for dataset_value in dataset[1:]:
_UpperCamelCase = euclidean(__snake_case , __snake_case )
if dist > temp_dist:
_UpperCamelCase = temp_dist
_UpperCamelCase = dataset_value.tolist()
answer.append([vector, dist] )
return answer
def _snake_case ( __snake_case , __snake_case ):
return np.dot(__snake_case , __snake_case ) / (norm(__snake_case ) * norm(__snake_case ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 10 | 0 |
import baseaa
def _A ( _lowercase ) -> bytes:
"""simple docstring"""
return baseaa.aaaencode(string.encode('utf-8' ) )
def _A ( _lowercase ) -> str:
"""simple docstring"""
return baseaa.aaadecode(_lowercase ).decode('utf-8' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 | import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class lowerCAmelCase_ ( __lowercase, unittest.TestCase ):
UpperCAmelCase = ShapEPipeline
UpperCAmelCase = ["prompt"]
UpperCAmelCase = ["prompt"]
UpperCAmelCase = [
"num_images_per_prompt",
"num_inference_steps",
"generator",
"latents",
"guidance_scale",
"frame_size",
"output_type",
"return_dict",
]
UpperCAmelCase = False
@property
def UpperCamelCase_ ( self : Union[str, Any] ):
return 32
@property
def UpperCamelCase_ ( self : int ):
return 32
@property
def UpperCamelCase_ ( self : List[str] ):
return self.time_input_dim * 4
@property
def UpperCamelCase_ ( self : Optional[Any] ):
return 8
@property
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
return tokenizer
@property
def UpperCamelCase_ ( self : List[Any] ):
torch.manual_seed(0 )
_UpperCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(_A )
@property
def UpperCamelCase_ ( self : int ):
torch.manual_seed(0 )
_UpperCamelCase = {
'''num_attention_heads''': 2,
'''attention_head_dim''': 16,
'''embedding_dim''': self.time_input_dim,
'''num_embeddings''': 32,
'''embedding_proj_dim''': self.text_embedder_hidden_size,
'''time_embed_dim''': self.time_embed_dim,
'''num_layers''': 1,
'''clip_embed_dim''': self.time_input_dim * 2,
'''additional_embeddings''': 0,
'''time_embed_act_fn''': '''gelu''',
'''norm_in_type''': '''layer''',
'''encoder_hid_proj_type''': None,
'''added_emb_type''': None,
}
_UpperCamelCase = PriorTransformer(**_A )
return model
@property
def UpperCamelCase_ ( self : Union[str, Any] ):
torch.manual_seed(0 )
_UpperCamelCase = {
'''param_shapes''': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'''d_latent''': self.time_input_dim,
'''d_hidden''': self.renderer_dim,
'''n_output''': 12,
'''background''': (
0.1,
0.1,
0.1,
),
}
_UpperCamelCase = ShapERenderer(**_A )
return model
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = self.dummy_prior
_UpperCamelCase = self.dummy_text_encoder
_UpperCamelCase = self.dummy_tokenizer
_UpperCamelCase = self.dummy_renderer
_UpperCamelCase = HeunDiscreteScheduler(
beta_schedule='''exp''' , num_train_timesteps=1024 , prediction_type='''sample''' , use_karras_sigmas=_A , clip_sample=_A , clip_sample_range=1.0 , )
_UpperCamelCase = {
'''prior''': prior,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''renderer''': renderer,
'''scheduler''': scheduler,
}
return components
def UpperCamelCase_ ( self : Tuple , _A : Tuple , _A : Optional[int]=0 ):
if str(_A ).startswith('''mps''' ):
_UpperCamelCase = torch.manual_seed(_A )
else:
_UpperCamelCase = torch.Generator(device=_A ).manual_seed(_A )
_UpperCamelCase = {
'''prompt''': '''horse''',
'''generator''': generator,
'''num_inference_steps''': 1,
'''frame_size''': 32,
'''output_type''': '''np''',
}
return inputs
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = '''cpu'''
_UpperCamelCase = self.get_dummy_components()
_UpperCamelCase = self.pipeline_class(**_A )
_UpperCamelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
_UpperCamelCase = pipe(**self.get_dummy_inputs(_A ) )
_UpperCamelCase = output.images[0]
_UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
_UpperCamelCase = np.array(
[
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def UpperCamelCase_ ( self : Any ):
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = torch_device == '''cpu'''
_UpperCamelCase = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=_A , relax_max_difference=_A , )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = self.get_dummy_components()
_UpperCamelCase = self.pipeline_class(**_A )
_UpperCamelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
_UpperCamelCase = 1
_UpperCamelCase = 2
_UpperCamelCase = self.get_dummy_inputs(_A )
for key in inputs.keys():
if key in self.batch_params:
_UpperCamelCase = batch_size * [inputs[key]]
_UpperCamelCase = pipe(**_A , num_images_per_prompt=_A )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class lowerCAmelCase_ ( unittest.TestCase ):
def UpperCamelCase_ ( self : str ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/shap_e/test_shap_e_np_out.npy''' )
_UpperCamelCase = ShapEPipeline.from_pretrained('''openai/shap-e''' )
_UpperCamelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
_UpperCamelCase = torch.Generator(device=_A ).manual_seed(0 )
_UpperCamelCase = pipe(
'''a shark''' , generator=_A , guidance_scale=15.0 , num_inference_steps=64 , frame_size=64 , output_type='''np''' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(_A , _A )
| 10 | 0 |
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE_ ( _snake_case :str = "AAPL" ) -> str:
_A = F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
_A = BeautifulSoup(requests.get(_snake_case ).text , '''html.parser''' )
_A = '''My(6px) Pos(r) smartphone_Mt(6px)'''
return soup.find('''div''' , class_=class_ ).find('''span''' ).text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(f'Current {symbol:<4} stock price is {stock_price(symbol):>8}')
| 2 | import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
_lowerCAmelCase = HfApi()
_lowerCAmelCase = {}
# fmt: off
_lowerCAmelCase = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189,
-1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839,
0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557
])
_lowerCAmelCase = torch.tensor([
-2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436,
1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208,
-2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948,
2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365
])
_lowerCAmelCase = torch.tensor([
-0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,
-0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304,
-0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925,
0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943
])
_lowerCAmelCase = torch.tensor([
0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172,
-0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309,
0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805,
-0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505
])
_lowerCAmelCase = torch.tensor([
0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133,
-0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395,
0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559,
-0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386
])
_lowerCAmelCase = torch.tensor([
0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078,
-0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330,
0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683,
-0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431
])
_lowerCAmelCase = torch.tensor([
0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042,
-0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398,
0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574,
-0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390
])
_lowerCAmelCase = torch.tensor([
0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042,
-0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290,
0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746,
-0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473
])
_lowerCAmelCase = torch.tensor([
-1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330,
1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243,
-2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810,
1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251])
_lowerCAmelCase = torch.tensor([
-1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324,
0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181,
-2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259,
1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266
])
_lowerCAmelCase = torch.tensor([
-1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212,
0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027,
-2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131,
1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355
])
_lowerCAmelCase = torch.tensor([
-2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959,
1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351,
-3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341,
3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066
])
_lowerCAmelCase = torch.tensor([
-2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740,
1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398,
-2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395,
2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243
])
_lowerCAmelCase = torch.tensor([
-2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336,
1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908,
-3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560,
3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343
])
_lowerCAmelCase = torch.tensor([
-1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344,
1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391,
-2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439,
1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219
])
# fmt: on
_lowerCAmelCase = api.list_models(filter="diffusers")
for mod in models:
if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
_lowerCAmelCase = "/home/patrick/google_checkpoints/" + mod.modelId.split("/")[-1]
print(f'Started running {mod.modelId}!!!')
if mod.modelId.startswith("CompVis"):
_lowerCAmelCase = UNetaDModel.from_pretrained(local_checkpoint, subfolder="unet")
else:
_lowerCAmelCase = UNetaDModel.from_pretrained(local_checkpoint)
torch.manual_seed(0)
random.seed(0)
_lowerCAmelCase = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
_lowerCAmelCase = torch.tensor([10] * noise.shape[0])
with torch.no_grad():
_lowerCAmelCase = model(noise, time_step).sample
assert torch.allclose(
logits[0, 0, 0, :30], results["_".join("_".join(mod.modelId.split("/")).split("-"))], atol=1E-3
)
print(f'{mod.modelId} has passed successfully!!!')
| 10 | 0 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
from ...utils.dataclasses import (
ComputeEnvironment,
DistributedType,
DynamoBackend,
PrecisionType,
SageMakerDistributedType,
)
from ..menu import BulletMenu
lowerCAmelCase : Dict = [
'EAGER',
'AOT_EAGER',
'INDUCTOR',
'NVFUSER',
'AOT_NVFUSER',
'AOT_CUDAGRAPHS',
'OFI',
'FX2TRT',
'ONNXRT',
'IPEX',
]
def A_( A : str , A : Tuple=None , A : int=None , A : Union[str, Any]=None):
UpperCamelCase = True
while ask_again:
UpperCamelCase = input(A)
try:
if default is not None and len(A) == 0:
return default
return convert_value(A) if convert_value is not None else result
except Exception:
if error_message is not None:
print(A)
def A_( A : Any , A : Union[str, Any]=[] , A : Dict=None , A : List[str]=0):
UpperCamelCase = BulletMenu(A , A)
UpperCamelCase = menu.run(default_choice=A)
return convert_value(A) if convert_value is not None else result
def A_( A : Tuple):
UpperCamelCase = int(A)
return ComputeEnvironment(['LOCAL_MACHINE', 'AMAZON_SAGEMAKER'][value])
def A_( A : List[str]):
UpperCamelCase = int(A)
return DistributedType(['NO', 'MULTI_CPU', 'MULTI_XPU', 'MULTI_GPU', 'MULTI_NPU', 'TPU'][value])
def A_( A : str):
UpperCamelCase = int(A)
return DynamoBackend(DYNAMO_BACKENDS[value]).value
def A_( A : Any):
UpperCamelCase = int(A)
return PrecisionType(['no', 'fp16', 'bf16', 'fp8'][value])
def A_( A : Optional[Any]):
UpperCamelCase = int(A)
return SageMakerDistributedType(['NO', 'DATA_PARALLEL', 'MODEL_PARALLEL'][value])
def A_( A : int):
return {"yes": True, "no": False}[value.lower()]
class SCREAMING_SNAKE_CASE__ ( argparse.RawDescriptionHelpFormatter):
def UpperCAmelCase_ ( self , A_ , A_ , A_ , A_ )-> int:
'''simple docstring'''
UpperCamelCase = super()._format_usage(A_ , A_ , A_ , A_ )
UpperCamelCase = usage.replace('<command> [<args>] ' , '' )
return usage
| 3 | from typing import List
from .keymap import KEYMAP, get_character
def _snake_case ( __snake_case ):
def decorator(__snake_case ):
_UpperCamelCase = getattr(__snake_case , '''handle_key''' , [] )
handle += [key]
setattr(__snake_case , '''handle_key''' , __snake_case )
return func
return decorator
def _snake_case ( *__snake_case ):
def decorator(__snake_case ):
_UpperCamelCase = getattr(__snake_case , '''handle_key''' , [] )
handle += keys
setattr(__snake_case , '''handle_key''' , __snake_case )
return func
return decorator
class lowerCAmelCase_ ( __lowercase ):
def __new__( cls : Optional[Any] , _A : Optional[Any] , _A : Optional[int] , _A : Union[str, Any] ):
_UpperCamelCase = super().__new__(cls , _A , _A , _A )
if not hasattr(_A , '''key_handler''' ):
setattr(_A , '''key_handler''' , {} )
setattr(_A , '''handle_input''' , KeyHandler.handle_input )
for value in attrs.values():
_UpperCamelCase = getattr(_A , '''handle_key''' , [] )
for key in handled_keys:
_UpperCamelCase = value
return new_cls
@staticmethod
def UpperCamelCase_ ( cls : str ):
_UpperCamelCase = get_character()
if char != KEYMAP["undefined"]:
_UpperCamelCase = ord(_A )
_UpperCamelCase = cls.key_handler.get(_A )
if handler:
_UpperCamelCase = char
return handler(cls )
else:
return None
def _snake_case ( cls ):
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 10 | 0 |
"""simple docstring"""
import qiskit
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int , _UpperCAmelCase : int ):
lowerCAmelCase = qiskit.Aer.get_backend('aer_simulator' )
lowerCAmelCase = qiskit.QuantumCircuit(4 , 2 )
# encode inputs in qubits 0 and 1
if bita == 1:
qc_ha.x(0 )
if bita == 1:
qc_ha.x(1 )
qc_ha.barrier()
# use cnots to write XOR of the inputs on qubit2
qc_ha.cx(0 , 2 )
qc_ha.cx(1 , 2 )
# use ccx / toffoli gate to write AND of the inputs on qubit3
qc_ha.ccx(0 , 1 , 3 )
qc_ha.barrier()
# extract outputs
qc_ha.measure(2 , 0 ) # extract XOR value
qc_ha.measure(3 , 1 ) # extract AND value
# Execute the circuit on the qasm simulator
lowerCAmelCase = qiskit.execute(_UpperCAmelCase , _UpperCAmelCase , shots=1000 )
# Return the histogram data of the results of the experiment
return job.result().get_counts(_UpperCAmelCase )
if __name__ == "__main__":
__UpperCamelCase : Union[str, Any] = half_adder(1, 1)
print(f'''Half Adder Output Qubit Counts: {counts}''')
| 4 | import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
require_torch_gpu,
require_torch_or_tf,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
class lowerCAmelCase_ ( unittest.TestCase ):
UpperCAmelCase = MODEL_FOR_CAUSAL_LM_MAPPING
UpperCAmelCase = TF_MODEL_FOR_CAUSAL_LM_MAPPING
@require_torch
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = pipeline(task='''text-generation''' , model='''sshleifer/tiny-ctrl''' , framework='''pt''' )
# Using `do_sample=False` to force deterministic output
_UpperCamelCase = text_generator('''This is a test''' , do_sample=_A )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
''' oscope. FiliFili@@'''
)
}
] , )
_UpperCamelCase = text_generator(['''This is a test''', '''This is a second test'''] )
self.assertEqual(
_A , [
[
{
'''generated_text''': (
'''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
''' oscope. FiliFili@@'''
)
}
],
[
{
'''generated_text''': (
'''This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'''
''' oscope. oscope. FiliFili@@'''
)
}
],
] , )
_UpperCamelCase = text_generator('''This is a test''' , do_sample=_A , num_return_sequences=2 , return_tensors=_A )
self.assertEqual(
_A , [
{'''generated_token_ids''': ANY(_A )},
{'''generated_token_ids''': ANY(_A )},
] , )
_UpperCamelCase = text_generator.model.config.eos_token_id
_UpperCamelCase = '''<pad>'''
_UpperCamelCase = text_generator(
['''This is a test''', '''This is a second test'''] , do_sample=_A , num_return_sequences=2 , batch_size=2 , return_tensors=_A , )
self.assertEqual(
_A , [
[
{'''generated_token_ids''': ANY(_A )},
{'''generated_token_ids''': ANY(_A )},
],
[
{'''generated_token_ids''': ANY(_A )},
{'''generated_token_ids''': ANY(_A )},
],
] , )
@require_tf
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = pipeline(task='''text-generation''' , model='''sshleifer/tiny-ctrl''' , framework='''tf''' )
# Using `do_sample=False` to force deterministic output
_UpperCamelCase = text_generator('''This is a test''' , do_sample=_A )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
''' please,'''
)
}
] , )
_UpperCamelCase = text_generator(['''This is a test''', '''This is a second test'''] , do_sample=_A )
self.assertEqual(
_A , [
[
{
'''generated_text''': (
'''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
''' please,'''
)
}
],
[
{
'''generated_text''': (
'''This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'''
''' Cannes 閲閲Cannes Cannes Cannes 攵 please,'''
)
}
],
] , )
def UpperCamelCase_ ( self : int , _A : str , _A : Union[str, Any] , _A : Any ):
_UpperCamelCase = TextGenerationPipeline(model=_A , tokenizer=_A )
return text_generator, ["This is a test", "Another test"]
def UpperCamelCase_ ( self : Union[str, Any] ):
_UpperCamelCase = '''Hello I believe in'''
_UpperCamelCase = pipeline('''text-generation''' , model='''hf-internal-testing/tiny-random-gpt2''' )
_UpperCamelCase = text_generator(_A )
self.assertEqual(
_A , [{'''generated_text''': '''Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'''}] , )
_UpperCamelCase = text_generator(_A , stop_sequence=''' fe''' )
self.assertEqual(_A , [{'''generated_text''': '''Hello I believe in fe'''}] )
def UpperCamelCase_ ( self : Any , _A : List[Any] , _A : Union[str, Any] ):
_UpperCamelCase = text_generator.model
_UpperCamelCase = text_generator.tokenizer
_UpperCamelCase = text_generator('''This is a test''' )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )
_UpperCamelCase = text_generator('''This is a test''' , return_full_text=_A )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
self.assertNotIn('''This is a test''' , outputs[0]['''generated_text'''] )
_UpperCamelCase = pipeline(task='''text-generation''' , model=_A , tokenizer=_A , return_full_text=_A )
_UpperCamelCase = text_generator('''This is a test''' )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
self.assertNotIn('''This is a test''' , outputs[0]['''generated_text'''] )
_UpperCamelCase = text_generator('''This is a test''' , return_full_text=_A )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )
_UpperCamelCase = text_generator(['''This is great !''', '''Something else'''] , num_return_sequences=2 , do_sample=_A )
self.assertEqual(
_A , [
[{'''generated_text''': ANY(_A )}, {'''generated_text''': ANY(_A )}],
[{'''generated_text''': ANY(_A )}, {'''generated_text''': ANY(_A )}],
] , )
if text_generator.tokenizer.pad_token is not None:
_UpperCamelCase = text_generator(
['''This is great !''', '''Something else'''] , num_return_sequences=2 , batch_size=2 , do_sample=_A )
self.assertEqual(
_A , [
[{'''generated_text''': ANY(_A )}, {'''generated_text''': ANY(_A )}],
[{'''generated_text''': ANY(_A )}, {'''generated_text''': ANY(_A )}],
] , )
with self.assertRaises(_A ):
_UpperCamelCase = text_generator('''test''' , return_full_text=_A , return_text=_A )
with self.assertRaises(_A ):
_UpperCamelCase = text_generator('''test''' , return_full_text=_A , return_tensors=_A )
with self.assertRaises(_A ):
_UpperCamelCase = text_generator('''test''' , return_text=_A , return_tensors=_A )
# Empty prompt is slighly special
# it requires BOS token to exist.
# Special case for Pegasus which will always append EOS so will
# work even without BOS.
if (
text_generator.tokenizer.bos_token_id is not None
or "Pegasus" in tokenizer.__class__.__name__
or "Git" in model.__class__.__name__
):
_UpperCamelCase = text_generator('''''' )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
else:
with self.assertRaises((ValueError, AssertionError) ):
_UpperCamelCase = text_generator('''''' )
if text_generator.framework == "tf":
# TF generation does not support max_new_tokens, and it's impossible
# to control long generation with only max_length without
# fancy calculation, dismissing tests for now.
return
# We don't care about infinite range models.
# They already work.
# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
_UpperCamelCase = ['''RwkvForCausalLM''', '''XGLMForCausalLM''', '''GPTNeoXForCausalLM''']
if (
tokenizer.model_max_length < 1_0000
and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
):
# Handling of large generations
with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError) ):
text_generator('''This is a test''' * 500 , max_new_tokens=20 )
_UpperCamelCase = text_generator('''This is a test''' * 500 , handle_long_generation='''hole''' , max_new_tokens=20 )
# Hole strategy cannot work
with self.assertRaises(_A ):
text_generator(
'''This is a test''' * 500 , handle_long_generation='''hole''' , max_new_tokens=tokenizer.model_max_length + 10 , )
@require_torch
@require_accelerate
@require_torch_gpu
def UpperCamelCase_ ( self : Optional[int] ):
import torch
# Classic `model_kwargs`
_UpperCamelCase = pipeline(
model='''hf-internal-testing/tiny-random-bloom''' , model_kwargs={'''device_map''': '''auto''', '''torch_dtype''': torch.bfloataa} , )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
_UpperCamelCase = pipe('''This is a test''' )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
_UpperCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' , torch_dtype=torch.bfloataa )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
_UpperCamelCase = pipe('''This is a test''' )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
_UpperCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.floataa )
_UpperCamelCase = pipe('''This is a test''' )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
@require_torch
@require_torch_gpu
def UpperCamelCase_ ( self : Union[str, Any] ):
import torch
_UpperCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device=0 , torch_dtype=torch.floataa )
pipe('''This is a test''' )
@require_torch
@require_accelerate
@require_torch_gpu
def UpperCamelCase_ ( self : Optional[int] ):
import torch
_UpperCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' , torch_dtype=torch.floataa )
pipe('''This is a test''' , do_sample=_A , top_p=0.5 )
def UpperCamelCase_ ( self : Tuple ):
_UpperCamelCase = '''Hello world'''
_UpperCamelCase = pipeline('''text-generation''' , model='''hf-internal-testing/tiny-random-gpt2''' )
if text_generator.model.framework == "tf":
_UpperCamelCase = logging.get_logger('''transformers.generation.tf_utils''' )
else:
_UpperCamelCase = logging.get_logger('''transformers.generation.utils''' )
_UpperCamelCase = '''Both `max_new_tokens`''' # The beggining of the message to be checked in this test
# Both are set by the user -> log warning
with CaptureLogger(_A ) as cl:
_UpperCamelCase = text_generator(_A , max_length=10 , max_new_tokens=1 )
self.assertIn(_A , cl.out )
# The user only sets one -> no warning
with CaptureLogger(_A ) as cl:
_UpperCamelCase = text_generator(_A , max_new_tokens=1 )
self.assertNotIn(_A , cl.out )
with CaptureLogger(_A ) as cl:
_UpperCamelCase = text_generator(_A , max_length=10 )
self.assertNotIn(_A , cl.out )
| 10 | 0 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("""Googling.....""")
_lowercase = """https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
_lowercase = requests.get(url, headers={"""UserAgent""": UserAgent().random})
# res.raise_for_status()
with open("""project1a.html""", """wb""") as out_file: # only for knowing the class
for data in res.iter_content(10000):
out_file.write(data)
_lowercase = BeautifulSoup(res.text, """html.parser""")
_lowercase = list(soup.select(""".eZt8xd"""))[:5]
print(len(links))
for link in links:
if link.text == "Maps":
webbrowser.open(link.get("""href"""))
else:
webbrowser.open(F"""https://google.com{link.get('href')}""")
| 5 | def _snake_case ( __snake_case = 100 ):
_UpperCamelCase = (n * (n + 1) // 2) ** 2
_UpperCamelCase = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f'{solution() = }')
| 10 | 0 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
_lowerCamelCase = TypeVar('KT')
_lowerCamelCase = TypeVar('VT')
class UpperCamelCase_ ( Generic[KT, VT] ):
def __init__( self :List[str] , __A :KT | str = "root" , __A :VT | None = None ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = key
SCREAMING_SNAKE_CASE__ = value
SCREAMING_SNAKE_CASE__ = []
def __repr__( self :Optional[Any] ) -> str:
"""simple docstring"""
return f'''Node({self.key}: {self.value})'''
@property
def _snake_case ( self :str ) -> int:
"""simple docstring"""
return len(self.forward )
class UpperCamelCase_ ( Generic[KT, VT] ):
def __init__( self :Union[str, Any] , __A :float = 0.5 , __A :int = 16 ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = Node[KT, VT]()
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = p
SCREAMING_SNAKE_CASE__ = max_level
def __str__( self :Optional[int] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = list(self )
if len(__A ) == 0:
return f'''SkipList(level={self.level})'''
SCREAMING_SNAKE_CASE__ = max((len(str(__A ) ) for item in items) , default=4 )
SCREAMING_SNAKE_CASE__ = max(__A , 4 ) + 4
SCREAMING_SNAKE_CASE__ = self.head
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = node.forward.copy()
lines.append(f'''[{node.key}]'''.ljust(__A , """-""" ) + """* """ * len(__A ) )
lines.append(""" """ * label_size + """| """ * len(__A ) )
while len(node.forward ) != 0:
SCREAMING_SNAKE_CASE__ = node.forward[0]
lines.append(
f'''[{node.key}]'''.ljust(__A , """-""" )
+ """ """.join(str(n.key ) if n.key == node.key else """|""" for n in forwards ) )
lines.append(""" """ * label_size + """| """ * len(__A ) )
SCREAMING_SNAKE_CASE__ = node.forward
lines.append("""None""".ljust(__A ) + """* """ * len(__A ) )
return f'''SkipList(level={self.level})\n''' + "\n".join(__A )
def __iter__( self :Dict ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.head
while len(node.forward ) != 0:
yield node.forward[0].key
SCREAMING_SNAKE_CASE__ = node.forward[0]
def _snake_case ( self :str ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = 1
while random() < self.p and level < self.max_level:
level += 1
return level
def _snake_case ( self :Dict , __A :Optional[int] ) -> tuple[Node[KT, VT] | None, list[Node[KT, VT]]]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = self.head
for i in reversed(range(self.level ) ):
# i < node.level - When node level is lesser than `i` decrement `i`.
# node.forward[i].key < key - Jumping to node with key value higher
# or equal to searched key would result
# in skipping searched key.
while i < node.level and node.forward[i].key < key:
SCREAMING_SNAKE_CASE__ = node.forward[i]
# Each leftmost node (relative to searched node) will potentially have to
# be updated.
update_vector.append(__A )
update_vector.reverse() # Note that we were inserting values in reverse order.
# len(node.forward) != 0 - If current node doesn't contain any further
# references then searched key is not present.
# node.forward[0].key == key - Next node key should be equal to search key
# if key is present.
if len(node.forward ) != 0 and node.forward[0].key == key:
return node.forward[0], update_vector
else:
return None, update_vector
def _snake_case ( self :Optional[int] , __A :KT ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self._locate_node(__A )
if node is not None:
for i, update_node in enumerate(__A ):
# Remove or replace all references to removed node.
if update_node.level > i and update_node.forward[i].key == key:
if node.level > i:
SCREAMING_SNAKE_CASE__ = node.forward[i]
else:
SCREAMING_SNAKE_CASE__ = update_node.forward[:i]
def _snake_case ( self :List[Any] , __A :KT , __A :VT ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self._locate_node(__A )
if node is not None:
SCREAMING_SNAKE_CASE__ = value
else:
SCREAMING_SNAKE_CASE__ = self.random_level()
if level > self.level:
# After level increase we have to add additional nodes to head.
for _ in range(self.level - 1 , __A ):
update_vector.append(self.head )
SCREAMING_SNAKE_CASE__ = level
SCREAMING_SNAKE_CASE__ = Node(__A , __A )
for i, update_node in enumerate(update_vector[:level] ):
# Change references to pass through new node.
if update_node.level > i:
new_node.forward.append(update_node.forward[i] )
if update_node.level < i + 1:
update_node.forward.append(__A )
else:
SCREAMING_SNAKE_CASE__ = new_node
def _snake_case ( self :Optional[Any] , __A :VT ) -> VT | None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self._locate_node(__A )
if node is not None:
return node.value
return None
def SCREAMING_SNAKE_CASE__ ( ):
SCREAMING_SNAKE_CASE__ = SkipList()
skip_list.insert("""Key1""" , 3 )
skip_list.insert("""Key2""" , 12 )
skip_list.insert("""Key3""" , 41 )
skip_list.insert("""Key4""" , -19 )
SCREAMING_SNAKE_CASE__ = skip_list.head
SCREAMING_SNAKE_CASE__ = {}
while node.level != 0:
SCREAMING_SNAKE_CASE__ = node.forward[0]
SCREAMING_SNAKE_CASE__ = node.value
assert len(UpperCamelCase__ ) == 4
assert all_values["Key1"] == 3
assert all_values["Key2"] == 12
assert all_values["Key3"] == 41
assert all_values["Key4"] == -19
def SCREAMING_SNAKE_CASE__ ( ):
SCREAMING_SNAKE_CASE__ = SkipList()
skip_list.insert("""Key1""" , 10 )
skip_list.insert("""Key1""" , 12 )
skip_list.insert("""Key5""" , 7 )
skip_list.insert("""Key7""" , 10 )
skip_list.insert("""Key10""" , 5 )
skip_list.insert("""Key7""" , 7 )
skip_list.insert("""Key5""" , 5 )
skip_list.insert("""Key10""" , 10 )
SCREAMING_SNAKE_CASE__ = skip_list.head
SCREAMING_SNAKE_CASE__ = {}
while node.level != 0:
SCREAMING_SNAKE_CASE__ = node.forward[0]
SCREAMING_SNAKE_CASE__ = node.value
if len(UpperCamelCase__ ) != 4:
print()
assert len(UpperCamelCase__ ) == 4
assert all_values["Key1"] == 12
assert all_values["Key7"] == 7
assert all_values["Key5"] == 5
assert all_values["Key10"] == 10
def SCREAMING_SNAKE_CASE__ ( ):
SCREAMING_SNAKE_CASE__ = SkipList()
assert skip_list.find("""Some key""" ) is None
def SCREAMING_SNAKE_CASE__ ( ):
SCREAMING_SNAKE_CASE__ = SkipList()
skip_list.insert("""Key2""" , 20 )
assert skip_list.find("""Key2""" ) == 20
skip_list.insert("""Some Key""" , 10 )
skip_list.insert("""Key2""" , 8 )
skip_list.insert("""V""" , 13 )
assert skip_list.find("""Y""" ) is None
assert skip_list.find("""Key2""" ) == 8
assert skip_list.find("""Some Key""" ) == 10
assert skip_list.find("""V""" ) == 13
def SCREAMING_SNAKE_CASE__ ( ):
SCREAMING_SNAKE_CASE__ = SkipList()
skip_list.delete("""Some key""" )
assert len(skip_list.head.forward ) == 0
def SCREAMING_SNAKE_CASE__ ( ):
SCREAMING_SNAKE_CASE__ = SkipList()
skip_list.insert("""Key1""" , 12 )
skip_list.insert("""V""" , 13 )
skip_list.insert("""X""" , 14 )
skip_list.insert("""Key2""" , 15 )
skip_list.delete("""V""" )
skip_list.delete("""Key2""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""Key2""" ) is None
def SCREAMING_SNAKE_CASE__ ( ):
SCREAMING_SNAKE_CASE__ = SkipList()
skip_list.insert("""Key1""" , 12 )
skip_list.insert("""V""" , 13 )
skip_list.insert("""X""" , 14 )
skip_list.insert("""Key2""" , 15 )
skip_list.delete("""V""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""X""" ) == 14
assert skip_list.find("""Key1""" ) == 12
assert skip_list.find("""Key2""" ) == 15
skip_list.delete("""X""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""X""" ) is None
assert skip_list.find("""Key1""" ) == 12
assert skip_list.find("""Key2""" ) == 15
skip_list.delete("""Key1""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""X""" ) is None
assert skip_list.find("""Key1""" ) is None
assert skip_list.find("""Key2""" ) == 15
skip_list.delete("""Key2""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""X""" ) is None
assert skip_list.find("""Key1""" ) is None
assert skip_list.find("""Key2""" ) is None
def SCREAMING_SNAKE_CASE__ ( ):
SCREAMING_SNAKE_CASE__ = SkipList()
skip_list.insert("""Key1""" , 12 )
skip_list.insert("""V""" , 13 )
skip_list.insert("""X""" , 142 )
skip_list.insert("""Key2""" , 15 )
skip_list.delete("""X""" )
def traverse_keys(UpperCamelCase__: List[Any] ):
yield node.key
for forward_node in node.forward:
yield from traverse_keys(UpperCamelCase__ )
assert len(set(traverse_keys(skip_list.head ) ) ) == 4
def SCREAMING_SNAKE_CASE__ ( ):
def is_sorted(UpperCamelCase__: str ):
return all(next_item >= item for item, next_item in zip(UpperCamelCase__ , lst[1:] ) )
SCREAMING_SNAKE_CASE__ = SkipList()
for i in range(10 ):
skip_list.insert(UpperCamelCase__ , UpperCamelCase__ )
assert is_sorted(list(UpperCamelCase__ ) )
skip_list.delete(5 )
skip_list.delete(8 )
skip_list.delete(2 )
assert is_sorted(list(UpperCamelCase__ ) )
skip_list.insert(-12 , -12 )
skip_list.insert(77 , 77 )
assert is_sorted(list(UpperCamelCase__ ) )
def SCREAMING_SNAKE_CASE__ ( ):
for _ in range(100 ):
# Repeat test 100 times due to the probabilistic nature of skip list
# random values == random bugs
test_insert()
test_insert_overrides_existing_value()
test_searching_empty_list_returns_none()
test_search()
test_deleting_item_from_empty_list_do_nothing()
test_deleted_items_are_not_founded_by_find_method()
test_delete_removes_only_given_key()
test_delete_doesnt_leave_dead_nodes()
test_iter_always_yields_sorted_values()
def SCREAMING_SNAKE_CASE__ ( ):
SCREAMING_SNAKE_CASE__ = SkipList()
skip_list.insert(2 , """2""" )
skip_list.insert(4 , """4""" )
skip_list.insert(6 , """4""" )
skip_list.insert(4 , """5""" )
skip_list.insert(8 , """4""" )
skip_list.insert(9 , """4""" )
skip_list.delete(4 )
print(UpperCamelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
main() | 6 | import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
_lowerCAmelCase = logging.get_logger(__name__)
def _snake_case ( __snake_case , __snake_case , __snake_case , __snake_case ):
def constraint_to_multiple_of(__snake_case , __snake_case , __snake_case=0 , __snake_case=None ):
_UpperCamelCase = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
_UpperCamelCase = math.floor(val / multiple ) * multiple
if x < min_val:
_UpperCamelCase = math.ceil(val / multiple ) * multiple
return x
_UpperCamelCase = (output_size, output_size) if isinstance(__snake_case , __snake_case ) else output_size
_UpperCamelCase , _UpperCamelCase = get_image_size(__snake_case )
_UpperCamelCase , _UpperCamelCase = output_size
# determine new height and width
_UpperCamelCase = output_height / input_height
_UpperCamelCase = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
_UpperCamelCase = scale_width
else:
# fit height
_UpperCamelCase = scale_height
_UpperCamelCase = constraint_to_multiple_of(scale_height * input_height , multiple=__snake_case )
_UpperCamelCase = constraint_to_multiple_of(scale_width * input_width , multiple=__snake_case )
return (new_height, new_width)
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = ["pixel_values"]
def __init__( self : List[Any] , _A : bool = True , _A : Dict[str, int] = None , _A : PILImageResampling = PILImageResampling.BILINEAR , _A : bool = False , _A : int = 1 , _A : bool = True , _A : Union[int, float] = 1 / 255 , _A : bool = True , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[float, List[float]]] = None , **_A : List[str] , ):
super().__init__(**_A )
_UpperCamelCase = size if size is not None else {'''height''': 384, '''width''': 384}
_UpperCamelCase = get_size_dict(_A )
_UpperCamelCase = do_resize
_UpperCamelCase = size
_UpperCamelCase = keep_aspect_ratio
_UpperCamelCase = ensure_multiple_of
_UpperCamelCase = resample
_UpperCamelCase = do_rescale
_UpperCamelCase = rescale_factor
_UpperCamelCase = do_normalize
_UpperCamelCase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_UpperCamelCase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def UpperCamelCase_ ( self : List[str] , _A : np.ndarray , _A : Dict[str, int] , _A : bool = False , _A : int = 1 , _A : PILImageResampling = PILImageResampling.BICUBIC , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Any , ):
_UpperCamelCase = get_size_dict(_A )
if "height" not in size or "width" not in size:
raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
_UpperCamelCase = get_resize_output_image_size(
_A , output_size=(size['''height'''], size['''width''']) , keep_aspect_ratio=_A , multiple=_A , )
return resize(_A , size=_A , resample=_A , data_format=_A , **_A )
def UpperCamelCase_ ( self : str , _A : np.ndarray , _A : Union[int, float] , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Any , ):
return rescale(_A , scale=_A , data_format=_A , **_A )
def UpperCamelCase_ ( self : int , _A : np.ndarray , _A : Union[float, List[float]] , _A : Union[float, List[float]] , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Any , ):
return normalize(_A , mean=_A , std=_A , data_format=_A , **_A )
def UpperCamelCase_ ( self : Optional[int] , _A : ImageInput , _A : bool = None , _A : int = None , _A : bool = None , _A : int = None , _A : PILImageResampling = None , _A : bool = None , _A : float = None , _A : bool = None , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[str, TensorType]] = None , _A : ChannelDimension = ChannelDimension.FIRST , **_A : str , ):
_UpperCamelCase = do_resize if do_resize is not None else self.do_resize
_UpperCamelCase = size if size is not None else self.size
_UpperCamelCase = get_size_dict(_A )
_UpperCamelCase = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
_UpperCamelCase = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
_UpperCamelCase = resample if resample is not None else self.resample
_UpperCamelCase = do_rescale if do_rescale is not None else self.do_rescale
_UpperCamelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
_UpperCamelCase = do_normalize if do_normalize is not None else self.do_normalize
_UpperCamelCase = image_mean if image_mean is not None else self.image_mean
_UpperCamelCase = image_std if image_std is not None else self.image_std
_UpperCamelCase = make_list_of_images(_A )
if not valid_images(_A ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None or resample is None:
raise ValueError('''Size and resample must be specified if do_resize is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
_UpperCamelCase = [to_numpy_array(_A ) for image in images]
if do_resize:
_UpperCamelCase = [self.resize(image=_A , size=_A , resample=_A ) for image in images]
if do_rescale:
_UpperCamelCase = [self.rescale(image=_A , scale=_A ) for image in images]
if do_normalize:
_UpperCamelCase = [self.normalize(image=_A , mean=_A , std=_A ) for image in images]
_UpperCamelCase = [to_channel_dimension_format(_A , _A ) for image in images]
_UpperCamelCase = {'''pixel_values''': images}
return BatchFeature(data=_A , tensor_type=_A )
def UpperCamelCase_ ( self : Any , _A : Any , _A : List[Tuple] = None ):
_UpperCamelCase = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(_A ) != len(_A ):
raise ValueError(
'''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )
if is_torch_tensor(_A ):
_UpperCamelCase = target_sizes.numpy()
_UpperCamelCase = []
for idx in range(len(_A ) ):
_UpperCamelCase = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='''bilinear''' , align_corners=_A )
_UpperCamelCase = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(_A )
else:
_UpperCamelCase = logits.argmax(dim=1 )
_UpperCamelCase = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 10 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : str , _snake_case : str ) -> int:
'''simple docstring'''
if len(_snake_case ) != len(_snake_case ):
raise ValueError('String lengths must match!' )
_A = 0
for chara, chara in zip(_snake_case , _snake_case ):
if chara != chara:
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 7 | import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_lowerCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_lowerCAmelCase = " \"\"\"\n Output class for the scheduler's step function output.\n\n Args:\n prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.FloatTensor\n pred_original_sample: Optional[torch.FloatTensor] = None\n"
class lowerCAmelCase_ ( unittest.TestCase ):
def UpperCamelCase_ ( self : List[Any] ):
_UpperCamelCase = tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir , '''schedulers/''' ) )
_UpperCamelCase = self.diffusers_dir
shutil.copy(
os.path.join(_A , '''src/diffusers/schedulers/scheduling_ddpm.py''' ) , os.path.join(self.diffusers_dir , '''schedulers/scheduling_ddpm.py''' ) , )
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = '''src/diffusers'''
shutil.rmtree(self.diffusers_dir )
def UpperCamelCase_ ( self : str , _A : List[str] , _A : Optional[Any] , _A : List[str] , _A : Optional[int]=None ):
_UpperCamelCase = comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
if overwrite_result is not None:
_UpperCamelCase = comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
_UpperCamelCase = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 )
_UpperCamelCase = black.format_str(_A , mode=_A )
_UpperCamelCase = os.path.join(self.diffusers_dir , '''new_code.py''' )
with open(_A , '''w''' , newline='''\n''' ) as f:
f.write(_A )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(_A ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=_A )
with open(_A , '''r''' ) as f:
self.assertTrue(f.read() , _A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput''' )
self.assertEqual(_A , _A )
def UpperCamelCase_ ( self : List[str] ):
# Base copy consistency
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' , '''DDPMSchedulerOutput''' , REFERENCE_CODE + '''\n''' , )
# With no empty line at the end
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' , '''DDPMSchedulerOutput''' , _A , )
# Copy consistency with rename
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' , '''TestSchedulerOutput''' , re.sub('''DDPM''' , '''Test''' , _A ) , )
# Copy consistency with a really long name
_UpperCamelCase = '''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
self.check_copy_consistency(
F"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}""" , F"""{long_class_name}SchedulerOutput""" , re.sub('''Bert''' , _A , _A ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' , '''TestSchedulerOutput''' , _A , overwrite_result=re.sub('''DDPM''' , '''Test''' , _A ) , )
| 10 | 0 |
'''simple docstring'''
from manim import *
class SCREAMING_SNAKE_CASE (a__ ):
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Union[str, Any] = Rectangle(height=0.5 , width=0.5)
__A : Tuple = Rectangle(height=0.46 , width=0.46).set_stroke(width=0)
__A : Dict = Rectangle(height=0.25 , width=0.25)
__A : str = [mem.copy() for i in range(6)]
__A : Union[str, Any] = [mem.copy() for i in range(6)]
__A : int = VGroup(*_UpperCAmelCase).arrange(_UpperCAmelCase , buff=0)
__A : Dict = VGroup(*_UpperCAmelCase).arrange(_UpperCAmelCase , buff=0)
__A : List[str] = VGroup(_UpperCAmelCase , _UpperCAmelCase).arrange(_UpperCAmelCase , buff=0)
__A : Any = Text('CPU' , font_size=24)
__A : Any = Group(_UpperCAmelCase , _UpperCAmelCase).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase)
cpu.move_to([-2.5, -0.5, 0])
self.add(_UpperCAmelCase)
__A : Any = [mem.copy() for i in range(4)]
__A : Optional[Any] = VGroup(*_UpperCAmelCase).arrange(_UpperCAmelCase , buff=0)
__A : Any = Text('GPU' , font_size=24)
__A : int = Group(_UpperCAmelCase , _UpperCAmelCase).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase)
gpu.move_to([-1, -1, 0])
self.add(_UpperCAmelCase)
__A : Optional[Any] = [mem.copy() for i in range(6)]
__A : List[Any] = VGroup(*_UpperCAmelCase).arrange(_UpperCAmelCase , buff=0)
__A : Dict = Text('Model' , font_size=24)
__A : int = Group(_UpperCAmelCase , _UpperCAmelCase).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase)
model.move_to([3, -1.0, 0])
self.add(_UpperCAmelCase)
__A : int = []
__A : Union[str, Any] = []
for i, rect in enumerate(_UpperCAmelCase):
__A : List[str] = fill.copy().set_fill(_UpperCAmelCase , opacity=0.8)
target.move_to(_UpperCAmelCase)
model_arr.append(_UpperCAmelCase)
__A : Dict = Rectangle(height=0.46 , width=0.46).set_stroke(width=0.0).set_fill(_UpperCAmelCase , opacity=0.8)
cpu_target.move_to(cpu_left_col_base[i])
model_cpu_arr.append(_UpperCAmelCase)
self.add(*_UpperCAmelCase , *_UpperCAmelCase)
__A : str = [meta_mem.copy() for i in range(6)]
__A : Optional[Any] = [meta_mem.copy() for i in range(6)]
__A : Optional[int] = VGroup(*_UpperCAmelCase).arrange(_UpperCAmelCase , buff=0)
__A : Any = VGroup(*_UpperCAmelCase).arrange(_UpperCAmelCase , buff=0)
__A : List[Any] = VGroup(_UpperCAmelCase , _UpperCAmelCase).arrange(_UpperCAmelCase , buff=0)
__A : List[Any] = Text('Disk' , font_size=24)
__A : int = Group(_UpperCAmelCase , _UpperCAmelCase).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase)
disk.move_to([-4, -1.25, 0])
self.add(_UpperCAmelCase , _UpperCAmelCase)
__A : List[Any] = Square(side_length=2.2)
key.move_to([-5, 2, 0])
__A : Dict = MarkupText(
F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model' , font_size=18 , )
key_text.move_to([-5, 2.4, 0])
self.add(_UpperCAmelCase , _UpperCAmelCase)
__A : Dict = MarkupText(
F'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint' , font_size=18 , )
blue_text.next_to(_UpperCAmelCase , DOWN * 2.4 , aligned_edge=key_text.get_left())
self.add(_UpperCAmelCase)
__A : Union[str, Any] = MarkupText(
F'Now watch as an input is passed through the model\nand how the memory is utilized and handled.' , font_size=24 , )
step_a.move_to([2, 2, 0])
self.play(Write(_UpperCAmelCase))
__A : Dict = Square(0.3)
input.set_fill(_UpperCAmelCase , opacity=1.0)
input.set_stroke(width=0.0)
input.next_to(model_base[0] , _UpperCAmelCase , buff=0.5)
self.play(Write(_UpperCAmelCase))
input.generate_target()
input.target.next_to(model_arr[0] , direction=_UpperCAmelCase , buff=0.02)
self.play(MoveToTarget(_UpperCAmelCase))
self.play(FadeOut(_UpperCAmelCase))
__A : Dict = Arrow(start=_UpperCAmelCase , end=_UpperCAmelCase , color=_UpperCAmelCase , buff=0.5)
a.next_to(model_arr[0].get_left() , _UpperCAmelCase , buff=0.2)
model_cpu_arr[0].generate_target()
model_cpu_arr[0].target.move_to(gpu_rect[0])
__A : List[str] = MarkupText(
F'As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.' , font_size=24 , )
step_a.move_to([2, 2, 0])
self.play(Write(_UpperCAmelCase , run_time=3))
__A : Optional[Any] = {'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.02}
self.play(
Write(_UpperCAmelCase) , Circumscribe(model_arr[0] , color=_UpperCAmelCase , **_UpperCAmelCase) , Circumscribe(model_cpu_arr[0] , color=_UpperCAmelCase , **_UpperCAmelCase) , Circumscribe(gpu_rect[0] , color=_UpperCAmelCase , **_UpperCAmelCase) , )
self.play(MoveToTarget(model_cpu_arr[0]))
__A : List[Any] = a.copy()
for i in range(6):
a_c.next_to(model_arr[i].get_right() + 0.02 , _UpperCAmelCase , buff=0.2)
input.generate_target()
input.target.move_to(model_arr[i].get_right() + 0.02)
__A : List[Any] = AnimationGroup(
FadeOut(_UpperCAmelCase , run_time=0.5) , MoveToTarget(_UpperCAmelCase , run_time=0.5) , FadeIn(_UpperCAmelCase , run_time=0.5) , lag_ratio=0.2)
self.play(_UpperCAmelCase)
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[i])
if i < 5:
model_cpu_arr[i + 1].generate_target()
model_cpu_arr[i + 1].target.move_to(gpu_rect[0])
if i >= 1:
__A : Union[str, Any] = 0.7
self.play(
Circumscribe(model_arr[i] , **_UpperCAmelCase) , Circumscribe(cpu_left_col_base[i] , **_UpperCAmelCase) , Circumscribe(cpu_left_col_base[i + 1] , color=_UpperCAmelCase , **_UpperCAmelCase) , Circumscribe(gpu_rect[0] , color=_UpperCAmelCase , **_UpperCAmelCase) , Circumscribe(model_arr[i + 1] , color=_UpperCAmelCase , **_UpperCAmelCase) , )
if i < 1:
self.play(
MoveToTarget(model_cpu_arr[i]) , MoveToTarget(model_cpu_arr[i + 1]) , )
else:
self.play(
MoveToTarget(model_cpu_arr[i] , run_time=0.7) , MoveToTarget(model_cpu_arr[i + 1] , run_time=0.7) , )
else:
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[-1])
input.generate_target()
input.target.next_to(model_arr[-1].get_right() , RIGHT + 0.02 , buff=0.2)
self.play(
Circumscribe(model_arr[-1] , color=_UpperCAmelCase , **_UpperCAmelCase) , Circumscribe(cpu_left_col_base[-1] , color=_UpperCAmelCase , **_UpperCAmelCase) , Circumscribe(gpu_rect[0] , color=_UpperCAmelCase , **_UpperCAmelCase) , )
self.play(MoveToTarget(model_cpu_arr[i]))
__A : str = a_c
__A : Union[str, Any] = a_c.copy()
input.generate_target()
input.target.next_to(model_base[-1] , RIGHT + 0.02 , buff=0.5)
self.play(
FadeOut(_UpperCAmelCase) , FadeOut(_UpperCAmelCase , run_time=0.5) , )
__A : Optional[Any] = MarkupText(F'Inference on a model too large for GPU memory\nis successfully completed.' , font_size=24)
step_a.move_to([2, 2, 0])
self.play(Write(_UpperCAmelCase , run_time=3) , MoveToTarget(_UpperCAmelCase))
self.wait() | 8 | import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaProcessor,
is_apex_available,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
_lowerCAmelCase = True
from torch.cuda.amp import autocast
_lowerCAmelCase = logging.getLogger(__name__)
def _snake_case ( __snake_case=None , __snake_case=None ):
return field(default_factory=lambda: default , metadata=__snake_case )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
UpperCAmelCase = field(
default=0.1, metadata={"help": "The dropout ratio for the attention probabilities."} )
UpperCAmelCase = field(
default=0.1, metadata={"help": "The dropout ratio for activations inside the fully connected layer."} )
UpperCAmelCase = field(
default=0.1, metadata={
"help": "The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."
}, )
UpperCAmelCase = field(
default=0.1, metadata={"help": "The dropout probabilitiy for all 1D convolutional layers in feature extractor."}, )
UpperCAmelCase = field(
default=0.0_5, metadata={
"help": (
"Propability of each feature vector along the time axis to be chosen as the start of the vector"
"span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"
"vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."
)
}, )
UpperCAmelCase = field(default=0.0, metadata={"help": "The LayerDrop probability."} )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
UpperCAmelCase = field(
default="train+validation", metadata={
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "The number of processes to use for the preprocessing."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
}, )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of validation examples to this "
"value if set."
)
}, )
UpperCAmelCase = list_field(
default=[",", "?", ".", "!", "-", ";", ":", "\"\"", "%", "'", "\"", "�"], metadata={"help": "A list of characters to remove from the transcripts."}, )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = 42
UpperCAmelCase = True
UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = None
def __call__( self : Union[str, Any] , _A : List[Dict[str, Union[List[int], torch.Tensor]]] ):
# split inputs and labels since they have to be of different lenghts and need
# different padding methods
_UpperCamelCase = [{'''input_values''': feature['''input_values''']} for feature in features]
_UpperCamelCase = [{'''input_ids''': feature['''labels''']} for feature in features]
_UpperCamelCase = self.processor.pad(
_A , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' , )
_UpperCamelCase = self.processor.pad(
labels=_A , padding=self.padding , max_length=self.max_length_labels , pad_to_multiple_of=self.pad_to_multiple_of_labels , return_tensors='''pt''' , )
# replace padding with -100 to ignore loss correctly
_UpperCamelCase = labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1 ) , -100 )
_UpperCamelCase = labels
return batch
class lowerCAmelCase_ ( __lowercase ):
def UpperCamelCase_ ( self : Dict , _A : nn.Module , _A : Dict[str, Union[torch.Tensor, Any]] ):
model.train()
_UpperCamelCase = self._prepare_inputs(_A )
if self.use_amp:
with autocast():
_UpperCamelCase = self.compute_loss(_A , _A )
else:
_UpperCamelCase = self.compute_loss(_A , _A )
if self.args.n_gpu > 1:
if model.module.config.ctc_loss_reduction == "mean":
_UpperCamelCase = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
_UpperCamelCase = loss.sum() / (inputs['''labels'''] >= 0).sum()
else:
raise ValueError(F"""{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']""" )
if self.args.gradient_accumulation_steps > 1:
_UpperCamelCase = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(_A ).backward()
elif self.use_apex:
with amp.scale_loss(_A , self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(_A )
else:
loss.backward()
return loss.detach()
def _snake_case ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_UpperCamelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
_UpperCamelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCamelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'''Use --overwrite_output_dir to overcome.''' )
elif last_checkpoint is not None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info('''Training/evaluation parameters %s''' , __snake_case )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets:
_UpperCamelCase = datasets.load_dataset(
'''common_voice''' , data_args.dataset_config_name , split=data_args.train_split_name )
_UpperCamelCase = datasets.load_dataset('''common_voice''' , data_args.dataset_config_name , split='''test''' )
# Create and save tokenizer
_UpperCamelCase = f"""[{"".join(data_args.chars_to_ignore )}]"""
def remove_special_characters(__snake_case ):
_UpperCamelCase = re.sub(__snake_case , '''''' , batch['''sentence'''] ).lower() + ''' '''
return batch
_UpperCamelCase = train_dataset.map(__snake_case , remove_columns=['''sentence'''] )
_UpperCamelCase = eval_dataset.map(__snake_case , remove_columns=['''sentence'''] )
def extract_all_chars(__snake_case ):
_UpperCamelCase = ''' '''.join(batch['''text'''] )
_UpperCamelCase = list(set(__snake_case ) )
return {"vocab": [vocab], "all_text": [all_text]}
_UpperCamelCase = train_dataset.map(
__snake_case , batched=__snake_case , batch_size=-1 , keep_in_memory=__snake_case , remove_columns=train_dataset.column_names , )
_UpperCamelCase = train_dataset.map(
__snake_case , batched=__snake_case , batch_size=-1 , keep_in_memory=__snake_case , remove_columns=eval_dataset.column_names , )
_UpperCamelCase = list(set(vocab_train['''vocab'''][0] ) | set(vocab_test['''vocab'''][0] ) )
_UpperCamelCase = {v: k for k, v in enumerate(__snake_case )}
_UpperCamelCase = vocab_dict[''' ''']
del vocab_dict[" "]
_UpperCamelCase = len(__snake_case )
_UpperCamelCase = len(__snake_case )
with open('''vocab.json''' , '''w''' ) as vocab_file:
json.dump(__snake_case , __snake_case )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCamelCase = WavaVecaCTCTokenizer(
'''vocab.json''' , unk_token='''[UNK]''' , pad_token='''[PAD]''' , word_delimiter_token='''|''' , )
_UpperCamelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0.0 , do_normalize=__snake_case , return_attention_mask=__snake_case )
_UpperCamelCase = WavaVecaProcessor(feature_extractor=__snake_case , tokenizer=__snake_case )
_UpperCamelCase = WavaVecaForCTC.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , activation_dropout=model_args.activation_dropout , attention_dropout=model_args.attention_dropout , hidden_dropout=model_args.hidden_dropout , feat_proj_dropout=model_args.feat_proj_dropout , mask_time_prob=model_args.mask_time_prob , gradient_checkpointing=training_args.gradient_checkpointing , layerdrop=model_args.layerdrop , ctc_loss_reduction='''mean''' , pad_token_id=processor.tokenizer.pad_token_id , vocab_size=len(processor.tokenizer ) , )
if data_args.max_train_samples is not None:
_UpperCamelCase = min(len(__snake_case ) , data_args.max_train_samples )
_UpperCamelCase = train_dataset.select(range(__snake_case ) )
if data_args.max_val_samples is not None:
_UpperCamelCase = eval_dataset.select(range(data_args.max_val_samples ) )
_UpperCamelCase = torchaudio.transforms.Resample(48000 , 16000 )
# Preprocessing the datasets.
# We need to read the aduio files as arrays and tokenize the targets.
def speech_file_to_array_fn(__snake_case ):
_UpperCamelCase , _UpperCamelCase = torchaudio.load(batch['''path'''] )
_UpperCamelCase = resampler(__snake_case ).squeeze().numpy()
_UpperCamelCase = 16000
_UpperCamelCase = batch['''text''']
return batch
_UpperCamelCase = train_dataset.map(
__snake_case , remove_columns=train_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
_UpperCamelCase = eval_dataset.map(
__snake_case , remove_columns=eval_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
def prepare_dataset(__snake_case ):
# check that all files have the correct sampling rate
assert (
len(set(batch['''sampling_rate'''] ) ) == 1
), f"""Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."""
_UpperCamelCase = processor(
audio=batch['''speech'''] , text=batch['''target_text'''] , sampling_rate=batch['''sampling_rate'''][0] )
batch.update(__snake_case )
return batch
_UpperCamelCase = train_dataset.map(
__snake_case , remove_columns=train_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=__snake_case , num_proc=data_args.preprocessing_num_workers , )
_UpperCamelCase = eval_dataset.map(
__snake_case , remove_columns=eval_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=__snake_case , num_proc=data_args.preprocessing_num_workers , )
# Metric
_UpperCamelCase = datasets.load_metric('''wer''' )
def compute_metrics(__snake_case ):
_UpperCamelCase = pred.predictions
_UpperCamelCase = np.argmax(__snake_case , axis=-1 )
_UpperCamelCase = processor.tokenizer.pad_token_id
_UpperCamelCase = processor.batch_decode(__snake_case )
# we do not want to group tokens when computing the metrics
_UpperCamelCase = processor.batch_decode(pred.label_ids , group_tokens=__snake_case )
_UpperCamelCase = wer_metric.compute(predictions=__snake_case , references=__snake_case )
return {"wer": wer}
if model_args.freeze_feature_extractor:
model.freeze_feature_extractor()
# Data collator
_UpperCamelCase = DataCollatorCTCWithPadding(processor=__snake_case , padding=__snake_case )
# Initialize our Trainer
_UpperCamelCase = CTCTrainer(
model=__snake_case , data_collator=__snake_case , args=__snake_case , compute_metrics=__snake_case , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=processor.feature_extractor , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
_UpperCamelCase = last_checkpoint
elif os.path.isdir(model_args.model_name_or_path ):
_UpperCamelCase = model_args.model_name_or_path
else:
_UpperCamelCase = None
# Save the feature_extractor and the tokenizer
if is_main_process(training_args.local_rank ):
processor.save_pretrained(training_args.output_dir )
_UpperCamelCase = trainer.train(resume_from_checkpoint=__snake_case )
trainer.save_model()
_UpperCamelCase = train_result.metrics
_UpperCamelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__snake_case )
)
_UpperCamelCase = min(__snake_case , len(__snake_case ) )
trainer.log_metrics('''train''' , __snake_case )
trainer.save_metrics('''train''' , __snake_case )
trainer.save_state()
# Evaluation
_UpperCamelCase = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
_UpperCamelCase = trainer.evaluate()
_UpperCamelCase = data_args.max_val_samples if data_args.max_val_samples is not None else len(__snake_case )
_UpperCamelCase = min(__snake_case , len(__snake_case ) )
trainer.log_metrics('''eval''' , __snake_case )
trainer.save_metrics('''eval''' , __snake_case )
return results
if __name__ == "__main__":
main()
| 10 | 0 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
A__ : Optional[Any] = ["input_values", "attention_mask"]
def __init__( self : str , _snake_case : int = 1 , _snake_case : int = 1_60_00 , _snake_case : float = 0.0 , _snake_case : bool = False , _snake_case : int = 80 , _snake_case : int = 16 , _snake_case : int = 64 , _snake_case : str = "hann_window" , _snake_case : float = 1.0 , _snake_case : float = 80 , _snake_case : float = 76_00 , _snake_case : float = 1E-10 , _snake_case : int = 2 , _snake_case : bool = True , **_snake_case : Union[str, Any] , ):
"""simple docstring"""
super().__init__(feature_size=_snake_case , sampling_rate=_snake_case , padding_value=_snake_case , **_snake_case )
A__ = do_normalize
A__ = return_attention_mask
A__ = num_mel_bins
A__ = hop_length
A__ = win_length
A__ = win_function
A__ = frame_signal_scale
A__ = fmin
A__ = fmax
A__ = mel_floor
A__ = reduction_factor
A__ = win_length * sampling_rate // 10_00
A__ = hop_length * sampling_rate // 10_00
A__ = optimal_fft_length(self.sample_size )
A__ = (self.n_fft // 2) + 1
A__ = window_function(window_length=self.sample_size , name=self.win_function , periodic=_snake_case )
A__ = mel_filter_bank(
num_frequency_bins=self.n_freqs , num_mel_filters=self.num_mel_bins , min_frequency=self.fmin , max_frequency=self.fmax , sampling_rate=self.sampling_rate , norm='slaney' , mel_scale='slaney' , )
if frame_signal_scale != 1.0:
warnings.warn(
'The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers' , _snake_case , )
if reduction_factor != 2.0:
warnings.warn(
'The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers' , _snake_case , )
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def _a ( _snake_case : List[np.ndarray] , _snake_case : List[np.ndarray] , _snake_case : float = 0.0 ):
"""simple docstring"""
if attention_mask is not None:
A__ = np.array(_snake_case , np.intaa )
A__ = []
for vector, length in zip(_snake_case , attention_mask.sum(-1 ) ):
A__ = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
if length < normed_slice.shape[0]:
A__ = padding_value
normed_input_values.append(_snake_case )
else:
A__ = [(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]
return normed_input_values
def _a ( self : Tuple , _snake_case : np.ndarray , ):
"""simple docstring"""
A__ = spectrogram(
_snake_case , window=self.window , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , mel_filters=self.mel_filters , mel_floor=self.mel_floor , log_mel='log10' , )
return log_mel_spec.T
def __call__( self : List[str] , _snake_case : Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None , _snake_case : Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None , _snake_case : Union[bool, str, PaddingStrategy] = False , _snake_case : Optional[int] = None , _snake_case : bool = False , _snake_case : Optional[int] = None , _snake_case : Optional[bool] = None , _snake_case : Optional[Union[str, TensorType]] = None , _snake_case : Optional[int] = None , **_snake_case : Tuple , ):
"""simple docstring"""
if audio is None and audio_target is None:
raise ValueError('You must provide either `audio` or `audio_target` values.' )
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
F''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
F''' {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
'It is strongly recommended to pass the ``sampling_rate`` argument to this function. '
'Failing to do so can result in silent errors that might be hard to debug.' )
if audio is not None:
A__ = self._process_audio(
_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , **_snake_case , )
else:
A__ = None
if audio_target is not None:
A__ = self._process_audio(
_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , **_snake_case , )
if inputs is None:
return inputs_target
else:
A__ = inputs_target['input_values']
A__ = inputs_target.get('attention_mask' )
if decoder_attention_mask is not None:
A__ = decoder_attention_mask
return inputs
def _a ( self : Tuple , _snake_case : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , _snake_case : bool = False , _snake_case : Union[bool, str, PaddingStrategy] = False , _snake_case : Optional[int] = None , _snake_case : bool = False , _snake_case : Optional[int] = None , _snake_case : Optional[bool] = None , _snake_case : Optional[Union[str, TensorType]] = None , **_snake_case : Tuple , ):
"""simple docstring"""
A__ = isinstance(_snake_case , np.ndarray ) and len(speech.shape ) > 1
if is_batched_numpy and len(speech.shape ) > 2:
raise ValueError(F'''Only mono-channel audio is supported for input to {self}''' )
A__ = is_batched_numpy or (
isinstance(_snake_case , (list, tuple) ) and (isinstance(speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
A__ = [np.asarray(_snake_case , dtype=np.floataa ) for speech in speech]
elif not is_batched and not isinstance(_snake_case , np.ndarray ):
A__ = np.asarray(_snake_case , dtype=np.floataa )
elif isinstance(_snake_case , np.ndarray ) and speech.dtype is np.dtype(np.floataa ):
A__ = speech.astype(np.floataa )
# always return batch
if not is_batched:
A__ = [speech]
# needed to make pad() work on spectrogram inputs
A__ = self.feature_size
# convert into correct format for padding
if is_target:
A__ = [self._extract_mel_features(_snake_case ) for waveform in speech]
A__ = BatchFeature({'input_values': features} )
A__ = self.num_mel_bins
else:
A__ = BatchFeature({'input_values': speech} )
A__ = self.pad(
_snake_case , padding=_snake_case , max_length=_snake_case , truncation=_snake_case , pad_to_multiple_of=_snake_case , return_attention_mask=_snake_case , **_snake_case , )
A__ = feature_size_hack
# convert input values to correct format
A__ = padded_inputs['input_values']
if not isinstance(input_values[0] , np.ndarray ):
A__ = [np.asarray(_snake_case , dtype=np.floataa ) for array in input_values]
elif (
not isinstance(_snake_case , np.ndarray )
and isinstance(input_values[0] , np.ndarray )
and input_values[0].dtype is np.dtype(np.floataa )
):
A__ = [array.astype(np.floataa ) for array in input_values]
elif isinstance(_snake_case , np.ndarray ) and input_values.dtype is np.dtype(np.floataa ):
A__ = input_values.astype(np.floataa )
# convert attention_mask to correct format
A__ = padded_inputs.get('attention_mask' )
if attention_mask is not None:
A__ = [np.asarray(_snake_case , dtype=np.intaa ) for array in attention_mask]
# zero-mean and unit-variance normalization
if not is_target and self.do_normalize:
A__ = (
attention_mask
if self._get_padding_strategies(_snake_case , max_length=_snake_case ) is not PaddingStrategy.DO_NOT_PAD
else None
)
A__ = self.zero_mean_unit_var_norm(
padded_inputs['input_values'] , attention_mask=_snake_case , padding_value=self.padding_value )
if return_tensors is not None:
A__ = padded_inputs.convert_to_tensors(_snake_case )
return padded_inputs
def _a ( self : Optional[Any] ):
"""simple docstring"""
A__ = super().to_dict()
# Don't serialize these as they are derived from the other properties.
A__ = ['window', 'mel_filters', 'sample_size', 'sample_stride', 'n_fft', 'n_freqs']
for name in names:
if name in output:
del output[name]
return output
| 9 | import math
class lowerCAmelCase_ :
def __init__( self : Tuple , _A : int=0 ): # a graph with Node 0,1,...,N-1
_UpperCamelCase = n
_UpperCamelCase = [
[math.inf for j in range(0 , _A )] for i in range(0 , _A )
] # adjacency matrix for weight
_UpperCamelCase = [
[math.inf for j in range(0 , _A )] for i in range(0 , _A )
] # dp[i][j] stores minimum distance from i to j
def UpperCamelCase_ ( self : Dict , _A : str , _A : List[str] , _A : Optional[Any] ):
_UpperCamelCase = w
def UpperCamelCase_ ( self : Optional[int] ):
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
_UpperCamelCase = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def UpperCamelCase_ ( self : List[str] , _A : Optional[int] , _A : Optional[int] ):
return self.dp[u][v]
if __name__ == "__main__":
_lowerCAmelCase = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 10 | 0 |
'''simple docstring'''
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : str = None
__lowerCamelCase : Optional[Any] = None
@property
def a__ (self ) -> List[Any]:
"""simple docstring"""
return self.feat_extract_tester.prepare_feat_extract_dict()
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(A , '''feature_size''' ) )
self.assertTrue(hasattr(A , '''sampling_rate''' ) )
self.assertTrue(hasattr(A , '''padding_value''' ) )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.feat_extract_tester.prepare_inputs_for_common()
_a = self.feature_extraction_class(**self.feat_extract_dict )
_a = feat_extract.model_input_names[0]
_a = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(A ) == len(A ) for x, y in zip(A , processed_features[input_name] ) ) )
_a = self.feat_extract_tester.prepare_inputs_for_common(equal_length=A )
_a = BatchFeature({input_name: speech_inputs} , tensor_type='''np''' )
_a = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
_a = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def a__ (self ) -> Dict:
"""simple docstring"""
_a = self.feat_extract_tester.prepare_inputs_for_common(equal_length=A )
_a = self.feature_extraction_class(**self.feat_extract_dict )
_a = feat_extract.model_input_names[0]
_a = BatchFeature({input_name: speech_inputs} , tensor_type='''pt''' )
_a = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
_a = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def a__ (self ) -> str:
"""simple docstring"""
_a = self.feat_extract_tester.prepare_inputs_for_common(equal_length=A )
_a = self.feature_extraction_class(**self.feat_extract_dict )
_a = feat_extract.model_input_names[0]
_a = BatchFeature({input_name: speech_inputs} , tensor_type='''tf''' )
_a = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
_a = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def a__ (self , A=False ) -> Any:
"""simple docstring"""
def _inputs_have_equal_length(A ):
_a = len(input[0] )
for input_slice in input[1:]:
if len(A ) != length:
return False
return True
def _inputs_are_equal(A , A ):
if len(A ) != len(A ):
return False
for input_slice_a, input_slice_a in zip(A , A ):
if not np.allclose(np.asarray(A ) , np.asarray(A ) , atol=1E-3 ):
return False
return True
_a = self.feature_extraction_class(**self.feat_extract_dict )
_a = self.feat_extract_tester.prepare_inputs_for_common(numpify=A )
_a = feat_extract.model_input_names[0]
_a = BatchFeature({input_name: speech_inputs} )
_a = self.feat_extract_tester.seq_length_diff
_a = self.feat_extract_tester.max_seq_length + pad_diff
_a = self.feat_extract_tester.min_seq_length
_a = self.feat_extract_tester.batch_size
_a = self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
_a = feat_extract.pad(A , padding=A )
_a = input_a[input_name]
_a = feat_extract.pad(A , padding='''longest''' )
_a = input_a[input_name]
_a = feat_extract.pad(A , padding='''max_length''' , max_length=len(speech_inputs[-1] ) )
_a = input_a[input_name]
_a = feat_extract.pad(A , padding='''longest''' , return_tensors='''np''' )
_a = input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(A ):
feat_extract.pad(A , padding='''max_length''' )[input_name]
_a = feat_extract.pad(
A , padding='''max_length''' , max_length=A , return_tensors='''np''' )
_a = input_a[input_name]
self.assertFalse(_inputs_have_equal_length(A ) )
self.assertTrue(_inputs_have_equal_length(A ) )
self.assertTrue(_inputs_have_equal_length(A ) )
self.assertTrue(_inputs_are_equal(A , A ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
_a = feat_extract.pad(A , pad_to_multiple_of=10 )
_a = input_a[input_name]
_a = feat_extract.pad(A , padding='''longest''' , pad_to_multiple_of=10 )
_a = input_a[input_name]
_a = feat_extract.pad(
A , padding='''max_length''' , pad_to_multiple_of=10 , max_length=A )
_a = input_a[input_name]
_a = feat_extract.pad(
A , padding='''max_length''' , pad_to_multiple_of=10 , max_length=A , return_tensors='''np''' , )
_a = input_a[input_name]
self.assertTrue(all(len(A ) % 10 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(A , A ) )
_a = pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(A ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] , (batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
_a = (np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1E-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1E-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1E-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1E-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1E-3 )
def a__ (self , A=False ) -> List[Any]:
"""simple docstring"""
def _inputs_have_equal_length(A ):
_a = len(input[0] )
for input_slice in input[1:]:
if len(A ) != length:
return False
return True
def _inputs_are_equal(A , A ):
if len(A ) != len(A ):
return False
for input_slice_a, input_slice_a in zip(A , A ):
if not np.allclose(np.asarray(A ) , np.asarray(A ) , atol=1E-3 ):
return False
return True
_a = self.feature_extraction_class(**self.feat_extract_dict )
_a = self.feat_extract_tester.prepare_inputs_for_common(numpify=A )
_a = feat_extract.model_input_names[0]
_a = BatchFeature({input_name: speech_inputs} )
# truncate to smallest
_a = feat_extract.pad(
A , padding='''max_length''' , max_length=len(speech_inputs[0] ) , truncation=A )
_a = input_a[input_name]
_a = feat_extract.pad(A , padding='''max_length''' , max_length=len(speech_inputs[0] ) )
_a = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(A ) )
self.assertFalse(_inputs_have_equal_length(A ) )
# truncate to smallest with np
_a = feat_extract.pad(
A , padding='''max_length''' , max_length=len(speech_inputs[0] ) , return_tensors='''np''' , truncation=A , )
_a = input_a[input_name]
_a = feat_extract.pad(
A , padding='''max_length''' , max_length=len(speech_inputs[0] ) , return_tensors='''np''' )
_a = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(A ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(A ) )
# truncate to middle
_a = feat_extract.pad(
A , padding='''max_length''' , max_length=len(speech_inputs[1] ) , truncation=A , return_tensors='''np''' , )
_a = input_a[input_name]
_a = feat_extract.pad(
A , padding='''max_length''' , max_length=len(speech_inputs[1] ) , truncation=A )
_a = input_a[input_name]
_a = feat_extract.pad(
A , padding='''max_length''' , max_length=len(speech_inputs[1] ) , return_tensors='''np''' )
_a = input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(A ) )
self.assertTrue(_inputs_have_equal_length(A ) )
self.assertTrue(_inputs_are_equal(A , A ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(A ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(A ):
feat_extract.pad(A , truncation=A )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(A ):
feat_extract.pad(A , padding='''longest''' , truncation=A )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(A ):
feat_extract.pad(A , padding='''longest''' , truncation=A )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(A ):
feat_extract.pad(A , padding='''max_length''' , truncation=A )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
_a = 12
_a = feat_extract.pad(
A , padding='''max_length''' , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=A , truncation=A , )
_a = input_a[input_name]
_a = feat_extract.pad(
A , padding='''max_length''' , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=A , )
_a = input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
_a = len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
_a = ((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(A ) )
self.assertFalse(_inputs_have_equal_length(A ) )
def a__ (self ) -> List[Any]:
"""simple docstring"""
self._check_padding(numpify=A )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
self._check_padding(numpify=A )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
self._check_truncation(numpify=A )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
self._check_truncation(numpify=A )
@require_torch
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = self.feature_extraction_class(**self.feat_extract_dict )
_a = self.feat_extract_tester.prepare_inputs_for_common()
_a = feat_extract.model_input_names[0]
_a = BatchFeature({input_name: speech_inputs} )
_a = feat_extract.pad(A , padding='''longest''' , return_tensors='''np''' )[input_name]
_a = feat_extract.pad(A , padding='''longest''' , return_tensors='''pt''' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1E-2 )
@require_tf
def a__ (self ) -> Any:
"""simple docstring"""
_a = self.feature_extraction_class(**self.feat_extract_dict )
_a = self.feat_extract_tester.prepare_inputs_for_common()
_a = feat_extract.model_input_names[0]
_a = BatchFeature({input_name: speech_inputs} )
_a = feat_extract.pad(A , padding='''longest''' , return_tensors='''np''' )[input_name]
_a = feat_extract.pad(A , padding='''longest''' , return_tensors='''tf''' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1E-2 )
def a__ (self ) -> Dict:
"""simple docstring"""
_a = self.feat_extract_dict
_a = True
_a = self.feature_extraction_class(**A )
_a = self.feat_extract_tester.prepare_inputs_for_common()
_a = [len(A ) for x in speech_inputs]
_a = feat_extract.model_input_names[0]
_a = BatchFeature({input_name: speech_inputs} )
_a = feat_extract.pad(A , padding='''longest''' , return_tensors='''np''' )
self.assertIn('''attention_mask''' , A )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , A )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = self.feat_extract_dict
_a = True
_a = self.feature_extraction_class(**A )
_a = self.feat_extract_tester.prepare_inputs_for_common()
_a = [len(A ) for x in speech_inputs]
_a = feat_extract.model_input_names[0]
_a = BatchFeature({input_name: speech_inputs} )
_a = min(A )
_a = feat_extract.pad(
A , padding='''max_length''' , max_length=A , truncation=A , return_tensors='''np''' )
self.assertIn('''attention_mask''' , A )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
| 11 | import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
def _snake_case ( __snake_case=None , __snake_case=None ):
return field(default_factory=lambda: default , metadata=__snake_case )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = list_field(
default=[], metadata={
"help": (
"Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
" of all available models"
)
}, )
UpperCAmelCase = list_field(
default=[8], metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"} )
UpperCAmelCase = list_field(
default=[8, 32, 128, 512], metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Use FP16 to accelerate inference."} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Benchmark training of model"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Verbose memory tracing"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
}, )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Trace memory line by line"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Save result to a CSV file"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Save all print statements in a log file"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Whether to print environment information"} )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
" multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
" for debugging / testing and on TPU."
)
}, )
UpperCAmelCase = field(
default=F"""inference_time_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving time results to csv."}, )
UpperCAmelCase = field(
default=F"""inference_memory_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving memory results to csv."}, )
UpperCAmelCase = field(
default=F"""train_time_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving time results to csv for training."}, )
UpperCAmelCase = field(
default=F"""train_memory_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving memory results to csv for training."}, )
UpperCAmelCase = field(
default=F"""env_info_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving environment information."}, )
UpperCAmelCase = field(
default=F"""log_{round(time() )}.csv""", metadata={"help": "Log filename used if print statements are saved in log."}, )
UpperCAmelCase = field(default=3, metadata={"help": "Times an experiment will be run."} )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
" model weights."
)
}, )
def UpperCamelCase_ ( self : Union[str, Any] ):
warnings.warn(
F"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
''' are deprecated in general and it is advised to use external Benchmarking libraries '''
''' to benchmark Transformer models.''' , _A , )
def UpperCamelCase_ ( self : str ):
return json.dumps(dataclasses.asdict(self ) , indent=2 )
@property
def UpperCamelCase_ ( self : List[Any] ):
if len(self.models ) <= 0:
raise ValueError(
'''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
''' bert-base-cased` or `args.models = [\'bert-base-cased\'].''' )
return self.models
@property
def UpperCamelCase_ ( self : Optional[int] ):
if not self.multi_process:
return False
elif self.is_tpu:
logger.info('''Multiprocessing is currently not possible on TPU.''' )
return False
else:
return True
| 10 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class _snake_case ( unittest.TestCase ):
@slow
def lowercase__ ( self):
'''simple docstring'''
lowercase__ : Dict = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base""")
lowercase__ : Optional[int] = tf.convert_to_tensor(
[[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
lowercase__ : Optional[Any] = model(SCREAMING_SNAKE_CASE_)["""last_hidden_state"""]
lowercase__ : Tuple = tf.TensorShape((1, 10, 7_68))
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_)
# compare the actual values for a slice.
lowercase__ : Optional[Any] = tf.convert_to_tensor(
[[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4))
| 12 | import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _snake_case ( *__snake_case , __snake_case = None , __snake_case=True , __snake_case=2 ):
from .. import __version__
_UpperCamelCase = take_from
_UpperCamelCase = ()
if not isinstance(args[0] , __snake_case ):
_UpperCamelCase = (args,)
for attribute, version_name, message in args:
if version.parse(version.parse(__snake_case ).base_version ) >= version.parse(__snake_case ):
raise ValueError(
f"""The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"""
f""" version {__version__} is >= {version_name}""" )
_UpperCamelCase = None
if isinstance(__snake_case , __snake_case ) and attribute in deprecated_kwargs:
values += (deprecated_kwargs.pop(__snake_case ),)
_UpperCamelCase = f"""The `{attribute}` argument is deprecated and will be removed in version {version_name}."""
elif hasattr(__snake_case , __snake_case ):
values += (getattr(__snake_case , __snake_case ),)
_UpperCamelCase = f"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
elif deprecated_kwargs is None:
_UpperCamelCase = f"""`{attribute}` is deprecated and will be removed in version {version_name}."""
if warning is not None:
_UpperCamelCase = warning + ''' ''' if standard_warn else ''''''
warnings.warn(warning + message , __snake_case , stacklevel=__snake_case )
if isinstance(__snake_case , __snake_case ) and len(__snake_case ) > 0:
_UpperCamelCase = inspect.getouterframes(inspect.currentframe() )[1]
_UpperCamelCase = call_frame.filename
_UpperCamelCase = call_frame.lineno
_UpperCamelCase = call_frame.function
_UpperCamelCase , _UpperCamelCase = next(iter(deprecated_kwargs.items() ) )
raise TypeError(f"""{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`""" )
if len(__snake_case ) == 0:
return
elif len(__snake_case ) == 1:
return values[0]
return values
| 10 | 0 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : List[str] = logging.get_logger(__name__)
# TODO Update this
A__ : Tuple = {
"""facebook/esm-1b""": """https://huggingface.co/facebook/esm-1b/resolve/main/config.json""",
# See all ESM models at https://huggingface.co/models?filter=esm
}
class UpperCAmelCase_ (_UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase : Tuple = 'esm'
def __init__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=7_68 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=30_72 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=10_26 , SCREAMING_SNAKE_CASE_=0.0_2 , SCREAMING_SNAKE_CASE_=1E-12 , SCREAMING_SNAKE_CASE_="absolute" , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ , ) -> List[str]:
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_ , mask_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : int = vocab_size
__lowerCamelCase : List[Any] = hidden_size
__lowerCamelCase : str = num_hidden_layers
__lowerCamelCase : List[str] = num_attention_heads
__lowerCamelCase : Any = intermediate_size
__lowerCamelCase : Optional[Any] = hidden_dropout_prob
__lowerCamelCase : Tuple = attention_probs_dropout_prob
__lowerCamelCase : Optional[int] = max_position_embeddings
__lowerCamelCase : str = initializer_range
__lowerCamelCase : Optional[int] = layer_norm_eps
__lowerCamelCase : List[str] = position_embedding_type
__lowerCamelCase : int = use_cache
__lowerCamelCase : Optional[Any] = emb_layer_norm_before
__lowerCamelCase : Optional[Any] = token_dropout
__lowerCamelCase : str = is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info('No esmfold_config supplied for folding model, using default values.' )
__lowerCamelCase : Dict = EsmFoldConfig()
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
__lowerCamelCase : Optional[int] = EsmFoldConfig(**SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : List[Any] = esmfold_config
if vocab_list is None:
logger.warning('No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!' )
__lowerCamelCase : List[str] = get_default_vocab_list()
else:
__lowerCamelCase : Optional[Any] = vocab_list
else:
__lowerCamelCase : Dict = None
__lowerCamelCase : Optional[Any] = None
if self.esmfold_config is not None and getattr(self.esmfold_config , 'use_esm_attn_map' , SCREAMING_SNAKE_CASE_ ):
raise ValueError('The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!' )
def lowercase_ ( self ) -> Any:
__lowerCamelCase : Any = super().to_dict()
if isinstance(self.esmfold_config , SCREAMING_SNAKE_CASE_ ):
__lowerCamelCase : int = self.esmfold_config.to_dict()
return output
@dataclass
class UpperCAmelCase_ :
"""simple docstring"""
lowerCamelCase : str = None
lowerCamelCase : bool = True
lowerCamelCase : bool = False
lowerCamelCase : bool = False
lowerCamelCase : bool = False
lowerCamelCase : float = 0
lowerCamelCase : bool = True
lowerCamelCase : bool = False
lowerCamelCase : int = 1_2_8
lowerCamelCase : "TrunkConfig" = None
def lowercase_ ( self ) -> Any:
if self.trunk is None:
__lowerCamelCase : List[str] = TrunkConfig()
elif isinstance(self.trunk , SCREAMING_SNAKE_CASE_ ):
__lowerCamelCase : Any = TrunkConfig(**self.trunk )
def lowercase_ ( self ) -> int:
__lowerCamelCase : Optional[int] = asdict(self )
__lowerCamelCase : str = self.trunk.to_dict()
return output
@dataclass
class UpperCAmelCase_ :
"""simple docstring"""
lowerCamelCase : int = 4_8
lowerCamelCase : int = 1_0_2_4
lowerCamelCase : int = 1_2_8
lowerCamelCase : int = 3_2
lowerCamelCase : int = 3_2
lowerCamelCase : int = 3_2
lowerCamelCase : float = 0
lowerCamelCase : float = 0
lowerCamelCase : bool = False
lowerCamelCase : int = 4
lowerCamelCase : Optional[int] = 1_2_8
lowerCamelCase : "StructureModuleConfig" = None
def lowercase_ ( self ) -> Optional[int]:
if self.structure_module is None:
__lowerCamelCase : Dict = StructureModuleConfig()
elif isinstance(self.structure_module , SCREAMING_SNAKE_CASE_ ):
__lowerCamelCase : Optional[Any] = StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(f'`max_recycles` should be positive, got {self.max_recycles}.' )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
'`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'
f' {self.sequence_state_dim} and {self.sequence_state_dim}.' )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
'`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'
f' {self.pairwise_state_dim} and {self.pairwise_state_dim}.' )
__lowerCamelCase : Tuple = self.sequence_state_dim // self.sequence_head_width
__lowerCamelCase : str = self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
'`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'
f' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.' )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
'`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'
f' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.' )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(f'`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.' )
if self.dropout >= 0.4:
raise ValueError(f'`dropout` should not be greater than 0.4, got {self.dropout}.' )
def lowercase_ ( self ) -> List[Any]:
__lowerCamelCase : List[str] = asdict(self )
__lowerCamelCase : int = self.structure_module.to_dict()
return output
@dataclass
class UpperCAmelCase_ :
"""simple docstring"""
lowerCamelCase : int = 3_8_4
lowerCamelCase : int = 1_2_8
lowerCamelCase : int = 1_6
lowerCamelCase : int = 1_2_8
lowerCamelCase : int = 1_2
lowerCamelCase : int = 4
lowerCamelCase : int = 8
lowerCamelCase : float = 0.1
lowerCamelCase : int = 8
lowerCamelCase : int = 1
lowerCamelCase : int = 2
lowerCamelCase : int = 7
lowerCamelCase : int = 1_0
lowerCamelCase : float = 1e-8
lowerCamelCase : float = 1e5
def lowercase_ ( self ) -> Any:
return asdict(self )
def UpperCAmelCase__ ( ) -> Optional[Any]:
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 13 | import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
_lowerCAmelCase = logging.getLogger(__name__)
def _snake_case ( __snake_case , __snake_case ):
return (preds == labels).mean()
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Pretrained config name or path if not the same as model_name"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys() )} )
UpperCAmelCase = field(metadata={"help": "Should contain the data files for the task."} )
UpperCAmelCase = field(
default=128, metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Overwrite the cached training and evaluation sets"} )
def _snake_case ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_UpperCamelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
''' --overwrite_output_dir to overcome.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('''Training/evaluation parameters %s''' , __snake_case )
# Set seed
set_seed(training_args.seed )
try:
_UpperCamelCase = processors[data_args.task_name]()
_UpperCamelCase = processor.get_labels()
_UpperCamelCase = len(__snake_case )
except KeyError:
raise ValueError('''Task not found: %s''' % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCamelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__snake_case , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
_UpperCamelCase = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
_UpperCamelCase = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=__snake_case , cache_dir=model_args.cache_dir , )
# Get datasets
_UpperCamelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__snake_case , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
_UpperCamelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__snake_case , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def compute_metrics(__snake_case ) -> Dict:
_UpperCamelCase = np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(__snake_case , p.label_ids )}
# Data collator
_UpperCamelCase = DataCollatorWithPadding(__snake_case , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
_UpperCamelCase = Trainer(
model=__snake_case , args=__snake_case , train_dataset=__snake_case , eval_dataset=__snake_case , compute_metrics=__snake_case , data_collator=__snake_case , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
_UpperCamelCase = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
_UpperCamelCase = trainer.evaluate()
_UpperCamelCase = os.path.join(training_args.output_dir , '''eval_results.txt''' )
if trainer.is_world_master():
with open(__snake_case , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''' , __snake_case , __snake_case )
writer.write('''%s = %s\n''' % (key, value) )
results.update(__snake_case )
return results
def _snake_case ( __snake_case ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 10 | 0 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
a__ = TypeVar('''T''')
def __UpperCAmelCase ( __a : int ) -> int:
"""simple docstring"""
return (position - 1) // 2
def __UpperCAmelCase ( __a : int ) -> int:
"""simple docstring"""
return (2 * position) + 1
def __UpperCAmelCase ( __a : int ) -> int:
"""simple docstring"""
return (2 * position) + 2
class UpperCAmelCase_ ( Generic[T] ):
"""simple docstring"""
def __init__( self ) -> None:
_a : list[tuple[T, int]] = []
_a : dict[T, int] = {}
_a : int = 0
def __len__( self ) -> int:
return self.elements
def __repr__( self ) -> str:
return str(self.heap )
def __lowercase ( self ) -> bool:
# Check if the priority queue is empty
return self.elements == 0
def __lowercase ( self , _a , _a ) -> None:
# Add an element with given priority to the queue
self.heap.append((elem, weight) )
_a : Union[str, Any] = self.elements
self.elements += 1
self._bubble_up(_a )
def __lowercase ( self ) -> T:
# Remove and return the element with lowest weight (highest priority)
if self.elements > 1:
self._swap_nodes(0 , self.elements - 1 )
_a , _a : str = self.heap.pop()
del self.position_map[elem]
self.elements -= 1
if self.elements > 0:
_a , _a : List[Any] = self.heap[0]
self._bubble_down(_a )
return elem
def __lowercase ( self , _a , _a ) -> None:
# Update the weight of the given key
_a : Optional[int] = self.position_map[elem]
_a : Any = (elem, weight)
if position > 0:
_a : Any = get_parent_position(_a )
_a , _a : Optional[int] = self.heap[parent_position]
if parent_weight > weight:
self._bubble_up(_a )
else:
self._bubble_down(_a )
else:
self._bubble_down(_a )
def __lowercase ( self , _a ) -> None:
# Place a node at the proper position (upward movement) [to be used internally
# only]
_a : Union[str, Any] = self.position_map[elem]
if curr_pos == 0:
return None
_a : str = get_parent_position(_a )
_a , _a : List[str] = self.heap[curr_pos]
_a , _a : List[str] = self.heap[parent_position]
if parent_weight > weight:
self._swap_nodes(_a , _a )
return self._bubble_up(_a )
return None
def __lowercase ( self , _a ) -> None:
# Place a node at the proper position (downward movement) [to be used
# internally only]
_a : Union[str, Any] = self.position_map[elem]
_a , _a : List[str] = self.heap[curr_pos]
_a : int = get_child_left_position(_a )
_a : Optional[int] = get_child_right_position(_a )
if child_left_position < self.elements and child_right_position < self.elements:
_a , _a : Union[str, Any] = self.heap[child_left_position]
_a , _a : Tuple = self.heap[child_right_position]
if child_right_weight < child_left_weight and child_right_weight < weight:
self._swap_nodes(_a , _a )
return self._bubble_down(_a )
if child_left_position < self.elements:
_a , _a : List[str] = self.heap[child_left_position]
if child_left_weight < weight:
self._swap_nodes(_a , _a )
return self._bubble_down(_a )
else:
return None
if child_right_position < self.elements:
_a , _a : Optional[Any] = self.heap[child_right_position]
if child_right_weight < weight:
self._swap_nodes(_a , _a )
return self._bubble_down(_a )
return None
def __lowercase ( self , _a , _a ) -> None:
# Swap the nodes at the given positions
_a : str = self.heap[nodea_pos][0]
_a : str = self.heap[nodea_pos][0]
_a , _a : Optional[Any] = (
self.heap[nodea_pos],
self.heap[nodea_pos],
)
_a : List[str] = nodea_pos
_a : List[str] = nodea_pos
class UpperCAmelCase_ ( Generic[T] ):
"""simple docstring"""
def __init__( self ) -> None:
_a : dict[T, dict[T, int]] = {}
_a : int = 0
def __repr__( self ) -> str:
return str(self.connections )
def __len__( self ) -> int:
return self.nodes
def __lowercase ( self , _a ) -> None:
# Add a node in the graph if it is not in the graph
if node not in self.connections:
_a : Optional[Any] = {}
self.nodes += 1
def __lowercase ( self , _a , _a , _a ) -> None:
# Add an edge between 2 nodes in the graph
self.add_node(_a )
self.add_node(_a )
_a : int = weight
_a : int = weight
def __UpperCAmelCase ( __a : GraphUndirectedWeighted[T] ,) -> tuple[dict[T, int], dict[T, T | None]]:
"""simple docstring"""
_a : dict[T, int] = {node: maxsize for node in graph.connections}
_a : dict[T, T | None] = {node: None for node in graph.connections}
_a : MinPriorityQueue[T] = MinPriorityQueue()
for node, weight in dist.items():
priority_queue.push(__a ,__a )
if priority_queue.is_empty():
return dist, parent
# initialization
_a : List[str] = priority_queue.extract_min()
_a : int = 0
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
_a : Tuple = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(__a ,dist[neighbour] )
_a : List[Any] = node
# running prim's algorithm
while not priority_queue.is_empty():
_a : List[str] = priority_queue.extract_min()
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
_a : Dict = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(__a ,dist[neighbour] )
_a : Optional[Any] = node
return dist, parent
| 14 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
),
# See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = "trocr"
UpperCAmelCase = ["past_key_values"]
UpperCAmelCase = {
"num_attention_heads": "decoder_attention_heads",
"hidden_size": "d_model",
"num_hidden_layers": "decoder_layers",
}
def __init__( self : List[str] , _A : Optional[Any]=5_0265 , _A : Optional[Any]=1024 , _A : Optional[Any]=12 , _A : Any=16 , _A : Any=4096 , _A : Optional[Any]="gelu" , _A : Union[str, Any]=512 , _A : Dict=0.1 , _A : List[str]=0.0 , _A : Optional[Any]=0.0 , _A : Union[str, Any]=2 , _A : Any=0.02 , _A : List[str]=0.0 , _A : List[str]=True , _A : str=False , _A : List[str]=True , _A : Optional[Any]=True , _A : Optional[int]=1 , _A : int=0 , _A : Any=2 , **_A : Optional[int] , ):
_UpperCamelCase = vocab_size
_UpperCamelCase = d_model
_UpperCamelCase = decoder_layers
_UpperCamelCase = decoder_attention_heads
_UpperCamelCase = decoder_ffn_dim
_UpperCamelCase = activation_function
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = activation_dropout
_UpperCamelCase = init_std
_UpperCamelCase = decoder_layerdrop
_UpperCamelCase = use_cache
_UpperCamelCase = scale_embedding
_UpperCamelCase = use_learned_position_embeddings
_UpperCamelCase = layernorm_embedding
super().__init__(
pad_token_id=_A , bos_token_id=_A , eos_token_id=_A , decoder_start_token_id=_A , **_A , )
| 10 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : List[str] = {
'configuration_trajectory_transformer': [
'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TrajectoryTransformerConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : str = [
'TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TrajectoryTransformerModel',
'TrajectoryTransformerPreTrainedModel',
'load_tf_weights_in_trajectory_transformer',
]
if TYPE_CHECKING:
from .configuration_trajectory_transformer import (
TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TrajectoryTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trajectory_transformer import (
TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TrajectoryTransformerModel,
TrajectoryTransformerPreTrainedModel,
load_tf_weights_in_trajectory_transformer,
)
else:
import sys
A : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 15 | import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
FlaubertForMultipleChoice,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertModel,
FlaubertWithLMHeadModel,
)
from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase_ ( __lowercase ):
def __init__( self : Union[str, Any] , _A : Optional[Any] , _A : Any=13 , _A : Union[str, Any]=7 , _A : List[str]=True , _A : List[str]=True , _A : List[str]=True , _A : List[str]=True , _A : List[Any]=True , _A : Optional[int]=False , _A : Any=False , _A : int=False , _A : Optional[Any]=2 , _A : Any=99 , _A : str=0 , _A : Union[str, Any]=32 , _A : List[Any]=5 , _A : Tuple=4 , _A : List[str]=0.1 , _A : Union[str, Any]=0.1 , _A : int=512 , _A : Union[str, Any]=12 , _A : List[str]=2 , _A : int=0.02 , _A : Optional[Any]=3 , _A : Any=4 , _A : Optional[int]="last" , _A : Any=None , _A : Dict=None , ):
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = seq_length
_UpperCamelCase = is_training
_UpperCamelCase = use_input_lengths
_UpperCamelCase = use_token_type_ids
_UpperCamelCase = use_labels
_UpperCamelCase = gelu_activation
_UpperCamelCase = sinusoidal_embeddings
_UpperCamelCase = causal
_UpperCamelCase = asm
_UpperCamelCase = n_langs
_UpperCamelCase = vocab_size
_UpperCamelCase = n_special
_UpperCamelCase = hidden_size
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = hidden_dropout_prob
_UpperCamelCase = attention_probs_dropout_prob
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = type_vocab_size
_UpperCamelCase = type_sequence_label_size
_UpperCamelCase = initializer_range
_UpperCamelCase = num_labels
_UpperCamelCase = num_choices
_UpperCamelCase = summary_type
_UpperCamelCase = use_proj
_UpperCamelCase = scope
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCamelCase = None
if self.use_input_lengths:
_UpperCamelCase = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
_UpperCamelCase = None
if self.use_token_type_ids:
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = None
if self.use_labels:
_UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCamelCase = ids_tensor([self.batch_size] , 2 ).float()
_UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices )
_UpperCamelCase = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def UpperCamelCase_ ( self : str ):
return FlaubertConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , )
def UpperCamelCase_ ( self : str , _A : Union[str, Any] , _A : Optional[Any] , _A : str , _A : Tuple , _A : List[str] , _A : List[Any] , _A : Any , _A : str , _A : Optional[int] , ):
_UpperCamelCase = FlaubertModel(config=_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A , lengths=_A , langs=_A )
_UpperCamelCase = model(_A , langs=_A )
_UpperCamelCase = model(_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase_ ( self : Tuple , _A : List[Any] , _A : str , _A : Optional[int] , _A : Optional[Any] , _A : List[str] , _A : int , _A : str , _A : List[Any] , _A : Any , ):
_UpperCamelCase = FlaubertWithLMHeadModel(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A , token_type_ids=_A , labels=_A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase_ ( self : Tuple , _A : List[str] , _A : List[str] , _A : Optional[Any] , _A : Union[str, Any] , _A : str , _A : List[str] , _A : Tuple , _A : Optional[int] , _A : Dict , ):
_UpperCamelCase = FlaubertForQuestionAnsweringSimple(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A )
_UpperCamelCase = model(_A , start_positions=_A , end_positions=_A )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase_ ( self : Tuple , _A : str , _A : Tuple , _A : Tuple , _A : Union[str, Any] , _A : List[str] , _A : int , _A : str , _A : Dict , _A : List[Any] , ):
_UpperCamelCase = FlaubertForQuestionAnswering(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A )
_UpperCamelCase = model(
_A , start_positions=_A , end_positions=_A , cls_index=_A , is_impossible=_A , p_mask=_A , )
_UpperCamelCase = model(
_A , start_positions=_A , end_positions=_A , cls_index=_A , is_impossible=_A , )
((_UpperCamelCase) , ) = result_with_labels.to_tuple()
_UpperCamelCase = model(_A , start_positions=_A , end_positions=_A )
((_UpperCamelCase) , ) = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def UpperCamelCase_ ( self : List[Any] , _A : Union[str, Any] , _A : Tuple , _A : str , _A : int , _A : int , _A : Optional[int] , _A : Optional[int] , _A : int , _A : List[str] , ):
_UpperCamelCase = FlaubertForSequenceClassification(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A )
_UpperCamelCase = model(_A , labels=_A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCamelCase_ ( self : Optional[int] , _A : List[str] , _A : Optional[Any] , _A : str , _A : Union[str, Any] , _A : List[Any] , _A : int , _A : List[Any] , _A : str , _A : List[str] , ):
_UpperCamelCase = self.num_labels
_UpperCamelCase = FlaubertForTokenClassification(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A , attention_mask=_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase_ ( self : Tuple , _A : Dict , _A : str , _A : Optional[Any] , _A : List[str] , _A : Any , _A : Optional[int] , _A : Optional[Any] , _A : List[Any] , _A : List[str] , ):
_UpperCamelCase = self.num_choices
_UpperCamelCase = FlaubertForMultipleChoice(config=_A )
model.to(_A )
model.eval()
_UpperCamelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCamelCase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCamelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCamelCase = model(
_A , attention_mask=_A , token_type_ids=_A , labels=_A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCamelCase_ ( self : Tuple ):
_UpperCamelCase = self.prepare_config_and_inputs()
(
(
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) ,
) = config_and_inputs
_UpperCamelCase = {
'''input_ids''': input_ids,
'''token_type_ids''': token_type_ids,
'''lengths''': input_lengths,
'''attention_mask''': input_mask,
}
return config, inputs_dict
@require_torch
class lowerCAmelCase_ ( __lowercase, __lowercase, unittest.TestCase ):
UpperCAmelCase = (
(
FlaubertModel,
FlaubertWithLMHeadModel,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertForMultipleChoice,
)
if is_torch_available()
else ()
)
UpperCAmelCase = (
{
"feature-extraction": FlaubertModel,
"fill-mask": FlaubertWithLMHeadModel,
"question-answering": FlaubertForQuestionAnsweringSimple,
"text-classification": FlaubertForSequenceClassification,
"token-classification": FlaubertForTokenClassification,
"zero-shot": FlaubertForSequenceClassification,
}
if is_torch_available()
else {}
)
def UpperCamelCase_ ( self : Union[str, Any] , _A : Dict , _A : Dict , _A : Tuple , _A : int , _A : Any ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('''Fast''' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def UpperCamelCase_ ( self : str , _A : Any , _A : List[str] , _A : Optional[int]=False ):
_UpperCamelCase = super()._prepare_for_class(_A , _A , return_labels=_A )
if return_labels:
if model_class.__name__ == "FlaubertForQuestionAnswering":
_UpperCamelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_A )
_UpperCamelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_A )
return inputs_dict
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = FlaubertModelTester(self )
_UpperCamelCase = ConfigTester(self , config_class=_A , emb_dim=37 )
def UpperCamelCase_ ( self : Optional[Any] ):
self.config_tester.run_common_tests()
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_model(*_A )
def UpperCamelCase_ ( self : Optional[Any] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_lm_head(*_A )
def UpperCamelCase_ ( self : Optional[Any] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_simple_qa(*_A )
def UpperCamelCase_ ( self : Union[str, Any] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_qa(*_A )
def UpperCamelCase_ ( self : Optional[int] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_sequence_classif(*_A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_token_classif(*_A )
def UpperCamelCase_ ( self : Optional[int] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_multiple_choice(*_A )
@slow
def UpperCamelCase_ ( self : str ):
for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = FlaubertModel.from_pretrained(_A )
self.assertIsNotNone(_A )
@slow
@require_torch_gpu
def UpperCamelCase_ ( self : List[Any] ):
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == FlaubertForMultipleChoice:
return
_UpperCamelCase = True
_UpperCamelCase = model_class(config=_A )
_UpperCamelCase = self._prepare_for_class(_A , _A )
_UpperCamelCase = torch.jit.trace(
_A , (inputs_dict['''input_ids'''].to('''cpu''' ), inputs_dict['''attention_mask'''].to('''cpu''' )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(_A , os.path.join(_A , '''traced_model.pt''' ) )
_UpperCamelCase = torch.jit.load(os.path.join(_A , '''traced_model.pt''' ) , map_location=_A )
loaded(inputs_dict['''input_ids'''].to(_A ) , inputs_dict['''attention_mask'''].to(_A ) )
@require_torch
class lowerCAmelCase_ ( unittest.TestCase ):
@slow
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = FlaubertModel.from_pretrained('''flaubert/flaubert_base_cased''' )
_UpperCamelCase = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
with torch.no_grad():
_UpperCamelCase = model(_A )[0]
_UpperCamelCase = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , _A )
_UpperCamelCase = torch.tensor(
[[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _A , atol=1e-4 ) )
| 10 | 0 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : str , __lowerCamelCase : str , __lowerCamelCase : Tuple=13 , __lowerCamelCase : Tuple=30 , __lowerCamelCase : Any=2 , __lowerCamelCase : Union[str, Any]=3 , __lowerCamelCase : int=True , __lowerCamelCase : Optional[int]=True , __lowerCamelCase : List[Any]=32 , __lowerCamelCase : Dict=5 , __lowerCamelCase : Union[str, Any]=4 , __lowerCamelCase : Optional[Any]=37 , __lowerCamelCase : Union[str, Any]="gelu" , __lowerCamelCase : str=0.1 , __lowerCamelCase : Any=0.1 , __lowerCamelCase : Union[str, Any]=10 , __lowerCamelCase : int=0.02 , ):
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = patch_size
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = is_training
SCREAMING_SNAKE_CASE = use_labels
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = type_sequence_label_size
SCREAMING_SNAKE_CASE = initializer_range
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
SCREAMING_SNAKE_CASE = (image_size // patch_size) ** 2
SCREAMING_SNAKE_CASE = num_patches + 1
def _snake_case ( self : int ):
SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE = ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__lowerCamelCase , initializer_range=self.initializer_range , )
return config, pixel_values
def _snake_case ( self : List[Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Tuple ):
SCREAMING_SNAKE_CASE = FlaxViTModel(config=__lowerCamelCase )
SCREAMING_SNAKE_CASE = model(__lowerCamelCase )
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
SCREAMING_SNAKE_CASE = (self.image_size, self.image_size)
SCREAMING_SNAKE_CASE = (self.patch_size, self.patch_size)
SCREAMING_SNAKE_CASE = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, num_patches + 1, self.hidden_size) )
def _snake_case ( self : List[Any] , __lowerCamelCase : Optional[int] , __lowerCamelCase : List[Any] ):
SCREAMING_SNAKE_CASE = self.type_sequence_label_size
SCREAMING_SNAKE_CASE = FlaxViTForImageClassification(config=__lowerCamelCase )
SCREAMING_SNAKE_CASE = model(__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = FlaxViTForImageClassification(__lowerCamelCase )
SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE = model(__lowerCamelCase )
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) ,
) = config_and_inputs
SCREAMING_SNAKE_CASE = {"pixel_values": pixel_values}
return config, inputs_dict
@require_flax
class _SCREAMING_SNAKE_CASE ( __snake_case , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = FlaxViTModelTester(self )
SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=__lowerCamelCase , has_text_modality=__lowerCamelCase , hidden_size=37 )
def _snake_case ( self : Dict ):
self.config_tester.run_common_tests()
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCamelCase )
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase )
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class(__lowerCamelCase )
SCREAMING_SNAKE_CASE = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCamelCase )
def _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
SCREAMING_SNAKE_CASE = self._prepare_for_class(__lowerCamelCase , __lowerCamelCase )
SCREAMING_SNAKE_CASE = model_class(__lowerCamelCase )
@jax.jit
def model_jitted(__lowerCamelCase : str , **__lowerCamelCase : Union[str, Any] ):
return model(pixel_values=__lowerCamelCase , **__lowerCamelCase )
with self.subTest("JIT Enabled" ):
SCREAMING_SNAKE_CASE = model_jitted(**__lowerCamelCase ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
SCREAMING_SNAKE_CASE = model_jitted(**__lowerCamelCase ).to_tuple()
self.assertEqual(len(__lowerCamelCase ) , len(__lowerCamelCase ) )
for jitted_output, output in zip(__lowerCamelCase , __lowerCamelCase ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def _snake_case ( self : Dict ):
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class_name.from_pretrained("google/vit-base-patch16-224" )
SCREAMING_SNAKE_CASE = model(np.ones((1, 3, 224, 224) ) )
self.assertIsNotNone(__lowerCamelCase ) | 16 | from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase_ :
def __init__( self : Any , _A : int , _A : int=12 , _A : int=7 , _A : Tuple=True , _A : Optional[int]=True , _A : Union[str, Any]=True , _A : str=99 , _A : str=32 , _A : int=32 , _A : Optional[Any]=2 , _A : Dict=4 , _A : int=37 , _A : List[Any]=0.1 , _A : str=0.1 , _A : Any=512 , _A : int=0.02 , _A : Optional[Any]=0 , _A : Dict=None , ):
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = seq_length
_UpperCamelCase = is_training
_UpperCamelCase = use_input_mask
_UpperCamelCase = use_labels
_UpperCamelCase = vocab_size
_UpperCamelCase = hidden_size
_UpperCamelCase = projection_dim
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = intermediate_size
_UpperCamelCase = dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = initializer_range
_UpperCamelCase = scope
_UpperCamelCase = bos_token_id
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCamelCase = None
if self.use_input_mask:
_UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
_UpperCamelCase = input_mask.numpy()
_UpperCamelCase , _UpperCamelCase = input_mask.shape
_UpperCamelCase = np.random.randint(1 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(_A ):
_UpperCamelCase = 1
_UpperCamelCase = 0
_UpperCamelCase = self.get_config()
return config, input_ids, tf.convert_to_tensor(_A )
def UpperCamelCase_ ( self : str ):
return BlipTextConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , projection_dim=self.projection_dim , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , dropout=self.dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , bos_token_id=self.bos_token_id , )
def UpperCamelCase_ ( self : List[str] , _A : Tuple , _A : str , _A : Optional[Any] ):
_UpperCamelCase = TFBlipTextModel(config=_A )
_UpperCamelCase = model(_A , attention_mask=_A , training=_A )
_UpperCamelCase = model(_A , training=_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def UpperCamelCase_ ( self : Tuple ):
_UpperCamelCase = self.prepare_config_and_inputs()
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = config_and_inputs
_UpperCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class lowerCAmelCase_ ( __lowercase, unittest.TestCase ):
UpperCAmelCase = (TFBlipTextModel,) if is_tf_available() else ()
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = False
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = BlipTextModelTester(self )
_UpperCamelCase = ConfigTester(self , config_class=_A , hidden_size=37 )
def UpperCamelCase_ ( self : Dict ):
self.config_tester.run_common_tests()
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_A )
def UpperCamelCase_ ( self : List[Any] ):
pass
def UpperCamelCase_ ( self : Tuple ):
pass
@unittest.skip(reason='''Blip does not use inputs_embeds''' )
def UpperCamelCase_ ( self : Dict ):
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def UpperCamelCase_ ( self : Dict ):
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def UpperCamelCase_ ( self : List[str] ):
pass
@slow
def UpperCamelCase_ ( self : Optional[int] ):
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = TFBlipTextModel.from_pretrained(_A )
self.assertIsNotNone(_A )
def UpperCamelCase_ ( self : int , _A : Optional[int]=True ):
super().test_pt_tf_model_equivalence(allow_missing_keys=_A )
| 10 | 0 |
from math import factorial
def __SCREAMING_SNAKE_CASE ( a__ : int = 100 ) -> int:
return sum(map(a__ ,str(factorial(a__ ) ) ) )
if __name__ == "__main__":
print(solution(int(input('''Enter the Number: ''').strip())))
| 17 | from __future__ import annotations
_lowerCAmelCase = [True] * 1_000_001
_lowerCAmelCase = 2
while i * i <= 1_000_000:
if seive[i]:
for j in range(i * i, 1_000_001, i):
_lowerCAmelCase = False
i += 1
def _snake_case ( __snake_case ):
return seive[n]
def _snake_case ( __snake_case ):
return any(digit in '''02468''' for digit in str(__snake_case ) )
def _snake_case ( __snake_case = 1000000 ):
_UpperCamelCase = [2] # result already includes the number 2.
for num in range(3 , limit + 1 , 2 ):
if is_prime(__snake_case ) and not contains_an_even_digit(__snake_case ):
_UpperCamelCase = str(__snake_case )
_UpperCamelCase = [int(str_num[j:] + str_num[:j] ) for j in range(len(__snake_case ) )]
if all(is_prime(__snake_case ) for i in list_nums ):
result.append(__snake_case )
return result
def _snake_case ( ):
return len(find_circular_primes() )
if __name__ == "__main__":
print(f'{len(find_circular_primes()) = }')
| 10 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_SCREAMING_SNAKE_CASE = {"processing_layoutxlm": ["LayoutXLMProcessor"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = ["LayoutXLMTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = ["LayoutXLMTokenizerFast"]
if TYPE_CHECKING:
from .processing_layoutxlm import LayoutXLMProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutxlm import LayoutXLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast
else:
import sys
_SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 18 | import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCAmelCase = get_tests_dir("fixtures/spiece.model")
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_ ( __lowercase, unittest.TestCase ):
UpperCAmelCase = DebertaVaTokenizer
UpperCAmelCase = DebertaVaTokenizerFast
UpperCAmelCase = True
UpperCAmelCase = True
def UpperCamelCase_ ( self : List[Any] ):
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCamelCase = DebertaVaTokenizer(_A , unk_token='''<unk>''' )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase_ ( self : Dict , _A : Union[str, Any] ):
_UpperCamelCase = '''this is a test'''
_UpperCamelCase = '''this is a test'''
return input_text, output_text
def UpperCamelCase_ ( self : Optional[Any] ):
_UpperCamelCase = '''<pad>'''
_UpperCamelCase = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A ) , _A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A ) , _A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<pad>''' )
self.assertEqual(vocab_keys[1] , '''<unk>''' )
self.assertEqual(vocab_keys[-1] , '''[PAD]''' )
self.assertEqual(len(_A ) , 3_0001 )
def UpperCamelCase_ ( self : List[Any] ):
self.assertEqual(self.get_tokenizer().vocab_size , 3_0000 )
def UpperCamelCase_ ( self : List[str] ):
# fmt: off
_UpperCamelCase = ''' \tHeLLo!how \n Are yoU? '''
_UpperCamelCase = ['''▁hello''', '''!''', '''how''', '''▁are''', '''▁you''', '''?''']
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
@unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.''' )
def UpperCamelCase_ ( self : Dict ):
pass
@unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.''' )
def UpperCamelCase_ ( self : Optional[Any] ):
pass
def UpperCamelCase_ ( self : Dict ):
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : List[Any] ):
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : Dict ):
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : int ):
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : Tuple ):
# fmt: off
_UpperCamelCase = ''' \tHeLLo!how \n Are yoU? '''
_UpperCamelCase = ['''▁''', '''<unk>''', '''e''', '''<unk>''', '''o''', '''!''', '''how''', '''▁''', '''<unk>''', '''re''', '''▁yo''', '''<unk>''', '''?''']
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = self.get_tokenizer()
_UpperCamelCase = self.get_rust_tokenizer()
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.encode(_A , add_special_tokens=_A )
_UpperCamelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = self.get_rust_tokenizer()
_UpperCamelCase = tokenizer.encode(_A )
_UpperCamelCase = rust_tokenizer.encode(_A )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = '''This is a test'''
_UpperCamelCase = [13, 1, 4398, 25, 21, 1289]
_UpperCamelCase = ['''▁''', '''T''', '''his''', '''▁is''', '''▁a''', '''▁test''']
_UpperCamelCase = ['''▁''', '''<unk>''', '''his''', '''▁is''', '''▁a''', '''▁test''']
_UpperCamelCase = DebertaVaTokenizer(_A , keep_accents=_A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , keep_accents=_A )
_UpperCamelCase = tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = [13, 1, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]
_UpperCamelCase = ['''▁''', '''I''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''', ]
_UpperCamelCase = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
# fmt: on
_UpperCamelCase = tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = DebertaVaTokenizer(_A )
_UpperCamelCase = tokenizer.encode('''sequence builders''' )
_UpperCamelCase = tokenizer.encode('''multi-sequence build''' )
_UpperCamelCase = tokenizer.build_inputs_with_special_tokens(_A )
_UpperCamelCase = tokenizer.build_inputs_with_special_tokens(_A , _A )
self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] , _A )
self.assertEqual(
[tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id] , _A , )
@slow
def UpperCamelCase_ ( self : Optional[Any] ):
# fmt: off
_UpperCamelCase = {'''input_ids''': [[1, 3_9867, 36, 1_9390, 486, 27, 3_5052, 8_1436, 18, 6_0685, 1225, 7, 3_5052, 8_1436, 18, 9367, 1_6899, 18, 1_5937, 53, 594, 773, 18, 1_6287, 3_0465, 36, 1_5937, 6, 4_1139, 38, 3_6979, 6_0763, 191, 6, 3_4132, 99, 6, 5_0538, 390, 4_3230, 6, 3_4132, 2779, 2_0850, 14, 699, 1072, 1194, 36, 382, 1_0901, 53, 7, 699, 1072, 2084, 36, 2_0422, 630, 53, 19, 105, 3049, 1896, 1053, 1_6899, 1506, 11, 3_7978, 4243, 7, 1237, 3_1869, 200, 1_6566, 654, 6, 3_5052, 8_1436, 7, 5_5630, 1_3593, 4, 2], [1, 26, 1_5011, 13, 667, 8, 1053, 18, 2_3611, 1237, 7_2356, 1_2820, 34, 10_4134, 1209, 35, 1_3313, 6627, 21, 202, 347, 7, 164, 2399, 11, 46, 4485, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 1232, 2864, 1_5785, 1_4951, 105, 5, 8581, 1250, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_A , model_name='''microsoft/deberta-v2-xlarge''' , revision='''ad6e42c1532ddf3a15c39246b63f5559d558b670''' , )
| 10 | 0 |
"""simple docstring"""
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
_a = logging.get_logger(__name__)
_a = {
"""b0""": efficientnet.EfficientNetBa,
"""b1""": efficientnet.EfficientNetBa,
"""b2""": efficientnet.EfficientNetBa,
"""b3""": efficientnet.EfficientNetBa,
"""b4""": efficientnet.EfficientNetBa,
"""b5""": efficientnet.EfficientNetBa,
"""b6""": efficientnet.EfficientNetBa,
"""b7""": efficientnet.EfficientNetBa,
}
_a = {
"""b0""": {
"""hidden_dim""": 1280,
"""width_coef""": 1.0,
"""depth_coef""": 1.0,
"""image_size""": 224,
"""dropout_rate""": 0.2,
"""dw_padding""": [],
},
"""b1""": {
"""hidden_dim""": 1280,
"""width_coef""": 1.0,
"""depth_coef""": 1.1,
"""image_size""": 240,
"""dropout_rate""": 0.2,
"""dw_padding""": [16],
},
"""b2""": {
"""hidden_dim""": 1408,
"""width_coef""": 1.1,
"""depth_coef""": 1.2,
"""image_size""": 260,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 8, 16],
},
"""b3""": {
"""hidden_dim""": 1536,
"""width_coef""": 1.2,
"""depth_coef""": 1.4,
"""image_size""": 300,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 18],
},
"""b4""": {
"""hidden_dim""": 1792,
"""width_coef""": 1.4,
"""depth_coef""": 1.8,
"""image_size""": 380,
"""dropout_rate""": 0.4,
"""dw_padding""": [6],
},
"""b5""": {
"""hidden_dim""": 2048,
"""width_coef""": 1.6,
"""depth_coef""": 2.2,
"""image_size""": 456,
"""dropout_rate""": 0.4,
"""dw_padding""": [13, 27],
},
"""b6""": {
"""hidden_dim""": 2304,
"""width_coef""": 1.8,
"""depth_coef""": 2.6,
"""image_size""": 528,
"""dropout_rate""": 0.5,
"""dw_padding""": [31],
},
"""b7""": {
"""hidden_dim""": 2560,
"""width_coef""": 2.0,
"""depth_coef""": 3.1,
"""image_size""": 600,
"""dropout_rate""": 0.5,
"""dw_padding""": [18],
},
}
def lowerCamelCase__ ( __snake_case ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase = EfficientNetConfig()
_UpperCamelCase = CONFIG_MAP[model_name]['''hidden_dim''']
_UpperCamelCase = CONFIG_MAP[model_name]['''width_coef''']
_UpperCamelCase = CONFIG_MAP[model_name]['''depth_coef''']
_UpperCamelCase = CONFIG_MAP[model_name]['''image_size''']
_UpperCamelCase = CONFIG_MAP[model_name]['''dropout_rate''']
_UpperCamelCase = CONFIG_MAP[model_name]['''dw_padding''']
_UpperCamelCase = '''huggingface/label-files'''
_UpperCamelCase = '''imagenet-1k-id2label.json'''
_UpperCamelCase = 10_00
_UpperCamelCase = json.load(open(hf_hub_download(__snake_case, __snake_case, repo_type='''dataset''' ), '''r''' ) )
_UpperCamelCase = {int(__snake_case ): v for k, v in idalabel.items()}
_UpperCamelCase = idalabel
_UpperCamelCase = {v: k for k, v in idalabel.items()}
return config
def lowerCamelCase__ ( ) -> str:
"""simple docstring"""
_UpperCamelCase = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
_UpperCamelCase = Image.open(requests.get(__snake_case, stream=__snake_case ).raw )
return im
def lowerCamelCase__ ( __snake_case ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = CONFIG_MAP[model_name]['''image_size''']
_UpperCamelCase = EfficientNetImageProcessor(
size={'''height''': size, '''width''': size}, image_mean=[0.485, 0.456, 0.406], image_std=[0.47853944, 0.4732864, 0.47434163], do_center_crop=__snake_case, )
return preprocessor
def lowerCamelCase__ ( __snake_case ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase = [v.split('''_''' )[0].split('''block''' )[1] for v in original_param_names if v.startswith('''block''' )]
_UpperCamelCase = sorted(set(__snake_case ) )
_UpperCamelCase = len(__snake_case )
_UpperCamelCase = {b: str(__snake_case ) for b, i in zip(__snake_case, range(__snake_case ) )}
_UpperCamelCase = []
rename_keys.append(('''stem_conv/kernel:0''', '''embeddings.convolution.weight''') )
rename_keys.append(('''stem_bn/gamma:0''', '''embeddings.batchnorm.weight''') )
rename_keys.append(('''stem_bn/beta:0''', '''embeddings.batchnorm.bias''') )
rename_keys.append(('''stem_bn/moving_mean:0''', '''embeddings.batchnorm.running_mean''') )
rename_keys.append(('''stem_bn/moving_variance:0''', '''embeddings.batchnorm.running_var''') )
for b in block_names:
_UpperCamelCase = block_name_mapping[b]
rename_keys.append((F'''block{b}_expand_conv/kernel:0''', F'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''') )
rename_keys.append((F'''block{b}_expand_bn/gamma:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''') )
rename_keys.append((F'''block{b}_expand_bn/beta:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''') )
rename_keys.append(
(F'''block{b}_expand_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''') )
rename_keys.append(
(F'''block{b}_expand_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''') )
rename_keys.append(
(F'''block{b}_dwconv/depthwise_kernel:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''') )
rename_keys.append((F'''block{b}_bn/gamma:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''') )
rename_keys.append((F'''block{b}_bn/beta:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''') )
rename_keys.append(
(F'''block{b}_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''') )
rename_keys.append(
(F'''block{b}_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''') )
rename_keys.append((F'''block{b}_se_reduce/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''') )
rename_keys.append((F'''block{b}_se_reduce/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''') )
rename_keys.append((F'''block{b}_se_expand/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''') )
rename_keys.append((F'''block{b}_se_expand/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''') )
rename_keys.append(
(F'''block{b}_project_conv/kernel:0''', F'''encoder.blocks.{hf_b}.projection.project_conv.weight''') )
rename_keys.append((F'''block{b}_project_bn/gamma:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.weight''') )
rename_keys.append((F'''block{b}_project_bn/beta:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.bias''') )
rename_keys.append(
(F'''block{b}_project_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''') )
rename_keys.append(
(F'''block{b}_project_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_var''') )
rename_keys.append(('''top_conv/kernel:0''', '''encoder.top_conv.weight''') )
rename_keys.append(('''top_bn/gamma:0''', '''encoder.top_bn.weight''') )
rename_keys.append(('''top_bn/beta:0''', '''encoder.top_bn.bias''') )
rename_keys.append(('''top_bn/moving_mean:0''', '''encoder.top_bn.running_mean''') )
rename_keys.append(('''top_bn/moving_variance:0''', '''encoder.top_bn.running_var''') )
_UpperCamelCase = {}
for item in rename_keys:
if item[0] in original_param_names:
_UpperCamelCase = '''efficientnet.''' + item[1]
_UpperCamelCase = '''classifier.weight'''
_UpperCamelCase = '''classifier.bias'''
return key_mapping
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case ) -> str:
"""simple docstring"""
for key, value in tf_params.items():
if "normalization" in key:
continue
_UpperCamelCase = key_mapping[key]
if "_conv" in key and "kernel" in key:
_UpperCamelCase = torch.from_numpy(__snake_case ).permute(3, 2, 0, 1 )
elif "depthwise_kernel" in key:
_UpperCamelCase = torch.from_numpy(__snake_case ).permute(2, 3, 0, 1 )
elif "kernel" in key:
_UpperCamelCase = torch.from_numpy(np.transpose(__snake_case ) )
else:
_UpperCamelCase = torch.from_numpy(__snake_case )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(__snake_case )
@torch.no_grad()
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case ) -> int:
"""simple docstring"""
_UpperCamelCase = model_classes[model_name](
include_top=__snake_case, weights='''imagenet''', input_tensor=__snake_case, input_shape=__snake_case, pooling=__snake_case, classes=10_00, classifier_activation='''softmax''', )
_UpperCamelCase = original_model.trainable_variables
_UpperCamelCase = original_model.non_trainable_variables
_UpperCamelCase = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
_UpperCamelCase = param.numpy()
_UpperCamelCase = list(tf_params.keys() )
# Load HuggingFace model
_UpperCamelCase = get_efficientnet_config(__snake_case )
_UpperCamelCase = EfficientNetForImageClassification(__snake_case ).eval()
_UpperCamelCase = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print('''Converting parameters...''' )
_UpperCamelCase = rename_keys(__snake_case )
replace_params(__snake_case, __snake_case, __snake_case )
# Initialize preprocessor and preprocess input image
_UpperCamelCase = convert_image_processor(__snake_case )
_UpperCamelCase = preprocessor(images=prepare_img(), return_tensors='''pt''' )
# HF model inference
hf_model.eval()
with torch.no_grad():
_UpperCamelCase = hf_model(**__snake_case )
_UpperCamelCase = outputs.logits.detach().numpy()
# Original model inference
_UpperCamelCase = False
_UpperCamelCase = CONFIG_MAP[model_name]['''image_size''']
_UpperCamelCase = prepare_img().resize((image_size, image_size), resample=PIL.Image.NEAREST )
_UpperCamelCase = image.img_to_array(__snake_case )
_UpperCamelCase = np.expand_dims(__snake_case, axis=0 )
_UpperCamelCase = original_model.predict(__snake_case )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(__snake_case, __snake_case, atol=1e-3 ), "The predicted logits are not the same."
print('''Model outputs match!''' )
if save_model:
# Create folder to save model
if not os.path.isdir(__snake_case ):
os.mkdir(__snake_case )
# Save converted model and image processor
hf_model.save_pretrained(__snake_case )
preprocessor.save_pretrained(__snake_case )
if push_to_hub:
# Push model and image processor to hub
print(F'''Pushing converted {model_name} to the hub...''' )
_UpperCamelCase = F'''efficientnet-{model_name}'''
preprocessor.push_to_hub(__snake_case )
hf_model.push_to_hub(__snake_case )
if __name__ == "__main__":
_a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""b0""",
type=str,
help="""Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""hf_model""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
_a = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 19 | import sys
from collections import defaultdict
class lowerCAmelCase_ :
def __init__( self : Optional[int] ):
_UpperCamelCase = []
def UpperCamelCase_ ( self : Any , _A : str ):
return self.node_position[vertex]
def UpperCamelCase_ ( self : Optional[Any] , _A : List[str] , _A : Union[str, Any] ):
_UpperCamelCase = pos
def UpperCamelCase_ ( self : Any , _A : List[str] , _A : int , _A : Optional[Any] , _A : Union[str, Any] ):
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
_UpperCamelCase = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
_UpperCamelCase = 2 * start + 1
else:
_UpperCamelCase = 2 * start + 2
if heap[smallest_child] < heap[start]:
_UpperCamelCase , _UpperCamelCase = heap[smallest_child], positions[smallest_child]
_UpperCamelCase , _UpperCamelCase = (
heap[start],
positions[start],
)
_UpperCamelCase , _UpperCamelCase = temp, tempa
_UpperCamelCase = self.get_position(positions[smallest_child] )
self.set_position(
positions[smallest_child] , self.get_position(positions[start] ) )
self.set_position(positions[start] , _A )
self.top_to_bottom(_A , _A , _A , _A )
def UpperCamelCase_ ( self : List[str] , _A : Tuple , _A : Optional[Any] , _A : int , _A : Optional[int] ):
_UpperCamelCase = position[index]
while index != 0:
_UpperCamelCase = int((index - 2) / 2 ) if index % 2 == 0 else int((index - 1) / 2 )
if val < heap[parent]:
_UpperCamelCase = heap[parent]
_UpperCamelCase = position[parent]
self.set_position(position[parent] , _A )
else:
_UpperCamelCase = val
_UpperCamelCase = temp
self.set_position(_A , _A )
break
_UpperCamelCase = parent
else:
_UpperCamelCase = val
_UpperCamelCase = temp
self.set_position(_A , 0 )
def UpperCamelCase_ ( self : int , _A : Tuple , _A : int ):
_UpperCamelCase = len(_A ) // 2 - 1
for i in range(_A , -1 , -1 ):
self.top_to_bottom(_A , _A , len(_A ) , _A )
def UpperCamelCase_ ( self : Any , _A : int , _A : List[str] ):
_UpperCamelCase = positions[0]
_UpperCamelCase = sys.maxsize
self.top_to_bottom(_A , 0 , len(_A ) , _A )
return temp
def _snake_case ( __snake_case ):
_UpperCamelCase = Heap()
_UpperCamelCase = [0] * len(__snake_case )
_UpperCamelCase = [-1] * len(__snake_case ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
_UpperCamelCase = [] # Heap of Distance of vertices from their neighboring vertex
_UpperCamelCase = []
for vertex in range(len(__snake_case ) ):
distance_tv.append(sys.maxsize )
positions.append(__snake_case )
heap.node_position.append(__snake_case )
_UpperCamelCase = []
_UpperCamelCase = 1
_UpperCamelCase = sys.maxsize
for neighbor, distance in adjacency_list[0]:
_UpperCamelCase = 0
_UpperCamelCase = distance
heap.heapify(__snake_case , __snake_case )
for _ in range(1 , len(__snake_case ) ):
_UpperCamelCase = heap.delete_minimum(__snake_case , __snake_case )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
_UpperCamelCase = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(__snake_case )]
):
_UpperCamelCase = distance
heap.bottom_to_top(
__snake_case , heap.get_position(__snake_case ) , __snake_case , __snake_case )
_UpperCamelCase = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
_lowerCAmelCase = int(input("Enter number of edges: ").strip())
_lowerCAmelCase = defaultdict(list)
for _ in range(edges_number):
_lowerCAmelCase = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 10 | 0 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def _lowercase( __a : List[Any] ):
if "cls_token" in name:
a__ =name.replace('cls_token' , 'vit.embeddings.cls_token' )
if "mask_token" in name:
a__ =name.replace('mask_token' , 'decoder.mask_token' )
if "decoder_pos_embed" in name:
a__ =name.replace('decoder_pos_embed' , 'decoder.decoder_pos_embed' )
if "pos_embed" in name and "decoder" not in name:
a__ =name.replace('pos_embed' , 'vit.embeddings.position_embeddings' )
if "patch_embed.proj" in name:
a__ =name.replace('patch_embed.proj' , 'vit.embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
a__ =name.replace('patch_embed.norm' , 'vit.embeddings.norm' )
if "decoder_blocks" in name:
a__ =name.replace('decoder_blocks' , 'decoder.decoder_layers' )
if "blocks" in name:
a__ =name.replace('blocks' , 'vit.encoder.layer' )
if "attn.proj" in name:
a__ =name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name:
a__ =name.replace('attn' , 'attention.self' )
if "norm1" in name:
a__ =name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
a__ =name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
a__ =name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
a__ =name.replace('mlp.fc2' , 'output.dense' )
if "decoder_embed" in name:
a__ =name.replace('decoder_embed' , 'decoder.decoder_embed' )
if "decoder_norm" in name:
a__ =name.replace('decoder_norm' , 'decoder.decoder_norm' )
if "decoder_pred" in name:
a__ =name.replace('decoder_pred' , 'decoder.decoder_pred' )
if "norm.weight" in name and "decoder" not in name:
a__ =name.replace('norm.weight' , 'vit.layernorm.weight' )
if "norm.bias" in name and "decoder" not in name:
a__ =name.replace('norm.bias' , 'vit.layernorm.bias' )
return name
def _lowercase( __a : Any , __a : Any ):
for key in orig_state_dict.copy().keys():
a__ =orig_state_dict.pop(__a )
if "qkv" in key:
a__ =key.split('.' )
a__ =int(key_split[1] )
if "decoder_blocks" in key:
a__ =config.decoder_hidden_size
a__ ='decoder.decoder_layers.'
if "weight" in key:
a__ =val[:dim, :]
a__ =val[dim : dim * 2, :]
a__ =val[-dim:, :]
elif "bias" in key:
a__ =val[:dim]
a__ =val[dim : dim * 2]
a__ =val[-dim:]
else:
a__ =config.hidden_size
a__ ='vit.encoder.layer.'
if "weight" in key:
a__ =val[:dim, :]
a__ =val[dim : dim * 2, :]
a__ =val[-dim:, :]
elif "bias" in key:
a__ =val[:dim]
a__ =val[dim : dim * 2]
a__ =val[-dim:]
else:
a__ =val
return orig_state_dict
def _lowercase( __a : Any , __a : Optional[Any] ):
a__ =ViTMAEConfig()
if "large" in checkpoint_url:
a__ =1024
a__ =4096
a__ =24
a__ =16
elif "huge" in checkpoint_url:
a__ =14
a__ =1280
a__ =5120
a__ =32
a__ =16
a__ =ViTMAEForPreTraining(__a )
a__ =torch.hub.load_state_dict_from_url(__a , map_location='cpu' )['model']
a__ =ViTMAEImageProcessor(size=config.image_size )
a__ =convert_state_dict(__a , __a )
model.load_state_dict(__a )
model.eval()
a__ ='https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'
a__ =Image.open(requests.get(__a , stream=__a ).raw )
a__ =ViTMAEImageProcessor(size=config.image_size )
a__ =image_processor(images=__a , return_tensors='pt' )
# forward pass
torch.manual_seed(2 )
a__ =model(**__a )
a__ =outputs.logits
if "large" in checkpoint_url:
a__ =torch.tensor(
[[-0.73_09, -0.71_28, -1.01_69], [-1.01_61, -0.90_58, -1.18_78], [-1.04_78, -0.94_11, -1.19_11]] )
elif "huge" in checkpoint_url:
a__ =torch.tensor(
[[-1.15_99, -0.91_99, -1.22_21], [-1.19_52, -0.92_69, -1.23_07], [-1.21_43, -0.93_37, -1.22_62]] )
else:
a__ =torch.tensor(
[[-0.91_92, -0.84_81, -1.12_59], [-1.13_49, -1.00_34, -1.25_99], [-1.17_57, -1.04_29, -1.27_26]] )
# verify logits
assert torch.allclose(logits[0, :3, :3] , __a , atol=1e-4 )
print(f"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(__a )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(__a )
if __name__ == "__main__":
_lowerCAmelCase: Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint_url',
default='https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth',
type=str,
help='URL of the checkpoint you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
_lowerCAmelCase: str = parser.parse_args()
convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 20 | import logging
import os
from .state import PartialState
class lowerCAmelCase_ ( logging.LoggerAdapter ):
@staticmethod
def UpperCamelCase_ ( _A : Any ):
_UpperCamelCase = PartialState()
return not main_process_only or (main_process_only and state.is_main_process)
def UpperCamelCase_ ( self : Union[str, Any] , _A : Optional[Any] , _A : str , *_A : int , **_A : List[Any] ):
if PartialState._shared_state == {}:
raise RuntimeError(
'''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.''' )
_UpperCamelCase = kwargs.pop('''main_process_only''' , _A )
_UpperCamelCase = kwargs.pop('''in_order''' , _A )
if self.isEnabledFor(_A ):
if self._should_log(_A ):
_UpperCamelCase , _UpperCamelCase = self.process(_A , _A )
self.logger.log(_A , _A , *_A , **_A )
elif in_order:
_UpperCamelCase = PartialState()
for i in range(state.num_processes ):
if i == state.process_index:
_UpperCamelCase , _UpperCamelCase = self.process(_A , _A )
self.logger.log(_A , _A , *_A , **_A )
state.wait_for_everyone()
def _snake_case ( __snake_case , __snake_case = None ):
if log_level is None:
_UpperCamelCase = os.environ.get('''ACCELERATE_LOG_LEVEL''' , __snake_case )
_UpperCamelCase = logging.getLogger(__snake_case )
if log_level is not None:
logger.setLevel(log_level.upper() )
logger.root.setLevel(log_level.upper() )
return MultiProcessAdapter(__snake_case , {} )
| 10 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase_ : Union[str, Any] = {
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ConditionalDetrConfig",
"ConditionalDetrOnnxConfig",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Tuple = ["ConditionalDetrFeatureExtractor"]
UpperCAmelCase_ : str = ["ConditionalDetrImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Optional[Any] = [
"CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
"ConditionalDetrForObjectDetection",
"ConditionalDetrForSegmentation",
"ConditionalDetrModel",
"ConditionalDetrPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
ConditionalDetrConfig,
ConditionalDetrOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
from .image_processing_conditional_detr import ConditionalDetrImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrModel,
ConditionalDetrPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : Any = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 | import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCAmelCase = "▁"
_lowerCAmelCase = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
class lowerCAmelCase_ ( __lowercase, unittest.TestCase ):
UpperCAmelCase = BertGenerationTokenizer
UpperCAmelCase = False
UpperCAmelCase = True
def UpperCamelCase_ ( self : List[str] ):
super().setUp()
_UpperCamelCase = BertGenerationTokenizer(_A , keep_accents=_A )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = '''<s>'''
_UpperCamelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A ) , _A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A ) , _A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<unk>''' )
self.assertEqual(vocab_keys[1] , '''<s>''' )
self.assertEqual(vocab_keys[-1] , '''<pad>''' )
self.assertEqual(len(_A ) , 1002 )
def UpperCamelCase_ ( self : Dict ):
self.assertEqual(self.get_tokenizer().vocab_size , 1000 )
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = BertGenerationTokenizer(_A , keep_accents=_A )
_UpperCamelCase = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(_A , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_A ) , [285, 46, 10, 170, 382] , )
_UpperCamelCase = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
_A , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
_UpperCamelCase = tokenizer.convert_tokens_to_ids(_A )
self.assertListEqual(
_A , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(
_A , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
@cached_property
def UpperCamelCase_ ( self : Union[str, Any] ):
return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''' )
@slow
def UpperCamelCase_ ( self : Optional[Any] ):
_UpperCamelCase = '''Hello World!'''
_UpperCamelCase = [1_8536, 2260, 101]
self.assertListEqual(_A , self.big_tokenizer.encode(_A ) )
@slow
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
_UpperCamelCase = [
871,
419,
358,
946,
991,
2521,
452,
358,
1357,
387,
7751,
3536,
112,
985,
456,
126,
865,
938,
5400,
5734,
458,
1368,
467,
786,
2462,
5246,
1159,
633,
865,
4519,
457,
582,
852,
2557,
427,
916,
508,
405,
3_4324,
497,
391,
408,
1_1342,
1244,
385,
100,
938,
985,
456,
574,
362,
1_2597,
3200,
3129,
1172,
]
self.assertListEqual(_A , self.big_tokenizer.encode(_A ) )
@require_torch
@slow
def UpperCamelCase_ ( self : Dict ):
import torch
from transformers import BertGenerationConfig, BertGenerationEncoder
# Build sequence
_UpperCamelCase = list(self.big_tokenizer.get_vocab().keys() )[:10]
_UpperCamelCase = ''' '''.join(_A )
_UpperCamelCase = self.big_tokenizer.encode_plus(_A , return_tensors='''pt''' , return_token_type_ids=_A )
_UpperCamelCase = self.big_tokenizer.batch_encode_plus(
[sequence + ''' ''' + sequence] , return_tensors='''pt''' , return_token_type_ids=_A )
_UpperCamelCase = BertGenerationConfig()
_UpperCamelCase = BertGenerationEncoder(_A )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**_A )
model(**_A )
@slow
def UpperCamelCase_ ( self : Dict ):
# fmt: off
_UpperCamelCase = {'''input_ids''': [[3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114], [448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_A , model_name='''google/bert_for_seq_generation_L-24_bbc_encoder''' , revision='''c817d1fd1be2ffa69431227a1fe320544943d4db''' , )
| 10 | 0 |
'''simple docstring'''
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class A ( _a ):
def __init__( self : Union[str, Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Tuple=None , lowerCAmelCase_ : Tuple=True , lowerCAmelCase_ : Union[str, Any]=None , **lowerCAmelCase_ : Optional[int] ) -> Dict:
"""simple docstring"""
_a = parent
_a = config_class
_a = has_text_modality
_a = kwargs
_a = common_properties
def __lowerCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
_a = self.config_class(**self.inputs_dict )
_a = (
['''hidden_size''', '''num_attention_heads''', '''num_hidden_layers''']
if self.common_properties is None
else self.common_properties
)
# Add common fields for text models
if self.has_text_modality:
common_properties.extend(['''vocab_size'''] )
# Test that config has the common properties as getters
for prop in common_properties:
self.parent.assertTrue(hasattr(lowerCAmelCase_ , lowerCAmelCase_ ) , msg=F'`{prop}` does not exist' )
# Test that config has the common properties as setter
for idx, name in enumerate(lowerCAmelCase_ ):
try:
setattr(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
self.parent.assertEqual(
getattr(lowerCAmelCase_ , lowerCAmelCase_ ) , lowerCAmelCase_ , msg=F'`{name} value {idx} expected, but was {getattr(lowerCAmelCase_ , lowerCAmelCase_ )}' )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
# Test if config class can be called with Config(prop_name=..)
for idx, name in enumerate(lowerCAmelCase_ ):
try:
_a = self.config_class(**{name: idx} )
self.parent.assertEqual(
getattr(lowerCAmelCase_ , lowerCAmelCase_ ) , lowerCAmelCase_ , msg=F'`{name} value {idx} expected, but was {getattr(lowerCAmelCase_ , lowerCAmelCase_ )}' )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
def __lowerCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
_a = self.config_class(**self.inputs_dict )
_a = json.loads(config.to_json_string() )
for key, value in self.inputs_dict.items():
self.parent.assertEqual(obj[key] , lowerCAmelCase_ )
def __lowerCAmelCase ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
_a = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_a = os.path.join(lowerCAmelCase_ , '''config.json''' )
config_first.to_json_file(lowerCAmelCase_ )
_a = self.config_class.from_json_file(lowerCAmelCase_ )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def __lowerCAmelCase ( self : List[Any] ) -> Any:
"""simple docstring"""
_a = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
config_first.save_pretrained(lowerCAmelCase_ )
_a = self.config_class.from_pretrained(lowerCAmelCase_ )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def __lowerCAmelCase ( self : Tuple ) -> int:
"""simple docstring"""
_a = self.config_class(**self.inputs_dict )
_a = '''test'''
with tempfile.TemporaryDirectory() as tmpdirname:
_a = os.path.join(lowerCAmelCase_ , lowerCAmelCase_ )
config_first.save_pretrained(lowerCAmelCase_ )
_a = self.config_class.from_pretrained(lowerCAmelCase_ , subfolder=lowerCAmelCase_ )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def __lowerCAmelCase ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
_a = self.config_class(**self.inputs_dict , num_labels=5 )
self.parent.assertEqual(len(config.idalabel ) , 5 )
self.parent.assertEqual(len(config.labelaid ) , 5 )
_a = 3
self.parent.assertEqual(len(config.idalabel ) , 3 )
self.parent.assertEqual(len(config.labelaid ) , 3 )
def __lowerCAmelCase ( self : Optional[Any] ) -> str:
"""simple docstring"""
if self.config_class.is_composition:
return
_a = self.config_class()
self.parent.assertIsNotNone(lowerCAmelCase_ )
def __lowerCAmelCase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
_a = copy.deepcopy(lowerCAmelCase_ )
_a = self.config_class(**lowerCAmelCase_ )
_a = []
for key, value in config_common_kwargs.items():
if key == "torch_dtype":
if not is_torch_available():
continue
else:
import torch
if config.torch_dtype != torch.floataa:
wrong_values.append(('''torch_dtype''', config.torch_dtype, torch.floataa) )
elif getattr(lowerCAmelCase_ , lowerCAmelCase_ ) != value:
wrong_values.append((key, getattr(lowerCAmelCase_ , lowerCAmelCase_ ), value) )
if len(lowerCAmelCase_ ) > 0:
_a = '''\n'''.join([F'- {v[0]}: got {v[1]} instead of {v[2]}' for v in wrong_values] )
raise ValueError(F'The following keys were not properly set in the config:\n{errors}' )
def __lowerCAmelCase ( self : int ) -> Union[str, Any]:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.create_and_test_config_to_json_string()
self.create_and_test_config_to_json_file()
self.create_and_test_config_from_and_save_pretrained()
self.create_and_test_config_from_and_save_pretrained_subfolder()
self.create_and_test_config_with_num_labels()
self.check_config_can_be_init_without_params()
self.check_config_arguments_init()
| 22 | import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class lowerCAmelCase_ ( __lowercase, __lowercase, __lowercase, unittest.TestCase ):
UpperCAmelCase = StableUnCLIPPipeline
UpperCAmelCase = TEXT_TO_IMAGE_PARAMS
UpperCAmelCase = TEXT_TO_IMAGE_BATCH_PARAMS
UpperCAmelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
UpperCAmelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
UpperCAmelCase = False
def UpperCamelCase_ ( self : Optional[int] ):
_UpperCamelCase = 32
_UpperCamelCase = embedder_hidden_size
# prior components
torch.manual_seed(0 )
_UpperCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
torch.manual_seed(0 )
_UpperCamelCase = CLIPTextModelWithProjection(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=_A , projection_dim=_A , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , ) )
torch.manual_seed(0 )
_UpperCamelCase = PriorTransformer(
num_attention_heads=2 , attention_head_dim=12 , embedding_dim=_A , num_layers=1 , )
torch.manual_seed(0 )
_UpperCamelCase = DDPMScheduler(
variance_type='''fixed_small_log''' , prediction_type='''sample''' , num_train_timesteps=1000 , clip_sample=_A , clip_sample_range=5.0 , beta_schedule='''squaredcos_cap_v2''' , )
# regular denoising components
torch.manual_seed(0 )
_UpperCamelCase = StableUnCLIPImageNormalizer(embedding_dim=_A )
_UpperCamelCase = DDPMScheduler(beta_schedule='''squaredcos_cap_v2''' )
torch.manual_seed(0 )
_UpperCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
torch.manual_seed(0 )
_UpperCamelCase = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=_A , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , ) )
torch.manual_seed(0 )
_UpperCamelCase = UNetaDConditionModel(
sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''') , up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''') , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type='''projection''' , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=_A , layers_per_block=1 , upcast_attention=_A , use_linear_projection=_A , )
torch.manual_seed(0 )
_UpperCamelCase = DDIMScheduler(
beta_schedule='''scaled_linear''' , beta_start=0.0_0085 , beta_end=0.012 , prediction_type='''v_prediction''' , set_alpha_to_one=_A , steps_offset=1 , )
torch.manual_seed(0 )
_UpperCamelCase = AutoencoderKL()
_UpperCamelCase = {
# prior components
'''prior_tokenizer''': prior_tokenizer,
'''prior_text_encoder''': prior_text_encoder,
'''prior''': prior,
'''prior_scheduler''': prior_scheduler,
# image noising components
'''image_normalizer''': image_normalizer,
'''image_noising_scheduler''': image_noising_scheduler,
# regular denoising components
'''tokenizer''': tokenizer,
'''text_encoder''': text_encoder,
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
}
return components
def UpperCamelCase_ ( self : Dict , _A : Tuple , _A : Dict=0 ):
if str(_A ).startswith('''mps''' ):
_UpperCamelCase = torch.manual_seed(_A )
else:
_UpperCamelCase = torch.Generator(device=_A ).manual_seed(_A )
_UpperCamelCase = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''prior_num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = torch_device == '''cpu'''
self._test_attention_slicing_forward_pass(test_max_difference=_A )
def UpperCamelCase_ ( self : List[Any] ):
_UpperCamelCase = torch_device in ['''cpu''', '''mps''']
self._test_inference_batch_single_identical(test_max_difference=_A )
@slow
@require_torch_gpu
class lowerCAmelCase_ ( unittest.TestCase ):
def UpperCamelCase_ ( self : Optional[Any] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy''' )
_UpperCamelCase = StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l''' , torch_dtype=torch.floataa )
pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
_UpperCamelCase = torch.Generator(device='''cpu''' ).manual_seed(0 )
_UpperCamelCase = pipe('''anime turle''' , generator=_A , output_type='''np''' )
_UpperCamelCase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_A , _A )
def UpperCamelCase_ ( self : Optional[Any] ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
_UpperCamelCase = StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l''' , torch_dtype=torch.floataa )
_UpperCamelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
_UpperCamelCase = pipe(
'''anime turtle''' , prior_num_inference_steps=2 , num_inference_steps=2 , output_type='''np''' , )
_UpperCamelCase = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 10 | 0 |
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
snake_case__ : Union[str, Any] = """platform"""
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class _a :
"""simple docstring"""
A_ = PegasusConfig
A_ = {}
A_ = """gelu"""
def __init__( self , _UpperCAmelCase , _UpperCAmelCase=13 , _UpperCAmelCase=7 , _UpperCAmelCase=True , _UpperCAmelCase=False , _UpperCAmelCase=99 , _UpperCAmelCase=32 , _UpperCAmelCase=5 , _UpperCAmelCase=4 , _UpperCAmelCase=37 , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.1 , _UpperCAmelCase=20 , _UpperCAmelCase=2 , _UpperCAmelCase=1 , _UpperCAmelCase=0 , ) -> Optional[int]:
UpperCamelCase_ = parent
UpperCamelCase_ = batch_size
UpperCamelCase_ = seq_length
UpperCamelCase_ = is_training
UpperCamelCase_ = use_labels
UpperCamelCase_ = vocab_size
UpperCamelCase_ = hidden_size
UpperCamelCase_ = num_hidden_layers
UpperCamelCase_ = num_attention_heads
UpperCamelCase_ = intermediate_size
UpperCamelCase_ = hidden_dropout_prob
UpperCamelCase_ = attention_probs_dropout_prob
UpperCamelCase_ = max_position_embeddings
UpperCamelCase_ = eos_token_id
UpperCamelCase_ = pad_token_id
UpperCamelCase_ = bos_token_id
def _UpperCAmelCase ( self ) -> str:
UpperCamelCase_ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ).clip(3 , self.vocab_size )
UpperCamelCase_ = np.expand_dims(np.array([self.eos_token_id] * self.batch_size ) , 1 )
UpperCamelCase_ = np.concatenate([input_ids, eos_tensor] , axis=1 )
UpperCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase_ = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
UpperCamelCase_ = prepare_pegasus_inputs_dict(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
return config, inputs_dict
def _UpperCAmelCase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Any:
UpperCamelCase_ = 20
UpperCamelCase_ = model_class_name(_UpperCAmelCase )
UpperCamelCase_ = model.encode(inputs_dict['input_ids'] )
UpperCamelCase_ , UpperCamelCase_ = (
inputs_dict['decoder_input_ids'],
inputs_dict['decoder_attention_mask'],
)
UpperCamelCase_ = model.init_cache(decoder_input_ids.shape[0] , _UpperCAmelCase , _UpperCAmelCase )
UpperCamelCase_ = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='i4' )
UpperCamelCase_ = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
UpperCamelCase_ = model.decode(
decoder_input_ids[:, :-1] , _UpperCAmelCase , decoder_attention_mask=_UpperCAmelCase , past_key_values=_UpperCAmelCase , decoder_position_ids=_UpperCAmelCase , )
UpperCamelCase_ = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='i4' )
UpperCamelCase_ = model.decode(
decoder_input_ids[:, -1:] , _UpperCAmelCase , decoder_attention_mask=_UpperCAmelCase , past_key_values=outputs_cache.past_key_values , decoder_position_ids=_UpperCAmelCase , )
UpperCamelCase_ = model.decode(_UpperCAmelCase , _UpperCAmelCase )
UpperCamelCase_ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f"""Max diff is {diff}""" )
def _UpperCAmelCase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> List[Any]:
UpperCamelCase_ = 20
UpperCamelCase_ = model_class_name(_UpperCAmelCase )
UpperCamelCase_ = model.encode(inputs_dict['input_ids'] )
UpperCamelCase_ , UpperCamelCase_ = (
inputs_dict['decoder_input_ids'],
inputs_dict['decoder_attention_mask'],
)
UpperCamelCase_ = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
UpperCamelCase_ = model.init_cache(decoder_input_ids.shape[0] , _UpperCAmelCase , _UpperCAmelCase )
UpperCamelCase_ = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
UpperCamelCase_ = model.decode(
decoder_input_ids[:, :-1] , _UpperCAmelCase , decoder_attention_mask=_UpperCAmelCase , past_key_values=_UpperCAmelCase , decoder_position_ids=_UpperCAmelCase , )
UpperCamelCase_ = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='i4' )
UpperCamelCase_ = model.decode(
decoder_input_ids[:, -1:] , _UpperCAmelCase , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=_UpperCAmelCase , decoder_position_ids=_UpperCAmelCase , )
UpperCamelCase_ = model.decode(_UpperCAmelCase , _UpperCAmelCase , decoder_attention_mask=_UpperCAmelCase )
UpperCamelCase_ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f"""Max diff is {diff}""" )
def _snake_case (__lowercase , __lowercase , __lowercase , __lowercase=None , __lowercase=None , ):
if attention_mask is None:
UpperCamelCase_ = np.not_equal(__lowercase , config.pad_token_id).astype(np.inta)
if decoder_attention_mask is None:
UpperCamelCase_ = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape , dtype=np.inta),
np.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id).astype(np.inta),
] , axis=-1 , )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class _a ( UpperCAmelCase__ , unittest.TestCase ):
"""simple docstring"""
A_ = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
A_ = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
A_ = True
A_ = False
A_ = False
A_ = False
def _UpperCAmelCase ( self ) -> Dict:
UpperCamelCase_ = FlaxPegasusModelTester(self )
UpperCamelCase_ = ConfigTester(self , config_class=_UpperCAmelCase )
def _UpperCAmelCase ( self ) -> str:
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self ) -> Dict:
UpperCamelCase_ , UpperCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
def _UpperCAmelCase ( self ) -> Tuple:
UpperCamelCase_ , UpperCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
def _UpperCAmelCase ( self ) -> Dict:
UpperCamelCase_ , UpperCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
UpperCamelCase_ = self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase )
UpperCamelCase_ = model_class(_UpperCAmelCase )
@jax.jit
def encode_jitted(_UpperCAmelCase , _UpperCAmelCase=None , **_UpperCAmelCase ):
return model.encode(input_ids=_UpperCAmelCase , attention_mask=_UpperCAmelCase )
with self.subTest('JIT Enabled' ):
UpperCamelCase_ = encode_jitted(**_UpperCAmelCase ).to_tuple()
with self.subTest('JIT Disabled' ):
with jax.disable_jit():
UpperCamelCase_ = encode_jitted(**_UpperCAmelCase ).to_tuple()
self.assertEqual(len(_UpperCAmelCase ) , len(_UpperCAmelCase ) )
for jitted_output, output in zip(_UpperCAmelCase , _UpperCAmelCase ):
self.assertEqual(jitted_output.shape , output.shape )
def _UpperCAmelCase ( self ) -> List[str]:
UpperCamelCase_ , UpperCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
UpperCamelCase_ = model_class(_UpperCAmelCase )
UpperCamelCase_ = model.encode(inputs_dict['input_ids'] , inputs_dict['attention_mask'] )
UpperCamelCase_ = {
'decoder_input_ids': inputs_dict['decoder_input_ids'],
'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
'encoder_outputs': encoder_outputs,
}
@jax.jit
def decode_jitted(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
return model.decode(
decoder_input_ids=_UpperCAmelCase , decoder_attention_mask=_UpperCAmelCase , encoder_outputs=_UpperCAmelCase , )
with self.subTest('JIT Enabled' ):
UpperCamelCase_ = decode_jitted(**_UpperCAmelCase ).to_tuple()
with self.subTest('JIT Disabled' ):
with jax.disable_jit():
UpperCamelCase_ = decode_jitted(**_UpperCAmelCase ).to_tuple()
self.assertEqual(len(_UpperCAmelCase ) , len(_UpperCAmelCase ) )
for jitted_output, output in zip(_UpperCAmelCase , _UpperCAmelCase ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def _UpperCAmelCase ( self ) -> int:
for model_class_name in self.all_model_classes:
UpperCamelCase_ = model_class_name.from_pretrained('google/pegasus-large' , from_pt=_UpperCAmelCase )
UpperCamelCase_ = np.ones((1, 1) )
UpperCamelCase_ = model(_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
@slow
def _UpperCAmelCase ( self ) -> List[Any]:
UpperCamelCase_ = FlaxPegasusForConditionalGeneration.from_pretrained('google/pegasus-xsum' )
UpperCamelCase_ = PegasusTokenizer.from_pretrained('google/pegasus-xsum' )
UpperCamelCase_ = [
' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.',
' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ',
]
UpperCamelCase_ = [
'California\'s largest electricity provider has turned off power to hundreds of thousands of customers.',
'Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.',
]
UpperCamelCase_ = tokenizer(_UpperCAmelCase , return_tensors='np' , truncation=_UpperCAmelCase , max_length=512 , padding=_UpperCAmelCase )
UpperCamelCase_ = model.generate(**_UpperCAmelCase , num_beams=2 ).sequences
UpperCamelCase_ = tokenizer.batch_decode(_UpperCAmelCase , skip_special_tokens=_UpperCAmelCase )
assert tgt_text == decoded
| 23 | from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _snake_case ( __snake_case , __snake_case ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(__snake_case , __snake_case ) ) )
def _snake_case ( __snake_case , __snake_case ):
if dataset.ndim != value_array.ndim:
_UpperCamelCase = (
'''Wrong input data\'s dimensions... '''
f"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
)
raise ValueError(__snake_case )
try:
if dataset.shape[1] != value_array.shape[1]:
_UpperCamelCase = (
'''Wrong input data\'s shape... '''
f"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
)
raise ValueError(__snake_case )
except IndexError:
if dataset.ndim != value_array.ndim:
raise TypeError('''Wrong shape''' )
if dataset.dtype != value_array.dtype:
_UpperCamelCase = (
'''Input data have different datatype... '''
f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
)
raise TypeError(__snake_case )
_UpperCamelCase = []
for value in value_array:
_UpperCamelCase = euclidean(__snake_case , dataset[0] )
_UpperCamelCase = dataset[0].tolist()
for dataset_value in dataset[1:]:
_UpperCamelCase = euclidean(__snake_case , __snake_case )
if dist > temp_dist:
_UpperCamelCase = temp_dist
_UpperCamelCase = dataset_value.tolist()
answer.append([vector, dist] )
return answer
def _snake_case ( __snake_case , __snake_case ):
return np.dot(__snake_case , __snake_case ) / (norm(__snake_case ) * norm(__snake_case ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 10 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class lowerCAmelCase ( unittest.TestCase):
def lowerCAmelCase ( self ) -> int:
'''simple docstring'''
__snake_case = tempfile.mkdtemp()
# fmt: off
__snake_case = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
# fmt: on
__snake_case = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
__snake_case = {
'''do_resize''': True,
'''size''': {'''height''': 18, '''width''': 18},
'''do_normalize''': True,
'''image_mean''': [0.5, 0.5, 0.5],
'''image_std''': [0.5, 0.5, 0.5],
}
__snake_case = os.path.join(self.tmpdirname , __SCREAMING_SNAKE_CASE )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def lowerCAmelCase ( self , **__SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
return BertTokenizer.from_pretrained(self.tmpdirname , **__SCREAMING_SNAKE_CASE )
def lowerCAmelCase ( self , **__SCREAMING_SNAKE_CASE ) -> Tuple:
'''simple docstring'''
return ViTImageProcessor.from_pretrained(self.tmpdirname , **__SCREAMING_SNAKE_CASE )
def lowerCAmelCase ( self ) -> str:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def lowerCAmelCase ( self ) -> Tuple:
'''simple docstring'''
__snake_case = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
__snake_case = [Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowerCAmelCase ( self ) -> Optional[Any]:
'''simple docstring'''
__snake_case = self.get_tokenizer()
__snake_case = self.get_image_processor()
__snake_case = VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE )
processor.save_pretrained(self.tmpdirname )
__snake_case = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , __SCREAMING_SNAKE_CASE )
def lowerCAmelCase ( self ) -> List[str]:
'''simple docstring'''
__snake_case = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__snake_case = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
__snake_case = self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE , padding_value=1.0 )
__snake_case = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=__SCREAMING_SNAKE_CASE , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __SCREAMING_SNAKE_CASE )
def lowerCAmelCase ( self ) -> int:
'''simple docstring'''
__snake_case = self.get_image_processor()
__snake_case = self.get_tokenizer()
__snake_case = VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE )
__snake_case = self.prepare_image_inputs()
__snake_case = image_processor(__SCREAMING_SNAKE_CASE , return_tensors='''np''' )
__snake_case = processor(images=__SCREAMING_SNAKE_CASE , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def lowerCAmelCase ( self ) -> List[str]:
'''simple docstring'''
__snake_case = self.get_image_processor()
__snake_case = self.get_tokenizer()
__snake_case = VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE )
__snake_case = '''lower newer'''
__snake_case = processor(text=__SCREAMING_SNAKE_CASE )
__snake_case = tokenizer(__SCREAMING_SNAKE_CASE )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowerCAmelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
__snake_case = self.get_image_processor()
__snake_case = self.get_tokenizer()
__snake_case = VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE )
__snake_case = '''lower newer'''
__snake_case = self.prepare_image_inputs()
__snake_case = processor(text=__SCREAMING_SNAKE_CASE , images=__SCREAMING_SNAKE_CASE )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with self.assertRaises(__SCREAMING_SNAKE_CASE ):
processor()
def lowerCAmelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
__snake_case = self.get_image_processor()
__snake_case = self.get_tokenizer()
__snake_case = VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE )
__snake_case = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__snake_case = processor.batch_decode(__SCREAMING_SNAKE_CASE )
__snake_case = tokenizer.batch_decode(__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def lowerCAmelCase ( self ) -> List[str]:
'''simple docstring'''
__snake_case = self.get_image_processor()
__snake_case = self.get_tokenizer()
__snake_case = VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE )
__snake_case = '''lower newer'''
__snake_case = self.prepare_image_inputs()
__snake_case = processor(text=__SCREAMING_SNAKE_CASE , images=__SCREAMING_SNAKE_CASE )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 24 | import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class lowerCAmelCase_ ( __lowercase, unittest.TestCase ):
UpperCAmelCase = ShapEPipeline
UpperCAmelCase = ["prompt"]
UpperCAmelCase = ["prompt"]
UpperCAmelCase = [
"num_images_per_prompt",
"num_inference_steps",
"generator",
"latents",
"guidance_scale",
"frame_size",
"output_type",
"return_dict",
]
UpperCAmelCase = False
@property
def UpperCamelCase_ ( self : Union[str, Any] ):
return 32
@property
def UpperCamelCase_ ( self : int ):
return 32
@property
def UpperCamelCase_ ( self : List[str] ):
return self.time_input_dim * 4
@property
def UpperCamelCase_ ( self : Optional[Any] ):
return 8
@property
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
return tokenizer
@property
def UpperCamelCase_ ( self : List[Any] ):
torch.manual_seed(0 )
_UpperCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(_A )
@property
def UpperCamelCase_ ( self : int ):
torch.manual_seed(0 )
_UpperCamelCase = {
'''num_attention_heads''': 2,
'''attention_head_dim''': 16,
'''embedding_dim''': self.time_input_dim,
'''num_embeddings''': 32,
'''embedding_proj_dim''': self.text_embedder_hidden_size,
'''time_embed_dim''': self.time_embed_dim,
'''num_layers''': 1,
'''clip_embed_dim''': self.time_input_dim * 2,
'''additional_embeddings''': 0,
'''time_embed_act_fn''': '''gelu''',
'''norm_in_type''': '''layer''',
'''encoder_hid_proj_type''': None,
'''added_emb_type''': None,
}
_UpperCamelCase = PriorTransformer(**_A )
return model
@property
def UpperCamelCase_ ( self : Union[str, Any] ):
torch.manual_seed(0 )
_UpperCamelCase = {
'''param_shapes''': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'''d_latent''': self.time_input_dim,
'''d_hidden''': self.renderer_dim,
'''n_output''': 12,
'''background''': (
0.1,
0.1,
0.1,
),
}
_UpperCamelCase = ShapERenderer(**_A )
return model
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = self.dummy_prior
_UpperCamelCase = self.dummy_text_encoder
_UpperCamelCase = self.dummy_tokenizer
_UpperCamelCase = self.dummy_renderer
_UpperCamelCase = HeunDiscreteScheduler(
beta_schedule='''exp''' , num_train_timesteps=1024 , prediction_type='''sample''' , use_karras_sigmas=_A , clip_sample=_A , clip_sample_range=1.0 , )
_UpperCamelCase = {
'''prior''': prior,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''renderer''': renderer,
'''scheduler''': scheduler,
}
return components
def UpperCamelCase_ ( self : Tuple , _A : Tuple , _A : Optional[int]=0 ):
if str(_A ).startswith('''mps''' ):
_UpperCamelCase = torch.manual_seed(_A )
else:
_UpperCamelCase = torch.Generator(device=_A ).manual_seed(_A )
_UpperCamelCase = {
'''prompt''': '''horse''',
'''generator''': generator,
'''num_inference_steps''': 1,
'''frame_size''': 32,
'''output_type''': '''np''',
}
return inputs
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = '''cpu'''
_UpperCamelCase = self.get_dummy_components()
_UpperCamelCase = self.pipeline_class(**_A )
_UpperCamelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
_UpperCamelCase = pipe(**self.get_dummy_inputs(_A ) )
_UpperCamelCase = output.images[0]
_UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
_UpperCamelCase = np.array(
[
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def UpperCamelCase_ ( self : Any ):
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = torch_device == '''cpu'''
_UpperCamelCase = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=_A , relax_max_difference=_A , )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = self.get_dummy_components()
_UpperCamelCase = self.pipeline_class(**_A )
_UpperCamelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
_UpperCamelCase = 1
_UpperCamelCase = 2
_UpperCamelCase = self.get_dummy_inputs(_A )
for key in inputs.keys():
if key in self.batch_params:
_UpperCamelCase = batch_size * [inputs[key]]
_UpperCamelCase = pipe(**_A , num_images_per_prompt=_A )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class lowerCAmelCase_ ( unittest.TestCase ):
def UpperCamelCase_ ( self : str ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/shap_e/test_shap_e_np_out.npy''' )
_UpperCamelCase = ShapEPipeline.from_pretrained('''openai/shap-e''' )
_UpperCamelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
_UpperCamelCase = torch.Generator(device=_A ).manual_seed(0 )
_UpperCamelCase = pipe(
'''a shark''' , generator=_A , guidance_scale=15.0 , num_inference_steps=64 , frame_size=64 , output_type='''np''' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(_A , _A )
| 10 | 0 |
from collections.abc import Callable
import numpy as np
def lowerCamelCase__ ( _a , _a , _a , _a , _a):
SCREAMING_SNAKE_CASE : List[Any] = int(np.ceil((x_end - xa) / step_size))
SCREAMING_SNAKE_CASE : Tuple = np.zeros((n + 1,))
SCREAMING_SNAKE_CASE : List[Any] = ya
SCREAMING_SNAKE_CASE : str = xa
for k in range(_a):
SCREAMING_SNAKE_CASE : Optional[int] = y[k] + step_size * ode_func(_a , y[k])
SCREAMING_SNAKE_CASE : Union[str, Any] = y[k] + (
(step_size / 2) * (ode_func(_a , y[k]) + ode_func(x + step_size , _a))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 | import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
_lowerCAmelCase = HfApi()
_lowerCAmelCase = {}
# fmt: off
_lowerCAmelCase = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189,
-1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839,
0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557
])
_lowerCAmelCase = torch.tensor([
-2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436,
1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208,
-2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948,
2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365
])
_lowerCAmelCase = torch.tensor([
-0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,
-0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304,
-0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925,
0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943
])
_lowerCAmelCase = torch.tensor([
0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172,
-0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309,
0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805,
-0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505
])
_lowerCAmelCase = torch.tensor([
0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133,
-0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395,
0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559,
-0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386
])
_lowerCAmelCase = torch.tensor([
0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078,
-0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330,
0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683,
-0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431
])
_lowerCAmelCase = torch.tensor([
0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042,
-0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398,
0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574,
-0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390
])
_lowerCAmelCase = torch.tensor([
0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042,
-0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290,
0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746,
-0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473
])
_lowerCAmelCase = torch.tensor([
-1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330,
1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243,
-2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810,
1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251])
_lowerCAmelCase = torch.tensor([
-1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324,
0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181,
-2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259,
1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266
])
_lowerCAmelCase = torch.tensor([
-1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212,
0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027,
-2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131,
1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355
])
_lowerCAmelCase = torch.tensor([
-2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959,
1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351,
-3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341,
3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066
])
_lowerCAmelCase = torch.tensor([
-2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740,
1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398,
-2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395,
2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243
])
_lowerCAmelCase = torch.tensor([
-2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336,
1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908,
-3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560,
3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343
])
_lowerCAmelCase = torch.tensor([
-1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344,
1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391,
-2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439,
1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219
])
# fmt: on
_lowerCAmelCase = api.list_models(filter="diffusers")
for mod in models:
if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
_lowerCAmelCase = "/home/patrick/google_checkpoints/" + mod.modelId.split("/")[-1]
print(f'Started running {mod.modelId}!!!')
if mod.modelId.startswith("CompVis"):
_lowerCAmelCase = UNetaDModel.from_pretrained(local_checkpoint, subfolder="unet")
else:
_lowerCAmelCase = UNetaDModel.from_pretrained(local_checkpoint)
torch.manual_seed(0)
random.seed(0)
_lowerCAmelCase = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
_lowerCAmelCase = torch.tensor([10] * noise.shape[0])
with torch.no_grad():
_lowerCAmelCase = model(noise, time_step).sample
assert torch.allclose(
logits[0, 0, 0, :30], results["_".join("_".join(mod.modelId.split("/")).split("-"))], atol=1E-3
)
print(f'{mod.modelId} has passed successfully!!!')
| 10 | 0 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEModel,
)
from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class _A :
def __init__( self : List[Any] , __magic_name__ : Optional[int] , __magic_name__ : str=13 , __magic_name__ : Tuple=10 , __magic_name__ : Union[str, Any]=3 , __magic_name__ : Union[str, Any]=2 , __magic_name__ : Optional[Any]=2 , __magic_name__ : str=2 , __magic_name__ : Dict=True , __magic_name__ : Union[str, Any]=True , __magic_name__ : Optional[int]=32 , __magic_name__ : str=5 , __magic_name__ : Tuple=4 , __magic_name__ : Tuple=37 , __magic_name__ : Union[str, Any]="gelu" , __magic_name__ : Any=0.1 , __magic_name__ : int=0.1 , __magic_name__ : Tuple=10 , __magic_name__ : Optional[int]=0.02 , __magic_name__ : Optional[Any]=0.9 , __magic_name__ : str=None , ) -> Any:
"""simple docstring"""
__snake_case : List[Any] = parent
__snake_case : Tuple = batch_size
__snake_case : List[Any] = image_size
__snake_case : Union[str, Any] = num_channels
__snake_case : int = patch_size
__snake_case : Dict = tubelet_size
__snake_case : Union[str, Any] = num_frames
__snake_case : Union[str, Any] = is_training
__snake_case : Dict = use_labels
__snake_case : Tuple = hidden_size
__snake_case : Union[str, Any] = num_hidden_layers
__snake_case : List[str] = num_attention_heads
__snake_case : str = intermediate_size
__snake_case : List[str] = hidden_act
__snake_case : List[str] = hidden_dropout_prob
__snake_case : Optional[Any] = attention_probs_dropout_prob
__snake_case : List[str] = type_sequence_label_size
__snake_case : List[str] = initializer_range
__snake_case : Optional[Any] = mask_ratio
__snake_case : int = scope
# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
__snake_case : Tuple = (image_size // patch_size) ** 2
__snake_case : Optional[Any] = (num_frames // tubelet_size) * self.num_patches_per_frame
# use this variable to define bool_masked_pos
__snake_case : Optional[Any] = int(mask_ratio * self.seq_length )
def lowercase__ ( self : Optional[int] ) -> str:
"""simple docstring"""
__snake_case : Optional[Any] = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
__snake_case : Optional[int] = None
if self.use_labels:
__snake_case : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__snake_case : List[str] = self.get_config()
return config, pixel_values, labels
def lowercase__ ( self : Union[str, Any] ) -> int:
"""simple docstring"""
return VideoMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , tubelet_size=self.tubelet_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__magic_name__ , initializer_range=self.initializer_range , )
def lowercase__ ( self : Optional[Any] , __magic_name__ : Any , __magic_name__ : str , __magic_name__ : Tuple ) -> int:
"""simple docstring"""
__snake_case : Tuple = VideoMAEModel(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
__snake_case : Optional[int] = model(__magic_name__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowercase__ ( self : Tuple , __magic_name__ : str , __magic_name__ : Tuple , __magic_name__ : Any ) -> int:
"""simple docstring"""
__snake_case : Dict = VideoMAEForPreTraining(__magic_name__ )
model.to(__magic_name__ )
model.eval()
# important: each video needs to have the same number of masked patches
# hence we define a single mask, which we then repeat for each example in the batch
__snake_case : List[str] = torch.ones((self.num_masks,) )
__snake_case : Tuple = torch.cat([mask, torch.zeros(self.seq_length - mask.size(0 ) )] )
__snake_case : Any = mask.expand(self.batch_size , -1 ).bool()
__snake_case : Union[str, Any] = model(__magic_name__ , __magic_name__ )
# model only returns predictions for masked patches
__snake_case : List[Any] = mask.sum().item()
__snake_case : Any = 3 * self.tubelet_size * self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_masked_patches, decoder_num_labels) )
def lowercase__ ( self : int ) -> Any:
"""simple docstring"""
__snake_case : List[str] = self.prepare_config_and_inputs()
__snake_case , __snake_case , __snake_case : Dict = config_and_inputs
__snake_case : Optional[Any] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class _A ( __lowercase , __lowercase , unittest.TestCase ):
lowercase__: Union[str, Any] = (
(VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
)
lowercase__: Any = (
{'''feature-extraction''': VideoMAEModel, '''video-classification''': VideoMAEForVideoClassification}
if is_torch_available()
else {}
)
lowercase__: int = False
lowercase__: Optional[Any] = False
lowercase__: int = False
lowercase__: List[Any] = False
def lowercase__ ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
__snake_case : str = VideoMAEModelTester(self )
__snake_case : str = ConfigTester(self , config_class=__magic_name__ , has_text_modality=__magic_name__ , hidden_size=37 )
def lowercase__ ( self : int , __magic_name__ : Union[str, Any] , __magic_name__ : Union[str, Any] , __magic_name__ : str=False ) -> List[str]:
"""simple docstring"""
__snake_case : Dict = copy.deepcopy(__magic_name__ )
if model_class == VideoMAEForPreTraining:
# important: each video needs to have the same number of masked patches
# hence we define a single mask, which we then repeat for each example in the batch
__snake_case : str = torch.ones((self.model_tester.num_masks,) )
__snake_case : str = torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0 ) )] )
__snake_case : Any = mask.expand(self.model_tester.batch_size , -1 ).bool()
__snake_case : str = bool_masked_pos.to(__magic_name__ )
if return_labels:
if model_class in [
*get_values(__magic_name__ ),
]:
__snake_case : int = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__magic_name__ )
return inputs_dict
def lowercase__ ( self : str ) -> Any:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="""VideoMAE does not use inputs_embeds""" )
def lowercase__ ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
pass
def lowercase__ ( self : List[str] ) -> str:
"""simple docstring"""
__snake_case , __snake_case : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__snake_case : List[Any] = model_class(__magic_name__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__snake_case : List[Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__magic_name__ , nn.Linear ) )
def lowercase__ ( self : Any ) -> List[Any]:
"""simple docstring"""
__snake_case , __snake_case : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__snake_case : Any = model_class(__magic_name__ )
__snake_case : Dict = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__snake_case : Any = [*signature.parameters.keys()]
__snake_case : str = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , __magic_name__ )
def lowercase__ ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__snake_case : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__magic_name__ )
def lowercase__ ( self : int ) -> List[Any]:
"""simple docstring"""
__snake_case : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__magic_name__ )
@slow
def lowercase__ ( self : Dict ) -> List[str]:
"""simple docstring"""
for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__snake_case : List[Any] = VideoMAEModel.from_pretrained(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def lowercase__ ( self : Any ) -> List[str]:
"""simple docstring"""
if not self.has_attentions:
pass
else:
__snake_case , __snake_case : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
__snake_case : Tuple = True
for model_class in self.all_model_classes:
__snake_case : List[str] = self.model_tester.seq_length - self.model_tester.num_masks
__snake_case : Union[str, Any] = (
num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
)
__snake_case : str = True
__snake_case : int = False
__snake_case : List[str] = True
__snake_case : Dict = model_class(__magic_name__ )
model.to(__magic_name__ )
model.eval()
with torch.no_grad():
__snake_case : List[str] = model(**self._prepare_for_class(__magic_name__ , __magic_name__ ) )
__snake_case : Any = outputs.attentions
self.assertEqual(len(__magic_name__ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__snake_case : str = True
__snake_case : Union[str, Any] = model_class(__magic_name__ )
model.to(__magic_name__ )
model.eval()
with torch.no_grad():
__snake_case : Optional[Any] = model(**self._prepare_for_class(__magic_name__ , __magic_name__ ) )
__snake_case : Optional[Any] = outputs.attentions
self.assertEqual(len(__magic_name__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
__snake_case : Tuple = len(__magic_name__ )
# Check attention is always last and order is fine
__snake_case : Dict = True
__snake_case : Optional[int] = True
__snake_case : Optional[int] = model_class(__magic_name__ )
model.to(__magic_name__ )
model.eval()
with torch.no_grad():
__snake_case : Any = model(**self._prepare_for_class(__magic_name__ , __magic_name__ ) )
self.assertEqual(out_len + 1 , len(__magic_name__ ) )
__snake_case : List[str] = outputs.attentions
self.assertEqual(len(__magic_name__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
def lowercase__ ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
def check_hidden_states_output(__magic_name__ : Optional[int] , __magic_name__ : Optional[int] , __magic_name__ : Optional[Any] ):
__snake_case : Optional[int] = model_class(__magic_name__ )
model.to(__magic_name__ )
model.eval()
with torch.no_grad():
__snake_case : Dict = model(**self._prepare_for_class(__magic_name__ , __magic_name__ ) )
__snake_case : Any = outputs.hidden_states
__snake_case : Union[str, Any] = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(__magic_name__ ) , __magic_name__ )
__snake_case : Any = self.model_tester.seq_length - self.model_tester.num_masks
__snake_case : List[str] = num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
__snake_case , __snake_case : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__snake_case : List[str] = True
check_hidden_states_output(__magic_name__ , __magic_name__ , __magic_name__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__snake_case : Optional[int] = True
check_hidden_states_output(__magic_name__ , __magic_name__ , __magic_name__ )
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def lowercase__ ( self : Any ) -> str:
"""simple docstring"""
pass
def _a ( ) -> int:
"""simple docstring"""
__snake_case : Union[str, Any] = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""" , filename="""eating_spaghetti.npy""" , repo_type="""dataset""" )
__snake_case : Union[str, Any] = np.load(_lowerCamelCase )
return list(_lowerCamelCase )
@require_torch
@require_vision
class _A ( unittest.TestCase ):
@cached_property
def lowercase__ ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def lowercase__ ( self : str ) -> List[str]:
"""simple docstring"""
__snake_case : Dict = VideoMAEForVideoClassification.from_pretrained("""MCG-NJU/videomae-base-finetuned-kinetics""" ).to(
__magic_name__ )
__snake_case : Optional[Any] = self.default_image_processor
__snake_case : List[Any] = prepare_video()
__snake_case : Union[str, Any] = image_processor(__magic_name__ , return_tensors="""pt""" ).to(__magic_name__ )
# forward pass
with torch.no_grad():
__snake_case : int = model(**__magic_name__ )
# verify the logits
__snake_case : Optional[int] = torch.Size((1, 4_00) )
self.assertEqual(outputs.logits.shape , __magic_name__ )
__snake_case : str = torch.tensor([0.3669, -0.0688, -0.2421] ).to(__magic_name__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __magic_name__ , atol=1E-4 ) )
@slow
def lowercase__ ( self : List[Any] ) -> List[str]:
"""simple docstring"""
__snake_case : Union[str, Any] = VideoMAEForPreTraining.from_pretrained("""MCG-NJU/videomae-base-short""" ).to(__magic_name__ )
__snake_case : List[Any] = self.default_image_processor
__snake_case : List[str] = prepare_video()
__snake_case : Union[str, Any] = image_processor(__magic_name__ , return_tensors="""pt""" ).to(__magic_name__ )
# add boolean mask, indicating which patches to mask
__snake_case : str = hf_hub_download(repo_id="""hf-internal-testing/bool-masked-pos""" , filename="""bool_masked_pos.pt""" )
__snake_case : Tuple = torch.load(__magic_name__ )
# forward pass
with torch.no_grad():
__snake_case : str = model(**__magic_name__ )
# verify the logits
__snake_case : Tuple = torch.Size([1, 14_08, 15_36] )
__snake_case : Tuple = torch.tensor(
[[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]] , device=__magic_name__ )
self.assertEqual(outputs.logits.shape , __magic_name__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , __magic_name__ , atol=1E-4 ) )
# verify the loss (`config.norm_pix_loss` = `True`)
__snake_case : int = torch.tensor([0.5142] , device=__magic_name__ )
self.assertTrue(torch.allclose(outputs.loss , __magic_name__ , atol=1E-4 ) )
# verify the loss (`config.norm_pix_loss` = `False`)
__snake_case : Union[str, Any] = VideoMAEForPreTraining.from_pretrained("""MCG-NJU/videomae-base-short""" , norm_pix_loss=__magic_name__ ).to(
__magic_name__ )
with torch.no_grad():
__snake_case : Optional[Any] = model(**__magic_name__ )
__snake_case : int = torch.tensor(torch.tensor([0.6469] ) , device=__magic_name__ )
self.assertTrue(torch.allclose(outputs.loss , __magic_name__ , atol=1E-4 ) )
| 26 | from typing import List
from .keymap import KEYMAP, get_character
def _snake_case ( __snake_case ):
def decorator(__snake_case ):
_UpperCamelCase = getattr(__snake_case , '''handle_key''' , [] )
handle += [key]
setattr(__snake_case , '''handle_key''' , __snake_case )
return func
return decorator
def _snake_case ( *__snake_case ):
def decorator(__snake_case ):
_UpperCamelCase = getattr(__snake_case , '''handle_key''' , [] )
handle += keys
setattr(__snake_case , '''handle_key''' , __snake_case )
return func
return decorator
class lowerCAmelCase_ ( __lowercase ):
def __new__( cls : Optional[Any] , _A : Optional[Any] , _A : Optional[int] , _A : Union[str, Any] ):
_UpperCamelCase = super().__new__(cls , _A , _A , _A )
if not hasattr(_A , '''key_handler''' ):
setattr(_A , '''key_handler''' , {} )
setattr(_A , '''handle_input''' , KeyHandler.handle_input )
for value in attrs.values():
_UpperCamelCase = getattr(_A , '''handle_key''' , [] )
for key in handled_keys:
_UpperCamelCase = value
return new_cls
@staticmethod
def UpperCamelCase_ ( cls : str ):
_UpperCamelCase = get_character()
if char != KEYMAP["undefined"]:
_UpperCamelCase = ord(_A )
_UpperCamelCase = cls.key_handler.get(_A )
if handler:
_UpperCamelCase = char
return handler(cls )
else:
return None
def _snake_case ( cls ):
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 10 | 0 |
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
NystromformerModel,
)
from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase:
'''simple docstring'''
def __init__( self , snake_case_ , snake_case_=13 , snake_case_=7 , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=99 , snake_case_=32 , snake_case_=5 , snake_case_=4 , snake_case_=37 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=512 , snake_case_=16 , snake_case_=2 , snake_case_=0.02 , snake_case_=3 , snake_case_=4 , snake_case_=None , ):
_A = parent
_A = batch_size
_A = seq_length
_A = is_training
_A = use_input_mask
_A = use_token_type_ids
_A = use_labels
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_act
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = type_sequence_label_size
_A = initializer_range
_A = num_labels
_A = num_choices
_A = scope
def lowerCAmelCase__ ( self ):
_A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A = None
if self.use_input_mask:
_A = random_attention_mask([self.batch_size, self.seq_length] )
_A = None
if self.use_token_type_ids:
_A = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_A = None
_A = None
_A = None
if self.use_labels:
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_A = ids_tensor([self.batch_size] , self.num_choices )
_A = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCAmelCase__ ( self ):
return NystromformerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=snake_case_ , initializer_range=self.initializer_range , )
def lowerCAmelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_A = NystromformerModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_A = model(snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ )
_A = model(snake_case_ , token_type_ids=snake_case_ )
_A = model(snake_case_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCAmelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_A = NystromformerForMaskedLM(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_A = model(snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCAmelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_A = NystromformerForQuestionAnswering(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_A = model(
snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , start_positions=snake_case_ , end_positions=snake_case_ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCAmelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_A = self.num_labels
_A = NystromformerForSequenceClassification(snake_case_ )
model.to(snake_case_ )
model.eval()
_A = model(snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCAmelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_A = self.num_labels
_A = NystromformerForTokenClassification(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_A = model(snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCAmelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_A = self.num_choices
_A = NystromformerForMultipleChoice(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_A = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_A = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_A = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_A = model(
snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , labels=snake_case_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCAmelCase__ ( self ):
_A = self.prepare_config_and_inputs()
(
(
_A
), (
_A
), (
_A
), (
_A
), (
_A
), (
_A
), (
_A
),
) = config_and_inputs
_A = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase( __snake_case , __snake_case , unittest.TestCase ):
'''simple docstring'''
__magic_name__ = (
(
NystromformerModel,
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
)
if is_torch_available()
else ()
)
__magic_name__ = (
{
'feature-extraction': NystromformerModel,
'fill-mask': NystromformerForMaskedLM,
'question-answering': NystromformerForQuestionAnswering,
'text-classification': NystromformerForSequenceClassification,
'token-classification': NystromformerForTokenClassification,
'zero-shot': NystromformerForSequenceClassification,
}
if is_torch_available()
else {}
)
__magic_name__ = False
__magic_name__ = False
def lowerCAmelCase__ ( self ):
_A = NystromformerModelTester(self )
_A = ConfigTester(self , config_class=snake_case_ , hidden_size=37 )
def lowerCAmelCase__ ( self ):
self.config_tester.run_common_tests()
def lowerCAmelCase__ ( self ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
def lowerCAmelCase__ ( self ):
_A = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_A = type
self.model_tester.create_and_check_model(*snake_case_ )
def lowerCAmelCase__ ( self ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*snake_case_ )
def lowerCAmelCase__ ( self ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*snake_case_ )
def lowerCAmelCase__ ( self ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*snake_case_ )
def lowerCAmelCase__ ( self ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*snake_case_ )
def lowerCAmelCase__ ( self ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*snake_case_ )
@slow
def lowerCAmelCase__ ( self ):
for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A = NystromformerModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
@require_torch
class lowerCamelCase( unittest.TestCase ):
'''simple docstring'''
@slow
def lowerCAmelCase__ ( self ):
_A = NystromformerModel.from_pretrained('uw-madison/nystromformer-512' )
_A = torch.tensor([[0, 1, 2, 3, 4, 5]] )
with torch.no_grad():
_A = model(snake_case_ )[0]
_A = torch.Size((1, 6, 768) )
self.assertEqual(output.shape , snake_case_ )
_A = torch.tensor(
[[[-0.4532, -0.0936, 0.5137], [-0.2676, 0.0628, 0.6186], [-0.3629, -0.1726, 0.4716]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , snake_case_ , atol=1E-4 ) )
@slow
def lowerCAmelCase__ ( self ):
_A = 'the [MASK] of Belgium is Brussels'
_A = AutoTokenizer.from_pretrained('uw-madison/nystromformer-512' )
_A = NystromformerForMaskedLM.from_pretrained('uw-madison/nystromformer-512' )
_A = tokenizer(snake_case_ , return_tensors='pt' )
with torch.no_grad():
_A = model(encoding.input_ids ).logits
_A = token_logits[:, 2, :].argmax(-1 )[0]
self.assertEqual(tokenizer.decode(snake_case_ ) , 'capital' )
| 27 | import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
require_torch_gpu,
require_torch_or_tf,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
class lowerCAmelCase_ ( unittest.TestCase ):
UpperCAmelCase = MODEL_FOR_CAUSAL_LM_MAPPING
UpperCAmelCase = TF_MODEL_FOR_CAUSAL_LM_MAPPING
@require_torch
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = pipeline(task='''text-generation''' , model='''sshleifer/tiny-ctrl''' , framework='''pt''' )
# Using `do_sample=False` to force deterministic output
_UpperCamelCase = text_generator('''This is a test''' , do_sample=_A )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
''' oscope. FiliFili@@'''
)
}
] , )
_UpperCamelCase = text_generator(['''This is a test''', '''This is a second test'''] )
self.assertEqual(
_A , [
[
{
'''generated_text''': (
'''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
''' oscope. FiliFili@@'''
)
}
],
[
{
'''generated_text''': (
'''This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'''
''' oscope. oscope. FiliFili@@'''
)
}
],
] , )
_UpperCamelCase = text_generator('''This is a test''' , do_sample=_A , num_return_sequences=2 , return_tensors=_A )
self.assertEqual(
_A , [
{'''generated_token_ids''': ANY(_A )},
{'''generated_token_ids''': ANY(_A )},
] , )
_UpperCamelCase = text_generator.model.config.eos_token_id
_UpperCamelCase = '''<pad>'''
_UpperCamelCase = text_generator(
['''This is a test''', '''This is a second test'''] , do_sample=_A , num_return_sequences=2 , batch_size=2 , return_tensors=_A , )
self.assertEqual(
_A , [
[
{'''generated_token_ids''': ANY(_A )},
{'''generated_token_ids''': ANY(_A )},
],
[
{'''generated_token_ids''': ANY(_A )},
{'''generated_token_ids''': ANY(_A )},
],
] , )
@require_tf
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = pipeline(task='''text-generation''' , model='''sshleifer/tiny-ctrl''' , framework='''tf''' )
# Using `do_sample=False` to force deterministic output
_UpperCamelCase = text_generator('''This is a test''' , do_sample=_A )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
''' please,'''
)
}
] , )
_UpperCamelCase = text_generator(['''This is a test''', '''This is a second test'''] , do_sample=_A )
self.assertEqual(
_A , [
[
{
'''generated_text''': (
'''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
''' please,'''
)
}
],
[
{
'''generated_text''': (
'''This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'''
''' Cannes 閲閲Cannes Cannes Cannes 攵 please,'''
)
}
],
] , )
def UpperCamelCase_ ( self : int , _A : str , _A : Union[str, Any] , _A : Any ):
_UpperCamelCase = TextGenerationPipeline(model=_A , tokenizer=_A )
return text_generator, ["This is a test", "Another test"]
def UpperCamelCase_ ( self : Union[str, Any] ):
_UpperCamelCase = '''Hello I believe in'''
_UpperCamelCase = pipeline('''text-generation''' , model='''hf-internal-testing/tiny-random-gpt2''' )
_UpperCamelCase = text_generator(_A )
self.assertEqual(
_A , [{'''generated_text''': '''Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'''}] , )
_UpperCamelCase = text_generator(_A , stop_sequence=''' fe''' )
self.assertEqual(_A , [{'''generated_text''': '''Hello I believe in fe'''}] )
def UpperCamelCase_ ( self : Any , _A : List[Any] , _A : Union[str, Any] ):
_UpperCamelCase = text_generator.model
_UpperCamelCase = text_generator.tokenizer
_UpperCamelCase = text_generator('''This is a test''' )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )
_UpperCamelCase = text_generator('''This is a test''' , return_full_text=_A )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
self.assertNotIn('''This is a test''' , outputs[0]['''generated_text'''] )
_UpperCamelCase = pipeline(task='''text-generation''' , model=_A , tokenizer=_A , return_full_text=_A )
_UpperCamelCase = text_generator('''This is a test''' )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
self.assertNotIn('''This is a test''' , outputs[0]['''generated_text'''] )
_UpperCamelCase = text_generator('''This is a test''' , return_full_text=_A )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )
_UpperCamelCase = text_generator(['''This is great !''', '''Something else'''] , num_return_sequences=2 , do_sample=_A )
self.assertEqual(
_A , [
[{'''generated_text''': ANY(_A )}, {'''generated_text''': ANY(_A )}],
[{'''generated_text''': ANY(_A )}, {'''generated_text''': ANY(_A )}],
] , )
if text_generator.tokenizer.pad_token is not None:
_UpperCamelCase = text_generator(
['''This is great !''', '''Something else'''] , num_return_sequences=2 , batch_size=2 , do_sample=_A )
self.assertEqual(
_A , [
[{'''generated_text''': ANY(_A )}, {'''generated_text''': ANY(_A )}],
[{'''generated_text''': ANY(_A )}, {'''generated_text''': ANY(_A )}],
] , )
with self.assertRaises(_A ):
_UpperCamelCase = text_generator('''test''' , return_full_text=_A , return_text=_A )
with self.assertRaises(_A ):
_UpperCamelCase = text_generator('''test''' , return_full_text=_A , return_tensors=_A )
with self.assertRaises(_A ):
_UpperCamelCase = text_generator('''test''' , return_text=_A , return_tensors=_A )
# Empty prompt is slighly special
# it requires BOS token to exist.
# Special case for Pegasus which will always append EOS so will
# work even without BOS.
if (
text_generator.tokenizer.bos_token_id is not None
or "Pegasus" in tokenizer.__class__.__name__
or "Git" in model.__class__.__name__
):
_UpperCamelCase = text_generator('''''' )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
else:
with self.assertRaises((ValueError, AssertionError) ):
_UpperCamelCase = text_generator('''''' )
if text_generator.framework == "tf":
# TF generation does not support max_new_tokens, and it's impossible
# to control long generation with only max_length without
# fancy calculation, dismissing tests for now.
return
# We don't care about infinite range models.
# They already work.
# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
_UpperCamelCase = ['''RwkvForCausalLM''', '''XGLMForCausalLM''', '''GPTNeoXForCausalLM''']
if (
tokenizer.model_max_length < 1_0000
and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
):
# Handling of large generations
with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError) ):
text_generator('''This is a test''' * 500 , max_new_tokens=20 )
_UpperCamelCase = text_generator('''This is a test''' * 500 , handle_long_generation='''hole''' , max_new_tokens=20 )
# Hole strategy cannot work
with self.assertRaises(_A ):
text_generator(
'''This is a test''' * 500 , handle_long_generation='''hole''' , max_new_tokens=tokenizer.model_max_length + 10 , )
@require_torch
@require_accelerate
@require_torch_gpu
def UpperCamelCase_ ( self : Optional[int] ):
import torch
# Classic `model_kwargs`
_UpperCamelCase = pipeline(
model='''hf-internal-testing/tiny-random-bloom''' , model_kwargs={'''device_map''': '''auto''', '''torch_dtype''': torch.bfloataa} , )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
_UpperCamelCase = pipe('''This is a test''' )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
_UpperCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' , torch_dtype=torch.bfloataa )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
_UpperCamelCase = pipe('''This is a test''' )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
_UpperCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.floataa )
_UpperCamelCase = pipe('''This is a test''' )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
@require_torch
@require_torch_gpu
def UpperCamelCase_ ( self : Union[str, Any] ):
import torch
_UpperCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device=0 , torch_dtype=torch.floataa )
pipe('''This is a test''' )
@require_torch
@require_accelerate
@require_torch_gpu
def UpperCamelCase_ ( self : Optional[int] ):
import torch
_UpperCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' , torch_dtype=torch.floataa )
pipe('''This is a test''' , do_sample=_A , top_p=0.5 )
def UpperCamelCase_ ( self : Tuple ):
_UpperCamelCase = '''Hello world'''
_UpperCamelCase = pipeline('''text-generation''' , model='''hf-internal-testing/tiny-random-gpt2''' )
if text_generator.model.framework == "tf":
_UpperCamelCase = logging.get_logger('''transformers.generation.tf_utils''' )
else:
_UpperCamelCase = logging.get_logger('''transformers.generation.utils''' )
_UpperCamelCase = '''Both `max_new_tokens`''' # The beggining of the message to be checked in this test
# Both are set by the user -> log warning
with CaptureLogger(_A ) as cl:
_UpperCamelCase = text_generator(_A , max_length=10 , max_new_tokens=1 )
self.assertIn(_A , cl.out )
# The user only sets one -> no warning
with CaptureLogger(_A ) as cl:
_UpperCamelCase = text_generator(_A , max_new_tokens=1 )
self.assertNotIn(_A , cl.out )
with CaptureLogger(_A ) as cl:
_UpperCamelCase = text_generator(_A , max_length=10 )
self.assertNotIn(_A , cl.out )
| 10 | 0 |
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
A : int = StableDiffusionDiffEditPipeline
A : str = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
A : int = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
A : str = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
A : Union[str, Any] = frozenset([] )
def UpperCamelCase_ ( self ):
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Optional[Any] = UNetaDConditionModel(
block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=4, out_channels=4, down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D'), up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D'), cross_attention_dim=32, attention_head_dim=(2, 4), use_linear_projection=A, )
SCREAMING_SNAKE_CASE : int = DDIMScheduler(
beta_start=0.0_00_85, beta_end=0.0_12, beta_schedule='scaled_linear', clip_sample=A, set_alpha_to_one=A, )
SCREAMING_SNAKE_CASE : str = DDIMInverseScheduler(
beta_start=0.0_00_85, beta_end=0.0_12, beta_schedule='scaled_linear', clip_sample=A, set_alpha_to_zero=A, )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Dict = AutoencoderKL(
block_out_channels=[32, 64], in_channels=3, out_channels=3, down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'], up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'], latent_channels=4, sample_size=128, )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Tuple = CLIPTextConfig(
bos_token_id=0, eos_token_id=2, hidden_size=32, intermediate_size=37, layer_norm_eps=1E-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1_000, hidden_act='gelu', projection_dim=512, )
SCREAMING_SNAKE_CASE : Union[str, Any] = CLIPTextModel(A )
SCREAMING_SNAKE_CASE : str = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
SCREAMING_SNAKE_CASE : int = {
'unet': unet,
'scheduler': scheduler,
'inverse_scheduler': inverse_scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def UpperCamelCase_ ( self, A, A=0 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = floats_tensor((1, 16, 16), rng=random.Random(A ) ).to(A )
SCREAMING_SNAKE_CASE : List[str] = floats_tensor((1, 2, 4, 16, 16), rng=random.Random(A ) ).to(A )
if str(A ).startswith('mps' ):
SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(A )
else:
SCREAMING_SNAKE_CASE : Tuple = torch.Generator(device=A ).manual_seed(A )
SCREAMING_SNAKE_CASE : Union[str, Any] = {
'prompt': 'a dog and a newt',
'mask_image': mask,
'image_latents': latents,
'generator': generator,
'num_inference_steps': 2,
'inpaint_strength': 1.0,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def UpperCamelCase_ ( self, A, A=0 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = floats_tensor((1, 3, 32, 32), rng=random.Random(A ) ).to(A )
SCREAMING_SNAKE_CASE : Any = image.cpu().permute(0, 2, 3, 1 )[0]
SCREAMING_SNAKE_CASE : Optional[int] = Image.fromarray(np.uinta(A ) ).convert('RGB' )
if str(A ).startswith('mps' ):
SCREAMING_SNAKE_CASE : Tuple = torch.manual_seed(A )
else:
SCREAMING_SNAKE_CASE : int = torch.Generator(device=A ).manual_seed(A )
SCREAMING_SNAKE_CASE : Dict = {
'image': image,
'source_prompt': 'a cat and a frog',
'target_prompt': 'a dog and a newt',
'generator': generator,
'num_inference_steps': 2,
'num_maps_per_mask': 2,
'mask_encode_strength': 1.0,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def UpperCamelCase_ ( self, A, A=0 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = floats_tensor((1, 3, 32, 32), rng=random.Random(A ) ).to(A )
SCREAMING_SNAKE_CASE : List[Any] = image.cpu().permute(0, 2, 3, 1 )[0]
SCREAMING_SNAKE_CASE : int = Image.fromarray(np.uinta(A ) ).convert('RGB' )
if str(A ).startswith('mps' ):
SCREAMING_SNAKE_CASE : Optional[Any] = torch.manual_seed(A )
else:
SCREAMING_SNAKE_CASE : Any = torch.Generator(device=A ).manual_seed(A )
SCREAMING_SNAKE_CASE : Any = {
'image': image,
'prompt': 'a cat and a frog',
'generator': generator,
'num_inference_steps': 2,
'inpaint_strength': 1.0,
'guidance_scale': 6.0,
'decode_latents': True,
'output_type': 'numpy',
}
return inputs
def UpperCamelCase_ ( self ):
'''simple docstring'''
if not hasattr(self.pipeline_class, '_optional_components' ):
return
SCREAMING_SNAKE_CASE : Tuple = self.get_dummy_components()
SCREAMING_SNAKE_CASE : Optional[int] = self.pipeline_class(**A )
pipe.to(A )
pipe.set_progress_bar_config(disable=A )
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(A, A, A )
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )
SCREAMING_SNAKE_CASE : Optional[int] = self.get_dummy_inputs(A )
SCREAMING_SNAKE_CASE : Dict = pipe(**A )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(A )
SCREAMING_SNAKE_CASE : List[Any] = self.pipeline_class.from_pretrained(A )
pipe_loaded.to(A )
pipe_loaded.set_progress_bar_config(disable=A )
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(A, A ) is None, F"`{optional_component}` did not stay set to None after loading.", )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_dummy_inputs(A )
SCREAMING_SNAKE_CASE : Tuple = pipe_loaded(**A )[0]
SCREAMING_SNAKE_CASE : List[str] = np.abs(output - output_loaded ).max()
self.assertLess(A, 1E-4 )
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = 'cpu'
SCREAMING_SNAKE_CASE : Optional[Any] = self.get_dummy_components()
SCREAMING_SNAKE_CASE : Union[str, Any] = self.pipeline_class(**A )
pipe.to(A )
pipe.set_progress_bar_config(disable=A )
SCREAMING_SNAKE_CASE : str = self.get_dummy_mask_inputs(A )
SCREAMING_SNAKE_CASE : Union[str, Any] = pipe.generate_mask(**A )
SCREAMING_SNAKE_CASE : Dict = mask[0, -3:, -3:]
self.assertEqual(mask.shape, (1, 16, 16) )
SCREAMING_SNAKE_CASE : Any = np.array([0] * 9 )
SCREAMING_SNAKE_CASE : Any = np.abs(mask_slice.flatten() - expected_slice ).max()
self.assertLessEqual(A, 1E-3 )
self.assertEqual(mask[0, -3, -4], 0 )
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = 'cpu'
SCREAMING_SNAKE_CASE : Dict = self.get_dummy_components()
SCREAMING_SNAKE_CASE : Dict = self.pipeline_class(**A )
pipe.to(A )
pipe.set_progress_bar_config(disable=A )
SCREAMING_SNAKE_CASE : Tuple = self.get_dummy_inversion_inputs(A )
SCREAMING_SNAKE_CASE : Optional[Any] = pipe.invert(**A ).images
SCREAMING_SNAKE_CASE : Optional[Any] = image[0, -1, -3:, -3:]
self.assertEqual(image.shape, (2, 32, 32, 3) )
SCREAMING_SNAKE_CASE : Tuple = np.array(
[0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99], )
SCREAMING_SNAKE_CASE : Dict = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(A, 1E-3 )
def UpperCamelCase_ ( self ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=5E-3 )
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = 'cpu'
SCREAMING_SNAKE_CASE : Optional[int] = self.get_dummy_components()
SCREAMING_SNAKE_CASE : Dict = {'beta_start': 0.0_00_85, 'beta_end': 0.0_12, 'beta_schedule': 'scaled_linear'}
SCREAMING_SNAKE_CASE : Union[str, Any] = DPMSolverMultistepScheduler(**A )
SCREAMING_SNAKE_CASE : Optional[int] = DPMSolverMultistepInverseScheduler(**A )
SCREAMING_SNAKE_CASE : Tuple = self.pipeline_class(**A )
pipe.to(A )
pipe.set_progress_bar_config(disable=A )
SCREAMING_SNAKE_CASE : Tuple = self.get_dummy_inversion_inputs(A )
SCREAMING_SNAKE_CASE : List[str] = pipe.invert(**A ).images
SCREAMING_SNAKE_CASE : Optional[Any] = image[0, -1, -3:, -3:]
self.assertEqual(image.shape, (2, 32, 32, 3) )
SCREAMING_SNAKE_CASE : Tuple = np.array(
[0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99], )
SCREAMING_SNAKE_CASE : Any = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(A, 1E-3 )
@require_torch_gpu
@slow
class _a ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def UpperCamelCase_ ( cls ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png' )
SCREAMING_SNAKE_CASE : Optional[int] = raw_image.convert('RGB' ).resize((768, 768) )
SCREAMING_SNAKE_CASE : List[str] = raw_image
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Dict = StableDiffusionDiffEditPipeline.from_pretrained(
'stabilityai/stable-diffusion-2-1', safety_checker=A, torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE : List[Any] = DDIMScheduler.from_config(pipe.scheduler.config )
SCREAMING_SNAKE_CASE : int = DDIMInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=A )
SCREAMING_SNAKE_CASE : List[Any] = 'a bowl of fruit'
SCREAMING_SNAKE_CASE : List[str] = 'a bowl of pears'
SCREAMING_SNAKE_CASE : Dict = pipe.generate_mask(
image=self.raw_image, source_prompt=A, target_prompt=A, generator=A, )
SCREAMING_SNAKE_CASE : Optional[int] = pipe.invert(
prompt=A, image=self.raw_image, inpaint_strength=0.7, generator=A ).latents
SCREAMING_SNAKE_CASE : List[str] = pipe(
prompt=A, mask_image=A, image_latents=A, generator=A, negative_prompt=A, inpaint_strength=0.7, output_type='numpy', ).images[0]
SCREAMING_SNAKE_CASE : List[Any] = (
np.array(
load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/diffedit/pears.png' ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5E-1
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : int = StableDiffusionDiffEditPipeline.from_pretrained(
'stabilityai/stable-diffusion-2-1', safety_checker=A, torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE : List[str] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
SCREAMING_SNAKE_CASE : List[str] = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=A )
SCREAMING_SNAKE_CASE : str = 'a bowl of fruit'
SCREAMING_SNAKE_CASE : Tuple = 'a bowl of pears'
SCREAMING_SNAKE_CASE : List[Any] = pipe.generate_mask(
image=self.raw_image, source_prompt=A, target_prompt=A, generator=A, )
SCREAMING_SNAKE_CASE : Union[str, Any] = pipe.invert(
prompt=A, image=self.raw_image, inpaint_strength=0.7, generator=A, num_inference_steps=25, ).latents
SCREAMING_SNAKE_CASE : str = pipe(
prompt=A, mask_image=A, image_latents=A, generator=A, negative_prompt=A, inpaint_strength=0.7, num_inference_steps=25, output_type='numpy', ).images[0]
SCREAMING_SNAKE_CASE : Tuple = (
np.array(
load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/diffedit/pears.png' ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5E-1
| 28 | def _snake_case ( __snake_case = 100 ):
_UpperCamelCase = (n * (n + 1) // 2) ** 2
_UpperCamelCase = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f'{solution() = }')
| 10 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"""facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
"""facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
"""facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
"""facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
"""facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
"""facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
"""facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
"""facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
"""facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}
class __lowerCamelCase ( lowerCAmelCase ):
a__: Dict = 'xmod'
def __init__( self , UpperCAmelCase=3_0522 , UpperCAmelCase=768 , UpperCAmelCase=12 , UpperCAmelCase=12 , UpperCAmelCase=3072 , UpperCAmelCase="gelu" , UpperCAmelCase=0.1 , UpperCAmelCase=0.1 , UpperCAmelCase=512 , UpperCAmelCase=2 , UpperCAmelCase=0.0_2 , UpperCAmelCase=1e-1_2 , UpperCAmelCase=1 , UpperCAmelCase=0 , UpperCAmelCase=2 , UpperCAmelCase="absolute" , UpperCAmelCase=True , UpperCAmelCase=None , UpperCAmelCase=False , UpperCAmelCase=2 , UpperCAmelCase=False , UpperCAmelCase=True , UpperCAmelCase=True , UpperCAmelCase=("en_XX",) , UpperCAmelCase=None , **UpperCAmelCase , ):
super().__init__(pad_token_id=UpperCAmelCase , bos_token_id=UpperCAmelCase , eos_token_id=UpperCAmelCase , **UpperCAmelCase )
lowerCamelCase_ = vocab_size
lowerCamelCase_ = hidden_size
lowerCamelCase_ = num_hidden_layers
lowerCamelCase_ = num_attention_heads
lowerCamelCase_ = hidden_act
lowerCamelCase_ = intermediate_size
lowerCamelCase_ = hidden_dropout_prob
lowerCamelCase_ = attention_probs_dropout_prob
lowerCamelCase_ = max_position_embeddings
lowerCamelCase_ = type_vocab_size
lowerCamelCase_ = initializer_range
lowerCamelCase_ = layer_norm_eps
lowerCamelCase_ = position_embedding_type
lowerCamelCase_ = use_cache
lowerCamelCase_ = classifier_dropout
lowerCamelCase_ = pre_norm
lowerCamelCase_ = adapter_reduction_factor
lowerCamelCase_ = adapter_layer_norm
lowerCamelCase_ = adapter_reuse_layer_norm
lowerCamelCase_ = ln_before_adapter
lowerCamelCase_ = list(UpperCAmelCase )
lowerCamelCase_ = default_language
class __lowerCamelCase ( lowerCAmelCase ):
@property
def UpperCAmelCase__ ( self ):
if self.task == "multiple-choice":
lowerCamelCase_ = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
lowerCamelCase_ = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 29 | import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
_lowerCAmelCase = logging.get_logger(__name__)
def _snake_case ( __snake_case , __snake_case , __snake_case , __snake_case ):
def constraint_to_multiple_of(__snake_case , __snake_case , __snake_case=0 , __snake_case=None ):
_UpperCamelCase = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
_UpperCamelCase = math.floor(val / multiple ) * multiple
if x < min_val:
_UpperCamelCase = math.ceil(val / multiple ) * multiple
return x
_UpperCamelCase = (output_size, output_size) if isinstance(__snake_case , __snake_case ) else output_size
_UpperCamelCase , _UpperCamelCase = get_image_size(__snake_case )
_UpperCamelCase , _UpperCamelCase = output_size
# determine new height and width
_UpperCamelCase = output_height / input_height
_UpperCamelCase = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
_UpperCamelCase = scale_width
else:
# fit height
_UpperCamelCase = scale_height
_UpperCamelCase = constraint_to_multiple_of(scale_height * input_height , multiple=__snake_case )
_UpperCamelCase = constraint_to_multiple_of(scale_width * input_width , multiple=__snake_case )
return (new_height, new_width)
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = ["pixel_values"]
def __init__( self : List[Any] , _A : bool = True , _A : Dict[str, int] = None , _A : PILImageResampling = PILImageResampling.BILINEAR , _A : bool = False , _A : int = 1 , _A : bool = True , _A : Union[int, float] = 1 / 255 , _A : bool = True , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[float, List[float]]] = None , **_A : List[str] , ):
super().__init__(**_A )
_UpperCamelCase = size if size is not None else {'''height''': 384, '''width''': 384}
_UpperCamelCase = get_size_dict(_A )
_UpperCamelCase = do_resize
_UpperCamelCase = size
_UpperCamelCase = keep_aspect_ratio
_UpperCamelCase = ensure_multiple_of
_UpperCamelCase = resample
_UpperCamelCase = do_rescale
_UpperCamelCase = rescale_factor
_UpperCamelCase = do_normalize
_UpperCamelCase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_UpperCamelCase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def UpperCamelCase_ ( self : List[str] , _A : np.ndarray , _A : Dict[str, int] , _A : bool = False , _A : int = 1 , _A : PILImageResampling = PILImageResampling.BICUBIC , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Any , ):
_UpperCamelCase = get_size_dict(_A )
if "height" not in size or "width" not in size:
raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
_UpperCamelCase = get_resize_output_image_size(
_A , output_size=(size['''height'''], size['''width''']) , keep_aspect_ratio=_A , multiple=_A , )
return resize(_A , size=_A , resample=_A , data_format=_A , **_A )
def UpperCamelCase_ ( self : str , _A : np.ndarray , _A : Union[int, float] , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Any , ):
return rescale(_A , scale=_A , data_format=_A , **_A )
def UpperCamelCase_ ( self : int , _A : np.ndarray , _A : Union[float, List[float]] , _A : Union[float, List[float]] , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Any , ):
return normalize(_A , mean=_A , std=_A , data_format=_A , **_A )
def UpperCamelCase_ ( self : Optional[int] , _A : ImageInput , _A : bool = None , _A : int = None , _A : bool = None , _A : int = None , _A : PILImageResampling = None , _A : bool = None , _A : float = None , _A : bool = None , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[str, TensorType]] = None , _A : ChannelDimension = ChannelDimension.FIRST , **_A : str , ):
_UpperCamelCase = do_resize if do_resize is not None else self.do_resize
_UpperCamelCase = size if size is not None else self.size
_UpperCamelCase = get_size_dict(_A )
_UpperCamelCase = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
_UpperCamelCase = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
_UpperCamelCase = resample if resample is not None else self.resample
_UpperCamelCase = do_rescale if do_rescale is not None else self.do_rescale
_UpperCamelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
_UpperCamelCase = do_normalize if do_normalize is not None else self.do_normalize
_UpperCamelCase = image_mean if image_mean is not None else self.image_mean
_UpperCamelCase = image_std if image_std is not None else self.image_std
_UpperCamelCase = make_list_of_images(_A )
if not valid_images(_A ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None or resample is None:
raise ValueError('''Size and resample must be specified if do_resize is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
_UpperCamelCase = [to_numpy_array(_A ) for image in images]
if do_resize:
_UpperCamelCase = [self.resize(image=_A , size=_A , resample=_A ) for image in images]
if do_rescale:
_UpperCamelCase = [self.rescale(image=_A , scale=_A ) for image in images]
if do_normalize:
_UpperCamelCase = [self.normalize(image=_A , mean=_A , std=_A ) for image in images]
_UpperCamelCase = [to_channel_dimension_format(_A , _A ) for image in images]
_UpperCamelCase = {'''pixel_values''': images}
return BatchFeature(data=_A , tensor_type=_A )
def UpperCamelCase_ ( self : Any , _A : Any , _A : List[Tuple] = None ):
_UpperCamelCase = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(_A ) != len(_A ):
raise ValueError(
'''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )
if is_torch_tensor(_A ):
_UpperCamelCase = target_sizes.numpy()
_UpperCamelCase = []
for idx in range(len(_A ) ):
_UpperCamelCase = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='''bilinear''' , align_corners=_A )
_UpperCamelCase = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(_A )
else:
_UpperCamelCase = logits.argmax(dim=1 )
_UpperCamelCase = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 10 | 0 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures
__a = logging.get_logger(__name__)
@dataclass
class __a:
"""simple docstring"""
lowerCAmelCase = field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(glue_processors.keys() )} )
lowerCAmelCase = field(
metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''} )
lowerCAmelCase = field(
default=128 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
lowerCAmelCase = field(
default=_a , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def a__ ( self ) -> int:
UpperCAmelCase_ : int = self.task_name.lower()
class __a( _a ):
"""simple docstring"""
lowerCAmelCase = '''train'''
lowerCAmelCase = '''dev'''
lowerCAmelCase = '''test'''
class __a( _a ):
"""simple docstring"""
lowerCAmelCase = 42
lowerCAmelCase = 42
lowerCAmelCase = 42
def __init__( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = Split.train ,_SCREAMING_SNAKE_CASE = None ,) -> str:
warnings.warn(
'''This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '''
'''library. You can have a look at this example script for pointers: '''
'''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py''' ,_SCREAMING_SNAKE_CASE ,)
UpperCAmelCase_ : List[str] = args
UpperCAmelCase_ : Optional[Any] = glue_processors[args.task_name]()
UpperCAmelCase_ : int = glue_output_modes[args.task_name]
if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ):
try:
UpperCAmelCase_ : Any = Split[mode]
except KeyError:
raise KeyError('''mode is not a valid split name''' )
# Load data features from cache or dataset file
UpperCAmelCase_ : List[str] = os.path.join(
cache_dir if cache_dir is not None else args.data_dir ,f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}''' ,)
UpperCAmelCase_ : Any = self.processor.get_labels()
if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
"RobertaTokenizer",
"RobertaTokenizerFast",
"XLMRobertaTokenizer",
"BartTokenizer",
"BartTokenizerFast",
):
# HACK(label indices are swapped in RoBERTa pretrained model)
UpperCAmelCase_, UpperCAmelCase_ : str = label_list[2], label_list[1]
UpperCAmelCase_ : str = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
UpperCAmelCase_ : Dict = cached_features_file + '''.lock'''
with FileLock(_SCREAMING_SNAKE_CASE ):
if os.path.exists(_SCREAMING_SNAKE_CASE ) and not args.overwrite_cache:
UpperCAmelCase_ : List[Any] = time.time()
UpperCAmelCase_ : Dict = torch.load(_SCREAMING_SNAKE_CASE )
logger.info(
f'''Loading features from cached file {cached_features_file} [took %.3f s]''' ,time.time() - start )
else:
logger.info(f'''Creating features from dataset file at {args.data_dir}''' )
if mode == Split.dev:
UpperCAmelCase_ : List[str] = self.processor.get_dev_examples(args.data_dir )
elif mode == Split.test:
UpperCAmelCase_ : Tuple = self.processor.get_test_examples(args.data_dir )
else:
UpperCAmelCase_ : int = self.processor.get_train_examples(args.data_dir )
if limit_length is not None:
UpperCAmelCase_ : Optional[int] = examples[:limit_length]
UpperCAmelCase_ : int = glue_convert_examples_to_features(
_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,max_length=args.max_seq_length ,label_list=_SCREAMING_SNAKE_CASE ,output_mode=self.output_mode ,)
UpperCAmelCase_ : Union[str, Any] = time.time()
torch.save(self.features ,_SCREAMING_SNAKE_CASE )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]''' )
def __len__( self ) -> Tuple:
return len(self.features )
def __getitem__( self ,_SCREAMING_SNAKE_CASE ) -> InputFeatures:
return self.features[i]
def a__ ( self ) -> Any:
return self.label_list | 30 | import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_lowerCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_lowerCAmelCase = " \"\"\"\n Output class for the scheduler's step function output.\n\n Args:\n prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.FloatTensor\n pred_original_sample: Optional[torch.FloatTensor] = None\n"
class lowerCAmelCase_ ( unittest.TestCase ):
def UpperCamelCase_ ( self : List[Any] ):
_UpperCamelCase = tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir , '''schedulers/''' ) )
_UpperCamelCase = self.diffusers_dir
shutil.copy(
os.path.join(_A , '''src/diffusers/schedulers/scheduling_ddpm.py''' ) , os.path.join(self.diffusers_dir , '''schedulers/scheduling_ddpm.py''' ) , )
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = '''src/diffusers'''
shutil.rmtree(self.diffusers_dir )
def UpperCamelCase_ ( self : str , _A : List[str] , _A : Optional[Any] , _A : List[str] , _A : Optional[int]=None ):
_UpperCamelCase = comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
if overwrite_result is not None:
_UpperCamelCase = comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
_UpperCamelCase = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 )
_UpperCamelCase = black.format_str(_A , mode=_A )
_UpperCamelCase = os.path.join(self.diffusers_dir , '''new_code.py''' )
with open(_A , '''w''' , newline='''\n''' ) as f:
f.write(_A )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(_A ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=_A )
with open(_A , '''r''' ) as f:
self.assertTrue(f.read() , _A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput''' )
self.assertEqual(_A , _A )
def UpperCamelCase_ ( self : List[str] ):
# Base copy consistency
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' , '''DDPMSchedulerOutput''' , REFERENCE_CODE + '''\n''' , )
# With no empty line at the end
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' , '''DDPMSchedulerOutput''' , _A , )
# Copy consistency with rename
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' , '''TestSchedulerOutput''' , re.sub('''DDPM''' , '''Test''' , _A ) , )
# Copy consistency with a really long name
_UpperCamelCase = '''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
self.check_copy_consistency(
F"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}""" , F"""{long_class_name}SchedulerOutput""" , re.sub('''Bert''' , _A , _A ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' , '''TestSchedulerOutput''' , _A , overwrite_result=re.sub('''DDPM''' , '''Test''' , _A ) , )
| 10 | 0 |
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppTokenizer,
MecabTokenizer,
SudachiTokenizer,
WordpieceTokenizer,
)
from transformers.testing_utils import custom_tokenizers, require_jumanpp, require_sudachi
from ...test_tokenization_common import TokenizerTesterMixin
@custom_tokenizers
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
lowercase_ = BertJapaneseTokenizer
lowercase_ = False
lowercase_ = True
def lowerCAmelCase_ ( self : int ):
super().setUp()
SCREAMING_SNAKE_CASE_ = [
'[UNK]',
'[CLS]',
'[SEP]',
'こんにちは',
'こん',
'にちは',
'ばんは',
'##こん',
'##にちは',
'##ばんは',
'世界',
'##世界',
'、',
'##、',
'。',
'##。',
]
SCREAMING_SNAKE_CASE_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
def lowerCAmelCase_ ( self : Dict , _lowerCAmelCase : int ):
SCREAMING_SNAKE_CASE_ = 'こんにちは、世界。 \nこんばんは、世界。'
SCREAMING_SNAKE_CASE_ = 'こんにちは 、 世界 。 こんばんは 、 世界 。'
return input_text, output_text
def lowerCAmelCase_ ( self : Any , _lowerCAmelCase : Tuple ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.get_input_output_texts(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = tokenizer.encode(_lowerCAmelCase , add_special_tokens=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = tokenizer.decode(_lowerCAmelCase , clean_up_tokenization_spaces=_lowerCAmelCase )
return text, ids
def lowerCAmelCase_ ( self : str ):
pass # TODO add if relevant
def lowerCAmelCase_ ( self : Dict ):
pass # TODO add if relevant
def lowerCAmelCase_ ( self : List[Any] ):
pass # TODO add if relevant
def lowerCAmelCase_ ( self : str ):
SCREAMING_SNAKE_CASE_ = self.tokenizer_class(self.vocab_file )
SCREAMING_SNAKE_CASE_ = tokenizer.tokenize('こんにちは、世界。\nこんばんは、世界。' )
self.assertListEqual(_lowerCAmelCase , ['こんにちは', '、', '世界', '。', 'こん', '##ばんは', '、', '世界', '。'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
def lowerCAmelCase_ ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE_ = self.tokenizer_class(self.vocab_file , word_tokenizer_type='mecab' )
self.assertIsNotNone(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = 'こんにちは、世界。\nこんばんは、世界。'
SCREAMING_SNAKE_CASE_ = tokenizer.tokenize(_lowerCAmelCase )
self.assertListEqual(_lowerCAmelCase , ['こんにちは', '、', '世界', '。', 'こん', '##ばんは', '、', '世界', '。'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
SCREAMING_SNAKE_CASE_ = os.path.join(self.tmpdirname , 'tokenizer.bin' )
with open(_lowerCAmelCase , 'wb' ) as handle:
pickle.dump(_lowerCAmelCase , _lowerCAmelCase )
with open(_lowerCAmelCase , 'rb' ) as handle:
SCREAMING_SNAKE_CASE_ = pickle.load(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = tokenizer_new.tokenize(_lowerCAmelCase )
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase )
def lowerCAmelCase_ ( self : Any ):
SCREAMING_SNAKE_CASE_ = MecabTokenizer(mecab_dic='ipadic' )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップルストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'] , )
def lowerCAmelCase_ ( self : int ):
try:
SCREAMING_SNAKE_CASE_ = MecabTokenizer(mecab_dic='unidic_lite' )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップル', 'ストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'] , )
def lowerCAmelCase_ ( self : Any ):
try:
SCREAMING_SNAKE_CASE_ = MecabTokenizer(mecab_dic='unidic' )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップル', 'ストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'] , )
def lowerCAmelCase_ ( self : List[str] ):
SCREAMING_SNAKE_CASE_ = MecabTokenizer(do_lower_case=_lowerCAmelCase , mecab_dic='ipadic' )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップルストア', 'で', 'iphone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'] , )
def lowerCAmelCase_ ( self : List[Any] ):
try:
SCREAMING_SNAKE_CASE_ = MecabTokenizer(
do_lower_case=_lowerCAmelCase , normalize_text=_lowerCAmelCase , mecab_option='-d /usr/local/lib/mecab/dic/jumandic' )
except RuntimeError:
# if dict doesn't exist in the system, previous code raises this error.
return
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['ｱｯﾌﾟﾙストア', 'で', 'iPhone', '８', 'が', '発売', 'さ', 'れた', '\u3000', '。'] , )
def lowerCAmelCase_ ( self : Optional[int] ):
SCREAMING_SNAKE_CASE_ = MecabTokenizer(normalize_text=_lowerCAmelCase , mecab_dic='ipadic' )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['ｱｯﾌﾟﾙストア', 'で', 'iPhone', '８', 'が', '発売', 'さ', 'れ', 'た', ' ', '。'] , )
@require_sudachi
def lowerCAmelCase_ ( self : Dict ):
SCREAMING_SNAKE_CASE_ = self.tokenizer_class(self.vocab_file , word_tokenizer_type='sudachi' )
self.assertIsNotNone(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = 'こんにちは、世界。\nこんばんは、世界。'
SCREAMING_SNAKE_CASE_ = tokenizer.tokenize(_lowerCAmelCase )
self.assertListEqual(_lowerCAmelCase , ['こんにちは', '、', '世界', '。', 'こん', '##ばんは', '、', '世界', '。'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
SCREAMING_SNAKE_CASE_ = os.path.join(self.tmpdirname , 'tokenizer.bin' )
with open(_lowerCAmelCase , 'wb' ) as handle:
pickle.dump(_lowerCAmelCase , _lowerCAmelCase )
with open(_lowerCAmelCase , 'rb' ) as handle:
SCREAMING_SNAKE_CASE_ = pickle.load(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = tokenizer_new.tokenize(_lowerCAmelCase )
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase )
@require_sudachi
def lowerCAmelCase_ ( self : List[str] ):
SCREAMING_SNAKE_CASE_ = SudachiTokenizer(sudachi_dict_type='core' )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , [' ', '\t', 'アップル', 'ストア', 'で', 'iPhone', '8', ' ', 'が', ' ', ' ', '\n ', '発売', 'さ', 'れ', 'た', ' ', '。', ' ', ' '] , )
@require_sudachi
def lowerCAmelCase_ ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE_ = SudachiTokenizer(sudachi_dict_type='core' , sudachi_split_mode='A' )
self.assertListEqual(tokenizer.tokenize('外国人参政権' ) , ['外国', '人', '参政', '権'] )
@require_sudachi
def lowerCAmelCase_ ( self : int ):
SCREAMING_SNAKE_CASE_ = SudachiTokenizer(sudachi_dict_type='core' , sudachi_split_mode='B' )
self.assertListEqual(tokenizer.tokenize('外国人参政権' ) , ['外国人', '参政権'] )
@require_sudachi
def lowerCAmelCase_ ( self : List[str] ):
SCREAMING_SNAKE_CASE_ = SudachiTokenizer(sudachi_dict_type='core' , sudachi_split_mode='C' )
self.assertListEqual(tokenizer.tokenize('外国人参政権' ) , ['外国人参政権'] )
@require_sudachi
def lowerCAmelCase_ ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE_ = SudachiTokenizer(do_lower_case=_lowerCAmelCase , sudachi_dict_type='core' )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , [' ', '\t', 'アップル', 'ストア', 'で', 'iphone', '8', ' ', 'が', ' ', ' ', '\n ', '発売', 'さ', 'れ', 'た', ' ', '。', ' ', ' '] , )
@require_sudachi
def lowerCAmelCase_ ( self : str ):
SCREAMING_SNAKE_CASE_ = SudachiTokenizer(normalize_text=_lowerCAmelCase , sudachi_dict_type='core' )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , [' ', '\t', 'ｱｯﾌﾟﾙ', 'ストア', 'で', 'iPhone', '８', ' ', 'が', ' ', ' ', '\n ', '発売', 'さ', 'れ', 'た', '\u3000', '。', ' ', ' '] , )
@require_sudachi
def lowerCAmelCase_ ( self : List[Any] ):
SCREAMING_SNAKE_CASE_ = SudachiTokenizer(trim_whitespace=_lowerCAmelCase , sudachi_dict_type='core' )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップル', 'ストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'] , )
@require_jumanpp
def lowerCAmelCase_ ( self : Dict ):
SCREAMING_SNAKE_CASE_ = self.tokenizer_class(self.vocab_file , word_tokenizer_type='jumanpp' )
self.assertIsNotNone(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = 'こんにちは、世界。\nこんばんは、世界。'
SCREAMING_SNAKE_CASE_ = tokenizer.tokenize(_lowerCAmelCase )
self.assertListEqual(_lowerCAmelCase , ['こんにちは', '、', '世界', '。', 'こん', '##ばんは', '、', '世界', '。'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
SCREAMING_SNAKE_CASE_ = os.path.join(self.tmpdirname , 'tokenizer.bin' )
with open(_lowerCAmelCase , 'wb' ) as handle:
pickle.dump(_lowerCAmelCase , _lowerCAmelCase )
with open(_lowerCAmelCase , 'rb' ) as handle:
SCREAMING_SNAKE_CASE_ = pickle.load(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = tokenizer_new.tokenize(_lowerCAmelCase )
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase )
@require_jumanpp
def lowerCAmelCase_ ( self : List[Any] ):
SCREAMING_SNAKE_CASE_ = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップル', 'ストア', 'で', 'iPhone', '8', '\u3000', 'が', '\u3000', '\u3000', '\u3000', '発売', 'さ', 'れた', '\u3000', '。'] , )
@require_jumanpp
def lowerCAmelCase_ ( self : Any ):
SCREAMING_SNAKE_CASE_ = JumanppTokenizer(do_lower_case=_lowerCAmelCase )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップル', 'ストア', 'で', 'iphone', '8', '\u3000', 'が', '\u3000', '\u3000', '\u3000', '発売', 'さ', 'れた', '\u3000', '。'] , )
@require_jumanpp
def lowerCAmelCase_ ( self : List[str] ):
SCREAMING_SNAKE_CASE_ = JumanppTokenizer(normalize_text=_lowerCAmelCase )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['ｱ', 'ｯ', 'ﾌ', 'ﾟ', 'ﾙ', 'ストア', 'で', 'iPhone', '８', '\u3000', 'が', '\u3000', '\u3000', '\u3000', '発売', 'さ', 'れた', '\u3000', '。'] , )
@require_jumanpp
def lowerCAmelCase_ ( self : List[Any] ):
SCREAMING_SNAKE_CASE_ = JumanppTokenizer(trim_whitespace=_lowerCAmelCase )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップル', 'ストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れた', '。'] , )
@require_jumanpp
def lowerCAmelCase_ ( self : Optional[int] ):
SCREAMING_SNAKE_CASE_ = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize('ありがとうございますm(_ _)ｍ見つけるのが大変です。' ) , ['ありがとう', 'ございます', 'm(_ _)m', '見つける', 'の', 'が', '大変です', '。'] , )
def lowerCAmelCase_ ( self : List[str] ):
SCREAMING_SNAKE_CASE_ = ['[UNK]', '[CLS]', '[SEP]', 'こんにちは', 'こん', 'にちは', 'ばんは', '##こん', '##にちは', '##ばんは']
SCREAMING_SNAKE_CASE_ = {}
for i, token in enumerate(_lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ = i
SCREAMING_SNAKE_CASE_ = WordpieceTokenizer(vocab=_lowerCAmelCase , unk_token='[UNK]' )
self.assertListEqual(tokenizer.tokenize('' ) , [] )
self.assertListEqual(tokenizer.tokenize('こんにちは' ) , ['こんにちは'] )
self.assertListEqual(tokenizer.tokenize('こんばんは' ) , ['こん', '##ばんは'] )
self.assertListEqual(tokenizer.tokenize('こんばんは こんばんにちは こんにちは' ) , ['こん', '##ばんは', '[UNK]', 'こんにちは'] )
def lowerCAmelCase_ ( self : Tuple ):
SCREAMING_SNAKE_CASE_ = BertJapaneseTokenizer.from_pretrained('nlp-waseda/roberta-base-japanese-with-auto-jumanpp' )
SCREAMING_SNAKE_CASE_ = tokenizer.subword_tokenizer
SCREAMING_SNAKE_CASE_ = subword_tokenizer.tokenize('国境 の 長い トンネル を 抜ける と 雪国 であった 。' )
self.assertListEqual(_lowerCAmelCase , ['▁国境', '▁の', '▁長い', '▁トンネル', '▁を', '▁抜ける', '▁と', '▁雪', '国', '▁であった', '▁。'] )
SCREAMING_SNAKE_CASE_ = subword_tokenizer.tokenize('こんばんは こんばん にち は こんにちは' )
self.assertListEqual(_lowerCAmelCase , ['▁こん', 'ばん', 'は', '▁こん', 'ばん', '▁に', 'ち', '▁は', '▁こんにちは'] )
def lowerCAmelCase_ ( self : Optional[int] ):
SCREAMING_SNAKE_CASE_ = self.tokenizer_class.from_pretrained('cl-tohoku/bert-base-japanese' )
SCREAMING_SNAKE_CASE_ = tokenizer.encode('ありがとう。' , add_special_tokens=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = tokenizer.encode('どういたしまして。' , add_special_tokens=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase , _lowerCAmelCase )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
lowercase_ = BertJapaneseTokenizer
lowercase_ = False
def lowerCAmelCase_ ( self : int ):
super().setUp()
SCREAMING_SNAKE_CASE_ = ['[UNK]', '[CLS]', '[SEP]', 'こ', 'ん', 'に', 'ち', 'は', 'ば', '世', '界', '、', '。']
SCREAMING_SNAKE_CASE_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
def lowerCAmelCase_ ( self : List[Any] , **_lowerCAmelCase : Union[str, Any] ):
return BertJapaneseTokenizer.from_pretrained(self.tmpdirname , subword_tokenizer_type='character' , **_lowerCAmelCase )
def lowerCAmelCase_ ( self : Union[str, Any] , _lowerCAmelCase : str ):
SCREAMING_SNAKE_CASE_ = 'こんにちは、世界。 \nこんばんは、世界。'
SCREAMING_SNAKE_CASE_ = 'こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。'
return input_text, output_text
def lowerCAmelCase_ ( self : Optional[Any] ):
pass # TODO add if relevant
def lowerCAmelCase_ ( self : int ):
pass # TODO add if relevant
def lowerCAmelCase_ ( self : List[Any] ):
pass # TODO add if relevant
def lowerCAmelCase_ ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE_ = self.tokenizer_class(self.vocab_file , subword_tokenizer_type='character' )
SCREAMING_SNAKE_CASE_ = tokenizer.tokenize('こんにちは、世界。 \nこんばんは、世界。' )
self.assertListEqual(
_lowerCAmelCase , ['こ', 'ん', 'に', 'ち', 'は', '、', '世', '界', '。', 'こ', 'ん', 'ば', 'ん', 'は', '、', '世', '界', '。'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_lowerCAmelCase ) , [3, 4, 5, 6, 7, 11, 9, 10, 12, 3, 4, 8, 4, 7, 11, 9, 10, 12] )
def lowerCAmelCase_ ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE_ = ['[UNK]', '[CLS]', '[SEP]', 'こ', 'ん', 'に', 'ち', 'は', 'ば', '世', '界', '、', '。']
SCREAMING_SNAKE_CASE_ = {}
for i, token in enumerate(_lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ = i
SCREAMING_SNAKE_CASE_ = CharacterTokenizer(vocab=_lowerCAmelCase , unk_token='[UNK]' )
self.assertListEqual(tokenizer.tokenize('' ) , [] )
self.assertListEqual(tokenizer.tokenize('こんにちは' ) , ['こ', 'ん', 'に', 'ち', 'は'] )
self.assertListEqual(tokenizer.tokenize('こんにちほ' ) , ['こ', 'ん', 'に', 'ち', '[UNK]'] )
def lowerCAmelCase_ ( self : List[Any] ):
SCREAMING_SNAKE_CASE_ = self.tokenizer_class.from_pretrained('cl-tohoku/bert-base-japanese-char' )
SCREAMING_SNAKE_CASE_ = tokenizer.encode('ありがとう。' , add_special_tokens=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = tokenizer.encode('どういたしまして。' , add_special_tokens=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase , _lowerCAmelCase )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Tuple ):
SCREAMING_SNAKE_CASE_ = 'cl-tohoku/bert-base-japanese'
SCREAMING_SNAKE_CASE_ = AutoTokenizer.from_pretrained(_lowerCAmelCase )
self.assertIsInstance(_lowerCAmelCase , _lowerCAmelCase )
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Optional[int] ):
SCREAMING_SNAKE_CASE_ = 'cl-tohoku/bert-base-japanese'
with self.assertLogs('transformers' , level='WARNING' ) as cm:
BertTokenizer.from_pretrained(_lowerCAmelCase )
self.assertTrue(
cm.records[0].message.startswith(
'The tokenizer class you load from this checkpoint is not the same type as the class this function'
' is called from.' ) )
SCREAMING_SNAKE_CASE_ = 'bert-base-cased'
with self.assertLogs('transformers' , level='WARNING' ) as cm:
BertJapaneseTokenizer.from_pretrained(_lowerCAmelCase )
self.assertTrue(
cm.records[0].message.startswith(
'The tokenizer class you load from this checkpoint is not the same type as the class this function'
' is called from.' ) ) | 31 | import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaProcessor,
is_apex_available,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
_lowerCAmelCase = True
from torch.cuda.amp import autocast
_lowerCAmelCase = logging.getLogger(__name__)
def _snake_case ( __snake_case=None , __snake_case=None ):
return field(default_factory=lambda: default , metadata=__snake_case )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
UpperCAmelCase = field(
default=0.1, metadata={"help": "The dropout ratio for the attention probabilities."} )
UpperCAmelCase = field(
default=0.1, metadata={"help": "The dropout ratio for activations inside the fully connected layer."} )
UpperCAmelCase = field(
default=0.1, metadata={
"help": "The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."
}, )
UpperCAmelCase = field(
default=0.1, metadata={"help": "The dropout probabilitiy for all 1D convolutional layers in feature extractor."}, )
UpperCAmelCase = field(
default=0.0_5, metadata={
"help": (
"Propability of each feature vector along the time axis to be chosen as the start of the vector"
"span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"
"vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."
)
}, )
UpperCAmelCase = field(default=0.0, metadata={"help": "The LayerDrop probability."} )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
UpperCAmelCase = field(
default="train+validation", metadata={
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "The number of processes to use for the preprocessing."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
}, )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of validation examples to this "
"value if set."
)
}, )
UpperCAmelCase = list_field(
default=[",", "?", ".", "!", "-", ";", ":", "\"\"", "%", "'", "\"", "�"], metadata={"help": "A list of characters to remove from the transcripts."}, )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = 42
UpperCAmelCase = True
UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = None
def __call__( self : Union[str, Any] , _A : List[Dict[str, Union[List[int], torch.Tensor]]] ):
# split inputs and labels since they have to be of different lenghts and need
# different padding methods
_UpperCamelCase = [{'''input_values''': feature['''input_values''']} for feature in features]
_UpperCamelCase = [{'''input_ids''': feature['''labels''']} for feature in features]
_UpperCamelCase = self.processor.pad(
_A , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' , )
_UpperCamelCase = self.processor.pad(
labels=_A , padding=self.padding , max_length=self.max_length_labels , pad_to_multiple_of=self.pad_to_multiple_of_labels , return_tensors='''pt''' , )
# replace padding with -100 to ignore loss correctly
_UpperCamelCase = labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1 ) , -100 )
_UpperCamelCase = labels
return batch
class lowerCAmelCase_ ( __lowercase ):
def UpperCamelCase_ ( self : Dict , _A : nn.Module , _A : Dict[str, Union[torch.Tensor, Any]] ):
model.train()
_UpperCamelCase = self._prepare_inputs(_A )
if self.use_amp:
with autocast():
_UpperCamelCase = self.compute_loss(_A , _A )
else:
_UpperCamelCase = self.compute_loss(_A , _A )
if self.args.n_gpu > 1:
if model.module.config.ctc_loss_reduction == "mean":
_UpperCamelCase = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
_UpperCamelCase = loss.sum() / (inputs['''labels'''] >= 0).sum()
else:
raise ValueError(F"""{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']""" )
if self.args.gradient_accumulation_steps > 1:
_UpperCamelCase = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(_A ).backward()
elif self.use_apex:
with amp.scale_loss(_A , self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(_A )
else:
loss.backward()
return loss.detach()
def _snake_case ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_UpperCamelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
_UpperCamelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCamelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'''Use --overwrite_output_dir to overcome.''' )
elif last_checkpoint is not None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info('''Training/evaluation parameters %s''' , __snake_case )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets:
_UpperCamelCase = datasets.load_dataset(
'''common_voice''' , data_args.dataset_config_name , split=data_args.train_split_name )
_UpperCamelCase = datasets.load_dataset('''common_voice''' , data_args.dataset_config_name , split='''test''' )
# Create and save tokenizer
_UpperCamelCase = f"""[{"".join(data_args.chars_to_ignore )}]"""
def remove_special_characters(__snake_case ):
_UpperCamelCase = re.sub(__snake_case , '''''' , batch['''sentence'''] ).lower() + ''' '''
return batch
_UpperCamelCase = train_dataset.map(__snake_case , remove_columns=['''sentence'''] )
_UpperCamelCase = eval_dataset.map(__snake_case , remove_columns=['''sentence'''] )
def extract_all_chars(__snake_case ):
_UpperCamelCase = ''' '''.join(batch['''text'''] )
_UpperCamelCase = list(set(__snake_case ) )
return {"vocab": [vocab], "all_text": [all_text]}
_UpperCamelCase = train_dataset.map(
__snake_case , batched=__snake_case , batch_size=-1 , keep_in_memory=__snake_case , remove_columns=train_dataset.column_names , )
_UpperCamelCase = train_dataset.map(
__snake_case , batched=__snake_case , batch_size=-1 , keep_in_memory=__snake_case , remove_columns=eval_dataset.column_names , )
_UpperCamelCase = list(set(vocab_train['''vocab'''][0] ) | set(vocab_test['''vocab'''][0] ) )
_UpperCamelCase = {v: k for k, v in enumerate(__snake_case )}
_UpperCamelCase = vocab_dict[''' ''']
del vocab_dict[" "]
_UpperCamelCase = len(__snake_case )
_UpperCamelCase = len(__snake_case )
with open('''vocab.json''' , '''w''' ) as vocab_file:
json.dump(__snake_case , __snake_case )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCamelCase = WavaVecaCTCTokenizer(
'''vocab.json''' , unk_token='''[UNK]''' , pad_token='''[PAD]''' , word_delimiter_token='''|''' , )
_UpperCamelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0.0 , do_normalize=__snake_case , return_attention_mask=__snake_case )
_UpperCamelCase = WavaVecaProcessor(feature_extractor=__snake_case , tokenizer=__snake_case )
_UpperCamelCase = WavaVecaForCTC.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , activation_dropout=model_args.activation_dropout , attention_dropout=model_args.attention_dropout , hidden_dropout=model_args.hidden_dropout , feat_proj_dropout=model_args.feat_proj_dropout , mask_time_prob=model_args.mask_time_prob , gradient_checkpointing=training_args.gradient_checkpointing , layerdrop=model_args.layerdrop , ctc_loss_reduction='''mean''' , pad_token_id=processor.tokenizer.pad_token_id , vocab_size=len(processor.tokenizer ) , )
if data_args.max_train_samples is not None:
_UpperCamelCase = min(len(__snake_case ) , data_args.max_train_samples )
_UpperCamelCase = train_dataset.select(range(__snake_case ) )
if data_args.max_val_samples is not None:
_UpperCamelCase = eval_dataset.select(range(data_args.max_val_samples ) )
_UpperCamelCase = torchaudio.transforms.Resample(48000 , 16000 )
# Preprocessing the datasets.
# We need to read the aduio files as arrays and tokenize the targets.
def speech_file_to_array_fn(__snake_case ):
_UpperCamelCase , _UpperCamelCase = torchaudio.load(batch['''path'''] )
_UpperCamelCase = resampler(__snake_case ).squeeze().numpy()
_UpperCamelCase = 16000
_UpperCamelCase = batch['''text''']
return batch
_UpperCamelCase = train_dataset.map(
__snake_case , remove_columns=train_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
_UpperCamelCase = eval_dataset.map(
__snake_case , remove_columns=eval_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
def prepare_dataset(__snake_case ):
# check that all files have the correct sampling rate
assert (
len(set(batch['''sampling_rate'''] ) ) == 1
), f"""Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."""
_UpperCamelCase = processor(
audio=batch['''speech'''] , text=batch['''target_text'''] , sampling_rate=batch['''sampling_rate'''][0] )
batch.update(__snake_case )
return batch
_UpperCamelCase = train_dataset.map(
__snake_case , remove_columns=train_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=__snake_case , num_proc=data_args.preprocessing_num_workers , )
_UpperCamelCase = eval_dataset.map(
__snake_case , remove_columns=eval_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=__snake_case , num_proc=data_args.preprocessing_num_workers , )
# Metric
_UpperCamelCase = datasets.load_metric('''wer''' )
def compute_metrics(__snake_case ):
_UpperCamelCase = pred.predictions
_UpperCamelCase = np.argmax(__snake_case , axis=-1 )
_UpperCamelCase = processor.tokenizer.pad_token_id
_UpperCamelCase = processor.batch_decode(__snake_case )
# we do not want to group tokens when computing the metrics
_UpperCamelCase = processor.batch_decode(pred.label_ids , group_tokens=__snake_case )
_UpperCamelCase = wer_metric.compute(predictions=__snake_case , references=__snake_case )
return {"wer": wer}
if model_args.freeze_feature_extractor:
model.freeze_feature_extractor()
# Data collator
_UpperCamelCase = DataCollatorCTCWithPadding(processor=__snake_case , padding=__snake_case )
# Initialize our Trainer
_UpperCamelCase = CTCTrainer(
model=__snake_case , data_collator=__snake_case , args=__snake_case , compute_metrics=__snake_case , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=processor.feature_extractor , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
_UpperCamelCase = last_checkpoint
elif os.path.isdir(model_args.model_name_or_path ):
_UpperCamelCase = model_args.model_name_or_path
else:
_UpperCamelCase = None
# Save the feature_extractor and the tokenizer
if is_main_process(training_args.local_rank ):
processor.save_pretrained(training_args.output_dir )
_UpperCamelCase = trainer.train(resume_from_checkpoint=__snake_case )
trainer.save_model()
_UpperCamelCase = train_result.metrics
_UpperCamelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__snake_case )
)
_UpperCamelCase = min(__snake_case , len(__snake_case ) )
trainer.log_metrics('''train''' , __snake_case )
trainer.save_metrics('''train''' , __snake_case )
trainer.save_state()
# Evaluation
_UpperCamelCase = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
_UpperCamelCase = trainer.evaluate()
_UpperCamelCase = data_args.max_val_samples if data_args.max_val_samples is not None else len(__snake_case )
_UpperCamelCase = min(__snake_case , len(__snake_case ) )
trainer.log_metrics('''eval''' , __snake_case )
trainer.save_metrics('''eval''' , __snake_case )
return results
if __name__ == "__main__":
main()
| 10 | 0 |
def A__ ( SCREAMING_SNAKE_CASE_ : int = 10_00 ) -> int:
"""simple docstring"""
_UpperCAmelCase = 3
_UpperCAmelCase = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name__ == "__main__":
print(f'''{solution() = }''') | 32 | import math
class lowerCAmelCase_ :
def __init__( self : Tuple , _A : int=0 ): # a graph with Node 0,1,...,N-1
_UpperCamelCase = n
_UpperCamelCase = [
[math.inf for j in range(0 , _A )] for i in range(0 , _A )
] # adjacency matrix for weight
_UpperCamelCase = [
[math.inf for j in range(0 , _A )] for i in range(0 , _A )
] # dp[i][j] stores minimum distance from i to j
def UpperCamelCase_ ( self : Dict , _A : str , _A : List[str] , _A : Optional[Any] ):
_UpperCamelCase = w
def UpperCamelCase_ ( self : Optional[int] ):
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
_UpperCamelCase = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def UpperCamelCase_ ( self : List[str] , _A : Optional[int] , _A : Optional[int] ):
return self.dp[u][v]
if __name__ == "__main__":
_lowerCAmelCase = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 10 | 0 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowerCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
lowerCamelCase__ : str = {
"""CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": (
"""https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"""
),
# See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}
class __magic_name__ (snake_case_ ):
'''simple docstring'''
__lowercase : Optional[int] = 'trajectory_transformer'
__lowercase : List[Any] = ['past_key_values']
__lowercase : Dict = {
'hidden_size': 'n_embd',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self:List[Any] , _a:Any=1_00 , _a:List[str]=5 , _a:Optional[Any]=1 , _a:List[Any]=1 , _a:Optional[int]=2_49 , _a:Dict=6 , _a:int=17 , _a:Union[str, Any]=25 , _a:Optional[Any]=4 , _a:Union[str, Any]=4 , _a:int=1_28 , _a:Optional[Any]=0.1 , _a:Tuple=0.1 , _a:List[Any]=0.1 , _a:List[str]=0.0006 , _a:List[str]=5_12 , _a:Tuple=0.02 , _a:Optional[Any]=1e-12 , _a:Tuple=1 , _a:Dict=True , _a:str=1 , _a:Union[str, Any]=5_02_56 , _a:List[str]=5_02_56 , **_a:List[str] , ):
snake_case__ = vocab_size
snake_case__ = action_weight
snake_case__ = reward_weight
snake_case__ = value_weight
snake_case__ = max_position_embeddings
snake_case__ = block_size
snake_case__ = action_dim
snake_case__ = observation_dim
snake_case__ = transition_dim
snake_case__ = learning_rate
snake_case__ = n_layer
snake_case__ = n_head
snake_case__ = n_embd
snake_case__ = embd_pdrop
snake_case__ = attn_pdrop
snake_case__ = resid_pdrop
snake_case__ = initializer_range
snake_case__ = layer_norm_eps
snake_case__ = kaiming_initializer_range
snake_case__ = use_cache
super().__init__(pad_token_id=_a , bos_token_id=_a , eos_token_id=_a , **_a )
| 33 | import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
def _snake_case ( __snake_case=None , __snake_case=None ):
return field(default_factory=lambda: default , metadata=__snake_case )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = list_field(
default=[], metadata={
"help": (
"Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
" of all available models"
)
}, )
UpperCAmelCase = list_field(
default=[8], metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"} )
UpperCAmelCase = list_field(
default=[8, 32, 128, 512], metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Use FP16 to accelerate inference."} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Benchmark training of model"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Verbose memory tracing"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
}, )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Trace memory line by line"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Save result to a CSV file"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Save all print statements in a log file"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Whether to print environment information"} )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
" multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
" for debugging / testing and on TPU."
)
}, )
UpperCAmelCase = field(
default=F"""inference_time_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving time results to csv."}, )
UpperCAmelCase = field(
default=F"""inference_memory_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving memory results to csv."}, )
UpperCAmelCase = field(
default=F"""train_time_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving time results to csv for training."}, )
UpperCAmelCase = field(
default=F"""train_memory_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving memory results to csv for training."}, )
UpperCAmelCase = field(
default=F"""env_info_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving environment information."}, )
UpperCAmelCase = field(
default=F"""log_{round(time() )}.csv""", metadata={"help": "Log filename used if print statements are saved in log."}, )
UpperCAmelCase = field(default=3, metadata={"help": "Times an experiment will be run."} )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
" model weights."
)
}, )
def UpperCamelCase_ ( self : Union[str, Any] ):
warnings.warn(
F"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
''' are deprecated in general and it is advised to use external Benchmarking libraries '''
''' to benchmark Transformer models.''' , _A , )
def UpperCamelCase_ ( self : str ):
return json.dumps(dataclasses.asdict(self ) , indent=2 )
@property
def UpperCamelCase_ ( self : List[Any] ):
if len(self.models ) <= 0:
raise ValueError(
'''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
''' bert-base-cased` or `args.models = [\'bert-base-cased\'].''' )
return self.models
@property
def UpperCamelCase_ ( self : Optional[int] ):
if not self.multi_process:
return False
elif self.is_tpu:
logger.info('''Multiprocessing is currently not possible on TPU.''' )
return False
else:
return True
| 10 | 0 |
"""simple docstring"""
import sys
SCREAMING_SNAKE_CASE_ = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856403098711121722383113'
'62229893423380308135336276614282806444486645238749'
'30358907296290491560440772390713810515859307960866'
'70172427121883998797908792274921901699720888093776'
'65727333001053367881220235421809751254540594752243'
'52584907711670556013604839586446706324415722155397'
'53697817977846174064955149290862569321978468622482'
'83972241375657056057490261407972968652414535100474'
'82166370484403199890008895243450658541227588666881'
'16427171479924442928230863465674813919123162824586'
'17866458359124566529476545682848912883142607690042'
'24219022671055626321111109370544217506941658960408'
'07198403850962455444362981230987879927244284909188'
'84580156166097919133875499200524063689912560717606'
'05886116467109405077541002256983155200055935729725'
'71636269561882670428252483600823257530420752963450'
)
def __snake_case ( _lowercase ):
"""simple docstring"""
UpperCamelCase = 1
for digit in s:
product *= int(_lowercase )
return product
def __snake_case ( _lowercase = N ):
"""simple docstring"""
UpperCamelCase = -sys.maxsize - 1
UpperCamelCase = n[:13]
UpperCamelCase = 13
while cur_index < len(_lowercase ) - 13:
if int(n[cur_index] ) >= int(substr[0] ):
UpperCamelCase = substr[1:] + n[cur_index]
cur_index += 1
else:
UpperCamelCase = max(_lowercase ,str_eval(_lowercase ) )
UpperCamelCase = n[cur_index : cur_index + 13]
cur_index += 13
return largest_product
if __name__ == "__main__":
print(f'{solution() = }') | 34 | import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _snake_case ( *__snake_case , __snake_case = None , __snake_case=True , __snake_case=2 ):
from .. import __version__
_UpperCamelCase = take_from
_UpperCamelCase = ()
if not isinstance(args[0] , __snake_case ):
_UpperCamelCase = (args,)
for attribute, version_name, message in args:
if version.parse(version.parse(__snake_case ).base_version ) >= version.parse(__snake_case ):
raise ValueError(
f"""The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"""
f""" version {__version__} is >= {version_name}""" )
_UpperCamelCase = None
if isinstance(__snake_case , __snake_case ) and attribute in deprecated_kwargs:
values += (deprecated_kwargs.pop(__snake_case ),)
_UpperCamelCase = f"""The `{attribute}` argument is deprecated and will be removed in version {version_name}."""
elif hasattr(__snake_case , __snake_case ):
values += (getattr(__snake_case , __snake_case ),)
_UpperCamelCase = f"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
elif deprecated_kwargs is None:
_UpperCamelCase = f"""`{attribute}` is deprecated and will be removed in version {version_name}."""
if warning is not None:
_UpperCamelCase = warning + ''' ''' if standard_warn else ''''''
warnings.warn(warning + message , __snake_case , stacklevel=__snake_case )
if isinstance(__snake_case , __snake_case ) and len(__snake_case ) > 0:
_UpperCamelCase = inspect.getouterframes(inspect.currentframe() )[1]
_UpperCamelCase = call_frame.filename
_UpperCamelCase = call_frame.lineno
_UpperCamelCase = call_frame.function
_UpperCamelCase , _UpperCamelCase = next(iter(deprecated_kwargs.items() ) )
raise TypeError(f"""{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`""" )
if len(__snake_case ) == 0:
return
elif len(__snake_case ) == 1:
return values[0]
return values
| 10 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ :str = logging.get_logger(__name__)
a_ :Any = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}
class lowercase ( _UpperCAmelCase ):
lowerCamelCase : List[str] = '''roc_bert'''
def __init__( self : List[str] , _lowercase : Dict=3_05_22 , _lowercase : Tuple=7_68 , _lowercase : Any=12 , _lowercase : str=12 , _lowercase : Any=30_72 , _lowercase : str="gelu" , _lowercase : Any=0.1 , _lowercase : int=0.1 , _lowercase : Optional[Any]=5_12 , _lowercase : Any=2 , _lowercase : Union[str, Any]=0.02 , _lowercase : Optional[int]=1E-12 , _lowercase : Any=True , _lowercase : Tuple=0 , _lowercase : Dict="absolute" , _lowercase : Optional[int]=None , _lowercase : Union[str, Any]=True , _lowercase : Dict=True , _lowercase : Optional[int]=7_68 , _lowercase : Tuple=9_10 , _lowercase : Optional[int]=5_12 , _lowercase : List[str]=2_48_58 , _lowercase : List[Any]=True , **_lowercase : List[Any] , ):
SCREAMING_SNAKE_CASE__ : Dict = vocab_size
SCREAMING_SNAKE_CASE__ : Tuple = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_size
SCREAMING_SNAKE_CASE__ : str = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Any = num_attention_heads
SCREAMING_SNAKE_CASE__ : Union[str, Any] = intermediate_size
SCREAMING_SNAKE_CASE__ : Any = hidden_act
SCREAMING_SNAKE_CASE__ : List[Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : List[Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : List[str] = initializer_range
SCREAMING_SNAKE_CASE__ : Tuple = type_vocab_size
SCREAMING_SNAKE_CASE__ : Optional[int] = layer_norm_eps
SCREAMING_SNAKE_CASE__ : List[Any] = use_cache
SCREAMING_SNAKE_CASE__ : List[Any] = enable_pronunciation
SCREAMING_SNAKE_CASE__ : Tuple = enable_shape
SCREAMING_SNAKE_CASE__ : Dict = pronunciation_embed_dim
SCREAMING_SNAKE_CASE__ : int = pronunciation_vocab_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = shape_embed_dim
SCREAMING_SNAKE_CASE__ : Optional[int] = shape_vocab_size
SCREAMING_SNAKE_CASE__ : List[Any] = concat_input
SCREAMING_SNAKE_CASE__ : str = position_embedding_type
SCREAMING_SNAKE_CASE__ : str = classifier_dropout
super().__init__(pad_token_id=_lowercase , **_lowercase )
| 35 | import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
_lowerCAmelCase = logging.getLogger(__name__)
def _snake_case ( __snake_case , __snake_case ):
return (preds == labels).mean()
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Pretrained config name or path if not the same as model_name"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys() )} )
UpperCAmelCase = field(metadata={"help": "Should contain the data files for the task."} )
UpperCAmelCase = field(
default=128, metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Overwrite the cached training and evaluation sets"} )
def _snake_case ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_UpperCamelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
''' --overwrite_output_dir to overcome.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('''Training/evaluation parameters %s''' , __snake_case )
# Set seed
set_seed(training_args.seed )
try:
_UpperCamelCase = processors[data_args.task_name]()
_UpperCamelCase = processor.get_labels()
_UpperCamelCase = len(__snake_case )
except KeyError:
raise ValueError('''Task not found: %s''' % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCamelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__snake_case , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
_UpperCamelCase = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
_UpperCamelCase = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=__snake_case , cache_dir=model_args.cache_dir , )
# Get datasets
_UpperCamelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__snake_case , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
_UpperCamelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__snake_case , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def compute_metrics(__snake_case ) -> Dict:
_UpperCamelCase = np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(__snake_case , p.label_ids )}
# Data collator
_UpperCamelCase = DataCollatorWithPadding(__snake_case , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
_UpperCamelCase = Trainer(
model=__snake_case , args=__snake_case , train_dataset=__snake_case , eval_dataset=__snake_case , compute_metrics=__snake_case , data_collator=__snake_case , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
_UpperCamelCase = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
_UpperCamelCase = trainer.evaluate()
_UpperCamelCase = os.path.join(training_args.output_dir , '''eval_results.txt''' )
if trainer.is_world_master():
with open(__snake_case , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''' , __snake_case , __snake_case )
writer.write('''%s = %s\n''' % (key, value) )
results.update(__snake_case )
return results
def _snake_case ( __snake_case ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 10 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__lowercase : List[Any] = logging.get_logger(__name__)
def lowercase ( __A : Any ) -> Dict:
'''simple docstring'''
snake_case : int = DPTConfig()
if "large" in checkpoint_url:
snake_case : Optional[Any] = 1024
snake_case : List[str] = 4096
snake_case : Optional[int] = 24
snake_case : Dict = 16
snake_case : Optional[Any] = [5, 11, 17, 23]
snake_case : int = [256, 512, 1024, 1024]
snake_case : int = (1, 384, 384)
if "ade" in checkpoint_url:
snake_case : str = True
snake_case : Optional[int] = 150
snake_case : List[str] = """huggingface/label-files"""
snake_case : Optional[int] = """ade20k-id2label.json"""
snake_case : Optional[int] = json.load(open(cached_download(hf_hub_url(__A , __A , repo_type="""dataset""" ) ) , """r""" ) )
snake_case : Tuple = {int(__A ): v for k, v in idalabel.items()}
snake_case : Any = idalabel
snake_case : Any = {v: k for k, v in idalabel.items()}
snake_case : Dict = [1, 150, 480, 480]
return config, expected_shape
def lowercase ( __A : int ) -> int:
'''simple docstring'''
snake_case : List[Any] = ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
for k in ignore_keys:
state_dict.pop(__A , __A )
def lowercase ( __A : Tuple ) -> str:
'''simple docstring'''
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
snake_case : List[str] = name.replace("""pretrained.model""" , """dpt.encoder""" )
if "pretrained.model" in name:
snake_case : int = name.replace("""pretrained.model""" , """dpt.embeddings""" )
if "patch_embed" in name:
snake_case : Tuple = name.replace("""patch_embed""" , """patch_embeddings""" )
if "pos_embed" in name:
snake_case : List[str] = name.replace("""pos_embed""" , """position_embeddings""" )
if "attn.proj" in name:
snake_case : int = name.replace("""attn.proj""" , """attention.output.dense""" )
if "proj" in name and "project" not in name:
snake_case : int = name.replace("""proj""" , """projection""" )
if "blocks" in name:
snake_case : Union[str, Any] = name.replace("""blocks""" , """layer""" )
if "mlp.fc1" in name:
snake_case : Optional[int] = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
snake_case : List[Any] = name.replace("""mlp.fc2""" , """output.dense""" )
if "norm1" in name:
snake_case : List[Any] = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
snake_case : List[str] = name.replace("""norm2""" , """layernorm_after""" )
if "scratch.output_conv" in name:
snake_case : str = name.replace("""scratch.output_conv""" , """head""" )
if "scratch" in name:
snake_case : Dict = name.replace("""scratch""" , """neck""" )
if "layer1_rn" in name:
snake_case : Optional[int] = name.replace("""layer1_rn""" , """convs.0""" )
if "layer2_rn" in name:
snake_case : Tuple = name.replace("""layer2_rn""" , """convs.1""" )
if "layer3_rn" in name:
snake_case : Any = name.replace("""layer3_rn""" , """convs.2""" )
if "layer4_rn" in name:
snake_case : Optional[Any] = name.replace("""layer4_rn""" , """convs.3""" )
if "refinenet" in name:
snake_case : int = int(name[len("""neck.refinenet""" ) : len("""neck.refinenet""" ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
snake_case : Tuple = name.replace(f"""refinenet{layer_idx}""" , f"""fusion_stage.layers.{abs(layer_idx-4 )}""" )
if "out_conv" in name:
snake_case : Tuple = name.replace("""out_conv""" , """projection""" )
if "resConfUnit1" in name:
snake_case : Optional[Any] = name.replace("""resConfUnit1""" , """residual_layer1""" )
if "resConfUnit2" in name:
snake_case : Optional[Any] = name.replace("""resConfUnit2""" , """residual_layer2""" )
if "conv1" in name:
snake_case : Dict = name.replace("""conv1""" , """convolution1""" )
if "conv2" in name:
snake_case : List[Any] = name.replace("""conv2""" , """convolution2""" )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
snake_case : List[Any] = name.replace("""pretrained.act_postprocess1.0.project.0""" , """neck.reassemble_stage.readout_projects.0.0""" )
if "pretrained.act_postprocess2.0.project.0" in name:
snake_case : List[str] = name.replace("""pretrained.act_postprocess2.0.project.0""" , """neck.reassemble_stage.readout_projects.1.0""" )
if "pretrained.act_postprocess3.0.project.0" in name:
snake_case : Optional[Any] = name.replace("""pretrained.act_postprocess3.0.project.0""" , """neck.reassemble_stage.readout_projects.2.0""" )
if "pretrained.act_postprocess4.0.project.0" in name:
snake_case : List[str] = name.replace("""pretrained.act_postprocess4.0.project.0""" , """neck.reassemble_stage.readout_projects.3.0""" )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
snake_case : Any = name.replace("""pretrained.act_postprocess1.3""" , """neck.reassemble_stage.layers.0.projection""" )
if "pretrained.act_postprocess1.4" in name:
snake_case : Tuple = name.replace("""pretrained.act_postprocess1.4""" , """neck.reassemble_stage.layers.0.resize""" )
if "pretrained.act_postprocess2.3" in name:
snake_case : Optional[int] = name.replace("""pretrained.act_postprocess2.3""" , """neck.reassemble_stage.layers.1.projection""" )
if "pretrained.act_postprocess2.4" in name:
snake_case : Any = name.replace("""pretrained.act_postprocess2.4""" , """neck.reassemble_stage.layers.1.resize""" )
if "pretrained.act_postprocess3.3" in name:
snake_case : List[Any] = name.replace("""pretrained.act_postprocess3.3""" , """neck.reassemble_stage.layers.2.projection""" )
if "pretrained.act_postprocess4.3" in name:
snake_case : List[str] = name.replace("""pretrained.act_postprocess4.3""" , """neck.reassemble_stage.layers.3.projection""" )
if "pretrained.act_postprocess4.4" in name:
snake_case : str = name.replace("""pretrained.act_postprocess4.4""" , """neck.reassemble_stage.layers.3.resize""" )
if "pretrained" in name:
snake_case : Any = name.replace("""pretrained""" , """dpt""" )
if "bn" in name:
snake_case : Optional[Any] = name.replace("""bn""" , """batch_norm""" )
if "head" in name:
snake_case : Optional[int] = name.replace("""head""" , """head.head""" )
if "encoder.norm" in name:
snake_case : Optional[int] = name.replace("""encoder.norm""" , """layernorm""" )
if "auxlayer" in name:
snake_case : Union[str, Any] = name.replace("""auxlayer""" , """auxiliary_head.head""" )
return name
def lowercase ( __A : Dict , __A : Tuple ) -> Dict:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
snake_case : Any = state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight""" )
snake_case : List[str] = state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
snake_case : Optional[int] = in_proj_weight[: config.hidden_size, :]
snake_case : Optional[Any] = in_proj_bias[: config.hidden_size]
snake_case : Union[str, Any] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
snake_case : str = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
snake_case : List[str] = in_proj_weight[
-config.hidden_size :, :
]
snake_case : Tuple = in_proj_bias[-config.hidden_size :]
def lowercase ( ) -> Any:
'''simple docstring'''
snake_case : Optional[int] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
snake_case : List[Any] = Image.open(requests.get(__A , stream=__A ).raw )
return im
@torch.no_grad()
def lowercase ( __A : Tuple , __A : Optional[int] , __A : Optional[Any] , __A : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case , snake_case : Union[str, Any] = get_dpt_config(__A )
# load original state_dict from URL
snake_case : Union[str, Any] = torch.hub.load_state_dict_from_url(__A , map_location="""cpu""" )
# remove certain keys
remove_ignore_keys_(__A )
# rename keys
for key in state_dict.copy().keys():
snake_case : Optional[Any] = state_dict.pop(__A )
snake_case : List[str] = val
# read in qkv matrices
read_in_q_k_v(__A , __A )
# load HuggingFace model
snake_case : int = DPTForSemanticSegmentation(__A ) if """ade""" in checkpoint_url else DPTForDepthEstimation(__A )
model.load_state_dict(__A )
model.eval()
# Check outputs on an image
snake_case : int = 480 if """ade""" in checkpoint_url else 384
snake_case : Optional[int] = DPTImageProcessor(size=__A )
snake_case : List[Any] = prepare_img()
snake_case : Optional[Any] = image_processor(__A , return_tensors="""pt""" )
# forward pass
snake_case : Any = model(**__A ).logits if """ade""" in checkpoint_url else model(**__A ).predicted_depth
# Assert logits
snake_case : int = torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]] )
if "ade" in checkpoint_url:
snake_case : List[str] = torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]] )
assert outputs.shape == torch.Size(__A )
assert (
torch.allclose(outputs[0, 0, :3, :3] , __A , atol=1E-4 )
if "ade" in checkpoint_url
else torch.allclose(outputs[0, :3, :3] , __A )
)
Path(__A ).mkdir(exist_ok=__A )
print(f"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(__A )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(__A )
if push_to_hub:
print("""Pushing model to hub...""" )
model.push_to_hub(
repo_path_or_name=Path(__A , __A ) , organization="""nielsr""" , commit_message="""Add model""" , use_temp_dir=__A , )
image_processor.push_to_hub(
repo_path_or_name=Path(__A , __A ) , organization="""nielsr""" , commit_message="""Add image processor""" , use_temp_dir=__A , )
if __name__ == "__main__":
__lowercase : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint_url''',
default='''https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt''',
type=str,
help='''URL of the original DPT checkpoint you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=str,
required=True,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
)
parser.add_argument(
'''--model_name''',
default='''dpt-large''',
type=str,
help='''Name of the model, in case you\'re pushing to the hub.''',
)
__lowercase : List[Any] = parser.parse_args()
convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 36 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
),
# See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = "trocr"
UpperCAmelCase = ["past_key_values"]
UpperCAmelCase = {
"num_attention_heads": "decoder_attention_heads",
"hidden_size": "d_model",
"num_hidden_layers": "decoder_layers",
}
def __init__( self : List[str] , _A : Optional[Any]=5_0265 , _A : Optional[Any]=1024 , _A : Optional[Any]=12 , _A : Any=16 , _A : Any=4096 , _A : Optional[Any]="gelu" , _A : Union[str, Any]=512 , _A : Dict=0.1 , _A : List[str]=0.0 , _A : Optional[Any]=0.0 , _A : Union[str, Any]=2 , _A : Any=0.02 , _A : List[str]=0.0 , _A : List[str]=True , _A : str=False , _A : List[str]=True , _A : Optional[Any]=True , _A : Optional[int]=1 , _A : int=0 , _A : Any=2 , **_A : Optional[int] , ):
_UpperCamelCase = vocab_size
_UpperCamelCase = d_model
_UpperCamelCase = decoder_layers
_UpperCamelCase = decoder_attention_heads
_UpperCamelCase = decoder_ffn_dim
_UpperCamelCase = activation_function
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = activation_dropout
_UpperCamelCase = init_std
_UpperCamelCase = decoder_layerdrop
_UpperCamelCase = use_cache
_UpperCamelCase = scale_embedding
_UpperCamelCase = use_learned_position_embeddings
_UpperCamelCase = layernorm_embedding
super().__init__(
pad_token_id=_A , bos_token_id=_A , eos_token_id=_A , decoder_start_token_id=_A , **_A , )
| 10 | 0 |
def UpperCamelCase_ ( __a , __a , __a , __a ) -> str:
if height >= 1:
move_tower(height - 1 , __a , __a , __a )
move_disk(__a , __a )
move_tower(height - 1 , __a , __a , __a )
def UpperCamelCase_ ( __a , __a ) -> List[Any]:
print("moving disk from" , __a , "to" , __a )
def UpperCamelCase_ ( ) -> Optional[Any]:
a__ : Any = int(input("Height of hanoi: " ).strip() )
move_tower(__a , "A" , "B" , "C" )
if __name__ == "__main__":
main()
| 37 | import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
FlaubertForMultipleChoice,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertModel,
FlaubertWithLMHeadModel,
)
from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase_ ( __lowercase ):
def __init__( self : Union[str, Any] , _A : Optional[Any] , _A : Any=13 , _A : Union[str, Any]=7 , _A : List[str]=True , _A : List[str]=True , _A : List[str]=True , _A : List[str]=True , _A : List[Any]=True , _A : Optional[int]=False , _A : Any=False , _A : int=False , _A : Optional[Any]=2 , _A : Any=99 , _A : str=0 , _A : Union[str, Any]=32 , _A : List[Any]=5 , _A : Tuple=4 , _A : List[str]=0.1 , _A : Union[str, Any]=0.1 , _A : int=512 , _A : Union[str, Any]=12 , _A : List[str]=2 , _A : int=0.02 , _A : Optional[Any]=3 , _A : Any=4 , _A : Optional[int]="last" , _A : Any=None , _A : Dict=None , ):
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = seq_length
_UpperCamelCase = is_training
_UpperCamelCase = use_input_lengths
_UpperCamelCase = use_token_type_ids
_UpperCamelCase = use_labels
_UpperCamelCase = gelu_activation
_UpperCamelCase = sinusoidal_embeddings
_UpperCamelCase = causal
_UpperCamelCase = asm
_UpperCamelCase = n_langs
_UpperCamelCase = vocab_size
_UpperCamelCase = n_special
_UpperCamelCase = hidden_size
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = hidden_dropout_prob
_UpperCamelCase = attention_probs_dropout_prob
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = type_vocab_size
_UpperCamelCase = type_sequence_label_size
_UpperCamelCase = initializer_range
_UpperCamelCase = num_labels
_UpperCamelCase = num_choices
_UpperCamelCase = summary_type
_UpperCamelCase = use_proj
_UpperCamelCase = scope
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCamelCase = None
if self.use_input_lengths:
_UpperCamelCase = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
_UpperCamelCase = None
if self.use_token_type_ids:
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = None
if self.use_labels:
_UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCamelCase = ids_tensor([self.batch_size] , 2 ).float()
_UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices )
_UpperCamelCase = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def UpperCamelCase_ ( self : str ):
return FlaubertConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , )
def UpperCamelCase_ ( self : str , _A : Union[str, Any] , _A : Optional[Any] , _A : str , _A : Tuple , _A : List[str] , _A : List[Any] , _A : Any , _A : str , _A : Optional[int] , ):
_UpperCamelCase = FlaubertModel(config=_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A , lengths=_A , langs=_A )
_UpperCamelCase = model(_A , langs=_A )
_UpperCamelCase = model(_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase_ ( self : Tuple , _A : List[Any] , _A : str , _A : Optional[int] , _A : Optional[Any] , _A : List[str] , _A : int , _A : str , _A : List[Any] , _A : Any , ):
_UpperCamelCase = FlaubertWithLMHeadModel(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A , token_type_ids=_A , labels=_A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase_ ( self : Tuple , _A : List[str] , _A : List[str] , _A : Optional[Any] , _A : Union[str, Any] , _A : str , _A : List[str] , _A : Tuple , _A : Optional[int] , _A : Dict , ):
_UpperCamelCase = FlaubertForQuestionAnsweringSimple(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A )
_UpperCamelCase = model(_A , start_positions=_A , end_positions=_A )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase_ ( self : Tuple , _A : str , _A : Tuple , _A : Tuple , _A : Union[str, Any] , _A : List[str] , _A : int , _A : str , _A : Dict , _A : List[Any] , ):
_UpperCamelCase = FlaubertForQuestionAnswering(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A )
_UpperCamelCase = model(
_A , start_positions=_A , end_positions=_A , cls_index=_A , is_impossible=_A , p_mask=_A , )
_UpperCamelCase = model(
_A , start_positions=_A , end_positions=_A , cls_index=_A , is_impossible=_A , )
((_UpperCamelCase) , ) = result_with_labels.to_tuple()
_UpperCamelCase = model(_A , start_positions=_A , end_positions=_A )
((_UpperCamelCase) , ) = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def UpperCamelCase_ ( self : List[Any] , _A : Union[str, Any] , _A : Tuple , _A : str , _A : int , _A : int , _A : Optional[int] , _A : Optional[int] , _A : int , _A : List[str] , ):
_UpperCamelCase = FlaubertForSequenceClassification(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A )
_UpperCamelCase = model(_A , labels=_A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCamelCase_ ( self : Optional[int] , _A : List[str] , _A : Optional[Any] , _A : str , _A : Union[str, Any] , _A : List[Any] , _A : int , _A : List[Any] , _A : str , _A : List[str] , ):
_UpperCamelCase = self.num_labels
_UpperCamelCase = FlaubertForTokenClassification(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A , attention_mask=_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase_ ( self : Tuple , _A : Dict , _A : str , _A : Optional[Any] , _A : List[str] , _A : Any , _A : Optional[int] , _A : Optional[Any] , _A : List[Any] , _A : List[str] , ):
_UpperCamelCase = self.num_choices
_UpperCamelCase = FlaubertForMultipleChoice(config=_A )
model.to(_A )
model.eval()
_UpperCamelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCamelCase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCamelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCamelCase = model(
_A , attention_mask=_A , token_type_ids=_A , labels=_A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCamelCase_ ( self : Tuple ):
_UpperCamelCase = self.prepare_config_and_inputs()
(
(
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) ,
) = config_and_inputs
_UpperCamelCase = {
'''input_ids''': input_ids,
'''token_type_ids''': token_type_ids,
'''lengths''': input_lengths,
'''attention_mask''': input_mask,
}
return config, inputs_dict
@require_torch
class lowerCAmelCase_ ( __lowercase, __lowercase, unittest.TestCase ):
UpperCAmelCase = (
(
FlaubertModel,
FlaubertWithLMHeadModel,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertForMultipleChoice,
)
if is_torch_available()
else ()
)
UpperCAmelCase = (
{
"feature-extraction": FlaubertModel,
"fill-mask": FlaubertWithLMHeadModel,
"question-answering": FlaubertForQuestionAnsweringSimple,
"text-classification": FlaubertForSequenceClassification,
"token-classification": FlaubertForTokenClassification,
"zero-shot": FlaubertForSequenceClassification,
}
if is_torch_available()
else {}
)
def UpperCamelCase_ ( self : Union[str, Any] , _A : Dict , _A : Dict , _A : Tuple , _A : int , _A : Any ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('''Fast''' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def UpperCamelCase_ ( self : str , _A : Any , _A : List[str] , _A : Optional[int]=False ):
_UpperCamelCase = super()._prepare_for_class(_A , _A , return_labels=_A )
if return_labels:
if model_class.__name__ == "FlaubertForQuestionAnswering":
_UpperCamelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_A )
_UpperCamelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_A )
return inputs_dict
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = FlaubertModelTester(self )
_UpperCamelCase = ConfigTester(self , config_class=_A , emb_dim=37 )
def UpperCamelCase_ ( self : Optional[Any] ):
self.config_tester.run_common_tests()
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_model(*_A )
def UpperCamelCase_ ( self : Optional[Any] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_lm_head(*_A )
def UpperCamelCase_ ( self : Optional[Any] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_simple_qa(*_A )
def UpperCamelCase_ ( self : Union[str, Any] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_qa(*_A )
def UpperCamelCase_ ( self : Optional[int] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_sequence_classif(*_A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_token_classif(*_A )
def UpperCamelCase_ ( self : Optional[int] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_multiple_choice(*_A )
@slow
def UpperCamelCase_ ( self : str ):
for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = FlaubertModel.from_pretrained(_A )
self.assertIsNotNone(_A )
@slow
@require_torch_gpu
def UpperCamelCase_ ( self : List[Any] ):
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == FlaubertForMultipleChoice:
return
_UpperCamelCase = True
_UpperCamelCase = model_class(config=_A )
_UpperCamelCase = self._prepare_for_class(_A , _A )
_UpperCamelCase = torch.jit.trace(
_A , (inputs_dict['''input_ids'''].to('''cpu''' ), inputs_dict['''attention_mask'''].to('''cpu''' )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(_A , os.path.join(_A , '''traced_model.pt''' ) )
_UpperCamelCase = torch.jit.load(os.path.join(_A , '''traced_model.pt''' ) , map_location=_A )
loaded(inputs_dict['''input_ids'''].to(_A ) , inputs_dict['''attention_mask'''].to(_A ) )
@require_torch
class lowerCAmelCase_ ( unittest.TestCase ):
@slow
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = FlaubertModel.from_pretrained('''flaubert/flaubert_base_cased''' )
_UpperCamelCase = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
with torch.no_grad():
_UpperCamelCase = model(_A )[0]
_UpperCamelCase = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , _A )
_UpperCamelCase = torch.tensor(
[[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _A , atol=1e-4 ) )
| 10 | 0 |
'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator
class __snake_case :
'''simple docstring'''
def __init__( self , __SCREAMING_SNAKE_CASE ):
snake_case__ : List[Any] = str(id_ )
snake_case__ : Dict = None
snake_case__ : List[Any] = None
snake_case__ : Optional[int] = []
snake_case__ : Tuple = {} # {vertex:distance}
def __lt__( self , __SCREAMING_SNAKE_CASE ):
return self.key < other.key
def __repr__( self ):
return self.id
def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE ):
self.neighbors.append(__SCREAMING_SNAKE_CASE )
def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
snake_case__ : Tuple = weight
def UpperCamelCase__ ( __magic_name__ : Optional[Any] , __magic_name__ : List[str] , __magic_name__ : int , __magic_name__ : Dict ) -> Union[str, Any]:
'''simple docstring'''
graph[a - 1].add_neighbor(graph[b - 1] )
graph[b - 1].add_neighbor(graph[a - 1] )
# add the edges:
graph[a - 1].add_edge(graph[b - 1] , __magic_name__ )
graph[b - 1].add_edge(graph[a - 1] , __magic_name__ )
def UpperCamelCase__ ( __magic_name__ : list , __magic_name__ : Vertex ) -> list:
'''simple docstring'''
snake_case__ : Optional[int] = []
for u in graph:
snake_case__ : str = math.inf
snake_case__ : List[Any] = None
snake_case__ : Dict = 0
snake_case__ : Tuple = graph[:]
while q:
snake_case__ : Any = min(__magic_name__ )
q.remove(__magic_name__ )
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
snake_case__ : Optional[int] = u
snake_case__ : Dict = u.edges[v.id]
for i in range(1 , len(__magic_name__ ) ):
a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
return a
def UpperCamelCase__ ( __magic_name__ : list , __magic_name__ : Vertex ) -> Iterator[tuple]:
'''simple docstring'''
for u in graph:
snake_case__ : Tuple = math.inf
snake_case__ : Tuple = None
snake_case__ : Optional[int] = 0
snake_case__ : str = list(__magic_name__ )
hq.heapify(__magic_name__ )
while h:
snake_case__ : str = hq.heappop(__magic_name__ )
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
snake_case__ : Union[str, Any] = u
snake_case__ : Dict = u.edges[v.id]
hq.heapify(__magic_name__ )
for i in range(1 , len(__magic_name__ ) ):
yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)
def UpperCamelCase__ ( ) -> None:
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 38 | from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase_ :
def __init__( self : Any , _A : int , _A : int=12 , _A : int=7 , _A : Tuple=True , _A : Optional[int]=True , _A : Union[str, Any]=True , _A : str=99 , _A : str=32 , _A : int=32 , _A : Optional[Any]=2 , _A : Dict=4 , _A : int=37 , _A : List[Any]=0.1 , _A : str=0.1 , _A : Any=512 , _A : int=0.02 , _A : Optional[Any]=0 , _A : Dict=None , ):
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = seq_length
_UpperCamelCase = is_training
_UpperCamelCase = use_input_mask
_UpperCamelCase = use_labels
_UpperCamelCase = vocab_size
_UpperCamelCase = hidden_size
_UpperCamelCase = projection_dim
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = intermediate_size
_UpperCamelCase = dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = initializer_range
_UpperCamelCase = scope
_UpperCamelCase = bos_token_id
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCamelCase = None
if self.use_input_mask:
_UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
_UpperCamelCase = input_mask.numpy()
_UpperCamelCase , _UpperCamelCase = input_mask.shape
_UpperCamelCase = np.random.randint(1 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(_A ):
_UpperCamelCase = 1
_UpperCamelCase = 0
_UpperCamelCase = self.get_config()
return config, input_ids, tf.convert_to_tensor(_A )
def UpperCamelCase_ ( self : str ):
return BlipTextConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , projection_dim=self.projection_dim , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , dropout=self.dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , bos_token_id=self.bos_token_id , )
def UpperCamelCase_ ( self : List[str] , _A : Tuple , _A : str , _A : Optional[Any] ):
_UpperCamelCase = TFBlipTextModel(config=_A )
_UpperCamelCase = model(_A , attention_mask=_A , training=_A )
_UpperCamelCase = model(_A , training=_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def UpperCamelCase_ ( self : Tuple ):
_UpperCamelCase = self.prepare_config_and_inputs()
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = config_and_inputs
_UpperCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class lowerCAmelCase_ ( __lowercase, unittest.TestCase ):
UpperCAmelCase = (TFBlipTextModel,) if is_tf_available() else ()
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = False
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = BlipTextModelTester(self )
_UpperCamelCase = ConfigTester(self , config_class=_A , hidden_size=37 )
def UpperCamelCase_ ( self : Dict ):
self.config_tester.run_common_tests()
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_A )
def UpperCamelCase_ ( self : List[Any] ):
pass
def UpperCamelCase_ ( self : Tuple ):
pass
@unittest.skip(reason='''Blip does not use inputs_embeds''' )
def UpperCamelCase_ ( self : Dict ):
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def UpperCamelCase_ ( self : Dict ):
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def UpperCamelCase_ ( self : List[str] ):
pass
@slow
def UpperCamelCase_ ( self : Optional[int] ):
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = TFBlipTextModel.from_pretrained(_A )
self.assertIsNotNone(_A )
def UpperCamelCase_ ( self : int , _A : Optional[int]=True ):
super().test_pt_tf_model_equivalence(allow_missing_keys=_A )
| 10 | 0 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class snake_case_ ( __A ):
'''simple docstring'''
def snake_case__( self : Tuple ) ->Optional[Any]:
return [
{"col_1": 3, "col_2": "a"},
{"col_1": 2, "col_2": "b"},
{"col_1": 1, "col_2": "c"},
{"col_1": 0, "col_2": "d"},
]
def snake_case__( self : List[str] ) ->List[str]:
snake_case_ = {'''col_1''': [3, 2, 1, 0], '''col_2''': ['''a''', '''b''', '''c''', '''d''']}
return Dataset.from_dict(_UpperCamelCase )
def snake_case__( self : Tuple ) ->str:
snake_case_ = self._create_example_records()
snake_case_ = Dataset.from_list(_UpperCamelCase )
self.assertListEqual(dset.column_names , ['''col_1''', '''col_2'''] )
for i, r in enumerate(_UpperCamelCase ):
self.assertDictEqual(_UpperCamelCase , example_records[i] )
def snake_case__( self : Optional[int] ) ->Any:
snake_case_ = self._create_example_records()
snake_case_ = Dataset.from_list(_UpperCamelCase )
snake_case_ = Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]} )
self.assertEqual(dset.info , dset_from_dict.info )
def snake_case__( self : Dict ) ->Optional[int]: # checks what happens with missing columns
snake_case_ = [{'''col_1''': 1}, {'''col_2''': '''x'''}]
snake_case_ = Dataset.from_list(_UpperCamelCase )
self.assertDictEqual(dset[0] , {'''col_1''': 1} )
self.assertDictEqual(dset[1] , {'''col_1''': None} ) # NB: first record is used for columns
def snake_case__( self : Dict ) ->str: # checks if the type can be inferred from the second record
snake_case_ = [{'''col_1''': []}, {'''col_1''': [1, 2]}]
snake_case_ = Dataset.from_list(_UpperCamelCase )
self.assertEqual(dset.info.features['''col_1'''] , Sequence(Value('''int64''' ) ) )
def snake_case__( self : Dict ) ->int:
snake_case_ = Dataset.from_list([] )
self.assertEqual(len(_UpperCamelCase ) , 0 )
self.assertListEqual(dset.column_names , [] ) | 39 | from __future__ import annotations
_lowerCAmelCase = [True] * 1_000_001
_lowerCAmelCase = 2
while i * i <= 1_000_000:
if seive[i]:
for j in range(i * i, 1_000_001, i):
_lowerCAmelCase = False
i += 1
def _snake_case ( __snake_case ):
return seive[n]
def _snake_case ( __snake_case ):
return any(digit in '''02468''' for digit in str(__snake_case ) )
def _snake_case ( __snake_case = 1000000 ):
_UpperCamelCase = [2] # result already includes the number 2.
for num in range(3 , limit + 1 , 2 ):
if is_prime(__snake_case ) and not contains_an_even_digit(__snake_case ):
_UpperCamelCase = str(__snake_case )
_UpperCamelCase = [int(str_num[j:] + str_num[:j] ) for j in range(len(__snake_case ) )]
if all(is_prime(__snake_case ) for i in list_nums ):
result.append(__snake_case )
return result
def _snake_case ( ):
return len(find_circular_primes() )
if __name__ == "__main__":
print(f'{len(find_circular_primes()) = }')
| 10 | 0 |
import math
import os
import sys
def UpperCamelCase ( snake_case__ : str ) -> str:
UpperCamelCase : Tuple = ''
try:
with open(snake_case__ , 'rb' ) as binary_file:
UpperCamelCase : Dict = binary_file.read()
for dat in data:
UpperCamelCase : str = F"""{dat:08b}"""
result += curr_byte
return result
except OSError:
print('File not accessible' )
sys.exit()
def UpperCamelCase ( snake_case__ : dict[str, str] , snake_case__ : str , snake_case__ : int , snake_case__ : str ) -> None:
lexicon.pop(snake_case__ )
UpperCamelCase : Tuple = last_match_id
if math.loga(snake_case__ ).is_integer():
for curr_key in lexicon:
UpperCamelCase : List[str] = '0' + lexicon[curr_key]
UpperCamelCase : List[str] = bin(snake_case__ )[2:]
def UpperCamelCase ( snake_case__ : str ) -> str:
UpperCamelCase : int = {'0': '0', '1': '1'}
UpperCamelCase , UpperCamelCase : Union[str, Any] = '', ''
UpperCamelCase : str = len(snake_case__ )
for i in range(len(snake_case__ ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
UpperCamelCase : List[Any] = lexicon[curr_string]
result += last_match_id
add_key_to_lexicon(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
index += 1
UpperCamelCase : Optional[int] = ''
while curr_string != "" and curr_string not in lexicon:
curr_string += "0"
if curr_string != "":
UpperCamelCase : Tuple = lexicon[curr_string]
result += last_match_id
return result
def UpperCamelCase ( snake_case__ : str , snake_case__ : str ) -> str:
UpperCamelCase : Optional[Any] = os.path.getsize(snake_case__ )
UpperCamelCase : str = bin(snake_case__ )[2:]
UpperCamelCase : int = len(snake_case__ )
return "0" * (length_length - 1) + file_length_binary + compressed
def UpperCamelCase ( snake_case__ : str , snake_case__ : str ) -> None:
UpperCamelCase : List[Any] = 8
try:
with open(snake_case__ , 'wb' ) as opened_file:
UpperCamelCase : Union[str, Any] = [
to_write[i : i + byte_length]
for i in range(0 , len(snake_case__ ) , snake_case__ )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append('10000000' )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array:
opened_file.write(int(snake_case__ , 2 ).to_bytes(1 , byteorder='big' ) )
except OSError:
print('File not accessible' )
sys.exit()
def UpperCamelCase ( snake_case__ : str , snake_case__ : str ) -> None:
UpperCamelCase : List[str] = read_file_binary(snake_case__ )
UpperCamelCase : Optional[int] = compress_data(snake_case__ )
UpperCamelCase : str = add_file_length(snake_case__ , snake_case__ )
write_file_binary(snake_case__ , snake_case__ )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 40 | import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCAmelCase = get_tests_dir("fixtures/spiece.model")
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_ ( __lowercase, unittest.TestCase ):
UpperCAmelCase = DebertaVaTokenizer
UpperCAmelCase = DebertaVaTokenizerFast
UpperCAmelCase = True
UpperCAmelCase = True
def UpperCamelCase_ ( self : List[Any] ):
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCamelCase = DebertaVaTokenizer(_A , unk_token='''<unk>''' )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase_ ( self : Dict , _A : Union[str, Any] ):
_UpperCamelCase = '''this is a test'''
_UpperCamelCase = '''this is a test'''
return input_text, output_text
def UpperCamelCase_ ( self : Optional[Any] ):
_UpperCamelCase = '''<pad>'''
_UpperCamelCase = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A ) , _A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A ) , _A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<pad>''' )
self.assertEqual(vocab_keys[1] , '''<unk>''' )
self.assertEqual(vocab_keys[-1] , '''[PAD]''' )
self.assertEqual(len(_A ) , 3_0001 )
def UpperCamelCase_ ( self : List[Any] ):
self.assertEqual(self.get_tokenizer().vocab_size , 3_0000 )
def UpperCamelCase_ ( self : List[str] ):
# fmt: off
_UpperCamelCase = ''' \tHeLLo!how \n Are yoU? '''
_UpperCamelCase = ['''▁hello''', '''!''', '''how''', '''▁are''', '''▁you''', '''?''']
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
@unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.''' )
def UpperCamelCase_ ( self : Dict ):
pass
@unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.''' )
def UpperCamelCase_ ( self : Optional[Any] ):
pass
def UpperCamelCase_ ( self : Dict ):
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : List[Any] ):
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : Dict ):
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : int ):
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : Tuple ):
# fmt: off
_UpperCamelCase = ''' \tHeLLo!how \n Are yoU? '''
_UpperCamelCase = ['''▁''', '''<unk>''', '''e''', '''<unk>''', '''o''', '''!''', '''how''', '''▁''', '''<unk>''', '''re''', '''▁yo''', '''<unk>''', '''?''']
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = self.get_tokenizer()
_UpperCamelCase = self.get_rust_tokenizer()
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.encode(_A , add_special_tokens=_A )
_UpperCamelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = self.get_rust_tokenizer()
_UpperCamelCase = tokenizer.encode(_A )
_UpperCamelCase = rust_tokenizer.encode(_A )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = '''This is a test'''
_UpperCamelCase = [13, 1, 4398, 25, 21, 1289]
_UpperCamelCase = ['''▁''', '''T''', '''his''', '''▁is''', '''▁a''', '''▁test''']
_UpperCamelCase = ['''▁''', '''<unk>''', '''his''', '''▁is''', '''▁a''', '''▁test''']
_UpperCamelCase = DebertaVaTokenizer(_A , keep_accents=_A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , keep_accents=_A )
_UpperCamelCase = tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = [13, 1, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]
_UpperCamelCase = ['''▁''', '''I''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''', ]
_UpperCamelCase = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
# fmt: on
_UpperCamelCase = tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = DebertaVaTokenizer(_A )
_UpperCamelCase = tokenizer.encode('''sequence builders''' )
_UpperCamelCase = tokenizer.encode('''multi-sequence build''' )
_UpperCamelCase = tokenizer.build_inputs_with_special_tokens(_A )
_UpperCamelCase = tokenizer.build_inputs_with_special_tokens(_A , _A )
self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] , _A )
self.assertEqual(
[tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id] , _A , )
@slow
def UpperCamelCase_ ( self : Optional[Any] ):
# fmt: off
_UpperCamelCase = {'''input_ids''': [[1, 3_9867, 36, 1_9390, 486, 27, 3_5052, 8_1436, 18, 6_0685, 1225, 7, 3_5052, 8_1436, 18, 9367, 1_6899, 18, 1_5937, 53, 594, 773, 18, 1_6287, 3_0465, 36, 1_5937, 6, 4_1139, 38, 3_6979, 6_0763, 191, 6, 3_4132, 99, 6, 5_0538, 390, 4_3230, 6, 3_4132, 2779, 2_0850, 14, 699, 1072, 1194, 36, 382, 1_0901, 53, 7, 699, 1072, 2084, 36, 2_0422, 630, 53, 19, 105, 3049, 1896, 1053, 1_6899, 1506, 11, 3_7978, 4243, 7, 1237, 3_1869, 200, 1_6566, 654, 6, 3_5052, 8_1436, 7, 5_5630, 1_3593, 4, 2], [1, 26, 1_5011, 13, 667, 8, 1053, 18, 2_3611, 1237, 7_2356, 1_2820, 34, 10_4134, 1209, 35, 1_3313, 6627, 21, 202, 347, 7, 164, 2399, 11, 46, 4485, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 1232, 2864, 1_5785, 1_4951, 105, 5, 8581, 1250, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_A , model_name='''microsoft/deberta-v2-xlarge''' , revision='''ad6e42c1532ddf3a15c39246b63f5559d558b670''' , )
| 10 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird import BigBirdTokenizer
else:
lowerCAmelCase__ = None
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
lowerCAmelCase__ = {
'''vocab_file''': {
'''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
'''google/bigbird-roberta-large''': (
'''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
),
'''google/bigbird-base-trivia-itc''': (
'''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
),
},
'''tokenizer_file''': {
'''google/bigbird-roberta-base''': (
'''https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'''
),
'''google/bigbird-roberta-large''': (
'''https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'''
),
'''google/bigbird-base-trivia-itc''': (
'''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'''
),
},
}
lowerCAmelCase__ = {
'''google/bigbird-roberta-base''': 4096,
'''google/bigbird-roberta-large''': 4096,
'''google/bigbird-base-trivia-itc''': 4096,
}
lowerCAmelCase__ = '''▁'''
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE : Union[str, Any] = BigBirdTokenizer
SCREAMING_SNAKE_CASE : Optional[Any] = ['input_ids', 'attention_mask']
SCREAMING_SNAKE_CASE : List[int] = []
def __init__( self : Union[str, Any] ,lowercase__ : str=None ,lowercase__ : List[str]=None ,lowercase__ : Dict="<unk>" ,lowercase__ : Dict="<s>" ,lowercase__ : Tuple="</s>" ,lowercase__ : int="<pad>" ,lowercase__ : Optional[Any]="[SEP]" ,lowercase__ : List[str]="[MASK]" ,lowercase__ : Tuple="[CLS]" ,**lowercase__ : List[str] ,):
__lowercase = AddedToken(lowercase__ ,lstrip=lowercase__ ,rstrip=lowercase__ ) if isinstance(lowercase__ ,lowercase__ ) else bos_token
__lowercase = AddedToken(lowercase__ ,lstrip=lowercase__ ,rstrip=lowercase__ ) if isinstance(lowercase__ ,lowercase__ ) else eos_token
__lowercase = AddedToken(lowercase__ ,lstrip=lowercase__ ,rstrip=lowercase__ ) if isinstance(lowercase__ ,lowercase__ ) else unk_token
__lowercase = AddedToken(lowercase__ ,lstrip=lowercase__ ,rstrip=lowercase__ ) if isinstance(lowercase__ ,lowercase__ ) else pad_token
__lowercase = AddedToken(lowercase__ ,lstrip=lowercase__ ,rstrip=lowercase__ ) if isinstance(lowercase__ ,lowercase__ ) else cls_token
__lowercase = AddedToken(lowercase__ ,lstrip=lowercase__ ,rstrip=lowercase__ ) if isinstance(lowercase__ ,lowercase__ ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
__lowercase = AddedToken(lowercase__ ,lstrip=lowercase__ ,rstrip=lowercase__ ) if isinstance(lowercase__ ,lowercase__ ) else mask_token
super().__init__(
lowercase__ ,tokenizer_file=lowercase__ ,bos_token=lowercase__ ,eos_token=lowercase__ ,unk_token=lowercase__ ,sep_token=lowercase__ ,pad_token=lowercase__ ,cls_token=lowercase__ ,mask_token=lowercase__ ,**lowercase__ ,)
__lowercase = vocab_file
__lowercase = False if not self.vocab_file else True
def SCREAMING_SNAKE_CASE ( self : List[Any] ,lowercase__ : List[int] ,lowercase__ : Optional[List[int]] = None ):
__lowercase = [self.sep_token_id]
__lowercase = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE ( self : Tuple ,lowercase__ : List[int] ,lowercase__ : Optional[List[int]] = None ,lowercase__ : bool = False ):
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'''You should not supply a second sequence if the provided sequence of '''
'''ids is already formatted with special tokens for the model.''' )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is None:
return [1] + ([0] * len(lowercase__ )) + [1]
return [1] + ([0] * len(lowercase__ )) + [1] + ([0] * len(lowercase__ )) + [1]
def SCREAMING_SNAKE_CASE ( self : List[Any] ,lowercase__ : List[int] ,lowercase__ : Optional[List[int]] = None ):
__lowercase = [self.sep_token_id]
__lowercase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def SCREAMING_SNAKE_CASE ( self : int ,lowercase__ : str ,lowercase__ : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(lowercase__ ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
__lowercase = os.path.join(
lowercase__ ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase__ ):
copyfile(self.vocab_file ,lowercase__ )
return (out_vocab_file,)
| 41 | import sys
from collections import defaultdict
class lowerCAmelCase_ :
def __init__( self : Optional[int] ):
_UpperCamelCase = []
def UpperCamelCase_ ( self : Any , _A : str ):
return self.node_position[vertex]
def UpperCamelCase_ ( self : Optional[Any] , _A : List[str] , _A : Union[str, Any] ):
_UpperCamelCase = pos
def UpperCamelCase_ ( self : Any , _A : List[str] , _A : int , _A : Optional[Any] , _A : Union[str, Any] ):
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
_UpperCamelCase = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
_UpperCamelCase = 2 * start + 1
else:
_UpperCamelCase = 2 * start + 2
if heap[smallest_child] < heap[start]:
_UpperCamelCase , _UpperCamelCase = heap[smallest_child], positions[smallest_child]
_UpperCamelCase , _UpperCamelCase = (
heap[start],
positions[start],
)
_UpperCamelCase , _UpperCamelCase = temp, tempa
_UpperCamelCase = self.get_position(positions[smallest_child] )
self.set_position(
positions[smallest_child] , self.get_position(positions[start] ) )
self.set_position(positions[start] , _A )
self.top_to_bottom(_A , _A , _A , _A )
def UpperCamelCase_ ( self : List[str] , _A : Tuple , _A : Optional[Any] , _A : int , _A : Optional[int] ):
_UpperCamelCase = position[index]
while index != 0:
_UpperCamelCase = int((index - 2) / 2 ) if index % 2 == 0 else int((index - 1) / 2 )
if val < heap[parent]:
_UpperCamelCase = heap[parent]
_UpperCamelCase = position[parent]
self.set_position(position[parent] , _A )
else:
_UpperCamelCase = val
_UpperCamelCase = temp
self.set_position(_A , _A )
break
_UpperCamelCase = parent
else:
_UpperCamelCase = val
_UpperCamelCase = temp
self.set_position(_A , 0 )
def UpperCamelCase_ ( self : int , _A : Tuple , _A : int ):
_UpperCamelCase = len(_A ) // 2 - 1
for i in range(_A , -1 , -1 ):
self.top_to_bottom(_A , _A , len(_A ) , _A )
def UpperCamelCase_ ( self : Any , _A : int , _A : List[str] ):
_UpperCamelCase = positions[0]
_UpperCamelCase = sys.maxsize
self.top_to_bottom(_A , 0 , len(_A ) , _A )
return temp
def _snake_case ( __snake_case ):
_UpperCamelCase = Heap()
_UpperCamelCase = [0] * len(__snake_case )
_UpperCamelCase = [-1] * len(__snake_case ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
_UpperCamelCase = [] # Heap of Distance of vertices from their neighboring vertex
_UpperCamelCase = []
for vertex in range(len(__snake_case ) ):
distance_tv.append(sys.maxsize )
positions.append(__snake_case )
heap.node_position.append(__snake_case )
_UpperCamelCase = []
_UpperCamelCase = 1
_UpperCamelCase = sys.maxsize
for neighbor, distance in adjacency_list[0]:
_UpperCamelCase = 0
_UpperCamelCase = distance
heap.heapify(__snake_case , __snake_case )
for _ in range(1 , len(__snake_case ) ):
_UpperCamelCase = heap.delete_minimum(__snake_case , __snake_case )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
_UpperCamelCase = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(__snake_case )]
):
_UpperCamelCase = distance
heap.bottom_to_top(
__snake_case , heap.get_position(__snake_case ) , __snake_case , __snake_case )
_UpperCamelCase = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
_lowerCAmelCase = int(input("Enter number of edges: ").strip())
_lowerCAmelCase = defaultdict(list)
for _ in range(edges_number):
_lowerCAmelCase = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 10 | 0 |
'''simple docstring'''
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> int:
while second != 0:
lowerCamelCase_ = first & second
first ^= second
lowerCamelCase_ = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.testmod()
A_ = int(input("Enter the first number: ").strip())
A_ = int(input("Enter the second number: ").strip())
print(f'''{add(first, second) = }''')
| 42 | import logging
import os
from .state import PartialState
class lowerCAmelCase_ ( logging.LoggerAdapter ):
@staticmethod
def UpperCamelCase_ ( _A : Any ):
_UpperCamelCase = PartialState()
return not main_process_only or (main_process_only and state.is_main_process)
def UpperCamelCase_ ( self : Union[str, Any] , _A : Optional[Any] , _A : str , *_A : int , **_A : List[Any] ):
if PartialState._shared_state == {}:
raise RuntimeError(
'''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.''' )
_UpperCamelCase = kwargs.pop('''main_process_only''' , _A )
_UpperCamelCase = kwargs.pop('''in_order''' , _A )
if self.isEnabledFor(_A ):
if self._should_log(_A ):
_UpperCamelCase , _UpperCamelCase = self.process(_A , _A )
self.logger.log(_A , _A , *_A , **_A )
elif in_order:
_UpperCamelCase = PartialState()
for i in range(state.num_processes ):
if i == state.process_index:
_UpperCamelCase , _UpperCamelCase = self.process(_A , _A )
self.logger.log(_A , _A , *_A , **_A )
state.wait_for_everyone()
def _snake_case ( __snake_case , __snake_case = None ):
if log_level is None:
_UpperCamelCase = os.environ.get('''ACCELERATE_LOG_LEVEL''' , __snake_case )
_UpperCamelCase = logging.getLogger(__snake_case )
if log_level is not None:
logger.setLevel(log_level.upper() )
logger.root.setLevel(log_level.upper() )
return MultiProcessAdapter(__snake_case , {} )
| 10 | 0 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class _a ( UpperCamelCase__ ):
def __init__( self: Tuple , UpperCamelCase_: UNetaDModel , UpperCamelCase_: UNetaDModel , UpperCamelCase_: DDPMScheduler , UpperCamelCase_: str , ) -> List[Any]:
"""simple docstring"""
super().__init__()
lowercase__ = value_function
lowercase__ = unet
lowercase__ = scheduler
lowercase__ = env
lowercase__ = env.get_dataset()
lowercase__ = {}
for key in self.data.keys():
try:
lowercase__ = self.data[key].mean()
except: # noqa: E722
pass
lowercase__ = {}
for key in self.data.keys():
try:
lowercase__ = self.data[key].std()
except: # noqa: E722
pass
lowercase__ = env.observation_space.shape[0]
lowercase__ = env.action_space.shape[0]
def lowerCamelCase_ ( self: Dict , UpperCamelCase_: List[str] , UpperCamelCase_: Tuple ) -> List[Any]:
"""simple docstring"""
return (x_in - self.means[key]) / self.stds[key]
def lowerCamelCase_ ( self: List[Any] , UpperCamelCase_: Optional[Any] , UpperCamelCase_: List[str] ) -> Dict:
"""simple docstring"""
return x_in * self.stds[key] + self.means[key]
def lowerCamelCase_ ( self: int , UpperCamelCase_: Union[str, Any] ) -> Any:
"""simple docstring"""
if type(UpperCamelCase_ ) is dict:
return {k: self.to_torch(UpperCamelCase_ ) for k, v in x_in.items()}
elif torch.is_tensor(UpperCamelCase_ ):
return x_in.to(self.unet.device )
return torch.tensor(UpperCamelCase_ , device=self.unet.device )
def lowerCamelCase_ ( self: Optional[int] , UpperCamelCase_: Optional[int] , UpperCamelCase_: Optional[Any] , UpperCamelCase_: Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
for key, val in cond.items():
lowercase__ = val.clone()
return x_in
def lowerCamelCase_ ( self: Tuple , UpperCamelCase_: Tuple , UpperCamelCase_: Union[str, Any] , UpperCamelCase_: Tuple , UpperCamelCase_: str ) -> str:
"""simple docstring"""
lowercase__ = x.shape[0]
lowercase__ = None
for i in tqdm.tqdm(self.scheduler.timesteps ):
# create batch of timesteps to pass into model
lowercase__ = torch.full((batch_size,) , UpperCamelCase_ , device=self.unet.device , dtype=torch.long )
for _ in range(UpperCamelCase_ ):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
lowercase__ = self.value_function(x.permute(0 , 2 , 1 ) , UpperCamelCase_ ).sample
lowercase__ = torch.autograd.grad([y.sum()] , [x] )[0]
lowercase__ = self.scheduler._get_variance(UpperCamelCase_ )
lowercase__ = torch.exp(0.5 * posterior_variance )
lowercase__ = model_std * grad
lowercase__ = 0
lowercase__ = x.detach()
lowercase__ = x + scale * grad
lowercase__ = self.reset_xa(UpperCamelCase_ , UpperCamelCase_ , self.action_dim )
lowercase__ = self.unet(x.permute(0 , 2 , 1 ) , UpperCamelCase_ ).sample.permute(0 , 2 , 1 )
# TODO: verify deprecation of this kwarg
lowercase__ = self.scheduler.step(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , predict_epsilon=UpperCamelCase_ )['''prev_sample''']
# apply conditions to the trajectory (set the initial state)
lowercase__ = self.reset_xa(UpperCamelCase_ , UpperCamelCase_ , self.action_dim )
lowercase__ = self.to_torch(UpperCamelCase_ )
return x, y
def __call__( self: Any , UpperCamelCase_: List[str] , UpperCamelCase_: Optional[int]=64 , UpperCamelCase_: Any=32 , UpperCamelCase_: Any=2 , UpperCamelCase_: Any=0.1 ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.normalize(UpperCamelCase_ , '''observations''' )
lowercase__ = obs[None].repeat(UpperCamelCase_ , axis=0 )
lowercase__ = {0: self.to_torch(UpperCamelCase_ )}
lowercase__ = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
lowercase__ = randn_tensor(UpperCamelCase_ , device=self.unet.device )
lowercase__ = self.reset_xa(UpperCamelCase_ , UpperCamelCase_ , self.action_dim )
lowercase__ = self.to_torch(UpperCamelCase_ )
# run the diffusion process
lowercase__ , lowercase__ = self.run_diffusion(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
# sort output trajectories by value
lowercase__ = y.argsort(0 , descending=UpperCamelCase_ ).squeeze()
lowercase__ = x[sorted_idx]
lowercase__ = sorted_values[:, :, : self.action_dim]
lowercase__ = actions.detach().cpu().numpy()
lowercase__ = self.de_normalize(UpperCamelCase_ , key='''actions''' )
# select the action with the highest value
if y is not None:
lowercase__ = 0
else:
# if we didn't run value guiding, select a random action
lowercase__ = np.random.randint(0 , UpperCamelCase_ )
lowercase__ = denorm_actions[selected_index, 0]
return denorm_actions
| 43 | import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCAmelCase = "▁"
_lowerCAmelCase = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
class lowerCAmelCase_ ( __lowercase, unittest.TestCase ):
UpperCAmelCase = BertGenerationTokenizer
UpperCAmelCase = False
UpperCAmelCase = True
def UpperCamelCase_ ( self : List[str] ):
super().setUp()
_UpperCamelCase = BertGenerationTokenizer(_A , keep_accents=_A )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = '''<s>'''
_UpperCamelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A ) , _A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A ) , _A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<unk>''' )
self.assertEqual(vocab_keys[1] , '''<s>''' )
self.assertEqual(vocab_keys[-1] , '''<pad>''' )
self.assertEqual(len(_A ) , 1002 )
def UpperCamelCase_ ( self : Dict ):
self.assertEqual(self.get_tokenizer().vocab_size , 1000 )
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = BertGenerationTokenizer(_A , keep_accents=_A )
_UpperCamelCase = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(_A , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_A ) , [285, 46, 10, 170, 382] , )
_UpperCamelCase = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
_A , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
_UpperCamelCase = tokenizer.convert_tokens_to_ids(_A )
self.assertListEqual(
_A , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(
_A , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
@cached_property
def UpperCamelCase_ ( self : Union[str, Any] ):
return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''' )
@slow
def UpperCamelCase_ ( self : Optional[Any] ):
_UpperCamelCase = '''Hello World!'''
_UpperCamelCase = [1_8536, 2260, 101]
self.assertListEqual(_A , self.big_tokenizer.encode(_A ) )
@slow
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
_UpperCamelCase = [
871,
419,
358,
946,
991,
2521,
452,
358,
1357,
387,
7751,
3536,
112,
985,
456,
126,
865,
938,
5400,
5734,
458,
1368,
467,
786,
2462,
5246,
1159,
633,
865,
4519,
457,
582,
852,
2557,
427,
916,
508,
405,
3_4324,
497,
391,
408,
1_1342,
1244,
385,
100,
938,
985,
456,
574,
362,
1_2597,
3200,
3129,
1172,
]
self.assertListEqual(_A , self.big_tokenizer.encode(_A ) )
@require_torch
@slow
def UpperCamelCase_ ( self : Dict ):
import torch
from transformers import BertGenerationConfig, BertGenerationEncoder
# Build sequence
_UpperCamelCase = list(self.big_tokenizer.get_vocab().keys() )[:10]
_UpperCamelCase = ''' '''.join(_A )
_UpperCamelCase = self.big_tokenizer.encode_plus(_A , return_tensors='''pt''' , return_token_type_ids=_A )
_UpperCamelCase = self.big_tokenizer.batch_encode_plus(
[sequence + ''' ''' + sequence] , return_tensors='''pt''' , return_token_type_ids=_A )
_UpperCamelCase = BertGenerationConfig()
_UpperCamelCase = BertGenerationEncoder(_A )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**_A )
model(**_A )
@slow
def UpperCamelCase_ ( self : Dict ):
# fmt: off
_UpperCamelCase = {'''input_ids''': [[3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114], [448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_A , model_name='''google/bert_for_seq_generation_L-24_bbc_encoder''' , revision='''c817d1fd1be2ffa69431227a1fe320544943d4db''' , )
| 10 | 0 |
'''simple docstring'''
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
UpperCAmelCase_ : List[Any] = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}
class UpperCAmelCase__ ( nn.Module ):
def __init__( self : List[Any],__A : List[str] ):
super().__init__()
_lowerCamelCase : Optional[Any] = torchvision.models.resnetaaa(pretrained=__A )
_lowerCamelCase : Optional[Any] = list(model.children() )[:-2]
_lowerCamelCase : Optional[Any] = nn.Sequential(*__A )
_lowerCamelCase : Optional[int] = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )
def lowerCamelCase_ ( self : int,__A : List[str] ):
# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
_lowerCamelCase : Optional[Any] = self.pool(self.model(__A ) )
_lowerCamelCase : Optional[Any] = torch.flatten(__A,start_dim=2 )
_lowerCamelCase : Optional[Any] = out.transpose(1,2 ).contiguous()
return out # BxNx2048
class UpperCAmelCase__ ( A ):
def __init__( self : List[str],__A : Union[str, Any],__A : Tuple,__A : str,__A : str,__A : Optional[int] ):
_lowerCamelCase : List[Any] = [json.loads(__A ) for l in open(__A )]
_lowerCamelCase : Dict = os.path.dirname(__A )
_lowerCamelCase : Union[str, Any] = tokenizer
_lowerCamelCase : str = labels
_lowerCamelCase : int = len(__A )
_lowerCamelCase : str = max_seq_length
_lowerCamelCase : Tuple = transforms
def __len__( self : Union[str, Any] ):
return len(self.data )
def __getitem__( self : List[str],__A : Any ):
_lowerCamelCase : List[Any] = torch.LongTensor(self.tokenizer.encode(self.data[index]["text"],add_special_tokens=__A ) )
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : List[Any] = sentence[0], sentence[1:-1], sentence[-1]
_lowerCamelCase : Tuple = sentence[: self.max_seq_length]
_lowerCamelCase : Optional[int] = torch.zeros(self.n_classes )
_lowerCamelCase : List[Any] = 1
_lowerCamelCase : str = Image.open(os.path.join(self.data_dir,self.data[index]["img"] ) ).convert("RGB" )
_lowerCamelCase : Optional[int] = self.transforms(__A )
return {
"image_start_token": start_token,
"image_end_token": end_token,
"sentence": sentence,
"image": image,
"label": label,
}
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : str = Counter()
for row in self.data:
label_freqs.update(row["label"] )
return label_freqs
def A_ ( _lowerCAmelCase : Any ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = [len(row["sentence"] ) for row in batch]
_lowerCamelCase , _lowerCamelCase : List[Any] = len(_lowerCAmelCase ), max(_lowerCAmelCase )
_lowerCamelCase : Tuple = torch.zeros(_lowerCAmelCase , _lowerCAmelCase , dtype=torch.long )
_lowerCamelCase : Tuple = torch.zeros(_lowerCAmelCase , _lowerCAmelCase , dtype=torch.long )
for i_batch, (input_row, length) in enumerate(zip(_lowerCAmelCase , _lowerCAmelCase ) ):
_lowerCamelCase : Optional[int] = input_row["sentence"]
_lowerCamelCase : Tuple = 1
_lowerCamelCase : Any = torch.stack([row["image"] for row in batch] )
_lowerCamelCase : Union[str, Any] = torch.stack([row["label"] for row in batch] )
_lowerCamelCase : int = torch.stack([row["image_start_token"] for row in batch] )
_lowerCamelCase : Optional[Any] = torch.stack([row["image_end_token"] for row in batch] )
return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def A_ ( ):
"""simple docstring"""
return [
"Crime",
"Drama",
"Thriller",
"Action",
"Comedy",
"Romance",
"Documentary",
"Short",
"Mystery",
"History",
"Family",
"Adventure",
"Fantasy",
"Sci-Fi",
"Western",
"Horror",
"Sport",
"War",
"Music",
"Musical",
"Animation",
"Biography",
"Film-Noir",
]
def A_ ( ):
"""simple docstring"""
return transforms.Compose(
[
transforms.Resize(256 ),
transforms.CenterCrop(224 ),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7] , std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9] , ),
] ) | 44 | import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class lowerCAmelCase_ ( __lowercase, __lowercase, __lowercase, unittest.TestCase ):
UpperCAmelCase = StableUnCLIPPipeline
UpperCAmelCase = TEXT_TO_IMAGE_PARAMS
UpperCAmelCase = TEXT_TO_IMAGE_BATCH_PARAMS
UpperCAmelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
UpperCAmelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
UpperCAmelCase = False
def UpperCamelCase_ ( self : Optional[int] ):
_UpperCamelCase = 32
_UpperCamelCase = embedder_hidden_size
# prior components
torch.manual_seed(0 )
_UpperCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
torch.manual_seed(0 )
_UpperCamelCase = CLIPTextModelWithProjection(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=_A , projection_dim=_A , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , ) )
torch.manual_seed(0 )
_UpperCamelCase = PriorTransformer(
num_attention_heads=2 , attention_head_dim=12 , embedding_dim=_A , num_layers=1 , )
torch.manual_seed(0 )
_UpperCamelCase = DDPMScheduler(
variance_type='''fixed_small_log''' , prediction_type='''sample''' , num_train_timesteps=1000 , clip_sample=_A , clip_sample_range=5.0 , beta_schedule='''squaredcos_cap_v2''' , )
# regular denoising components
torch.manual_seed(0 )
_UpperCamelCase = StableUnCLIPImageNormalizer(embedding_dim=_A )
_UpperCamelCase = DDPMScheduler(beta_schedule='''squaredcos_cap_v2''' )
torch.manual_seed(0 )
_UpperCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
torch.manual_seed(0 )
_UpperCamelCase = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=_A , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , ) )
torch.manual_seed(0 )
_UpperCamelCase = UNetaDConditionModel(
sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''') , up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''') , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type='''projection''' , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=_A , layers_per_block=1 , upcast_attention=_A , use_linear_projection=_A , )
torch.manual_seed(0 )
_UpperCamelCase = DDIMScheduler(
beta_schedule='''scaled_linear''' , beta_start=0.0_0085 , beta_end=0.012 , prediction_type='''v_prediction''' , set_alpha_to_one=_A , steps_offset=1 , )
torch.manual_seed(0 )
_UpperCamelCase = AutoencoderKL()
_UpperCamelCase = {
# prior components
'''prior_tokenizer''': prior_tokenizer,
'''prior_text_encoder''': prior_text_encoder,
'''prior''': prior,
'''prior_scheduler''': prior_scheduler,
# image noising components
'''image_normalizer''': image_normalizer,
'''image_noising_scheduler''': image_noising_scheduler,
# regular denoising components
'''tokenizer''': tokenizer,
'''text_encoder''': text_encoder,
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
}
return components
def UpperCamelCase_ ( self : Dict , _A : Tuple , _A : Dict=0 ):
if str(_A ).startswith('''mps''' ):
_UpperCamelCase = torch.manual_seed(_A )
else:
_UpperCamelCase = torch.Generator(device=_A ).manual_seed(_A )
_UpperCamelCase = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''prior_num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = torch_device == '''cpu'''
self._test_attention_slicing_forward_pass(test_max_difference=_A )
def UpperCamelCase_ ( self : List[Any] ):
_UpperCamelCase = torch_device in ['''cpu''', '''mps''']
self._test_inference_batch_single_identical(test_max_difference=_A )
@slow
@require_torch_gpu
class lowerCAmelCase_ ( unittest.TestCase ):
def UpperCamelCase_ ( self : Optional[Any] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy''' )
_UpperCamelCase = StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l''' , torch_dtype=torch.floataa )
pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
_UpperCamelCase = torch.Generator(device='''cpu''' ).manual_seed(0 )
_UpperCamelCase = pipe('''anime turle''' , generator=_A , output_type='''np''' )
_UpperCamelCase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_A , _A )
def UpperCamelCase_ ( self : Optional[Any] ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
_UpperCamelCase = StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l''' , torch_dtype=torch.floataa )
_UpperCamelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
_UpperCamelCase = pipe(
'''anime turtle''' , prior_num_inference_steps=2 , num_inference_steps=2 , output_type='''np''' , )
_UpperCamelCase = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 10 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTokenizer
else:
from ...utils.dummy_sentencepiece_objects import TaTokenizer
UpperCamelCase = TaTokenizer
if is_tokenizers_available():
from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
from ...utils.dummy_tokenizers_objects import TaTokenizerFast
UpperCamelCase = TaTokenizerFast
UpperCamelCase = {"configuration_mt5": ["MT5Config", "MT5OnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = [
"MT5EncoderModel",
"MT5ForConditionalGeneration",
"MT5ForQuestionAnswering",
"MT5Model",
"MT5PreTrainedModel",
"MT5Stack",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = ["TFMT5EncoderModel", "TFMT5ForConditionalGeneration", "TFMT5Model"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = ["FlaxMT5EncoderModel", "FlaxMT5ForConditionalGeneration", "FlaxMT5Model"]
if TYPE_CHECKING:
from .configuration_mta import MTaConfig, MTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mta import (
MTaEncoderModel,
MTaForConditionalGeneration,
MTaForQuestionAnswering,
MTaModel,
MTaPreTrainedModel,
MTaStack,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel
else:
import sys
UpperCamelCase = _LazyModule(
__name__,
globals()["__file__"],
_import_structure,
extra_objects={"MT5Tokenizer": MTaTokenizer, "MT5TokenizerFast": MTaTokenizerFast},
module_spec=__spec__,
) | 45 | from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _snake_case ( __snake_case , __snake_case ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(__snake_case , __snake_case ) ) )
def _snake_case ( __snake_case , __snake_case ):
if dataset.ndim != value_array.ndim:
_UpperCamelCase = (
'''Wrong input data\'s dimensions... '''
f"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
)
raise ValueError(__snake_case )
try:
if dataset.shape[1] != value_array.shape[1]:
_UpperCamelCase = (
'''Wrong input data\'s shape... '''
f"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
)
raise ValueError(__snake_case )
except IndexError:
if dataset.ndim != value_array.ndim:
raise TypeError('''Wrong shape''' )
if dataset.dtype != value_array.dtype:
_UpperCamelCase = (
'''Input data have different datatype... '''
f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
)
raise TypeError(__snake_case )
_UpperCamelCase = []
for value in value_array:
_UpperCamelCase = euclidean(__snake_case , dataset[0] )
_UpperCamelCase = dataset[0].tolist()
for dataset_value in dataset[1:]:
_UpperCamelCase = euclidean(__snake_case , __snake_case )
if dist > temp_dist:
_UpperCamelCase = temp_dist
_UpperCamelCase = dataset_value.tolist()
answer.append([vector, dist] )
return answer
def _snake_case ( __snake_case , __snake_case ):
return np.dot(__snake_case , __snake_case ) / (norm(__snake_case ) * norm(__snake_case ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 10 | 0 |
"""simple docstring"""
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class A_ :
def __init__( self: Optional[int] ,__lowerCAmelCase: List[str] ,__lowerCAmelCase: int=2 ,__lowerCAmelCase: Dict=8 ,__lowerCAmelCase: List[Any]=True ,__lowerCAmelCase: List[str]=True ,__lowerCAmelCase: List[Any]=True ,__lowerCAmelCase: Dict=True ,__lowerCAmelCase: Any=99 ,__lowerCAmelCase: int=16 ,__lowerCAmelCase: List[Any]=5 ,__lowerCAmelCase: List[str]=2 ,__lowerCAmelCase: str=36 ,__lowerCAmelCase: List[str]="gelu" ,__lowerCAmelCase: Any=0.0 ,__lowerCAmelCase: List[str]=0.0 ,__lowerCAmelCase: Any=512 ,__lowerCAmelCase: Union[str, Any]=16 ,__lowerCAmelCase: str=2 ,__lowerCAmelCase: str=0.02 ,__lowerCAmelCase: Dict=3 ,__lowerCAmelCase: List[str]=4 ,__lowerCAmelCase: Tuple=None ,):
'''simple docstring'''
_lowerCamelCase : Tuple = parent
_lowerCamelCase : Union[str, Any] = batch_size
_lowerCamelCase : Optional[Any] = seq_length
_lowerCamelCase : int = is_training
_lowerCamelCase : List[Any] = use_input_mask
_lowerCamelCase : Dict = use_token_type_ids
_lowerCamelCase : Tuple = use_labels
_lowerCamelCase : List[str] = vocab_size
_lowerCamelCase : List[str] = hidden_size
_lowerCamelCase : List[Any] = num_hidden_layers
_lowerCamelCase : List[str] = num_attention_heads
_lowerCamelCase : Dict = intermediate_size
_lowerCamelCase : List[str] = hidden_act
_lowerCamelCase : List[Any] = hidden_dropout_prob
_lowerCamelCase : Optional[int] = attention_probs_dropout_prob
_lowerCamelCase : List[str] = max_position_embeddings
_lowerCamelCase : Optional[Any] = type_vocab_size
_lowerCamelCase : Dict = type_sequence_label_size
_lowerCamelCase : Dict = initializer_range
_lowerCamelCase : List[Any] = num_labels
_lowerCamelCase : Any = num_choices
_lowerCamelCase : List[Any] = scope
def _lowercase ( self: List[Any] ):
'''simple docstring'''
_lowerCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
_lowerCamelCase : str = None
if self.use_input_mask:
_lowerCamelCase : Tuple = random_attention_mask([self.batch_size, self.seq_length] )
_lowerCamelCase : Optional[int] = None
if self.use_token_type_ids:
_lowerCamelCase : Dict = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
_lowerCamelCase : Dict = None
_lowerCamelCase : Union[str, Any] = None
_lowerCamelCase : int = None
if self.use_labels:
_lowerCamelCase : Optional[int] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
_lowerCamelCase : Any = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
_lowerCamelCase : Dict = ids_tensor([self.batch_size] ,self.num_choices )
_lowerCamelCase : List[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowercase ( self: Union[str, Any] ):
'''simple docstring'''
return MraConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,type_vocab_size=self.type_vocab_size ,is_decoder=__lowerCAmelCase ,initializer_range=self.initializer_range ,)
def _lowercase ( self: int ):
'''simple docstring'''
_lowerCamelCase : str = self.get_config()
_lowerCamelCase : Dict = 300
return config
def _lowercase ( self: Union[str, Any] ):
'''simple docstring'''
(
(
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
),
) : int = self.prepare_config_and_inputs()
_lowerCamelCase : Tuple = True
_lowerCamelCase : int = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
_lowerCamelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] ,vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def _lowercase ( self: List[Any] ,__lowerCAmelCase: Tuple ,__lowerCAmelCase: Optional[int] ,__lowerCAmelCase: str ,__lowerCAmelCase: Dict ,__lowerCAmelCase: int ,__lowerCAmelCase: Optional[Any] ,__lowerCAmelCase: Tuple ):
'''simple docstring'''
_lowerCamelCase : Optional[Any] = MraModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_lowerCamelCase : Optional[int] = model(__lowerCAmelCase ,attention_mask=__lowerCAmelCase ,token_type_ids=__lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = model(__lowerCAmelCase ,token_type_ids=__lowerCAmelCase )
_lowerCamelCase : List[Any] = model(__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _lowercase ( self: Union[str, Any] ,__lowerCAmelCase: Any ,__lowerCAmelCase: Union[str, Any] ,__lowerCAmelCase: Tuple ,__lowerCAmelCase: Tuple ,__lowerCAmelCase: int ,__lowerCAmelCase: Any ,__lowerCAmelCase: Optional[int] ,__lowerCAmelCase: Tuple ,__lowerCAmelCase: List[str] ,):
'''simple docstring'''
_lowerCamelCase : List[Any] = True
_lowerCamelCase : int = MraModel(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_lowerCamelCase : int = model(
__lowerCAmelCase ,attention_mask=__lowerCAmelCase ,token_type_ids=__lowerCAmelCase ,encoder_hidden_states=__lowerCAmelCase ,encoder_attention_mask=__lowerCAmelCase ,)
_lowerCamelCase : str = model(
__lowerCAmelCase ,attention_mask=__lowerCAmelCase ,token_type_ids=__lowerCAmelCase ,encoder_hidden_states=__lowerCAmelCase ,)
_lowerCamelCase : Optional[int] = model(__lowerCAmelCase ,attention_mask=__lowerCAmelCase ,token_type_ids=__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _lowercase ( self: int ,__lowerCAmelCase: Any ,__lowerCAmelCase: Union[str, Any] ,__lowerCAmelCase: Union[str, Any] ,__lowerCAmelCase: Optional[Any] ,__lowerCAmelCase: Tuple ,__lowerCAmelCase: int ,__lowerCAmelCase: List[str] ):
'''simple docstring'''
_lowerCamelCase : Tuple = MraForMaskedLM(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_lowerCamelCase : Any = model(__lowerCAmelCase ,attention_mask=__lowerCAmelCase ,token_type_ids=__lowerCAmelCase ,labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _lowercase ( self: Union[str, Any] ,__lowerCAmelCase: Dict ,__lowerCAmelCase: int ,__lowerCAmelCase: List[str] ,__lowerCAmelCase: List[Any] ,__lowerCAmelCase: Optional[int] ,__lowerCAmelCase: List[Any] ,__lowerCAmelCase: Optional[int] ):
'''simple docstring'''
_lowerCamelCase : Dict = MraForQuestionAnswering(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_lowerCamelCase : int = model(
__lowerCAmelCase ,attention_mask=__lowerCAmelCase ,token_type_ids=__lowerCAmelCase ,start_positions=__lowerCAmelCase ,end_positions=__lowerCAmelCase ,)
self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) )
def _lowercase ( self: Tuple ,__lowerCAmelCase: Optional[int] ,__lowerCAmelCase: str ,__lowerCAmelCase: Union[str, Any] ,__lowerCAmelCase: Tuple ,__lowerCAmelCase: Optional[Any] ,__lowerCAmelCase: Union[str, Any] ,__lowerCAmelCase: Optional[int] ):
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = self.num_labels
_lowerCamelCase : Any = MraForSequenceClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_lowerCamelCase : str = model(__lowerCAmelCase ,attention_mask=__lowerCAmelCase ,token_type_ids=__lowerCAmelCase ,labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def _lowercase ( self: str ,__lowerCAmelCase: Dict ,__lowerCAmelCase: Dict ,__lowerCAmelCase: int ,__lowerCAmelCase: List[Any] ,__lowerCAmelCase: Optional[int] ,__lowerCAmelCase: List[Any] ,__lowerCAmelCase: str ):
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = self.num_labels
_lowerCamelCase : int = MraForTokenClassification(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_lowerCamelCase : str = model(__lowerCAmelCase ,attention_mask=__lowerCAmelCase ,token_type_ids=__lowerCAmelCase ,labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def _lowercase ( self: Optional[int] ,__lowerCAmelCase: Optional[Any] ,__lowerCAmelCase: Any ,__lowerCAmelCase: Tuple ,__lowerCAmelCase: Tuple ,__lowerCAmelCase: Union[str, Any] ,__lowerCAmelCase: Union[str, Any] ,__lowerCAmelCase: Tuple ):
'''simple docstring'''
_lowerCamelCase : Optional[Any] = self.num_choices
_lowerCamelCase : Optional[Any] = MraForMultipleChoice(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_lowerCamelCase : Optional[Any] = input_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
_lowerCamelCase : List[str] = token_type_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
_lowerCamelCase : List[str] = input_mask.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
_lowerCamelCase : List[Any] = model(
__lowerCAmelCase ,attention_mask=__lowerCAmelCase ,token_type_ids=__lowerCAmelCase ,labels=__lowerCAmelCase ,)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) )
def _lowercase ( self: Optional[Any] ):
'''simple docstring'''
_lowerCamelCase : Optional[Any] = self.prepare_config_and_inputs()
(
(
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
),
) : Any = config_and_inputs
_lowerCamelCase : Union[str, Any] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class A_ ( _a , unittest.TestCase ):
lowerCAmelCase__ = (
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = ()
def _lowercase ( self: Dict ):
'''simple docstring'''
_lowerCamelCase : str = MraModelTester(self )
_lowerCamelCase : List[str] = ConfigTester(self ,config_class=__lowerCAmelCase ,hidden_size=37 )
def _lowercase ( self: Union[str, Any] ):
'''simple docstring'''
self.config_tester.run_common_tests()
def _lowercase ( self: Dict ):
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def _lowercase ( self: Union[str, Any] ):
'''simple docstring'''
_lowerCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_lowerCamelCase : Dict = type
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def _lowercase ( self: Union[str, Any] ):
'''simple docstring'''
_lowerCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase )
def _lowercase ( self: int ):
'''simple docstring'''
_lowerCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__lowerCAmelCase )
def _lowercase ( self: Tuple ):
'''simple docstring'''
_lowerCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase )
def _lowercase ( self: Dict ):
'''simple docstring'''
_lowerCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase )
def _lowercase ( self: List[Any] ):
'''simple docstring'''
_lowerCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase )
@slow
def _lowercase ( self: Tuple ):
'''simple docstring'''
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Dict = MraModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
@unittest.skip(reason="MRA does not output attentions" )
def _lowercase ( self: str ):
'''simple docstring'''
return
@require_torch
class A_ ( unittest.TestCase ):
@slow
def _lowercase ( self: Any ):
'''simple docstring'''
_lowerCamelCase : Dict = MraModel.from_pretrained("uw-madison/mra-base-512-4" )
_lowerCamelCase : List[Any] = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
_lowerCamelCase : List[str] = model(__lowerCAmelCase )[0]
_lowerCamelCase : str = torch.Size((1, 256, 768) )
self.assertEqual(output.shape ,__lowerCAmelCase )
_lowerCamelCase : List[Any] = torch.tensor(
[[[-0.01_40, 0.08_30, -0.03_81], [0.15_46, 0.14_02, 0.02_20], [0.11_62, 0.08_51, 0.01_65]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] ,__lowerCAmelCase ,atol=1e-4 ) )
@slow
def _lowercase ( self: Optional[int] ):
'''simple docstring'''
_lowerCamelCase : List[Any] = MraForMaskedLM.from_pretrained("uw-madison/mra-base-512-4" )
_lowerCamelCase : List[str] = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
_lowerCamelCase : Dict = model(__lowerCAmelCase )[0]
_lowerCamelCase : Optional[Any] = 50_265
_lowerCamelCase : Dict = torch.Size((1, 256, vocab_size) )
self.assertEqual(output.shape ,__lowerCAmelCase )
_lowerCamelCase : Optional[int] = torch.tensor(
[[[9.25_95, -3.60_38, 11.88_19], [9.38_69, -3.26_93, 11.09_56], [11.85_24, -3.49_38, 13.12_10]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] ,__lowerCAmelCase ,atol=1e-4 ) )
@slow
def _lowercase ( self: Dict ):
'''simple docstring'''
_lowerCamelCase : Any = MraForMaskedLM.from_pretrained("uw-madison/mra-base-4096-8-d3" )
_lowerCamelCase : str = torch.arange(4_096 ).unsqueeze(0 )
with torch.no_grad():
_lowerCamelCase : Any = model(__lowerCAmelCase )[0]
_lowerCamelCase : Any = 50_265
_lowerCamelCase : Dict = torch.Size((1, 4_096, vocab_size) )
self.assertEqual(output.shape ,__lowerCAmelCase )
_lowerCamelCase : int = torch.tensor(
[[[5.47_89, -2.35_64, 7.50_64], [7.90_67, -1.33_69, 9.96_68], [9.07_12, -1.81_06, 7.03_80]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] ,__lowerCAmelCase ,atol=1e-4 ) ) | 46 | import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class lowerCAmelCase_ ( __lowercase, unittest.TestCase ):
UpperCAmelCase = ShapEPipeline
UpperCAmelCase = ["prompt"]
UpperCAmelCase = ["prompt"]
UpperCAmelCase = [
"num_images_per_prompt",
"num_inference_steps",
"generator",
"latents",
"guidance_scale",
"frame_size",
"output_type",
"return_dict",
]
UpperCAmelCase = False
@property
def UpperCamelCase_ ( self : Union[str, Any] ):
return 32
@property
def UpperCamelCase_ ( self : int ):
return 32
@property
def UpperCamelCase_ ( self : List[str] ):
return self.time_input_dim * 4
@property
def UpperCamelCase_ ( self : Optional[Any] ):
return 8
@property
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
return tokenizer
@property
def UpperCamelCase_ ( self : List[Any] ):
torch.manual_seed(0 )
_UpperCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(_A )
@property
def UpperCamelCase_ ( self : int ):
torch.manual_seed(0 )
_UpperCamelCase = {
'''num_attention_heads''': 2,
'''attention_head_dim''': 16,
'''embedding_dim''': self.time_input_dim,
'''num_embeddings''': 32,
'''embedding_proj_dim''': self.text_embedder_hidden_size,
'''time_embed_dim''': self.time_embed_dim,
'''num_layers''': 1,
'''clip_embed_dim''': self.time_input_dim * 2,
'''additional_embeddings''': 0,
'''time_embed_act_fn''': '''gelu''',
'''norm_in_type''': '''layer''',
'''encoder_hid_proj_type''': None,
'''added_emb_type''': None,
}
_UpperCamelCase = PriorTransformer(**_A )
return model
@property
def UpperCamelCase_ ( self : Union[str, Any] ):
torch.manual_seed(0 )
_UpperCamelCase = {
'''param_shapes''': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'''d_latent''': self.time_input_dim,
'''d_hidden''': self.renderer_dim,
'''n_output''': 12,
'''background''': (
0.1,
0.1,
0.1,
),
}
_UpperCamelCase = ShapERenderer(**_A )
return model
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = self.dummy_prior
_UpperCamelCase = self.dummy_text_encoder
_UpperCamelCase = self.dummy_tokenizer
_UpperCamelCase = self.dummy_renderer
_UpperCamelCase = HeunDiscreteScheduler(
beta_schedule='''exp''' , num_train_timesteps=1024 , prediction_type='''sample''' , use_karras_sigmas=_A , clip_sample=_A , clip_sample_range=1.0 , )
_UpperCamelCase = {
'''prior''': prior,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''renderer''': renderer,
'''scheduler''': scheduler,
}
return components
def UpperCamelCase_ ( self : Tuple , _A : Tuple , _A : Optional[int]=0 ):
if str(_A ).startswith('''mps''' ):
_UpperCamelCase = torch.manual_seed(_A )
else:
_UpperCamelCase = torch.Generator(device=_A ).manual_seed(_A )
_UpperCamelCase = {
'''prompt''': '''horse''',
'''generator''': generator,
'''num_inference_steps''': 1,
'''frame_size''': 32,
'''output_type''': '''np''',
}
return inputs
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = '''cpu'''
_UpperCamelCase = self.get_dummy_components()
_UpperCamelCase = self.pipeline_class(**_A )
_UpperCamelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
_UpperCamelCase = pipe(**self.get_dummy_inputs(_A ) )
_UpperCamelCase = output.images[0]
_UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
_UpperCamelCase = np.array(
[
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def UpperCamelCase_ ( self : Any ):
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = torch_device == '''cpu'''
_UpperCamelCase = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=_A , relax_max_difference=_A , )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = self.get_dummy_components()
_UpperCamelCase = self.pipeline_class(**_A )
_UpperCamelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
_UpperCamelCase = 1
_UpperCamelCase = 2
_UpperCamelCase = self.get_dummy_inputs(_A )
for key in inputs.keys():
if key in self.batch_params:
_UpperCamelCase = batch_size * [inputs[key]]
_UpperCamelCase = pipe(**_A , num_images_per_prompt=_A )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class lowerCAmelCase_ ( unittest.TestCase ):
def UpperCamelCase_ ( self : str ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/shap_e/test_shap_e_np_out.npy''' )
_UpperCamelCase = ShapEPipeline.from_pretrained('''openai/shap-e''' )
_UpperCamelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
_UpperCamelCase = torch.Generator(device=_A ).manual_seed(0 )
_UpperCamelCase = pipe(
'''a shark''' , generator=_A , guidance_scale=15.0 , num_inference_steps=64 , frame_size=64 , output_type='''np''' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(_A , _A )
| 10 | 0 |
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
SCREAMING_SNAKE_CASE__ = 6_3_7_8_1_3_7.0
SCREAMING_SNAKE_CASE__ = 6_3_5_6_7_5_2.3_1_4_2_4_5
SCREAMING_SNAKE_CASE__ = 637_8137
def UpperCAmelCase__ ( lowerCamelCase_ : float , lowerCamelCase_ : float , lowerCamelCase_ : float , lowerCamelCase_ : float ):
__a : List[Any] = (AXIS_A - AXIS_B) / AXIS_A
# Parametric latitudes
# https://en.wikipedia.org/wiki/Latitude#Parametric_(or_reduced)_latitude
__a : Tuple = atan((1 - flattening) * tan(radians(lowerCamelCase_ ) ) )
__a : Any = atan((1 - flattening) * tan(radians(lowerCamelCase_ ) ) )
# Compute central angle between two points
# using haversine theta. sigma = haversine_distance / equatorial radius
__a : Optional[int] = haversine_distance(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) / EQUATORIAL_RADIUS
# Intermediate P and Q values
__a : Tuple = (b_lata + b_lata) / 2
__a : str = (b_lata - b_lata) / 2
# Intermediate X value
# X = (sigma - sin(sigma)) * sin^2Pcos^2Q / cos^2(sigma/2)
__a : Optional[Any] = (sin(lowerCamelCase_ ) ** 2) * (cos(lowerCamelCase_ ) ** 2)
__a : int = cos(sigma / 2 ) ** 2
__a : Any = (sigma - sin(lowerCamelCase_ )) * (x_numerator / x_demonimator)
# Intermediate Y value
# Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
__a : List[Any] = (cos(lowerCamelCase_ ) ** 2) * (sin(lowerCamelCase_ ) ** 2)
__a : int = sin(sigma / 2 ) ** 2
__a : List[Any] = (sigma + sin(lowerCamelCase_ )) * (y_numerator / y_denominator)
return EQUATORIAL_RADIUS * (sigma - ((flattening / 2) * (x_value + y_value)))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 47 | import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
_lowerCAmelCase = HfApi()
_lowerCAmelCase = {}
# fmt: off
_lowerCAmelCase = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189,
-1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839,
0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557
])
_lowerCAmelCase = torch.tensor([
-2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436,
1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208,
-2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948,
2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365
])
_lowerCAmelCase = torch.tensor([
-0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,
-0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304,
-0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925,
0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943
])
_lowerCAmelCase = torch.tensor([
0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172,
-0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309,
0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805,
-0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505
])
_lowerCAmelCase = torch.tensor([
0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133,
-0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395,
0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559,
-0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386
])
_lowerCAmelCase = torch.tensor([
0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078,
-0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330,
0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683,
-0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431
])
_lowerCAmelCase = torch.tensor([
0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042,
-0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398,
0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574,
-0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390
])
_lowerCAmelCase = torch.tensor([
0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042,
-0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290,
0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746,
-0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473
])
_lowerCAmelCase = torch.tensor([
-1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330,
1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243,
-2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810,
1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251])
_lowerCAmelCase = torch.tensor([
-1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324,
0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181,
-2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259,
1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266
])
_lowerCAmelCase = torch.tensor([
-1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212,
0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027,
-2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131,
1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355
])
_lowerCAmelCase = torch.tensor([
-2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959,
1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351,
-3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341,
3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066
])
_lowerCAmelCase = torch.tensor([
-2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740,
1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398,
-2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395,
2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243
])
_lowerCAmelCase = torch.tensor([
-2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336,
1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908,
-3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560,
3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343
])
_lowerCAmelCase = torch.tensor([
-1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344,
1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391,
-2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439,
1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219
])
# fmt: on
_lowerCAmelCase = api.list_models(filter="diffusers")
for mod in models:
if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
_lowerCAmelCase = "/home/patrick/google_checkpoints/" + mod.modelId.split("/")[-1]
print(f'Started running {mod.modelId}!!!')
if mod.modelId.startswith("CompVis"):
_lowerCAmelCase = UNetaDModel.from_pretrained(local_checkpoint, subfolder="unet")
else:
_lowerCAmelCase = UNetaDModel.from_pretrained(local_checkpoint)
torch.manual_seed(0)
random.seed(0)
_lowerCAmelCase = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
_lowerCAmelCase = torch.tensor([10] * noise.shape[0])
with torch.no_grad():
_lowerCAmelCase = model(noise, time_step).sample
assert torch.allclose(
logits[0, 0, 0, :30], results["_".join("_".join(mod.modelId.split("/")).split("-"))], atol=1E-3
)
print(f'{mod.modelId} has passed successfully!!!')
| 10 | 0 |
'''simple docstring'''
UpperCAmelCase__ : Tuple = 6_55_21
def A ( UpperCamelCase_ : str ) -> int:
'''simple docstring'''
lowerCAmelCase__ = 1
lowerCAmelCase__ = 0
for plain_chr in plain_text:
lowerCAmelCase__ = (a + ord(UpperCamelCase_ )) % MOD_ADLER
lowerCAmelCase__ = (b + a) % MOD_ADLER
return (b << 16) | a
| 48 | from typing import List
from .keymap import KEYMAP, get_character
def _snake_case ( __snake_case ):
def decorator(__snake_case ):
_UpperCamelCase = getattr(__snake_case , '''handle_key''' , [] )
handle += [key]
setattr(__snake_case , '''handle_key''' , __snake_case )
return func
return decorator
def _snake_case ( *__snake_case ):
def decorator(__snake_case ):
_UpperCamelCase = getattr(__snake_case , '''handle_key''' , [] )
handle += keys
setattr(__snake_case , '''handle_key''' , __snake_case )
return func
return decorator
class lowerCAmelCase_ ( __lowercase ):
def __new__( cls : Optional[Any] , _A : Optional[Any] , _A : Optional[int] , _A : Union[str, Any] ):
_UpperCamelCase = super().__new__(cls , _A , _A , _A )
if not hasattr(_A , '''key_handler''' ):
setattr(_A , '''key_handler''' , {} )
setattr(_A , '''handle_input''' , KeyHandler.handle_input )
for value in attrs.values():
_UpperCamelCase = getattr(_A , '''handle_key''' , [] )
for key in handled_keys:
_UpperCamelCase = value
return new_cls
@staticmethod
def UpperCamelCase_ ( cls : str ):
_UpperCamelCase = get_character()
if char != KEYMAP["undefined"]:
_UpperCamelCase = ord(_A )
_UpperCamelCase = cls.key_handler.get(_A )
if handler:
_UpperCamelCase = char
return handler(cls )
else:
return None
def _snake_case ( cls ):
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 10 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : List[str] = logging.get_logger(__name__)
_lowercase : Any = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}
class _UpperCAmelCase ( _lowerCAmelCase ):
a__ : Union[str, Any] = "pegasus"
a__ : Optional[int] = ["past_key_values"]
a__ : str = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self : Optional[int] , _lowercase : Tuple=5_02_65 , _lowercase : Dict=10_24 , _lowercase : int=12 , _lowercase : Any=40_96 , _lowercase : Tuple=16 , _lowercase : List[str]=12 , _lowercase : List[Any]=40_96 , _lowercase : List[str]=16 , _lowercase : Any=0.0 , _lowercase : int=0.0 , _lowercase : str=True , _lowercase : Union[str, Any]=True , _lowercase : Tuple="gelu" , _lowercase : Any=10_24 , _lowercase : Optional[int]=0.1 , _lowercase : Tuple=0.0 , _lowercase : Tuple=0.0 , _lowercase : Union[str, Any]=0.02 , _lowercase : Tuple=0 , _lowercase : str=False , _lowercase : Tuple=0 , _lowercase : Any=1 , _lowercase : List[Any]=1 , **_lowercase : Dict , ):
__UpperCAmelCase = vocab_size
__UpperCAmelCase = max_position_embeddings
__UpperCAmelCase = d_model
__UpperCAmelCase = encoder_ffn_dim
__UpperCAmelCase = encoder_layers
__UpperCAmelCase = encoder_attention_heads
__UpperCAmelCase = decoder_ffn_dim
__UpperCAmelCase = decoder_layers
__UpperCAmelCase = decoder_attention_heads
__UpperCAmelCase = dropout
__UpperCAmelCase = attention_dropout
__UpperCAmelCase = activation_dropout
__UpperCAmelCase = activation_function
__UpperCAmelCase = init_std
__UpperCAmelCase = encoder_layerdrop
__UpperCAmelCase = decoder_layerdrop
__UpperCAmelCase = use_cache
__UpperCAmelCase = encoder_layers
__UpperCAmelCase = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=_lowercase , eos_token_id=_lowercase , is_encoder_decoder=_lowercase , decoder_start_token_id=_lowercase , forced_eos_token_id=_lowercase , **_lowercase , )
@property
def a ( self : Any ):
return self.encoder_attention_heads
@property
def a ( self : List[Any] ):
return self.d_model
| 49 | import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
require_torch_gpu,
require_torch_or_tf,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
class lowerCAmelCase_ ( unittest.TestCase ):
UpperCAmelCase = MODEL_FOR_CAUSAL_LM_MAPPING
UpperCAmelCase = TF_MODEL_FOR_CAUSAL_LM_MAPPING
@require_torch
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = pipeline(task='''text-generation''' , model='''sshleifer/tiny-ctrl''' , framework='''pt''' )
# Using `do_sample=False` to force deterministic output
_UpperCamelCase = text_generator('''This is a test''' , do_sample=_A )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
''' oscope. FiliFili@@'''
)
}
] , )
_UpperCamelCase = text_generator(['''This is a test''', '''This is a second test'''] )
self.assertEqual(
_A , [
[
{
'''generated_text''': (
'''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
''' oscope. FiliFili@@'''
)
}
],
[
{
'''generated_text''': (
'''This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'''
''' oscope. oscope. FiliFili@@'''
)
}
],
] , )
_UpperCamelCase = text_generator('''This is a test''' , do_sample=_A , num_return_sequences=2 , return_tensors=_A )
self.assertEqual(
_A , [
{'''generated_token_ids''': ANY(_A )},
{'''generated_token_ids''': ANY(_A )},
] , )
_UpperCamelCase = text_generator.model.config.eos_token_id
_UpperCamelCase = '''<pad>'''
_UpperCamelCase = text_generator(
['''This is a test''', '''This is a second test'''] , do_sample=_A , num_return_sequences=2 , batch_size=2 , return_tensors=_A , )
self.assertEqual(
_A , [
[
{'''generated_token_ids''': ANY(_A )},
{'''generated_token_ids''': ANY(_A )},
],
[
{'''generated_token_ids''': ANY(_A )},
{'''generated_token_ids''': ANY(_A )},
],
] , )
@require_tf
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = pipeline(task='''text-generation''' , model='''sshleifer/tiny-ctrl''' , framework='''tf''' )
# Using `do_sample=False` to force deterministic output
_UpperCamelCase = text_generator('''This is a test''' , do_sample=_A )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
''' please,'''
)
}
] , )
_UpperCamelCase = text_generator(['''This is a test''', '''This is a second test'''] , do_sample=_A )
self.assertEqual(
_A , [
[
{
'''generated_text''': (
'''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
''' please,'''
)
}
],
[
{
'''generated_text''': (
'''This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'''
''' Cannes 閲閲Cannes Cannes Cannes 攵 please,'''
)
}
],
] , )
def UpperCamelCase_ ( self : int , _A : str , _A : Union[str, Any] , _A : Any ):
_UpperCamelCase = TextGenerationPipeline(model=_A , tokenizer=_A )
return text_generator, ["This is a test", "Another test"]
def UpperCamelCase_ ( self : Union[str, Any] ):
_UpperCamelCase = '''Hello I believe in'''
_UpperCamelCase = pipeline('''text-generation''' , model='''hf-internal-testing/tiny-random-gpt2''' )
_UpperCamelCase = text_generator(_A )
self.assertEqual(
_A , [{'''generated_text''': '''Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'''}] , )
_UpperCamelCase = text_generator(_A , stop_sequence=''' fe''' )
self.assertEqual(_A , [{'''generated_text''': '''Hello I believe in fe'''}] )
def UpperCamelCase_ ( self : Any , _A : List[Any] , _A : Union[str, Any] ):
_UpperCamelCase = text_generator.model
_UpperCamelCase = text_generator.tokenizer
_UpperCamelCase = text_generator('''This is a test''' )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )
_UpperCamelCase = text_generator('''This is a test''' , return_full_text=_A )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
self.assertNotIn('''This is a test''' , outputs[0]['''generated_text'''] )
_UpperCamelCase = pipeline(task='''text-generation''' , model=_A , tokenizer=_A , return_full_text=_A )
_UpperCamelCase = text_generator('''This is a test''' )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
self.assertNotIn('''This is a test''' , outputs[0]['''generated_text'''] )
_UpperCamelCase = text_generator('''This is a test''' , return_full_text=_A )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )
_UpperCamelCase = text_generator(['''This is great !''', '''Something else'''] , num_return_sequences=2 , do_sample=_A )
self.assertEqual(
_A , [
[{'''generated_text''': ANY(_A )}, {'''generated_text''': ANY(_A )}],
[{'''generated_text''': ANY(_A )}, {'''generated_text''': ANY(_A )}],
] , )
if text_generator.tokenizer.pad_token is not None:
_UpperCamelCase = text_generator(
['''This is great !''', '''Something else'''] , num_return_sequences=2 , batch_size=2 , do_sample=_A )
self.assertEqual(
_A , [
[{'''generated_text''': ANY(_A )}, {'''generated_text''': ANY(_A )}],
[{'''generated_text''': ANY(_A )}, {'''generated_text''': ANY(_A )}],
] , )
with self.assertRaises(_A ):
_UpperCamelCase = text_generator('''test''' , return_full_text=_A , return_text=_A )
with self.assertRaises(_A ):
_UpperCamelCase = text_generator('''test''' , return_full_text=_A , return_tensors=_A )
with self.assertRaises(_A ):
_UpperCamelCase = text_generator('''test''' , return_text=_A , return_tensors=_A )
# Empty prompt is slighly special
# it requires BOS token to exist.
# Special case for Pegasus which will always append EOS so will
# work even without BOS.
if (
text_generator.tokenizer.bos_token_id is not None
or "Pegasus" in tokenizer.__class__.__name__
or "Git" in model.__class__.__name__
):
_UpperCamelCase = text_generator('''''' )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
else:
with self.assertRaises((ValueError, AssertionError) ):
_UpperCamelCase = text_generator('''''' )
if text_generator.framework == "tf":
# TF generation does not support max_new_tokens, and it's impossible
# to control long generation with only max_length without
# fancy calculation, dismissing tests for now.
return
# We don't care about infinite range models.
# They already work.
# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
_UpperCamelCase = ['''RwkvForCausalLM''', '''XGLMForCausalLM''', '''GPTNeoXForCausalLM''']
if (
tokenizer.model_max_length < 1_0000
and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
):
# Handling of large generations
with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError) ):
text_generator('''This is a test''' * 500 , max_new_tokens=20 )
_UpperCamelCase = text_generator('''This is a test''' * 500 , handle_long_generation='''hole''' , max_new_tokens=20 )
# Hole strategy cannot work
with self.assertRaises(_A ):
text_generator(
'''This is a test''' * 500 , handle_long_generation='''hole''' , max_new_tokens=tokenizer.model_max_length + 10 , )
@require_torch
@require_accelerate
@require_torch_gpu
def UpperCamelCase_ ( self : Optional[int] ):
import torch
# Classic `model_kwargs`
_UpperCamelCase = pipeline(
model='''hf-internal-testing/tiny-random-bloom''' , model_kwargs={'''device_map''': '''auto''', '''torch_dtype''': torch.bfloataa} , )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
_UpperCamelCase = pipe('''This is a test''' )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
_UpperCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' , torch_dtype=torch.bfloataa )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
_UpperCamelCase = pipe('''This is a test''' )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
_UpperCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.floataa )
_UpperCamelCase = pipe('''This is a test''' )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
@require_torch
@require_torch_gpu
def UpperCamelCase_ ( self : Union[str, Any] ):
import torch
_UpperCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device=0 , torch_dtype=torch.floataa )
pipe('''This is a test''' )
@require_torch
@require_accelerate
@require_torch_gpu
def UpperCamelCase_ ( self : Optional[int] ):
import torch
_UpperCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' , torch_dtype=torch.floataa )
pipe('''This is a test''' , do_sample=_A , top_p=0.5 )
def UpperCamelCase_ ( self : Tuple ):
_UpperCamelCase = '''Hello world'''
_UpperCamelCase = pipeline('''text-generation''' , model='''hf-internal-testing/tiny-random-gpt2''' )
if text_generator.model.framework == "tf":
_UpperCamelCase = logging.get_logger('''transformers.generation.tf_utils''' )
else:
_UpperCamelCase = logging.get_logger('''transformers.generation.utils''' )
_UpperCamelCase = '''Both `max_new_tokens`''' # The beggining of the message to be checked in this test
# Both are set by the user -> log warning
with CaptureLogger(_A ) as cl:
_UpperCamelCase = text_generator(_A , max_length=10 , max_new_tokens=1 )
self.assertIn(_A , cl.out )
# The user only sets one -> no warning
with CaptureLogger(_A ) as cl:
_UpperCamelCase = text_generator(_A , max_new_tokens=1 )
self.assertNotIn(_A , cl.out )
with CaptureLogger(_A ) as cl:
_UpperCamelCase = text_generator(_A , max_length=10 )
self.assertNotIn(_A , cl.out )
| 10 | 0 |
'''simple docstring'''
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def A__ ( __lowerCAmelCase : int , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] ):
if isinstance(__lowerCAmelCase , torch.Tensor ):
return image
elif isinstance(__lowerCAmelCase , PIL.Image.Image ):
lowerCamelCase__ = [image]
if isinstance(image[0] , PIL.Image.Image ):
lowerCamelCase__ = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION["""lanczos"""] ) )[None, :] for i in image]
lowerCamelCase__ = np.concatenate(__lowerCAmelCase , axis=0 )
lowerCamelCase__ = np.array(__lowerCAmelCase ).astype(np.floataa ) / 255.0
lowerCamelCase__ = image.transpose(0 , 3 , 1 , 2 )
lowerCamelCase__ = 2.0 * image - 1.0
lowerCamelCase__ = torch.from_numpy(__lowerCAmelCase )
elif isinstance(image[0] , torch.Tensor ):
lowerCamelCase__ = torch.cat(__lowerCAmelCase , dim=0 )
return image
def A__ ( __lowerCAmelCase : List[str] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Any=0.9995 ):
if not isinstance(__lowerCAmelCase , np.ndarray ):
lowerCamelCase__ = True
lowerCamelCase__ = va.device
lowerCamelCase__ = va.cpu().numpy()
lowerCamelCase__ = va.cpu().numpy()
lowerCamelCase__ = np.sum(va * va / (np.linalg.norm(__lowerCAmelCase ) * np.linalg.norm(__lowerCAmelCase )) )
if np.abs(__lowerCAmelCase ) > DOT_THRESHOLD:
lowerCamelCase__ = (1 - t) * va + t * va
else:
lowerCamelCase__ = np.arccos(__lowerCAmelCase )
lowerCamelCase__ = np.sin(__lowerCAmelCase )
lowerCamelCase__ = theta_a * t
lowerCamelCase__ = np.sin(__lowerCAmelCase )
lowerCamelCase__ = np.sin(theta_a - theta_t ) / sin_theta_a
lowerCamelCase__ = sin_theta_t / sin_theta_a
lowerCamelCase__ = sa * va + sa * va
if inputs_are_torch:
lowerCamelCase__ = torch.from_numpy(__lowerCAmelCase ).to(__lowerCAmelCase )
return va
def A__ ( __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] ):
lowerCamelCase__ = F.normalize(__lowerCAmelCase , dim=-1 )
lowerCamelCase__ = F.normalize(__lowerCAmelCase , dim=-1 )
return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )
def A__ ( __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : str ):
for param in model.parameters():
lowerCamelCase__ = value
class UpperCamelCase__ (a ):
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,):
super().__init__()
self.register_modules(
vae=_lowerCAmelCase ,text_encoder=_lowerCAmelCase ,clip_model=_lowerCAmelCase ,tokenizer=_lowerCAmelCase ,unet=_lowerCAmelCase ,scheduler=_lowerCAmelCase ,feature_extractor=_lowerCAmelCase ,coca_model=_lowerCAmelCase ,coca_tokenizer=_lowerCAmelCase ,coca_transform=_lowerCAmelCase ,)
lowerCamelCase__ = (
feature_extractor.size
if isinstance(feature_extractor.size ,_lowerCAmelCase )
else feature_extractor.size["""shortest_edge"""]
)
lowerCamelCase__ = transforms.Normalize(mean=feature_extractor.image_mean ,std=feature_extractor.image_std )
set_requires_grad(self.text_encoder ,_lowerCAmelCase )
set_requires_grad(self.clip_model ,_lowerCAmelCase )
def UpperCamelCase_ ( self ,_lowerCAmelCase = "auto" ):
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
lowerCamelCase__ = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(_lowerCAmelCase )
def UpperCamelCase_ ( self ):
self.enable_attention_slicing(_lowerCAmelCase )
def UpperCamelCase_ ( self ):
set_requires_grad(self.vae ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
set_requires_grad(self.vae ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
set_requires_grad(self.unet ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
set_requires_grad(self.unet ,_lowerCAmelCase )
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
# get the original timestep using init_timestep
lowerCamelCase__ = min(int(num_inference_steps * strength ) ,_lowerCAmelCase )
lowerCamelCase__ = max(num_inference_steps - init_timestep ,0 )
lowerCamelCase__ = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase=None ):
if not isinstance(_lowerCAmelCase ,torch.Tensor ):
raise ValueError(F'''`image` has to be of type `torch.Tensor` but is {type(_lowerCAmelCase )}''' )
lowerCamelCase__ = image.to(device=_lowerCAmelCase ,dtype=_lowerCAmelCase )
if isinstance(_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(_lowerCAmelCase )
]
lowerCamelCase__ = torch.cat(_lowerCAmelCase ,dim=0 )
else:
lowerCamelCase__ = self.vae.encode(_lowerCAmelCase ).latent_dist.sample(_lowerCAmelCase )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
lowerCamelCase__ = 0.1_8215 * init_latents
lowerCamelCase__ = init_latents.repeat_interleave(_lowerCAmelCase ,dim=0 )
lowerCamelCase__ = randn_tensor(init_latents.shape ,generator=_lowerCAmelCase ,device=_lowerCAmelCase ,dtype=_lowerCAmelCase )
# get latents
lowerCamelCase__ = self.scheduler.add_noise(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = init_latents
return latents
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
lowerCamelCase__ = self.coca_transform(_lowerCAmelCase ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
lowerCamelCase__ = self.coca_model.generate(transformed_image.to(device=self.device ,dtype=self.coca_model.dtype ) )
lowerCamelCase__ = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split("""<end_of_text>""" )[0].replace("""<start_of_text>""" ,"""""" ).rstrip(""" .,""" )
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = self.feature_extractor.preprocess(_lowerCAmelCase )
lowerCamelCase__ = torch.from_numpy(clip_image_input["""pixel_values"""][0] ).unsqueeze(0 ).to(self.device ).half()
lowerCamelCase__ = self.clip_model.get_image_features(_lowerCAmelCase )
lowerCamelCase__ = image_embeddings_clip / image_embeddings_clip.norm(p=2 ,dim=-1 ,keepdim=_lowerCAmelCase )
lowerCamelCase__ = image_embeddings_clip.repeat_interleave(_lowerCAmelCase ,dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,):
lowerCamelCase__ = latents.detach().requires_grad_()
lowerCamelCase__ = self.scheduler.scale_model_input(_lowerCAmelCase ,_lowerCAmelCase )
# predict the noise residual
lowerCamelCase__ = self.unet(_lowerCAmelCase ,_lowerCAmelCase ,encoder_hidden_states=_lowerCAmelCase ).sample
if isinstance(self.scheduler ,(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
lowerCamelCase__ = self.scheduler.alphas_cumprod[timestep]
lowerCamelCase__ = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
lowerCamelCase__ = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
lowerCamelCase__ = torch.sqrt(_lowerCAmelCase )
lowerCamelCase__ = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler ,_lowerCAmelCase ):
lowerCamelCase__ = self.scheduler.sigmas[index]
lowerCamelCase__ = latents - sigma * noise_pred
else:
raise ValueError(F'''scheduler type {type(self.scheduler )} not supported''' )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
lowerCamelCase__ = 1 / 0.1_8215 * sample
lowerCamelCase__ = self.vae.decode(_lowerCAmelCase ).sample
lowerCamelCase__ = (image / 2 + 0.5).clamp(0 ,1 )
lowerCamelCase__ = transforms.Resize(self.feature_extractor_size )(_lowerCAmelCase )
lowerCamelCase__ = self.normalize(_lowerCAmelCase ).to(latents.dtype )
lowerCamelCase__ = self.clip_model.get_image_features(_lowerCAmelCase )
lowerCamelCase__ = image_embeddings_clip / image_embeddings_clip.norm(p=2 ,dim=-1 ,keepdim=_lowerCAmelCase )
lowerCamelCase__ = spherical_dist_loss(_lowerCAmelCase ,_lowerCAmelCase ).mean() * clip_guidance_scale
lowerCamelCase__ = -torch.autograd.grad(_lowerCAmelCase ,_lowerCAmelCase )[0]
if isinstance(self.scheduler ,_lowerCAmelCase ):
lowerCamelCase__ = latents.detach() + grads * (sigma**2)
lowerCamelCase__ = noise_pred_original
else:
lowerCamelCase__ = noise_pred_original - torch.sqrt(_lowerCAmelCase ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase = None ,_lowerCAmelCase = None ,_lowerCAmelCase = 5_12 ,_lowerCAmelCase = 5_12 ,_lowerCAmelCase = 0.6 ,_lowerCAmelCase = 50 ,_lowerCAmelCase = 7.5 ,_lowerCAmelCase = 1 ,_lowerCAmelCase = 0.0 ,_lowerCAmelCase = 1_00 ,_lowerCAmelCase = None ,_lowerCAmelCase = "pil" ,_lowerCAmelCase = True ,_lowerCAmelCase = 0.8 ,_lowerCAmelCase = 0.1 ,_lowerCAmelCase = 0.1 ,):
if isinstance(_lowerCAmelCase ,_lowerCAmelCase ) and len(_lowerCAmelCase ) != batch_size:
raise ValueError(F'''You have passed {batch_size} batch_size, but only {len(_lowerCAmelCase )} generators.''' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''' )
if isinstance(_lowerCAmelCase ,torch.Generator ) and batch_size > 1:
lowerCamelCase__ = [generator] + [None] * (batch_size - 1)
lowerCamelCase__ = [
("""model""", self.coca_model is None),
("""tokenizer""", self.coca_tokenizer is None),
("""transform""", self.coca_transform is None),
]
lowerCamelCase__ = [x[0] for x in coca_is_none if x[1]]
lowerCamelCase__ = """, """.join(_lowerCAmelCase )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(_lowerCAmelCase ):
raise ValueError(
F'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
F'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' )
lowerCamelCase__ = self.get_image_description(_lowerCAmelCase )
if style_prompt is None:
if len(_lowerCAmelCase ):
raise ValueError(
F'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
F''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' )
lowerCamelCase__ = self.get_image_description(_lowerCAmelCase )
# get prompt text embeddings for content and style
lowerCamelCase__ = self.tokenizer(
_lowerCAmelCase ,padding="""max_length""" ,max_length=self.tokenizer.model_max_length ,truncation=_lowerCAmelCase ,return_tensors="""pt""" ,)
lowerCamelCase__ = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
lowerCamelCase__ = self.tokenizer(
_lowerCAmelCase ,padding="""max_length""" ,max_length=self.tokenizer.model_max_length ,truncation=_lowerCAmelCase ,return_tensors="""pt""" ,)
lowerCamelCase__ = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
lowerCamelCase__ = slerp(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
# duplicate text embeddings for each generation per prompt
lowerCamelCase__ = text_embeddings.repeat_interleave(_lowerCAmelCase ,dim=0 )
# set timesteps
lowerCamelCase__ = """offset""" in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
lowerCamelCase__ = {}
if accepts_offset:
lowerCamelCase__ = 1
self.scheduler.set_timesteps(_lowerCAmelCase ,**_lowerCAmelCase )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
lowerCamelCase__ , lowerCamelCase__ = self.get_timesteps(_lowerCAmelCase ,_lowerCAmelCase ,self.device )
lowerCamelCase__ = timesteps[:1].repeat(_lowerCAmelCase )
# Preprocess image
lowerCamelCase__ = preprocess(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = self.prepare_latents(
_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,text_embeddings.dtype ,self.device ,_lowerCAmelCase )
lowerCamelCase__ = preprocess(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = self.prepare_latents(
_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,text_embeddings.dtype ,self.device ,_lowerCAmelCase )
lowerCamelCase__ = slerp(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
if clip_guidance_scale > 0:
lowerCamelCase__ = self.get_clip_image_embeddings(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = self.get_clip_image_embeddings(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = slerp(
_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
lowerCamelCase__ = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
lowerCamelCase__ = content_text_input.input_ids.shape[-1]
lowerCamelCase__ = self.tokenizer([""""""] ,padding="""max_length""" ,max_length=_lowerCAmelCase ,return_tensors="""pt""" )
lowerCamelCase__ = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
lowerCamelCase__ = uncond_embeddings.repeat_interleave(_lowerCAmelCase ,dim=0 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
lowerCamelCase__ = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
lowerCamelCase__ = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
lowerCamelCase__ = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
lowerCamelCase__ = torch.randn(_lowerCAmelCase ,generator=_lowerCAmelCase ,device="""cpu""" ,dtype=_lowerCAmelCase ).to(
self.device )
else:
lowerCamelCase__ = torch.randn(_lowerCAmelCase ,generator=_lowerCAmelCase ,device=self.device ,dtype=_lowerCAmelCase )
else:
if latents.shape != latents_shape:
raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
lowerCamelCase__ = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
lowerCamelCase__ = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
lowerCamelCase__ = """eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
lowerCamelCase__ = {}
if accepts_eta:
lowerCamelCase__ = eta
# check if the scheduler accepts generator
lowerCamelCase__ = """generator""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
lowerCamelCase__ = generator
with self.progress_bar(total=_lowerCAmelCase ):
for i, t in enumerate(_lowerCAmelCase ):
# expand the latents if we are doing classifier free guidance
lowerCamelCase__ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowerCamelCase__ = self.scheduler.scale_model_input(_lowerCAmelCase ,_lowerCAmelCase )
# predict the noise residual
lowerCamelCase__ = self.unet(_lowerCAmelCase ,_lowerCAmelCase ,encoder_hidden_states=_lowerCAmelCase ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
lowerCamelCase__ , lowerCamelCase__ = noise_pred.chunk(2 )
lowerCamelCase__ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
lowerCamelCase__ = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
lowerCamelCase__ , lowerCamelCase__ = self.cond_fn(
_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,)
# compute the previous noisy sample x_t -> x_t-1
lowerCamelCase__ = self.scheduler.step(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,**_lowerCAmelCase ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
lowerCamelCase__ = 1 / 0.1_8215 * latents
lowerCamelCase__ = self.vae.decode(_lowerCAmelCase ).sample
lowerCamelCase__ = (image / 2 + 0.5).clamp(0 ,1 )
lowerCamelCase__ = image.cpu().permute(0 ,2 ,3 ,1 ).numpy()
if output_type == "pil":
lowerCamelCase__ = self.numpy_to_pil(_lowerCAmelCase )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=_lowerCAmelCase ,nsfw_content_detected=_lowerCAmelCase )
| 50 | def _snake_case ( __snake_case = 100 ):
_UpperCamelCase = (n * (n + 1) // 2) ** 2
_UpperCamelCase = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f'{solution() = }')
| 10 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class lowerCAmelCase__ ( UpperCAmelCase_ ):
'''simple docstring'''
pass
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self : int , a__ : Any ):
UpperCAmelCase = data
UpperCAmelCase = None
def __iter__( self : List[str] ):
UpperCAmelCase = self
UpperCAmelCase = []
while node:
if node in visited:
raise ContainsLoopError
visited.append(a__ )
yield node.data
UpperCAmelCase = node.next_node
@property
def __snake_case ( self : Optional[int] ):
try:
list(self )
return False
except ContainsLoopError:
return True
if __name__ == "__main__":
a__ : List[str] = Node(1)
a__ : int = Node(2)
a__ : Optional[int] = Node(3)
a__ : Optional[Any] = Node(4)
print(root_node.has_loop) # False
a__ : Any = root_node.next_node
print(root_node.has_loop) # True
a__ : Any = Node(5)
a__ : List[Any] = Node(6)
a__ : Optional[int] = Node(5)
a__ : List[Any] = Node(6)
print(root_node.has_loop) # False
a__ : Dict = Node(1)
print(root_node.has_loop) # False
| 51 | import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
_lowerCAmelCase = logging.get_logger(__name__)
def _snake_case ( __snake_case , __snake_case , __snake_case , __snake_case ):
def constraint_to_multiple_of(__snake_case , __snake_case , __snake_case=0 , __snake_case=None ):
_UpperCamelCase = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
_UpperCamelCase = math.floor(val / multiple ) * multiple
if x < min_val:
_UpperCamelCase = math.ceil(val / multiple ) * multiple
return x
_UpperCamelCase = (output_size, output_size) if isinstance(__snake_case , __snake_case ) else output_size
_UpperCamelCase , _UpperCamelCase = get_image_size(__snake_case )
_UpperCamelCase , _UpperCamelCase = output_size
# determine new height and width
_UpperCamelCase = output_height / input_height
_UpperCamelCase = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
_UpperCamelCase = scale_width
else:
# fit height
_UpperCamelCase = scale_height
_UpperCamelCase = constraint_to_multiple_of(scale_height * input_height , multiple=__snake_case )
_UpperCamelCase = constraint_to_multiple_of(scale_width * input_width , multiple=__snake_case )
return (new_height, new_width)
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = ["pixel_values"]
def __init__( self : List[Any] , _A : bool = True , _A : Dict[str, int] = None , _A : PILImageResampling = PILImageResampling.BILINEAR , _A : bool = False , _A : int = 1 , _A : bool = True , _A : Union[int, float] = 1 / 255 , _A : bool = True , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[float, List[float]]] = None , **_A : List[str] , ):
super().__init__(**_A )
_UpperCamelCase = size if size is not None else {'''height''': 384, '''width''': 384}
_UpperCamelCase = get_size_dict(_A )
_UpperCamelCase = do_resize
_UpperCamelCase = size
_UpperCamelCase = keep_aspect_ratio
_UpperCamelCase = ensure_multiple_of
_UpperCamelCase = resample
_UpperCamelCase = do_rescale
_UpperCamelCase = rescale_factor
_UpperCamelCase = do_normalize
_UpperCamelCase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_UpperCamelCase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def UpperCamelCase_ ( self : List[str] , _A : np.ndarray , _A : Dict[str, int] , _A : bool = False , _A : int = 1 , _A : PILImageResampling = PILImageResampling.BICUBIC , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Any , ):
_UpperCamelCase = get_size_dict(_A )
if "height" not in size or "width" not in size:
raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
_UpperCamelCase = get_resize_output_image_size(
_A , output_size=(size['''height'''], size['''width''']) , keep_aspect_ratio=_A , multiple=_A , )
return resize(_A , size=_A , resample=_A , data_format=_A , **_A )
def UpperCamelCase_ ( self : str , _A : np.ndarray , _A : Union[int, float] , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Any , ):
return rescale(_A , scale=_A , data_format=_A , **_A )
def UpperCamelCase_ ( self : int , _A : np.ndarray , _A : Union[float, List[float]] , _A : Union[float, List[float]] , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Any , ):
return normalize(_A , mean=_A , std=_A , data_format=_A , **_A )
def UpperCamelCase_ ( self : Optional[int] , _A : ImageInput , _A : bool = None , _A : int = None , _A : bool = None , _A : int = None , _A : PILImageResampling = None , _A : bool = None , _A : float = None , _A : bool = None , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[str, TensorType]] = None , _A : ChannelDimension = ChannelDimension.FIRST , **_A : str , ):
_UpperCamelCase = do_resize if do_resize is not None else self.do_resize
_UpperCamelCase = size if size is not None else self.size
_UpperCamelCase = get_size_dict(_A )
_UpperCamelCase = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
_UpperCamelCase = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
_UpperCamelCase = resample if resample is not None else self.resample
_UpperCamelCase = do_rescale if do_rescale is not None else self.do_rescale
_UpperCamelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
_UpperCamelCase = do_normalize if do_normalize is not None else self.do_normalize
_UpperCamelCase = image_mean if image_mean is not None else self.image_mean
_UpperCamelCase = image_std if image_std is not None else self.image_std
_UpperCamelCase = make_list_of_images(_A )
if not valid_images(_A ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None or resample is None:
raise ValueError('''Size and resample must be specified if do_resize is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
_UpperCamelCase = [to_numpy_array(_A ) for image in images]
if do_resize:
_UpperCamelCase = [self.resize(image=_A , size=_A , resample=_A ) for image in images]
if do_rescale:
_UpperCamelCase = [self.rescale(image=_A , scale=_A ) for image in images]
if do_normalize:
_UpperCamelCase = [self.normalize(image=_A , mean=_A , std=_A ) for image in images]
_UpperCamelCase = [to_channel_dimension_format(_A , _A ) for image in images]
_UpperCamelCase = {'''pixel_values''': images}
return BatchFeature(data=_A , tensor_type=_A )
def UpperCamelCase_ ( self : Any , _A : Any , _A : List[Tuple] = None ):
_UpperCamelCase = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(_A ) != len(_A ):
raise ValueError(
'''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )
if is_torch_tensor(_A ):
_UpperCamelCase = target_sizes.numpy()
_UpperCamelCase = []
for idx in range(len(_A ) ):
_UpperCamelCase = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='''bilinear''' , align_corners=_A )
_UpperCamelCase = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(_A )
else:
_UpperCamelCase = logits.argmax(dim=1 )
_UpperCamelCase = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 10 | 0 |
"""simple docstring"""
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = (UnCLIPScheduler,)
def _lowerCamelCase ( self , **_UpperCAmelCase ):
__a : List[Any] = {
'''num_train_timesteps''': 1000,
'''variance_type''': '''fixed_small_log''',
'''clip_sample''': True,
'''clip_sample_range''': 1.0,
'''prediction_type''': '''epsilon''',
}
config.update(**_UpperCAmelCase )
return config
def _lowerCamelCase ( self ):
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=_UpperCAmelCase )
def _lowerCamelCase ( self ):
for variance in ["fixed_small_log", "learned_range"]:
self.check_over_configs(variance_type=_UpperCAmelCase )
def _lowerCamelCase ( self ):
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_UpperCAmelCase )
def _lowerCamelCase ( self ):
for clip_sample_range in [1, 5, 10, 20]:
self.check_over_configs(clip_sample_range=_UpperCAmelCase )
def _lowerCamelCase ( self ):
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(prediction_type=_UpperCAmelCase )
def _lowerCamelCase ( self ):
for time_step in [0, 500, 999]:
for prev_timestep in [None, 5, 100, 250, 500, 750]:
if prev_timestep is not None and prev_timestep >= time_step:
continue
self.check_over_forward(time_step=_UpperCAmelCase , prev_timestep=_UpperCAmelCase )
def _lowerCamelCase ( self ):
__a : Dict = self.scheduler_classes[0]
__a : Any = self.get_scheduler_config(variance_type='''fixed_small_log''' )
__a : Optional[Any] = scheduler_class(**_UpperCAmelCase )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 1.0_0_0_0e-1_0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_5_4_9_6_2_5 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.9_9_9_4_9_8_7 ) ) < 1e-5
def _lowerCamelCase ( self ):
__a : List[str] = self.scheduler_classes[0]
__a : Any = self.get_scheduler_config(variance_type='''learned_range''' )
__a : Optional[int] = scheduler_class(**_UpperCAmelCase )
__a : int = 0.5
assert scheduler._get_variance(1 , predicted_variance=_UpperCAmelCase ) - -1_0.1_7_1_2_7_9_0 < 1e-5
assert scheduler._get_variance(487 , predicted_variance=_UpperCAmelCase ) - -5.7_9_9_8_0_5_2 < 1e-5
assert scheduler._get_variance(999 , predicted_variance=_UpperCAmelCase ) - -0.0_0_1_0_0_1_1 < 1e-5
def _lowerCamelCase ( self ):
__a : Optional[int] = self.scheduler_classes[0]
__a : Dict = self.get_scheduler_config()
__a : List[str] = scheduler_class(**_UpperCAmelCase )
__a : Dict = scheduler.timesteps
__a : Optional[int] = self.dummy_model()
__a : Any = self.dummy_sample_deter
__a : Optional[int] = torch.manual_seed(0 )
for i, t in enumerate(_UpperCAmelCase ):
# 1. predict noise residual
__a : List[Any] = model(_UpperCAmelCase , _UpperCAmelCase )
# 2. predict previous mean of sample x_t-1
__a : Dict = scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , generator=_UpperCAmelCase ).prev_sample
__a : Tuple = pred_prev_sample
__a : Optional[Any] = torch.sum(torch.abs(_UpperCAmelCase ) )
__a : Any = torch.mean(torch.abs(_UpperCAmelCase ) )
assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5 ) < 1e-2
assert abs(result_mean.item() - 0.3_2_8_4_7_4_3 ) < 1e-3
def _lowerCamelCase ( self ):
__a : Tuple = self.scheduler_classes[0]
__a : str = self.get_scheduler_config()
__a : Union[str, Any] = scheduler_class(**_UpperCAmelCase )
scheduler.set_timesteps(25 )
__a : Tuple = scheduler.timesteps
__a : Any = self.dummy_model()
__a : Optional[Any] = self.dummy_sample_deter
__a : Optional[int] = torch.manual_seed(0 )
for i, t in enumerate(_UpperCAmelCase ):
# 1. predict noise residual
__a : List[Any] = model(_UpperCAmelCase , _UpperCAmelCase )
if i + 1 == timesteps.shape[0]:
__a : List[Any] = None
else:
__a : str = timesteps[i + 1]
# 2. predict previous mean of sample x_t-1
__a : List[str] = scheduler.step(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , prev_timestep=_UpperCAmelCase , generator=_UpperCAmelCase ).prev_sample
__a : Tuple = pred_prev_sample
__a : Any = torch.sum(torch.abs(_UpperCAmelCase ) )
__a : Dict = torch.mean(torch.abs(_UpperCAmelCase ) )
assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3 ) < 1e-2
assert abs(result_mean.item() - 0.3_3_6_2_0_3_8 ) < 1e-3
def _lowerCamelCase ( self ):
pass
def _lowerCamelCase ( self ):
pass | 52 | import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_lowerCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_lowerCAmelCase = " \"\"\"\n Output class for the scheduler's step function output.\n\n Args:\n prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.FloatTensor\n pred_original_sample: Optional[torch.FloatTensor] = None\n"
class lowerCAmelCase_ ( unittest.TestCase ):
def UpperCamelCase_ ( self : List[Any] ):
_UpperCamelCase = tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir , '''schedulers/''' ) )
_UpperCamelCase = self.diffusers_dir
shutil.copy(
os.path.join(_A , '''src/diffusers/schedulers/scheduling_ddpm.py''' ) , os.path.join(self.diffusers_dir , '''schedulers/scheduling_ddpm.py''' ) , )
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = '''src/diffusers'''
shutil.rmtree(self.diffusers_dir )
def UpperCamelCase_ ( self : str , _A : List[str] , _A : Optional[Any] , _A : List[str] , _A : Optional[int]=None ):
_UpperCamelCase = comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
if overwrite_result is not None:
_UpperCamelCase = comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
_UpperCamelCase = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 )
_UpperCamelCase = black.format_str(_A , mode=_A )
_UpperCamelCase = os.path.join(self.diffusers_dir , '''new_code.py''' )
with open(_A , '''w''' , newline='''\n''' ) as f:
f.write(_A )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(_A ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=_A )
with open(_A , '''r''' ) as f:
self.assertTrue(f.read() , _A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput''' )
self.assertEqual(_A , _A )
def UpperCamelCase_ ( self : List[str] ):
# Base copy consistency
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' , '''DDPMSchedulerOutput''' , REFERENCE_CODE + '''\n''' , )
# With no empty line at the end
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' , '''DDPMSchedulerOutput''' , _A , )
# Copy consistency with rename
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' , '''TestSchedulerOutput''' , re.sub('''DDPM''' , '''Test''' , _A ) , )
# Copy consistency with a really long name
_UpperCamelCase = '''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
self.check_copy_consistency(
F"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}""" , F"""{long_class_name}SchedulerOutput""" , re.sub('''Bert''' , _A , _A ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' , '''TestSchedulerOutput''' , _A , overwrite_result=re.sub('''DDPM''' , '''Test''' , _A ) , )
| 10 | 0 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_snake_case : Union[str, Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
_snake_case : Union[str, Any] = ' def __init__(self, config):\n super().__init__()\n self.transform = BertPredictionHeadTransform(config)\n\n # The output weights are the same as the input embeddings, but there is\n # an output-only bias for each token.\n self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n self.decoder.bias = self.bias\n\n def forward(self, hidden_states):\n hidden_states = self.transform(hidden_states)\n hidden_states = self.decoder(hidden_states)\n return hidden_states\n'
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase ( self : List[str] ) -> Tuple:
__lowerCAmelCase = tempfile.mkdtemp()
os.makedirs(os.path.join(self.transformer_dir , 'models/bert/' ) )
__lowerCAmelCase = self.transformer_dir
shutil.copy(
os.path.join(lowerCAmelCase_ , 'src/transformers/models/bert/modeling_bert.py' ) , os.path.join(self.transformer_dir , 'models/bert/modeling_bert.py' ) , )
def lowercase ( self : Optional[Any] ) -> Optional[int]:
__lowerCAmelCase = 'src/transformers'
shutil.rmtree(self.transformer_dir )
def lowercase ( self : Tuple , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : int=None ) -> Dict:
__lowerCAmelCase = comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
if overwrite_result is not None:
__lowerCAmelCase = comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
__lowerCAmelCase = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=1_1_9 )
__lowerCAmelCase = black.format_str(lowerCAmelCase_ , mode=lowerCAmelCase_ )
__lowerCAmelCase = os.path.join(self.transformer_dir , 'new_code.py' )
with open(lowerCAmelCase_ , 'w' , newline='\n' ) as f:
f.write(lowerCAmelCase_ )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(lowerCAmelCase_ ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=lowerCAmelCase_ )
with open(lowerCAmelCase_ , 'r' ) as f:
self.assertTrue(f.read() , lowerCAmelCase_ )
def lowercase ( self : int ) -> List[str]:
__lowerCAmelCase = check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead' )
self.assertEqual(lowerCAmelCase_ , lowerCAmelCase_ )
def lowercase ( self : Union[str, Any] ) -> List[Any]:
# Base copy consistency
self.check_copy_consistency(
'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead' , 'BertLMPredictionHead' , REFERENCE_CODE + '\n' , )
# With no empty line at the end
self.check_copy_consistency(
'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead' , 'BertLMPredictionHead' , lowerCAmelCase_ , )
# Copy consistency with rename
self.check_copy_consistency(
'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel' , 'TestModelLMPredictionHead' , re.sub('Bert' , 'TestModel' , lowerCAmelCase_ ) , )
# Copy consistency with a really long name
__lowerCAmelCase = 'TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
self.check_copy_consistency(
f"""# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}""" , f"""{long_class_name}LMPredictionHead""" , re.sub('Bert' , lowerCAmelCase_ , lowerCAmelCase_ ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel' , 'TestModelLMPredictionHead' , lowerCAmelCase_ , overwrite_result=re.sub('Bert' , 'TestModel' , lowerCAmelCase_ ) , )
def lowercase ( self : List[str] ) -> Any:
__lowerCAmelCase = check_copies.LOCALIZED_READMES['README_zh-hans.md']
__lowerCAmelCase = (
'1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1.'
' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),'
' released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
' lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same'
' method has been applied to compress GPT2 into'
' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
' Multilingual BERT into'
' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**'
' (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders'
' as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang'
' Luong, Quoc V. Le, Christopher D. Manning.'
)
__lowerCAmelCase = (
'1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
)
__lowerCAmelCase = (
'1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1.'
' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文'
' [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
' lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same'
' method has been applied to compress GPT2 into'
' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
' Multilingual BERT into'
' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自'
' Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather'
' than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,'
' Christopher D. Manning 发布。\n'
)
__lowerCAmelCase , __lowerCAmelCase = check_copies.convert_to_localized_md(
lowerCAmelCase_ , lowerCAmelCase_ , localized_readme['format_model_list'] )
self.assertFalse(lowerCAmelCase_ )
self.assertEqual(lowerCAmelCase_ , lowerCAmelCase_ )
__lowerCAmelCase , __lowerCAmelCase = check_copies.convert_to_localized_md(
lowerCAmelCase_ , lowerCAmelCase_ , localized_readme['format_model_list'] )
# Check whether the number of models is equal to README.md after conversion.
self.assertTrue(lowerCAmelCase_ )
__lowerCAmelCase = (
'1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.'
)
__lowerCAmelCase = (
'1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and'
' the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
)
__lowerCAmelCase = (
'1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
)
__lowerCAmelCase , __lowerCAmelCase = check_copies.convert_to_localized_md(
lowerCAmelCase_ , lowerCAmelCase_ , localized_readme['format_model_list'] )
# Check if the model link is synchronized.
self.assertEqual(lowerCAmelCase_ , lowerCAmelCase_ )
| 53 | import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaProcessor,
is_apex_available,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
_lowerCAmelCase = True
from torch.cuda.amp import autocast
_lowerCAmelCase = logging.getLogger(__name__)
def _snake_case ( __snake_case=None , __snake_case=None ):
return field(default_factory=lambda: default , metadata=__snake_case )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
UpperCAmelCase = field(
default=0.1, metadata={"help": "The dropout ratio for the attention probabilities."} )
UpperCAmelCase = field(
default=0.1, metadata={"help": "The dropout ratio for activations inside the fully connected layer."} )
UpperCAmelCase = field(
default=0.1, metadata={
"help": "The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."
}, )
UpperCAmelCase = field(
default=0.1, metadata={"help": "The dropout probabilitiy for all 1D convolutional layers in feature extractor."}, )
UpperCAmelCase = field(
default=0.0_5, metadata={
"help": (
"Propability of each feature vector along the time axis to be chosen as the start of the vector"
"span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"
"vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."
)
}, )
UpperCAmelCase = field(default=0.0, metadata={"help": "The LayerDrop probability."} )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
UpperCAmelCase = field(
default="train+validation", metadata={
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "The number of processes to use for the preprocessing."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
}, )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of validation examples to this "
"value if set."
)
}, )
UpperCAmelCase = list_field(
default=[",", "?", ".", "!", "-", ";", ":", "\"\"", "%", "'", "\"", "�"], metadata={"help": "A list of characters to remove from the transcripts."}, )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = 42
UpperCAmelCase = True
UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = None
def __call__( self : Union[str, Any] , _A : List[Dict[str, Union[List[int], torch.Tensor]]] ):
# split inputs and labels since they have to be of different lenghts and need
# different padding methods
_UpperCamelCase = [{'''input_values''': feature['''input_values''']} for feature in features]
_UpperCamelCase = [{'''input_ids''': feature['''labels''']} for feature in features]
_UpperCamelCase = self.processor.pad(
_A , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' , )
_UpperCamelCase = self.processor.pad(
labels=_A , padding=self.padding , max_length=self.max_length_labels , pad_to_multiple_of=self.pad_to_multiple_of_labels , return_tensors='''pt''' , )
# replace padding with -100 to ignore loss correctly
_UpperCamelCase = labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1 ) , -100 )
_UpperCamelCase = labels
return batch
class lowerCAmelCase_ ( __lowercase ):
def UpperCamelCase_ ( self : Dict , _A : nn.Module , _A : Dict[str, Union[torch.Tensor, Any]] ):
model.train()
_UpperCamelCase = self._prepare_inputs(_A )
if self.use_amp:
with autocast():
_UpperCamelCase = self.compute_loss(_A , _A )
else:
_UpperCamelCase = self.compute_loss(_A , _A )
if self.args.n_gpu > 1:
if model.module.config.ctc_loss_reduction == "mean":
_UpperCamelCase = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
_UpperCamelCase = loss.sum() / (inputs['''labels'''] >= 0).sum()
else:
raise ValueError(F"""{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']""" )
if self.args.gradient_accumulation_steps > 1:
_UpperCamelCase = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(_A ).backward()
elif self.use_apex:
with amp.scale_loss(_A , self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(_A )
else:
loss.backward()
return loss.detach()
def _snake_case ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_UpperCamelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
_UpperCamelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCamelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'''Use --overwrite_output_dir to overcome.''' )
elif last_checkpoint is not None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info('''Training/evaluation parameters %s''' , __snake_case )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets:
_UpperCamelCase = datasets.load_dataset(
'''common_voice''' , data_args.dataset_config_name , split=data_args.train_split_name )
_UpperCamelCase = datasets.load_dataset('''common_voice''' , data_args.dataset_config_name , split='''test''' )
# Create and save tokenizer
_UpperCamelCase = f"""[{"".join(data_args.chars_to_ignore )}]"""
def remove_special_characters(__snake_case ):
_UpperCamelCase = re.sub(__snake_case , '''''' , batch['''sentence'''] ).lower() + ''' '''
return batch
_UpperCamelCase = train_dataset.map(__snake_case , remove_columns=['''sentence'''] )
_UpperCamelCase = eval_dataset.map(__snake_case , remove_columns=['''sentence'''] )
def extract_all_chars(__snake_case ):
_UpperCamelCase = ''' '''.join(batch['''text'''] )
_UpperCamelCase = list(set(__snake_case ) )
return {"vocab": [vocab], "all_text": [all_text]}
_UpperCamelCase = train_dataset.map(
__snake_case , batched=__snake_case , batch_size=-1 , keep_in_memory=__snake_case , remove_columns=train_dataset.column_names , )
_UpperCamelCase = train_dataset.map(
__snake_case , batched=__snake_case , batch_size=-1 , keep_in_memory=__snake_case , remove_columns=eval_dataset.column_names , )
_UpperCamelCase = list(set(vocab_train['''vocab'''][0] ) | set(vocab_test['''vocab'''][0] ) )
_UpperCamelCase = {v: k for k, v in enumerate(__snake_case )}
_UpperCamelCase = vocab_dict[''' ''']
del vocab_dict[" "]
_UpperCamelCase = len(__snake_case )
_UpperCamelCase = len(__snake_case )
with open('''vocab.json''' , '''w''' ) as vocab_file:
json.dump(__snake_case , __snake_case )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCamelCase = WavaVecaCTCTokenizer(
'''vocab.json''' , unk_token='''[UNK]''' , pad_token='''[PAD]''' , word_delimiter_token='''|''' , )
_UpperCamelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0.0 , do_normalize=__snake_case , return_attention_mask=__snake_case )
_UpperCamelCase = WavaVecaProcessor(feature_extractor=__snake_case , tokenizer=__snake_case )
_UpperCamelCase = WavaVecaForCTC.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , activation_dropout=model_args.activation_dropout , attention_dropout=model_args.attention_dropout , hidden_dropout=model_args.hidden_dropout , feat_proj_dropout=model_args.feat_proj_dropout , mask_time_prob=model_args.mask_time_prob , gradient_checkpointing=training_args.gradient_checkpointing , layerdrop=model_args.layerdrop , ctc_loss_reduction='''mean''' , pad_token_id=processor.tokenizer.pad_token_id , vocab_size=len(processor.tokenizer ) , )
if data_args.max_train_samples is not None:
_UpperCamelCase = min(len(__snake_case ) , data_args.max_train_samples )
_UpperCamelCase = train_dataset.select(range(__snake_case ) )
if data_args.max_val_samples is not None:
_UpperCamelCase = eval_dataset.select(range(data_args.max_val_samples ) )
_UpperCamelCase = torchaudio.transforms.Resample(48000 , 16000 )
# Preprocessing the datasets.
# We need to read the aduio files as arrays and tokenize the targets.
def speech_file_to_array_fn(__snake_case ):
_UpperCamelCase , _UpperCamelCase = torchaudio.load(batch['''path'''] )
_UpperCamelCase = resampler(__snake_case ).squeeze().numpy()
_UpperCamelCase = 16000
_UpperCamelCase = batch['''text''']
return batch
_UpperCamelCase = train_dataset.map(
__snake_case , remove_columns=train_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
_UpperCamelCase = eval_dataset.map(
__snake_case , remove_columns=eval_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
def prepare_dataset(__snake_case ):
# check that all files have the correct sampling rate
assert (
len(set(batch['''sampling_rate'''] ) ) == 1
), f"""Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."""
_UpperCamelCase = processor(
audio=batch['''speech'''] , text=batch['''target_text'''] , sampling_rate=batch['''sampling_rate'''][0] )
batch.update(__snake_case )
return batch
_UpperCamelCase = train_dataset.map(
__snake_case , remove_columns=train_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=__snake_case , num_proc=data_args.preprocessing_num_workers , )
_UpperCamelCase = eval_dataset.map(
__snake_case , remove_columns=eval_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=__snake_case , num_proc=data_args.preprocessing_num_workers , )
# Metric
_UpperCamelCase = datasets.load_metric('''wer''' )
def compute_metrics(__snake_case ):
_UpperCamelCase = pred.predictions
_UpperCamelCase = np.argmax(__snake_case , axis=-1 )
_UpperCamelCase = processor.tokenizer.pad_token_id
_UpperCamelCase = processor.batch_decode(__snake_case )
# we do not want to group tokens when computing the metrics
_UpperCamelCase = processor.batch_decode(pred.label_ids , group_tokens=__snake_case )
_UpperCamelCase = wer_metric.compute(predictions=__snake_case , references=__snake_case )
return {"wer": wer}
if model_args.freeze_feature_extractor:
model.freeze_feature_extractor()
# Data collator
_UpperCamelCase = DataCollatorCTCWithPadding(processor=__snake_case , padding=__snake_case )
# Initialize our Trainer
_UpperCamelCase = CTCTrainer(
model=__snake_case , data_collator=__snake_case , args=__snake_case , compute_metrics=__snake_case , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=processor.feature_extractor , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
_UpperCamelCase = last_checkpoint
elif os.path.isdir(model_args.model_name_or_path ):
_UpperCamelCase = model_args.model_name_or_path
else:
_UpperCamelCase = None
# Save the feature_extractor and the tokenizer
if is_main_process(training_args.local_rank ):
processor.save_pretrained(training_args.output_dir )
_UpperCamelCase = trainer.train(resume_from_checkpoint=__snake_case )
trainer.save_model()
_UpperCamelCase = train_result.metrics
_UpperCamelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__snake_case )
)
_UpperCamelCase = min(__snake_case , len(__snake_case ) )
trainer.log_metrics('''train''' , __snake_case )
trainer.save_metrics('''train''' , __snake_case )
trainer.save_state()
# Evaluation
_UpperCamelCase = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
_UpperCamelCase = trainer.evaluate()
_UpperCamelCase = data_args.max_val_samples if data_args.max_val_samples is not None else len(__snake_case )
_UpperCamelCase = min(__snake_case , len(__snake_case ) )
trainer.log_metrics('''eval''' , __snake_case )
trainer.save_metrics('''eval''' , __snake_case )
return results
if __name__ == "__main__":
main()
| 10 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A ( __lowercase ):
_snake_case =42
_snake_case =42
def __init__( self: Tuple , _lowerCAmelCase: UNetaDModel , _lowerCAmelCase: ScoreSdeVeScheduler ) -> Optional[int]:
'''simple docstring'''
super().__init__()
self.register_modules(unet=_lowerCAmelCase , scheduler=_lowerCAmelCase )
@torch.no_grad()
def __call__( self: int , _lowerCAmelCase: int = 1 , _lowerCAmelCase: int = 2000 , _lowerCAmelCase: Optional[Union[torch.Generator, List[torch.Generator]]] = None , _lowerCAmelCase: Optional[str] = "pil" , _lowerCAmelCase: bool = True , **_lowerCAmelCase: Tuple , ) -> Union[ImagePipelineOutput, Tuple]:
'''simple docstring'''
UpperCAmelCase_ =self.unet.config.sample_size
UpperCAmelCase_ =(batch_size, 3, img_size, img_size)
UpperCAmelCase_ =self.unet
UpperCAmelCase_ =randn_tensor(_lowerCAmelCase , generator=_lowerCAmelCase ) * self.scheduler.init_noise_sigma
UpperCAmelCase_ =sample.to(self.device )
self.scheduler.set_timesteps(_lowerCAmelCase )
self.scheduler.set_sigmas(_lowerCAmelCase )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
UpperCAmelCase_ =self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
UpperCAmelCase_ =self.unet(_lowerCAmelCase , _lowerCAmelCase ).sample
UpperCAmelCase_ =self.scheduler.step_correct(_lowerCAmelCase , _lowerCAmelCase , generator=_lowerCAmelCase ).prev_sample
# prediction step
UpperCAmelCase_ =model(_lowerCAmelCase , _lowerCAmelCase ).sample
UpperCAmelCase_ =self.scheduler.step_pred(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , generator=_lowerCAmelCase )
UpperCAmelCase_ , UpperCAmelCase_ =output.prev_sample, output.prev_sample_mean
UpperCAmelCase_ =sample_mean.clamp(0 , 1 )
UpperCAmelCase_ =sample.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
UpperCAmelCase_ =self.numpy_to_pil(_lowerCAmelCase )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=_lowerCAmelCase )
| 54 | import math
class lowerCAmelCase_ :
def __init__( self : Tuple , _A : int=0 ): # a graph with Node 0,1,...,N-1
_UpperCamelCase = n
_UpperCamelCase = [
[math.inf for j in range(0 , _A )] for i in range(0 , _A )
] # adjacency matrix for weight
_UpperCamelCase = [
[math.inf for j in range(0 , _A )] for i in range(0 , _A )
] # dp[i][j] stores minimum distance from i to j
def UpperCamelCase_ ( self : Dict , _A : str , _A : List[str] , _A : Optional[Any] ):
_UpperCamelCase = w
def UpperCamelCase_ ( self : Optional[int] ):
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
_UpperCamelCase = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def UpperCamelCase_ ( self : List[str] , _A : Optional[int] , _A : Optional[int] ):
return self.dp[u][v]
if __name__ == "__main__":
_lowerCAmelCase = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 10 | 0 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
DYNAMO_BACKENDS,
_ask_field,
_ask_options,
_convert_dynamo_backend,
_convert_mixed_precision,
_convert_sagemaker_distributed_mode,
_convert_yes_no_to_bool,
)
if is_botoa_available():
import botoa # noqa: F401
def UpperCAmelCase ( a_ ) -> Optional[int]:
"""simple docstring"""
__A = botoa.client("iam" )
__A = {
"Version": "2012-10-17",
"Statement": [
{"Effect": "Allow", "Principal": {"Service": "sagemaker.amazonaws.com"}, "Action": "sts:AssumeRole"}
],
}
try:
# create the role, associated with the chosen trust policy
iam_client.create_role(
RoleName=a_ , AssumeRolePolicyDocument=json.dumps(a_ , indent=2 ) )
__A = {
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"sagemaker:*",
"ecr:GetDownloadUrlForLayer",
"ecr:BatchGetImage",
"ecr:BatchCheckLayerAvailability",
"ecr:GetAuthorizationToken",
"cloudwatch:PutMetricData",
"cloudwatch:GetMetricData",
"cloudwatch:GetMetricStatistics",
"cloudwatch:ListMetrics",
"logs:CreateLogGroup",
"logs:CreateLogStream",
"logs:DescribeLogStreams",
"logs:PutLogEvents",
"logs:GetLogEvents",
"s3:CreateBucket",
"s3:ListBucket",
"s3:GetBucketLocation",
"s3:GetObject",
"s3:PutObject",
],
"Resource": "*",
}
],
}
# attach policy to role
iam_client.put_role_policy(
RoleName=a_ , PolicyName=F'''{role_name}_policy_permission''' , PolicyDocument=json.dumps(a_ , indent=2 ) , )
except iam_client.exceptions.EntityAlreadyExistsException:
print(F'''role {role_name} already exists. Using existing one''' )
def UpperCAmelCase ( a_ ) -> Optional[int]:
"""simple docstring"""
__A = botoa.client("iam" )
return iam_client.get_role(RoleName=a_ )["Role"]["Arn"]
def UpperCAmelCase ( ) -> Optional[int]:
"""simple docstring"""
__A = _ask_options(
"How do you want to authorize?" , ["AWS Profile", "Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) "] , a_ , )
__A = None
if credentials_configuration == 0:
__A = _ask_field("Enter your AWS Profile name: [default] " , default="default" )
__A = aws_profile
else:
print(
"Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,"
"`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`" )
__A = _ask_field("AWS Access Key ID: " )
__A = aws_access_key_id
__A = _ask_field("AWS Secret Access Key: " )
__A = aws_secret_access_key
__A = _ask_field("Enter your AWS Region: [us-east-1]" , default="us-east-1" )
__A = aws_region
__A = _ask_options(
"Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?" , ["Provide IAM Role name", "Create new IAM role using credentials"] , a_ , )
if role_management == 0:
__A = _ask_field("Enter your IAM role name: " )
else:
__A = "accelerate_sagemaker_execution_role"
print(F'''Accelerate will create an iam role "{iam_role_name}" using the provided credentials''' )
_create_iam_role_for_sagemaker(a_ )
__A = _ask_field(
"Do you want to use custom Docker image? [yes/NO]: " , _convert_yes_no_to_bool , default=a_ , error_message="Please enter yes or no." , )
__A = None
if is_custom_docker_image:
__A = _ask_field("Enter your Docker image: " , lambda a_ : str(a_ ).lower() )
__A = _ask_field(
"Do you want to provide SageMaker input channels with data locations? [yes/NO]: " , _convert_yes_no_to_bool , default=a_ , error_message="Please enter yes or no." , )
__A = None
if is_sagemaker_inputs_enabled:
__A = _ask_field(
"Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): " , lambda a_ : str(a_ ).lower() , )
__A = _ask_field(
"Do you want to enable SageMaker metrics? [yes/NO]: " , _convert_yes_no_to_bool , default=a_ , error_message="Please enter yes or no." , )
__A = None
if is_sagemaker_metrics_enabled:
__A = _ask_field(
"Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): " , lambda a_ : str(a_ ).lower() , )
__A = _ask_options(
"What is the distributed mode?" , ["No distributed training", "Data parallelism"] , _convert_sagemaker_distributed_mode , )
__A = {}
__A = _ask_field(
"Do you wish to optimize your script with torch dynamo?[yes/NO]:" , _convert_yes_no_to_bool , default=a_ , error_message="Please enter yes or no." , )
if use_dynamo:
__A = "dynamo_"
__A = _ask_options(
"Which dynamo backend would you like to use?" , [x.lower() for x in DYNAMO_BACKENDS] , _convert_dynamo_backend , default=2 , )
__A = _ask_field(
"Do you want to customize the defaults sent to torch.compile? [yes/NO]: " , _convert_yes_no_to_bool , default=a_ , error_message="Please enter yes or no." , )
if use_custom_options:
__A = _ask_options(
"Which mode do you want to use?" , a_ , lambda a_ : TORCH_DYNAMO_MODES[int(a_ )] , default="default" , )
__A = _ask_field(
"Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: " , _convert_yes_no_to_bool , default=a_ , error_message="Please enter yes or no." , )
__A = _ask_field(
"Do you want to enable dynamic shape tracing? [yes/NO]: " , _convert_yes_no_to_bool , default=a_ , error_message="Please enter yes or no." , )
__A = "Which EC2 instance type you want to use for your training?"
if distributed_type != SageMakerDistributedType.NO:
__A = _ask_options(
a_ , a_ , lambda a_ : SAGEMAKER_PARALLEL_EC2_INSTANCES[int(a_ )] )
else:
eca_instance_query += "? [ml.p3.2xlarge]:"
__A = _ask_field(a_ , lambda a_ : str(a_ ).lower() , default="ml.p3.2xlarge" )
__A = 1
if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
__A = _ask_field(
"How many machines do you want use? [1]: " , a_ , default=1 , )
__A = _ask_options(
"Do you wish to use FP16 or BF16 (mixed precision)?" , ["no", "fp16", "bf16", "fp8"] , _convert_mixed_precision , )
if use_dynamo and mixed_precision == "no":
print(
"Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts." )
return SageMakerConfig(
image_uri=a_ , compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER , distributed_type=a_ , use_cpu=a_ , dynamo_config=a_ , eca_instance_type=a_ , profile=a_ , region=a_ , iam_role_name=a_ , mixed_precision=a_ , num_machines=a_ , sagemaker_inputs_file=a_ , sagemaker_metrics_file=a_ , )
| 55 | import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
def _snake_case ( __snake_case=None , __snake_case=None ):
return field(default_factory=lambda: default , metadata=__snake_case )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = list_field(
default=[], metadata={
"help": (
"Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
" of all available models"
)
}, )
UpperCAmelCase = list_field(
default=[8], metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"} )
UpperCAmelCase = list_field(
default=[8, 32, 128, 512], metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Use FP16 to accelerate inference."} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Benchmark training of model"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Verbose memory tracing"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
}, )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Trace memory line by line"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Save result to a CSV file"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Save all print statements in a log file"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Whether to print environment information"} )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
" multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
" for debugging / testing and on TPU."
)
}, )
UpperCAmelCase = field(
default=F"""inference_time_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving time results to csv."}, )
UpperCAmelCase = field(
default=F"""inference_memory_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving memory results to csv."}, )
UpperCAmelCase = field(
default=F"""train_time_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving time results to csv for training."}, )
UpperCAmelCase = field(
default=F"""train_memory_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving memory results to csv for training."}, )
UpperCAmelCase = field(
default=F"""env_info_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving environment information."}, )
UpperCAmelCase = field(
default=F"""log_{round(time() )}.csv""", metadata={"help": "Log filename used if print statements are saved in log."}, )
UpperCAmelCase = field(default=3, metadata={"help": "Times an experiment will be run."} )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
" model weights."
)
}, )
def UpperCamelCase_ ( self : Union[str, Any] ):
warnings.warn(
F"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
''' are deprecated in general and it is advised to use external Benchmarking libraries '''
''' to benchmark Transformer models.''' , _A , )
def UpperCamelCase_ ( self : str ):
return json.dumps(dataclasses.asdict(self ) , indent=2 )
@property
def UpperCamelCase_ ( self : List[Any] ):
if len(self.models ) <= 0:
raise ValueError(
'''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
''' bert-base-cased` or `args.models = [\'bert-base-cased\'].''' )
return self.models
@property
def UpperCamelCase_ ( self : Optional[int] ):
if not self.multi_process:
return False
elif self.is_tpu:
logger.info('''Multiprocessing is currently not possible on TPU.''' )
return False
else:
return True
| 10 | 0 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class _lowercase ( __lowercase ):
_SCREAMING_SNAKE_CASE : torch.FloatTensor
_SCREAMING_SNAKE_CASE : torch.FloatTensor
class _lowercase ( __lowercase , __lowercase ):
_SCREAMING_SNAKE_CASE : int = 1
@register_to_config
def __init__( self : Dict , SCREAMING_SNAKE_CASE_ : int = 2000 , SCREAMING_SNAKE_CASE_ : float = 0.1_5 , SCREAMING_SNAKE_CASE_ : float = 0.0_1 , SCREAMING_SNAKE_CASE_ : float = 1_3_4_8.0 , SCREAMING_SNAKE_CASE_ : float = 1e-5 , SCREAMING_SNAKE_CASE_ : int = 1 , ) -> str:
# standard deviation of the initial noise distribution
__snake_case = sigma_max
# setable values
__snake_case = None
self.set_sigmas(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : torch.FloatTensor , SCREAMING_SNAKE_CASE_ : Optional[int] = None ) -> torch.FloatTensor:
return sample
def a ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : float = None , SCREAMING_SNAKE_CASE_ : Union[str, torch.device] = None ) -> Any:
__snake_case = sampling_eps if sampling_eps is not None else self.config.sampling_eps
__snake_case = torch.linspace(1 , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , device=SCREAMING_SNAKE_CASE_ )
def a ( self : Dict , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : float = None , SCREAMING_SNAKE_CASE_ : float = None , SCREAMING_SNAKE_CASE_ : float = None ) -> str:
__snake_case = sigma_min if sigma_min is not None else self.config.sigma_min
__snake_case = sigma_max if sigma_max is not None else self.config.sigma_max
__snake_case = sampling_eps if sampling_eps is not None else self.config.sampling_eps
if self.timesteps is None:
self.set_timesteps(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
__snake_case = sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
__snake_case = torch.exp(torch.linspace(math.log(SCREAMING_SNAKE_CASE_ ) , math.log(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ ) )
__snake_case = torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps] )
def a ( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : int ) -> Union[str, Any]:
return torch.where(
timesteps == 0 , torch.zeros_like(t.to(timesteps.device ) ) , self.discrete_sigmas[timesteps - 1].to(timesteps.device ) , )
def a ( self : Tuple , SCREAMING_SNAKE_CASE_ : torch.FloatTensor , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : torch.FloatTensor , SCREAMING_SNAKE_CASE_ : Optional[torch.Generator] = None , SCREAMING_SNAKE_CASE_ : bool = True , ) -> Union[SdeVeOutput, Tuple]:
if self.timesteps is None:
raise ValueError(
'`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler' )
__snake_case = timestep * torch.ones(
sample.shape[0] , device=sample.device ) # torch.repeat_interleave(timestep, sample.shape[0])
__snake_case = (timestep * (len(self.timesteps ) - 1)).long()
# mps requires indices to be in the same device, so we use cpu as is the default with cuda
__snake_case = timesteps.to(self.discrete_sigmas.device )
__snake_case = self.discrete_sigmas[timesteps].to(sample.device )
__snake_case = self.get_adjacent_sigma(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).to(sample.device )
__snake_case = torch.zeros_like(SCREAMING_SNAKE_CASE_ )
__snake_case = (sigma**2 - adjacent_sigma**2) ** 0.5
# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
# also equation 47 shows the analog from SDE models to ancestral sampling methods
__snake_case = diffusion.flatten()
while len(diffusion.shape ) < len(sample.shape ):
__snake_case = diffusion.unsqueeze(-1 )
__snake_case = drift - diffusion**2 * model_output
# equation 6: sample noise for the diffusion term of
__snake_case = randn_tensor(
sample.shape , layout=sample.layout , generator=SCREAMING_SNAKE_CASE_ , device=sample.device , dtype=sample.dtype )
__snake_case = sample - drift # subtract because `dt` is a small negative timestep
# TODO is the variable diffusion the correct scaling term for the noise?
__snake_case = prev_sample_mean + diffusion * noise # add impact of diffusion field g
if not return_dict:
return (prev_sample, prev_sample_mean)
return SdeVeOutput(prev_sample=SCREAMING_SNAKE_CASE_ , prev_sample_mean=SCREAMING_SNAKE_CASE_ )
def a ( self : Tuple , SCREAMING_SNAKE_CASE_ : torch.FloatTensor , SCREAMING_SNAKE_CASE_ : torch.FloatTensor , SCREAMING_SNAKE_CASE_ : Optional[torch.Generator] = None , SCREAMING_SNAKE_CASE_ : bool = True , ) -> Union[SchedulerOutput, Tuple]:
if self.timesteps is None:
raise ValueError(
'`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler' )
# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
# sample noise for correction
__snake_case = randn_tensor(sample.shape , layout=sample.layout , generator=SCREAMING_SNAKE_CASE_ ).to(sample.device )
# compute step size from the model_output, the noise, and the snr
__snake_case = torch.norm(model_output.reshape(model_output.shape[0] , -1 ) , dim=-1 ).mean()
__snake_case = torch.norm(noise.reshape(noise.shape[0] , -1 ) , dim=-1 ).mean()
__snake_case = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
__snake_case = step_size * torch.ones(sample.shape[0] ).to(sample.device )
# self.repeat_scalar(step_size, sample.shape[0])
# compute corrected sample: model_output term and noise term
__snake_case = step_size.flatten()
while len(step_size.shape ) < len(sample.shape ):
__snake_case = step_size.unsqueeze(-1 )
__snake_case = sample + step_size * model_output
__snake_case = prev_sample_mean + ((step_size * 2) ** 0.5) * noise
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_ )
def a ( self : List[str] , SCREAMING_SNAKE_CASE_ : torch.FloatTensor , SCREAMING_SNAKE_CASE_ : torch.FloatTensor , SCREAMING_SNAKE_CASE_ : torch.FloatTensor , ) -> torch.FloatTensor:
# Make sure sigmas and timesteps have the same device and dtype as original_samples
__snake_case = timesteps.to(original_samples.device )
__snake_case = self.discrete_sigmas.to(original_samples.device )[timesteps]
__snake_case = (
noise * sigmas[:, None, None, None]
if noise is not None
else torch.randn_like(SCREAMING_SNAKE_CASE_ ) * sigmas[:, None, None, None]
)
__snake_case = noise + original_samples
return noisy_samples
def __len__( self : Union[str, Any] ) -> Optional[Any]:
return self.config.num_train_timesteps
| 56 | import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _snake_case ( *__snake_case , __snake_case = None , __snake_case=True , __snake_case=2 ):
from .. import __version__
_UpperCamelCase = take_from
_UpperCamelCase = ()
if not isinstance(args[0] , __snake_case ):
_UpperCamelCase = (args,)
for attribute, version_name, message in args:
if version.parse(version.parse(__snake_case ).base_version ) >= version.parse(__snake_case ):
raise ValueError(
f"""The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"""
f""" version {__version__} is >= {version_name}""" )
_UpperCamelCase = None
if isinstance(__snake_case , __snake_case ) and attribute in deprecated_kwargs:
values += (deprecated_kwargs.pop(__snake_case ),)
_UpperCamelCase = f"""The `{attribute}` argument is deprecated and will be removed in version {version_name}."""
elif hasattr(__snake_case , __snake_case ):
values += (getattr(__snake_case , __snake_case ),)
_UpperCamelCase = f"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
elif deprecated_kwargs is None:
_UpperCamelCase = f"""`{attribute}` is deprecated and will be removed in version {version_name}."""
if warning is not None:
_UpperCamelCase = warning + ''' ''' if standard_warn else ''''''
warnings.warn(warning + message , __snake_case , stacklevel=__snake_case )
if isinstance(__snake_case , __snake_case ) and len(__snake_case ) > 0:
_UpperCamelCase = inspect.getouterframes(inspect.currentframe() )[1]
_UpperCamelCase = call_frame.filename
_UpperCamelCase = call_frame.lineno
_UpperCamelCase = call_frame.function
_UpperCamelCase , _UpperCamelCase = next(iter(deprecated_kwargs.items() ) )
raise TypeError(f"""{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`""" )
if len(__snake_case ) == 0:
return
elif len(__snake_case ) == 1:
return values[0]
return values
| 10 | 0 |
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
A_ : Optional[Any] = False
try:
A_ : List[str] = _is_package_available('google.colab')
except ModuleNotFoundError:
pass
@input.register
class _lowerCAmelCase:
"""simple docstring"""
def __init__( self , _lowerCamelCase = None , _lowerCamelCase = [] ):
UpperCamelCase_: List[Any] = 0
UpperCamelCase_: Optional[Any] = choices
UpperCamelCase_: List[str] = prompt
if sys.platform == "win32":
UpperCamelCase_: Tuple = '*'
else:
UpperCamelCase_: Any = '➔ '
def _a ( self , _lowerCamelCase , _lowerCamelCase = "" ):
if sys.platform != "win32":
writeColor(self.choices[index] , 3_2 , _lowerCamelCase )
else:
forceWrite(self.choices[index] , _lowerCamelCase )
def _a ( self , _lowerCamelCase ):
if index == self.position:
forceWrite(f''' {self.arrow_char} ''' )
self.write_choice(_lowerCamelCase )
else:
forceWrite(f''' {self.choices[index]}''' )
reset_cursor()
def _a ( self , _lowerCamelCase , _lowerCamelCase = 1 ):
UpperCamelCase_: Tuple = self.position
if direction == Direction.DOWN:
if self.position + 1 >= len(self.choices ):
return
self.position += num_spaces
else:
if self.position - 1 < 0:
return
self.position -= num_spaces
clear_line()
self.print_choice(_lowerCamelCase )
move_cursor(_lowerCamelCase , direction.name )
self.print_choice(self.position )
@input.mark(KEYMAP['up'] )
def _a ( self ):
self.move_direction(Direction.UP )
@input.mark(KEYMAP['down'] )
def _a ( self ):
self.move_direction(Direction.DOWN )
@input.mark(KEYMAP['newline'] )
def _a ( self ):
move_cursor(len(self.choices ) - self.position , 'DOWN' )
return self.position
@input.mark(KEYMAP['interrupt'] )
def _a ( self ):
move_cursor(len(self.choices ) - self.position , 'DOWN' )
raise KeyboardInterrupt
@input.mark_multiple(*[KEYMAP[str(_lowerCamelCase )] for number in range(1_0 )] )
def _a ( self ):
UpperCamelCase_: Dict = int(chr(self.current_selection ) )
UpperCamelCase_: Tuple = index - self.position
if index == self.position:
return
if index < len(self.choices ):
if self.position > index:
self.move_direction(Direction.UP , -movement )
elif self.position < index:
self.move_direction(Direction.DOWN , _lowerCamelCase )
else:
return
else:
return
def _a ( self , _lowerCamelCase = 0 ):
if self.prompt:
linebreak()
forceWrite(self.prompt , '\n' )
if in_colab:
forceWrite('Please input a choice index (starting from 0), and press enter' , '\n' )
else:
forceWrite('Please select a choice using the arrow or number keys, and selecting with enter' , '\n' )
UpperCamelCase_: Tuple = default_choice
for i in range(len(self.choices ) ):
self.print_choice(_lowerCamelCase )
forceWrite('\n' )
move_cursor(len(self.choices ) - self.position , 'UP' )
with cursor.hide():
while True:
if in_colab:
try:
UpperCamelCase_: Tuple = int(builtins.input() )
except ValueError:
UpperCamelCase_: Any = default_choice
else:
UpperCamelCase_: Optional[Any] = self.handle_input()
if choice is not None:
reset_cursor()
for _ in range(len(self.choices ) + 1 ):
move_cursor(1 , 'UP' )
clear_line()
self.write_choice(_lowerCamelCase , '\n' )
return choice | 57 | import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
_lowerCAmelCase = logging.getLogger(__name__)
def _snake_case ( __snake_case , __snake_case ):
return (preds == labels).mean()
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Pretrained config name or path if not the same as model_name"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys() )} )
UpperCAmelCase = field(metadata={"help": "Should contain the data files for the task."} )
UpperCAmelCase = field(
default=128, metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Overwrite the cached training and evaluation sets"} )
def _snake_case ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_UpperCamelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
''' --overwrite_output_dir to overcome.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('''Training/evaluation parameters %s''' , __snake_case )
# Set seed
set_seed(training_args.seed )
try:
_UpperCamelCase = processors[data_args.task_name]()
_UpperCamelCase = processor.get_labels()
_UpperCamelCase = len(__snake_case )
except KeyError:
raise ValueError('''Task not found: %s''' % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCamelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__snake_case , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
_UpperCamelCase = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
_UpperCamelCase = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=__snake_case , cache_dir=model_args.cache_dir , )
# Get datasets
_UpperCamelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__snake_case , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
_UpperCamelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__snake_case , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def compute_metrics(__snake_case ) -> Dict:
_UpperCamelCase = np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(__snake_case , p.label_ids )}
# Data collator
_UpperCamelCase = DataCollatorWithPadding(__snake_case , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
_UpperCamelCase = Trainer(
model=__snake_case , args=__snake_case , train_dataset=__snake_case , eval_dataset=__snake_case , compute_metrics=__snake_case , data_collator=__snake_case , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
_UpperCamelCase = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
_UpperCamelCase = trainer.evaluate()
_UpperCamelCase = os.path.join(training_args.output_dir , '''eval_results.txt''' )
if trainer.is_world_master():
with open(__snake_case , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''' , __snake_case , __snake_case )
writer.write('''%s = %s\n''' % (key, value) )
results.update(__snake_case )
return results
def _snake_case ( __snake_case ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 10 | 0 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
__lowerCAmelCase : List[Any] = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
# See all BART models at https://huggingface.co/models?filter=bart
__lowerCAmelCase : Tuple = {
'''vocab_file''': {
'''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
'''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
'''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
'''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
'''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
},
'''merges_file''': {
'''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
'''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
'''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
'''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
'''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
},
}
__lowerCAmelCase : Optional[Any] = {
'''facebook/bart-base''': 1024,
'''facebook/bart-large''': 1024,
'''facebook/bart-large-mnli''': 1024,
'''facebook/bart-large-cnn''': 1024,
'''facebook/bart-large-xsum''': 1024,
'''yjernite/bart_eli5''': 1024,
}
@lru_cache()
def __lowerCAmelCase ( ):
'''simple docstring'''
snake_case_ : Optional[Any] = (
list(range(ord("""!""" ) , ord("""~""" ) + 1 ) ) + list(range(ord("""¡""" ) , ord("""¬""" ) + 1 ) ) + list(range(ord("""®""" ) , ord("""ÿ""" ) + 1 ) )
)
snake_case_ : Tuple = bs[:]
snake_case_ : Any = 0
for b in range(2**8 ):
if b not in bs:
bs.append(__UpperCamelCase )
cs.append(2**8 + n )
n += 1
snake_case_ : Dict = [chr(__UpperCamelCase ) for n in cs]
return dict(zip(__UpperCamelCase , __UpperCamelCase ) )
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : List[Any] = set()
snake_case_ : int = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
snake_case_ : str = char
return pairs
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = VOCAB_FILES_NAMES
_lowerCamelCase = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCamelCase = ['''input_ids''', '''attention_mask''']
def __init__( self , _lowercase , _lowercase , _lowercase="replace" , _lowercase="<s>" , _lowercase="</s>" , _lowercase="</s>" , _lowercase="<s>" , _lowercase="<unk>" , _lowercase="<pad>" , _lowercase="<mask>" , _lowercase=False , **_lowercase , ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Dict = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else bos_token
snake_case_ : Optional[int] = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else eos_token
snake_case_ : Dict = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else sep_token
snake_case_ : List[Any] = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else cls_token
snake_case_ : Any = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else unk_token
snake_case_ : str = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
snake_case_ : Tuple = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else mask_token
super().__init__(
errors=_lowercase , bos_token=_lowercase , eos_token=_lowercase , unk_token=_lowercase , sep_token=_lowercase , cls_token=_lowercase , pad_token=_lowercase , mask_token=_lowercase , add_prefix_space=_lowercase , **_lowercase , )
with open(_lowercase , encoding="""utf-8""" ) as vocab_handle:
snake_case_ : Optional[Any] = json.load(_lowercase )
snake_case_ : Any = {v: k for k, v in self.encoder.items()}
snake_case_ : Optional[Any] = errors # how to handle errors in decoding
snake_case_ : List[Any] = bytes_to_unicode()
snake_case_ : Tuple = {v: k for k, v in self.byte_encoder.items()}
with open(_lowercase , encoding="""utf-8""" ) as merges_handle:
snake_case_ : str = merges_handle.read().split("""\n""" )[1:-1]
snake_case_ : List[Any] = [tuple(merge.split() ) for merge in bpe_merges]
snake_case_ : List[Any] = dict(zip(_lowercase , range(len(_lowercase ) ) ) )
snake_case_ : Dict = {}
snake_case_ : List[str] = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
snake_case_ : int = re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""" )
@property
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
return len(self.encoder )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def UpperCAmelCase__ ( self , _lowercase ) -> List[str]:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
snake_case_ : Optional[int] = tuple(_lowercase )
snake_case_ : List[Any] = get_pairs(_lowercase )
if not pairs:
return token
while True:
snake_case_ : Optional[int] = min(_lowercase , key=lambda _lowercase : self.bpe_ranks.get(_lowercase , float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
snake_case_ , snake_case_ : List[str] = bigram
snake_case_ : List[str] = []
snake_case_ : Optional[int] = 0
while i < len(_lowercase ):
try:
snake_case_ : Any = word.index(_lowercase , _lowercase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
snake_case_ : List[str] = j
if word[i] == first and i < len(_lowercase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
snake_case_ : Union[str, Any] = tuple(_lowercase )
snake_case_ : int = new_word
if len(_lowercase ) == 1:
break
else:
snake_case_ : Tuple = get_pairs(_lowercase )
snake_case_ : Dict = """ """.join(_lowercase )
snake_case_ : Any = word
return word
def UpperCAmelCase__ ( self , _lowercase ) -> List[Any]:
'''simple docstring'''
snake_case_ : str = []
for token in re.findall(self.pat , _lowercase ):
snake_case_ : Optional[Any] = """""".join(
self.byte_encoder[b] for b in token.encode("""utf-8""" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowercase ).split(""" """ ) )
return bpe_tokens
def UpperCAmelCase__ ( self , _lowercase ) -> Tuple:
'''simple docstring'''
return self.encoder.get(_lowercase , self.encoder.get(self.unk_token ) )
def UpperCAmelCase__ ( self , _lowercase ) -> int:
'''simple docstring'''
return self.decoder.get(_lowercase )
def UpperCAmelCase__ ( self , _lowercase ) -> List[str]:
'''simple docstring'''
snake_case_ : str = """""".join(_lowercase )
snake_case_ : Optional[Any] = bytearray([self.byte_decoder[c] for c in text] ).decode("""utf-8""" , errors=self.errors )
return text
def UpperCAmelCase__ ( self , _lowercase , _lowercase = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(_lowercase ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
snake_case_ : int = os.path.join(
_lowercase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
snake_case_ : Optional[int] = os.path.join(
_lowercase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )
with open(_lowercase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=_lowercase , ensure_ascii=_lowercase ) + """\n""" )
snake_case_ : Optional[Any] = 0
with open(_lowercase , """w""" , encoding="""utf-8""" ) as writer:
writer.write("""#version: 0.2\n""" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda _lowercase : kv[1] ):
if index != token_index:
logger.warning(
f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
""" Please check that the tokenizer is not corrupted!""" )
snake_case_ : Union[str, Any] = token_index
writer.write(""" """.join(_lowercase ) + """\n""" )
index += 1
return vocab_file, merge_file
def UpperCAmelCase__ ( self , _lowercase , _lowercase = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
snake_case_ : Dict = [self.cls_token_id]
snake_case_ : str = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCAmelCase__ ( self , _lowercase , _lowercase = None , _lowercase = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowercase , token_ids_a=_lowercase , already_has_special_tokens=_lowercase )
if token_ids_a is None:
return [1] + ([0] * len(_lowercase )) + [1]
return [1] + ([0] * len(_lowercase )) + [1, 1] + ([0] * len(_lowercase )) + [1]
def UpperCAmelCase__ ( self , _lowercase , _lowercase = None ) -> List[int]:
'''simple docstring'''
snake_case_ : Optional[int] = [self.sep_token_id]
snake_case_ : Optional[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def UpperCAmelCase__ ( self , _lowercase , _lowercase=False , **_lowercase ) -> List[str]:
'''simple docstring'''
snake_case_ : Any = kwargs.pop("""add_prefix_space""" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(_lowercase ) > 0 and not text[0].isspace()):
snake_case_ : Any = """ """ + text
return (text, kwargs)
| 58 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
),
# See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = "trocr"
UpperCAmelCase = ["past_key_values"]
UpperCAmelCase = {
"num_attention_heads": "decoder_attention_heads",
"hidden_size": "d_model",
"num_hidden_layers": "decoder_layers",
}
def __init__( self : List[str] , _A : Optional[Any]=5_0265 , _A : Optional[Any]=1024 , _A : Optional[Any]=12 , _A : Any=16 , _A : Any=4096 , _A : Optional[Any]="gelu" , _A : Union[str, Any]=512 , _A : Dict=0.1 , _A : List[str]=0.0 , _A : Optional[Any]=0.0 , _A : Union[str, Any]=2 , _A : Any=0.02 , _A : List[str]=0.0 , _A : List[str]=True , _A : str=False , _A : List[str]=True , _A : Optional[Any]=True , _A : Optional[int]=1 , _A : int=0 , _A : Any=2 , **_A : Optional[int] , ):
_UpperCamelCase = vocab_size
_UpperCamelCase = d_model
_UpperCamelCase = decoder_layers
_UpperCamelCase = decoder_attention_heads
_UpperCamelCase = decoder_ffn_dim
_UpperCamelCase = activation_function
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = activation_dropout
_UpperCamelCase = init_std
_UpperCamelCase = decoder_layerdrop
_UpperCamelCase = use_cache
_UpperCamelCase = scale_embedding
_UpperCamelCase = use_learned_position_embeddings
_UpperCamelCase = layernorm_embedding
super().__init__(
pad_token_id=_A , bos_token_id=_A , eos_token_id=_A , decoder_start_token_id=_A , **_A , )
| 10 | 0 |
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
__A = logging.get_logger(__name__)
@add_end_docstrings(__SCREAMING_SNAKE_CASE )
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__(self : Optional[int] , **UpperCAmelCase_ : List[Any]) ->List[str]:
'''simple docstring'''
super().__init__(**UpperCAmelCase_)
requires_backends(self , "vision")
self.check_model_type(
TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if self.framework == "tf"
else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING)
def __call__(self : List[str] , UpperCAmelCase_ : Union[str, List[str], "Image", List["Image"]] , **UpperCAmelCase_ : List[Any]) ->Tuple:
'''simple docstring'''
return super().__call__(UpperCAmelCase_ , **UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : List[Any] , **UpperCAmelCase_ : Optional[int]) ->Any:
'''simple docstring'''
lowerCamelCase__: Optional[int] ={}
if "candidate_labels" in kwargs:
lowerCamelCase__: Tuple =kwargs["candidate_labels"]
if "hypothesis_template" in kwargs:
lowerCamelCase__: Tuple =kwargs["hypothesis_template"]
return preprocess_params, {}, {}
def SCREAMING_SNAKE_CASE_ (self : Optional[int] , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Dict=None , UpperCAmelCase_ : Optional[Any]="This is a photo of {}.") ->str:
'''simple docstring'''
lowerCamelCase__: int =load_image(UpperCAmelCase_)
lowerCamelCase__: Any =self.image_processor(images=[image] , return_tensors=self.framework)
lowerCamelCase__: Any =candidate_labels
lowerCamelCase__: List[str] =[hypothesis_template.format(UpperCAmelCase_) for x in candidate_labels]
lowerCamelCase__: int =self.tokenizer(UpperCAmelCase_ , return_tensors=self.framework , padding=UpperCAmelCase_)
lowerCamelCase__: str =[text_inputs]
return inputs
def SCREAMING_SNAKE_CASE_ (self : Optional[int] , UpperCAmelCase_ : Any) ->Tuple:
'''simple docstring'''
lowerCamelCase__: int =model_inputs.pop("candidate_labels")
lowerCamelCase__: List[str] =model_inputs.pop("text_inputs")
if isinstance(text_inputs[0] , UpperCAmelCase_):
lowerCamelCase__: List[Any] =text_inputs[0]
else:
# Batching case.
lowerCamelCase__: List[Any] =text_inputs[0][0]
lowerCamelCase__: List[str] =self.model(**UpperCAmelCase_ , **UpperCAmelCase_)
lowerCamelCase__: str ={
"candidate_labels": candidate_labels,
"logits": outputs.logits_per_image,
}
return model_outputs
def SCREAMING_SNAKE_CASE_ (self : Optional[Any] , UpperCAmelCase_ : Union[str, Any]) ->int:
'''simple docstring'''
lowerCamelCase__: List[Any] =model_outputs.pop("candidate_labels")
lowerCamelCase__: Optional[int] =model_outputs["logits"][0]
if self.framework == "pt":
lowerCamelCase__: Optional[Any] =logits.softmax(dim=-1).squeeze(-1)
lowerCamelCase__: Optional[Any] =probs.tolist()
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_):
lowerCamelCase__: Optional[int] =[scores]
elif self.framework == "tf":
lowerCamelCase__: List[str] =stable_softmax(UpperCAmelCase_ , axis=-1)
lowerCamelCase__: Optional[int] =probs.numpy().tolist()
else:
raise ValueError(F"""Unsupported framework: {self.framework}""")
lowerCamelCase__: Optional[int] =[
{"score": score, "label": candidate_label}
for score, candidate_label in sorted(zip(UpperCAmelCase_ , UpperCAmelCase_) , key=lambda UpperCAmelCase_: -x[0])
]
return result
| 59 | import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
FlaubertForMultipleChoice,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertModel,
FlaubertWithLMHeadModel,
)
from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase_ ( __lowercase ):
def __init__( self : Union[str, Any] , _A : Optional[Any] , _A : Any=13 , _A : Union[str, Any]=7 , _A : List[str]=True , _A : List[str]=True , _A : List[str]=True , _A : List[str]=True , _A : List[Any]=True , _A : Optional[int]=False , _A : Any=False , _A : int=False , _A : Optional[Any]=2 , _A : Any=99 , _A : str=0 , _A : Union[str, Any]=32 , _A : List[Any]=5 , _A : Tuple=4 , _A : List[str]=0.1 , _A : Union[str, Any]=0.1 , _A : int=512 , _A : Union[str, Any]=12 , _A : List[str]=2 , _A : int=0.02 , _A : Optional[Any]=3 , _A : Any=4 , _A : Optional[int]="last" , _A : Any=None , _A : Dict=None , ):
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = seq_length
_UpperCamelCase = is_training
_UpperCamelCase = use_input_lengths
_UpperCamelCase = use_token_type_ids
_UpperCamelCase = use_labels
_UpperCamelCase = gelu_activation
_UpperCamelCase = sinusoidal_embeddings
_UpperCamelCase = causal
_UpperCamelCase = asm
_UpperCamelCase = n_langs
_UpperCamelCase = vocab_size
_UpperCamelCase = n_special
_UpperCamelCase = hidden_size
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = hidden_dropout_prob
_UpperCamelCase = attention_probs_dropout_prob
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = type_vocab_size
_UpperCamelCase = type_sequence_label_size
_UpperCamelCase = initializer_range
_UpperCamelCase = num_labels
_UpperCamelCase = num_choices
_UpperCamelCase = summary_type
_UpperCamelCase = use_proj
_UpperCamelCase = scope
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCamelCase = None
if self.use_input_lengths:
_UpperCamelCase = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
_UpperCamelCase = None
if self.use_token_type_ids:
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = None
if self.use_labels:
_UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCamelCase = ids_tensor([self.batch_size] , 2 ).float()
_UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices )
_UpperCamelCase = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def UpperCamelCase_ ( self : str ):
return FlaubertConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , )
def UpperCamelCase_ ( self : str , _A : Union[str, Any] , _A : Optional[Any] , _A : str , _A : Tuple , _A : List[str] , _A : List[Any] , _A : Any , _A : str , _A : Optional[int] , ):
_UpperCamelCase = FlaubertModel(config=_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A , lengths=_A , langs=_A )
_UpperCamelCase = model(_A , langs=_A )
_UpperCamelCase = model(_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase_ ( self : Tuple , _A : List[Any] , _A : str , _A : Optional[int] , _A : Optional[Any] , _A : List[str] , _A : int , _A : str , _A : List[Any] , _A : Any , ):
_UpperCamelCase = FlaubertWithLMHeadModel(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A , token_type_ids=_A , labels=_A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase_ ( self : Tuple , _A : List[str] , _A : List[str] , _A : Optional[Any] , _A : Union[str, Any] , _A : str , _A : List[str] , _A : Tuple , _A : Optional[int] , _A : Dict , ):
_UpperCamelCase = FlaubertForQuestionAnsweringSimple(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A )
_UpperCamelCase = model(_A , start_positions=_A , end_positions=_A )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase_ ( self : Tuple , _A : str , _A : Tuple , _A : Tuple , _A : Union[str, Any] , _A : List[str] , _A : int , _A : str , _A : Dict , _A : List[Any] , ):
_UpperCamelCase = FlaubertForQuestionAnswering(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A )
_UpperCamelCase = model(
_A , start_positions=_A , end_positions=_A , cls_index=_A , is_impossible=_A , p_mask=_A , )
_UpperCamelCase = model(
_A , start_positions=_A , end_positions=_A , cls_index=_A , is_impossible=_A , )
((_UpperCamelCase) , ) = result_with_labels.to_tuple()
_UpperCamelCase = model(_A , start_positions=_A , end_positions=_A )
((_UpperCamelCase) , ) = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def UpperCamelCase_ ( self : List[Any] , _A : Union[str, Any] , _A : Tuple , _A : str , _A : int , _A : int , _A : Optional[int] , _A : Optional[int] , _A : int , _A : List[str] , ):
_UpperCamelCase = FlaubertForSequenceClassification(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A )
_UpperCamelCase = model(_A , labels=_A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCamelCase_ ( self : Optional[int] , _A : List[str] , _A : Optional[Any] , _A : str , _A : Union[str, Any] , _A : List[Any] , _A : int , _A : List[Any] , _A : str , _A : List[str] , ):
_UpperCamelCase = self.num_labels
_UpperCamelCase = FlaubertForTokenClassification(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A , attention_mask=_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase_ ( self : Tuple , _A : Dict , _A : str , _A : Optional[Any] , _A : List[str] , _A : Any , _A : Optional[int] , _A : Optional[Any] , _A : List[Any] , _A : List[str] , ):
_UpperCamelCase = self.num_choices
_UpperCamelCase = FlaubertForMultipleChoice(config=_A )
model.to(_A )
model.eval()
_UpperCamelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCamelCase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCamelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCamelCase = model(
_A , attention_mask=_A , token_type_ids=_A , labels=_A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCamelCase_ ( self : Tuple ):
_UpperCamelCase = self.prepare_config_and_inputs()
(
(
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) ,
) = config_and_inputs
_UpperCamelCase = {
'''input_ids''': input_ids,
'''token_type_ids''': token_type_ids,
'''lengths''': input_lengths,
'''attention_mask''': input_mask,
}
return config, inputs_dict
@require_torch
class lowerCAmelCase_ ( __lowercase, __lowercase, unittest.TestCase ):
UpperCAmelCase = (
(
FlaubertModel,
FlaubertWithLMHeadModel,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertForMultipleChoice,
)
if is_torch_available()
else ()
)
UpperCAmelCase = (
{
"feature-extraction": FlaubertModel,
"fill-mask": FlaubertWithLMHeadModel,
"question-answering": FlaubertForQuestionAnsweringSimple,
"text-classification": FlaubertForSequenceClassification,
"token-classification": FlaubertForTokenClassification,
"zero-shot": FlaubertForSequenceClassification,
}
if is_torch_available()
else {}
)
def UpperCamelCase_ ( self : Union[str, Any] , _A : Dict , _A : Dict , _A : Tuple , _A : int , _A : Any ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('''Fast''' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def UpperCamelCase_ ( self : str , _A : Any , _A : List[str] , _A : Optional[int]=False ):
_UpperCamelCase = super()._prepare_for_class(_A , _A , return_labels=_A )
if return_labels:
if model_class.__name__ == "FlaubertForQuestionAnswering":
_UpperCamelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_A )
_UpperCamelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_A )
return inputs_dict
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = FlaubertModelTester(self )
_UpperCamelCase = ConfigTester(self , config_class=_A , emb_dim=37 )
def UpperCamelCase_ ( self : Optional[Any] ):
self.config_tester.run_common_tests()
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_model(*_A )
def UpperCamelCase_ ( self : Optional[Any] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_lm_head(*_A )
def UpperCamelCase_ ( self : Optional[Any] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_simple_qa(*_A )
def UpperCamelCase_ ( self : Union[str, Any] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_qa(*_A )
def UpperCamelCase_ ( self : Optional[int] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_sequence_classif(*_A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_token_classif(*_A )
def UpperCamelCase_ ( self : Optional[int] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_multiple_choice(*_A )
@slow
def UpperCamelCase_ ( self : str ):
for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = FlaubertModel.from_pretrained(_A )
self.assertIsNotNone(_A )
@slow
@require_torch_gpu
def UpperCamelCase_ ( self : List[Any] ):
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == FlaubertForMultipleChoice:
return
_UpperCamelCase = True
_UpperCamelCase = model_class(config=_A )
_UpperCamelCase = self._prepare_for_class(_A , _A )
_UpperCamelCase = torch.jit.trace(
_A , (inputs_dict['''input_ids'''].to('''cpu''' ), inputs_dict['''attention_mask'''].to('''cpu''' )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(_A , os.path.join(_A , '''traced_model.pt''' ) )
_UpperCamelCase = torch.jit.load(os.path.join(_A , '''traced_model.pt''' ) , map_location=_A )
loaded(inputs_dict['''input_ids'''].to(_A ) , inputs_dict['''attention_mask'''].to(_A ) )
@require_torch
class lowerCAmelCase_ ( unittest.TestCase ):
@slow
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = FlaubertModel.from_pretrained('''flaubert/flaubert_base_cased''' )
_UpperCamelCase = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
with torch.no_grad():
_UpperCamelCase = model(_A )[0]
_UpperCamelCase = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , _A )
_UpperCamelCase = torch.tensor(
[[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _A , atol=1e-4 ) )
| 10 | 0 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
| 60 | from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase_ :
def __init__( self : Any , _A : int , _A : int=12 , _A : int=7 , _A : Tuple=True , _A : Optional[int]=True , _A : Union[str, Any]=True , _A : str=99 , _A : str=32 , _A : int=32 , _A : Optional[Any]=2 , _A : Dict=4 , _A : int=37 , _A : List[Any]=0.1 , _A : str=0.1 , _A : Any=512 , _A : int=0.02 , _A : Optional[Any]=0 , _A : Dict=None , ):
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = seq_length
_UpperCamelCase = is_training
_UpperCamelCase = use_input_mask
_UpperCamelCase = use_labels
_UpperCamelCase = vocab_size
_UpperCamelCase = hidden_size
_UpperCamelCase = projection_dim
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = intermediate_size
_UpperCamelCase = dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = initializer_range
_UpperCamelCase = scope
_UpperCamelCase = bos_token_id
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCamelCase = None
if self.use_input_mask:
_UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
_UpperCamelCase = input_mask.numpy()
_UpperCamelCase , _UpperCamelCase = input_mask.shape
_UpperCamelCase = np.random.randint(1 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(_A ):
_UpperCamelCase = 1
_UpperCamelCase = 0
_UpperCamelCase = self.get_config()
return config, input_ids, tf.convert_to_tensor(_A )
def UpperCamelCase_ ( self : str ):
return BlipTextConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , projection_dim=self.projection_dim , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , dropout=self.dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , bos_token_id=self.bos_token_id , )
def UpperCamelCase_ ( self : List[str] , _A : Tuple , _A : str , _A : Optional[Any] ):
_UpperCamelCase = TFBlipTextModel(config=_A )
_UpperCamelCase = model(_A , attention_mask=_A , training=_A )
_UpperCamelCase = model(_A , training=_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def UpperCamelCase_ ( self : Tuple ):
_UpperCamelCase = self.prepare_config_and_inputs()
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = config_and_inputs
_UpperCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class lowerCAmelCase_ ( __lowercase, unittest.TestCase ):
UpperCAmelCase = (TFBlipTextModel,) if is_tf_available() else ()
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = False
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = BlipTextModelTester(self )
_UpperCamelCase = ConfigTester(self , config_class=_A , hidden_size=37 )
def UpperCamelCase_ ( self : Dict ):
self.config_tester.run_common_tests()
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_A )
def UpperCamelCase_ ( self : List[Any] ):
pass
def UpperCamelCase_ ( self : Tuple ):
pass
@unittest.skip(reason='''Blip does not use inputs_embeds''' )
def UpperCamelCase_ ( self : Dict ):
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def UpperCamelCase_ ( self : Dict ):
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def UpperCamelCase_ ( self : List[str] ):
pass
@slow
def UpperCamelCase_ ( self : Optional[int] ):
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = TFBlipTextModel.from_pretrained(_A )
self.assertIsNotNone(_A )
def UpperCamelCase_ ( self : int , _A : Optional[int]=True ):
super().test_pt_tf_model_equivalence(allow_missing_keys=_A )
| 10 | 0 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCamelCase ( UpperCamelCase__ , unittest.TestCase ):
"""simple docstring"""
snake_case__ = BlenderbotSmallTokenizer
snake_case__ = False
def a ( self : List[str] ) -> List[str]:
super().setUp()
lowerCAmelCase__ = ["__start__", "adapt", "act", "ap@@", "te", "__end__", "__unk__"]
lowerCAmelCase__ = dict(zip(SCREAMING_SNAKE_CASE__ , range(len(SCREAMING_SNAKE_CASE__ ) ) ) )
lowerCAmelCase__ = ["#version: 0.2", "a p", "t e</w>", "ap t</w>", "a d", "ad apt</w>", "a c", "ac t</w>", ""]
lowerCAmelCase__ = {"unk_token": "__unk__", "bos_token": "__start__", "eos_token": "__end__"}
lowerCAmelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
lowerCAmelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(SCREAMING_SNAKE_CASE__ ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(SCREAMING_SNAKE_CASE__ ) )
def a ( self : Any , **SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> List[Any]:
kwargs.update(self.special_tokens_map )
return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def a ( self : str , SCREAMING_SNAKE_CASE__ : Any ) -> Optional[Any]:
lowerCAmelCase__ = "adapt act apte"
lowerCAmelCase__ = "adapt act apte"
return input_text, output_text
def a ( self : Dict ) -> Tuple:
lowerCAmelCase__ = BlenderbotSmallTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
lowerCAmelCase__ = "adapt act apte"
lowerCAmelCase__ = ["adapt", "act", "ap@@", "te"]
lowerCAmelCase__ = tokenizer.tokenize(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = [tokenizer.bos_token] + tokens + [tokenizer.eos_token]
lowerCAmelCase__ = [0, 1, 2, 3, 4, 5]
self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
def a ( self : Optional[int] ) -> Tuple:
lowerCAmelCase__ = BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M" )
assert tok("sam" ).input_ids == [1_384]
lowerCAmelCase__ = "I am a small frog."
lowerCAmelCase__ = tok([src_text] , padding=SCREAMING_SNAKE_CASE__ , truncation=SCREAMING_SNAKE_CASE__ )["input_ids"]
lowerCAmelCase__ = tok.batch_decode(SCREAMING_SNAKE_CASE__ , skip_special_tokens=SCREAMING_SNAKE_CASE__ , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )[0]
assert src_text != decoded # I wish it did!
assert decoded == "i am a small frog ."
def a ( self : Optional[Any] ) -> int:
lowerCAmelCase__ = BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M" )
lowerCAmelCase__ = "I am a small frog ."
lowerCAmelCase__ = "."
lowerCAmelCase__ = tok(SCREAMING_SNAKE_CASE__ )["input_ids"]
lowerCAmelCase__ = tok(SCREAMING_SNAKE_CASE__ )["input_ids"]
assert encoded[-1] == encoded_dot[0]
| 61 | from __future__ import annotations
_lowerCAmelCase = [True] * 1_000_001
_lowerCAmelCase = 2
while i * i <= 1_000_000:
if seive[i]:
for j in range(i * i, 1_000_001, i):
_lowerCAmelCase = False
i += 1
def _snake_case ( __snake_case ):
return seive[n]
def _snake_case ( __snake_case ):
return any(digit in '''02468''' for digit in str(__snake_case ) )
def _snake_case ( __snake_case = 1000000 ):
_UpperCamelCase = [2] # result already includes the number 2.
for num in range(3 , limit + 1 , 2 ):
if is_prime(__snake_case ) and not contains_an_even_digit(__snake_case ):
_UpperCamelCase = str(__snake_case )
_UpperCamelCase = [int(str_num[j:] + str_num[:j] ) for j in range(len(__snake_case ) )]
if all(is_prime(__snake_case ) for i in list_nums ):
result.append(__snake_case )
return result
def _snake_case ( ):
return len(find_circular_primes() )
if __name__ == "__main__":
print(f'{len(find_circular_primes()) = }')
| 10 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig
snake_case = [
"""openmmlab/upernet-convnext-tiny""",
# See all UperNet models at https://huggingface.co/models?filter=upernet
]
# General docstring
snake_case = """UperNetConfig"""
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : Union[int, Tuple[int, int]] , UpperCAmelCase_ : Union[int, Tuple[int, int], str] = 0 , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : Union[int, Tuple[int, int]] = 1 , ):
super().__init__()
SCREAMING_SNAKE_CASE : str = nn.Convad(
in_channels=UpperCAmelCase_ , out_channels=UpperCAmelCase_ , kernel_size=UpperCAmelCase_ , padding=UpperCAmelCase_ , bias=UpperCAmelCase_ , dilation=UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : Union[str, Any] = nn.BatchNormad(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = nn.ReLU()
def _A ( self : Union[str, Any] , UpperCAmelCase_ : torch.Tensor ):
SCREAMING_SNAKE_CASE : Any = self.conv(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Dict = self.batch_norm(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.activation(UpperCAmelCase_ )
return output
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
super().__init__()
SCREAMING_SNAKE_CASE : str = [
nn.AdaptiveAvgPoolad(UpperCAmelCase_ ),
UperNetConvModule(UpperCAmelCase_ , UpperCAmelCase_ , kernel_size=1 ),
]
for i, layer in enumerate(self.layers ):
self.add_module(str(UpperCAmelCase_ ) , UpperCAmelCase_ )
def _A ( self : Any , UpperCAmelCase_ : torch.Tensor ):
SCREAMING_SNAKE_CASE : Optional[int] = input
for layer in self.layers:
SCREAMING_SNAKE_CASE : Optional[Any] = layer(UpperCAmelCase_ )
return hidden_state
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict , UpperCAmelCase_ : Tuple[int, ...] , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : bool ):
super().__init__()
SCREAMING_SNAKE_CASE : Dict = pool_scales
SCREAMING_SNAKE_CASE : Optional[int] = align_corners
SCREAMING_SNAKE_CASE : Union[str, Any] = in_channels
SCREAMING_SNAKE_CASE : List[str] = channels
SCREAMING_SNAKE_CASE : str = []
for i, pool_scale in enumerate(UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : Optional[int] = UperNetPyramidPoolingBlock(pool_scale=UpperCAmelCase_ , in_channels=UpperCAmelCase_ , channels=UpperCAmelCase_ )
self.blocks.append(UpperCAmelCase_ )
self.add_module(str(UpperCAmelCase_ ) , UpperCAmelCase_ )
def _A ( self : Tuple , UpperCAmelCase_ : torch.Tensor ):
SCREAMING_SNAKE_CASE : List[Any] = []
for ppm in self.blocks:
SCREAMING_SNAKE_CASE : Dict = ppm(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = nn.functional.interpolate(
UpperCAmelCase_ , size=x.size()[2:] , mode="bilinear" , align_corners=self.align_corners )
ppm_outs.append(UpperCAmelCase_ )
return ppm_outs
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self : str , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Optional[Any] ):
super().__init__()
SCREAMING_SNAKE_CASE : Tuple = config
SCREAMING_SNAKE_CASE : List[str] = config.pool_scales # e.g. (1, 2, 3, 6)
SCREAMING_SNAKE_CASE : Dict = in_channels
SCREAMING_SNAKE_CASE : str = config.hidden_size
SCREAMING_SNAKE_CASE : str = False
SCREAMING_SNAKE_CASE : Any = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
# PSP Module
SCREAMING_SNAKE_CASE : Tuple = UperNetPyramidPoolingModule(
self.pool_scales , self.in_channels[-1] , self.channels , align_corners=self.align_corners , )
SCREAMING_SNAKE_CASE : Tuple = UperNetConvModule(
self.in_channels[-1] + len(self.pool_scales ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
# FPN Module
SCREAMING_SNAKE_CASE : Union[str, Any] = nn.ModuleList()
SCREAMING_SNAKE_CASE : List[Any] = nn.ModuleList()
for in_channels in self.in_channels[:-1]: # skip the top layer
SCREAMING_SNAKE_CASE : Optional[Any] = UperNetConvModule(UpperCAmelCase_ , self.channels , kernel_size=1 )
SCREAMING_SNAKE_CASE : Dict = UperNetConvModule(self.channels , self.channels , kernel_size=3 , padding=1 )
self.lateral_convs.append(UpperCAmelCase_ )
self.fpn_convs.append(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = UperNetConvModule(
len(self.in_channels ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
def _A ( self : Optional[int] ):
self.apply(self._init_weights )
def _A ( self : Tuple , UpperCAmelCase_ : Union[str, Any] ):
if isinstance(UpperCAmelCase_ , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def _A ( self : Tuple , UpperCAmelCase_ : str ):
SCREAMING_SNAKE_CASE : Any = inputs[-1]
SCREAMING_SNAKE_CASE : Union[str, Any] = [x]
psp_outs.extend(self.psp_modules(UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.cat(UpperCAmelCase_ , dim=1 )
SCREAMING_SNAKE_CASE : Optional[Any] = self.bottleneck(UpperCAmelCase_ )
return output
def _A ( self : Dict , UpperCAmelCase_ : torch.Tensor ):
# build laterals
SCREAMING_SNAKE_CASE : Tuple = [lateral_conv(encoder_hidden_states[i] ) for i, lateral_conv in enumerate(self.lateral_convs )]
laterals.append(self.psp_forward(UpperCAmelCase_ ) )
# build top-down path
SCREAMING_SNAKE_CASE : Union[str, Any] = len(UpperCAmelCase_ )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
SCREAMING_SNAKE_CASE : Optional[int] = laterals[i - 1].shape[2:]
SCREAMING_SNAKE_CASE : List[str] = laterals[i - 1] + nn.functional.interpolate(
laterals[i] , size=UpperCAmelCase_ , mode="bilinear" , align_corners=self.align_corners )
# build outputs
SCREAMING_SNAKE_CASE : Any = [self.fpn_convs[i](laterals[i] ) for i in range(used_backbone_levels - 1 )]
# append psp feature
fpn_outs.append(laterals[-1] )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
SCREAMING_SNAKE_CASE : List[Any] = nn.functional.interpolate(
fpn_outs[i] , size=fpn_outs[0].shape[2:] , mode="bilinear" , align_corners=self.align_corners )
SCREAMING_SNAKE_CASE : Optional[Any] = torch.cat(UpperCAmelCase_ , dim=1 )
SCREAMING_SNAKE_CASE : Tuple = self.fpn_bottleneck(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = self.classifier(UpperCAmelCase_ )
return output
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self : List[str] , UpperCAmelCase_ : str , UpperCAmelCase_ : int = 2 , UpperCAmelCase_ : int = 3 , UpperCAmelCase_ : Union[int, Tuple[int, int]] = 1 ):
super().__init__()
SCREAMING_SNAKE_CASE : int = config
SCREAMING_SNAKE_CASE : Optional[Any] = config.auxiliary_in_channels
SCREAMING_SNAKE_CASE : List[str] = config.auxiliary_channels
SCREAMING_SNAKE_CASE : List[Any] = config.auxiliary_num_convs
SCREAMING_SNAKE_CASE : Tuple = config.auxiliary_concat_input
SCREAMING_SNAKE_CASE : Optional[int] = in_index
SCREAMING_SNAKE_CASE : List[Any] = (kernel_size // 2) * dilation
SCREAMING_SNAKE_CASE : int = []
convs.append(
UperNetConvModule(
self.in_channels , self.channels , kernel_size=UpperCAmelCase_ , padding=UpperCAmelCase_ , dilation=UpperCAmelCase_ ) )
for i in range(self.num_convs - 1 ):
convs.append(
UperNetConvModule(
self.channels , self.channels , kernel_size=UpperCAmelCase_ , padding=UpperCAmelCase_ , dilation=UpperCAmelCase_ ) )
if self.num_convs == 0:
SCREAMING_SNAKE_CASE : Optional[int] = nn.Identity()
else:
SCREAMING_SNAKE_CASE : List[str] = nn.Sequential(*UpperCAmelCase_ )
if self.concat_input:
SCREAMING_SNAKE_CASE : Dict = UperNetConvModule(
self.in_channels + self.channels , self.channels , kernel_size=UpperCAmelCase_ , padding=kernel_size // 2 )
SCREAMING_SNAKE_CASE : Optional[Any] = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
def _A ( self : Optional[int] ):
self.apply(self._init_weights )
def _A ( self : int , UpperCAmelCase_ : Union[str, Any] ):
if isinstance(UpperCAmelCase_ , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def _A ( self : Tuple , UpperCAmelCase_ : torch.Tensor ):
# just take the relevant feature maps
SCREAMING_SNAKE_CASE : Union[str, Any] = encoder_hidden_states[self.in_index]
SCREAMING_SNAKE_CASE : Any = self.convs(UpperCAmelCase_ )
if self.concat_input:
SCREAMING_SNAKE_CASE : str = self.conv_cat(torch.cat([hidden_states, output] , dim=1 ) )
SCREAMING_SNAKE_CASE : int = self.classifier(UpperCAmelCase_ )
return output
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Tuple = UperNetConfig
UpperCamelCase_ : List[str] = '''pixel_values'''
UpperCamelCase_ : List[str] = True
def _A ( self : Tuple , UpperCAmelCase_ : Tuple ):
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
module.backbone.init_weights()
module.decode_head.init_weights()
module.auxiliary_head.init_weights()
def _A ( self : Tuple ):
self.backbone.init_weights()
self.decode_head.init_weights()
self.auxiliary_head.init_weights()
def _A ( self : Dict , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Dict=False ):
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : List[Any] = value
snake_case = r"""
Parameters:
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
snake_case = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
[`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See
`attentions` under returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under
returned tensors for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
'''UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.''' , lowerCAmelCase , )
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Any , UpperCAmelCase_ : Any ):
super().__init__(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = AutoBackbone.from_config(config.backbone_config )
# Semantic segmentation head(s)
SCREAMING_SNAKE_CASE : Union[str, Any] = UperNetHead(UpperCAmelCase_ , in_channels=self.backbone.channels )
SCREAMING_SNAKE_CASE : Union[str, Any] = UperNetFCNHead(UpperCAmelCase_ ) if config.use_auxiliary_head else None
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format("batch_size, sequence_length" ) )
@replace_return_docstrings(output_type=UpperCAmelCase_ , config_class=_CONFIG_FOR_DOC )
def _A ( self : Union[str, Any] , UpperCAmelCase_ : Optional[torch.Tensor] = None , UpperCAmelCase_ : Optional[bool] = None , UpperCAmelCase_ : Optional[bool] = None , UpperCAmelCase_ : Optional[torch.Tensor] = None , UpperCAmelCase_ : Optional[bool] = None , ):
SCREAMING_SNAKE_CASE : int = return_dict if return_dict is not None else self.config.use_return_dict
SCREAMING_SNAKE_CASE : List[Any] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
SCREAMING_SNAKE_CASE : List[Any] = output_attentions if output_attentions is not None else self.config.output_attentions
SCREAMING_SNAKE_CASE : Tuple = self.backbone.forward_with_filtered_kwargs(
UpperCAmelCase_ , output_hidden_states=UpperCAmelCase_ , output_attentions=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = outputs.feature_maps
SCREAMING_SNAKE_CASE : Optional[Any] = self.decode_head(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = nn.functional.interpolate(UpperCAmelCase_ , size=pixel_values.shape[2:] , mode="bilinear" , align_corners=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = None
if self.auxiliary_head is not None:
SCREAMING_SNAKE_CASE : Dict = self.auxiliary_head(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = nn.functional.interpolate(
UpperCAmelCase_ , size=pixel_values.shape[2:] , mode="bilinear" , align_corners=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = None
if labels is not None:
if self.config.num_labels == 1:
raise ValueError("The number of labels should be greater than one" )
else:
# compute weighted loss
SCREAMING_SNAKE_CASE : Optional[Any] = CrossEntropyLoss(ignore_index=self.config.loss_ignore_index )
SCREAMING_SNAKE_CASE : Dict = loss_fct(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = loss_fct(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss
if not return_dict:
if output_hidden_states:
SCREAMING_SNAKE_CASE : Any = (logits,) + outputs[1:]
else:
SCREAMING_SNAKE_CASE : Any = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SemanticSegmenterOutput(
loss=UpperCAmelCase_ , logits=UpperCAmelCase_ , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 62 | import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCAmelCase = get_tests_dir("fixtures/spiece.model")
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_ ( __lowercase, unittest.TestCase ):
UpperCAmelCase = DebertaVaTokenizer
UpperCAmelCase = DebertaVaTokenizerFast
UpperCAmelCase = True
UpperCAmelCase = True
def UpperCamelCase_ ( self : List[Any] ):
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCamelCase = DebertaVaTokenizer(_A , unk_token='''<unk>''' )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase_ ( self : Dict , _A : Union[str, Any] ):
_UpperCamelCase = '''this is a test'''
_UpperCamelCase = '''this is a test'''
return input_text, output_text
def UpperCamelCase_ ( self : Optional[Any] ):
_UpperCamelCase = '''<pad>'''
_UpperCamelCase = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A ) , _A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A ) , _A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<pad>''' )
self.assertEqual(vocab_keys[1] , '''<unk>''' )
self.assertEqual(vocab_keys[-1] , '''[PAD]''' )
self.assertEqual(len(_A ) , 3_0001 )
def UpperCamelCase_ ( self : List[Any] ):
self.assertEqual(self.get_tokenizer().vocab_size , 3_0000 )
def UpperCamelCase_ ( self : List[str] ):
# fmt: off
_UpperCamelCase = ''' \tHeLLo!how \n Are yoU? '''
_UpperCamelCase = ['''▁hello''', '''!''', '''how''', '''▁are''', '''▁you''', '''?''']
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
@unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.''' )
def UpperCamelCase_ ( self : Dict ):
pass
@unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.''' )
def UpperCamelCase_ ( self : Optional[Any] ):
pass
def UpperCamelCase_ ( self : Dict ):
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : List[Any] ):
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : Dict ):
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : int ):
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : Tuple ):
# fmt: off
_UpperCamelCase = ''' \tHeLLo!how \n Are yoU? '''
_UpperCamelCase = ['''▁''', '''<unk>''', '''e''', '''<unk>''', '''o''', '''!''', '''how''', '''▁''', '''<unk>''', '''re''', '''▁yo''', '''<unk>''', '''?''']
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = self.get_tokenizer()
_UpperCamelCase = self.get_rust_tokenizer()
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.encode(_A , add_special_tokens=_A )
_UpperCamelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = self.get_rust_tokenizer()
_UpperCamelCase = tokenizer.encode(_A )
_UpperCamelCase = rust_tokenizer.encode(_A )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = '''This is a test'''
_UpperCamelCase = [13, 1, 4398, 25, 21, 1289]
_UpperCamelCase = ['''▁''', '''T''', '''his''', '''▁is''', '''▁a''', '''▁test''']
_UpperCamelCase = ['''▁''', '''<unk>''', '''his''', '''▁is''', '''▁a''', '''▁test''']
_UpperCamelCase = DebertaVaTokenizer(_A , keep_accents=_A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , keep_accents=_A )
_UpperCamelCase = tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = [13, 1, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]
_UpperCamelCase = ['''▁''', '''I''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''', ]
_UpperCamelCase = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
# fmt: on
_UpperCamelCase = tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = DebertaVaTokenizer(_A )
_UpperCamelCase = tokenizer.encode('''sequence builders''' )
_UpperCamelCase = tokenizer.encode('''multi-sequence build''' )
_UpperCamelCase = tokenizer.build_inputs_with_special_tokens(_A )
_UpperCamelCase = tokenizer.build_inputs_with_special_tokens(_A , _A )
self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] , _A )
self.assertEqual(
[tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id] , _A , )
@slow
def UpperCamelCase_ ( self : Optional[Any] ):
# fmt: off
_UpperCamelCase = {'''input_ids''': [[1, 3_9867, 36, 1_9390, 486, 27, 3_5052, 8_1436, 18, 6_0685, 1225, 7, 3_5052, 8_1436, 18, 9367, 1_6899, 18, 1_5937, 53, 594, 773, 18, 1_6287, 3_0465, 36, 1_5937, 6, 4_1139, 38, 3_6979, 6_0763, 191, 6, 3_4132, 99, 6, 5_0538, 390, 4_3230, 6, 3_4132, 2779, 2_0850, 14, 699, 1072, 1194, 36, 382, 1_0901, 53, 7, 699, 1072, 2084, 36, 2_0422, 630, 53, 19, 105, 3049, 1896, 1053, 1_6899, 1506, 11, 3_7978, 4243, 7, 1237, 3_1869, 200, 1_6566, 654, 6, 3_5052, 8_1436, 7, 5_5630, 1_3593, 4, 2], [1, 26, 1_5011, 13, 667, 8, 1053, 18, 2_3611, 1237, 7_2356, 1_2820, 34, 10_4134, 1209, 35, 1_3313, 6627, 21, 202, 347, 7, 164, 2399, 11, 46, 4485, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 1232, 2864, 1_5785, 1_4951, 105, 5, 8581, 1250, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_A , model_name='''microsoft/deberta-v2-xlarge''' , revision='''ad6e42c1532ddf3a15c39246b63f5559d558b670''' , )
| 10 | 0 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class a ( lowercase__ , unittest.TestCase ):
"""simple docstring"""
a : List[Any] = AudioLDMPipeline
a : Optional[Any] = TEXT_TO_AUDIO_PARAMS
a : Dict = TEXT_TO_AUDIO_BATCH_PARAMS
a : Optional[int] = frozenset(
[
'num_inference_steps',
'num_waveforms_per_prompt',
'generator',
'latents',
'output_type',
'return_dict',
'callback',
'callback_steps',
] )
def UpperCAmelCase ( self : Any ) -> List[str]:
torch.manual_seed(0 )
__UpperCAmelCase : List[Any] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=(32, 64) , class_embed_type="""simple_projection""" , projection_class_embeddings_input_dim=32 , class_embeddings_concat=__lowercase , )
__UpperCAmelCase : Optional[int] = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=__lowercase , set_alpha_to_one=__lowercase , )
torch.manual_seed(0 )
__UpperCAmelCase : Optional[Any] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=1 , out_channels=1 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
__UpperCAmelCase : Optional[Any] = ClapTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , projection_dim=32 , )
__UpperCAmelCase : Optional[int] = ClapTextModelWithProjection(__lowercase )
__UpperCAmelCase : str = RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta""" , model_max_length=77 )
__UpperCAmelCase : Dict = SpeechTaHifiGanConfig(
model_in_dim=8 , sampling_rate=16000 , upsample_initial_channel=16 , upsample_rates=[2, 2] , upsample_kernel_sizes=[4, 4] , resblock_kernel_sizes=[3, 7] , resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] , normalize_before=__lowercase , )
__UpperCAmelCase : int = SpeechTaHifiGan(__lowercase )
__UpperCAmelCase : Tuple = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""vocoder""": vocoder,
}
return components
def UpperCAmelCase ( self : Optional[int] , __lowercase : Any , __lowercase : str=0 ) -> List[str]:
if str(__lowercase ).startswith("""mps""" ):
__UpperCAmelCase : Dict = torch.manual_seed(__lowercase )
else:
__UpperCAmelCase : Tuple = torch.Generator(device=__lowercase ).manual_seed(__lowercase )
__UpperCAmelCase : Tuple = {
"""prompt""": """A hammer hitting a wooden surface""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
}
return inputs
def UpperCAmelCase ( self : Tuple ) -> Union[str, Any]:
__UpperCAmelCase : Tuple = """cpu""" # ensure determinism for the device-dependent torch.Generator
__UpperCAmelCase : Dict = self.get_dummy_components()
__UpperCAmelCase : List[Any] = AudioLDMPipeline(**__lowercase )
__UpperCAmelCase : Tuple = audioldm_pipe.to(__lowercase )
audioldm_pipe.set_progress_bar_config(disable=__lowercase )
__UpperCAmelCase : List[Any] = self.get_dummy_inputs(__lowercase )
__UpperCAmelCase : Union[str, Any] = audioldm_pipe(**__lowercase )
__UpperCAmelCase : Union[str, Any] = output.audios[0]
assert audio.ndim == 1
assert len(__lowercase ) == 256
__UpperCAmelCase : str = audio[:10]
__UpperCAmelCase : List[Any] = np.array(
[-0.0_050, 0.0_050, -0.0_060, 0.0_033, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_033] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def UpperCAmelCase ( self : Tuple ) -> Optional[int]:
__UpperCAmelCase : List[str] = self.get_dummy_components()
__UpperCAmelCase : Any = AudioLDMPipeline(**__lowercase )
__UpperCAmelCase : Tuple = audioldm_pipe.to(__lowercase )
__UpperCAmelCase : str = audioldm_pipe.to(__lowercase )
audioldm_pipe.set_progress_bar_config(disable=__lowercase )
__UpperCAmelCase : Tuple = self.get_dummy_inputs(__lowercase )
__UpperCAmelCase : Dict = 3 * [inputs["""prompt"""]]
# forward
__UpperCAmelCase : Union[str, Any] = audioldm_pipe(**__lowercase )
__UpperCAmelCase : int = output.audios[0]
__UpperCAmelCase : List[str] = self.get_dummy_inputs(__lowercase )
__UpperCAmelCase : Any = 3 * [inputs.pop("""prompt""" )]
__UpperCAmelCase : Tuple = audioldm_pipe.tokenizer(
__lowercase , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=__lowercase , return_tensors="""pt""" , )
__UpperCAmelCase : Optional[Any] = text_inputs["""input_ids"""].to(__lowercase )
__UpperCAmelCase : int = audioldm_pipe.text_encoder(
__lowercase , )
__UpperCAmelCase : Dict = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
__UpperCAmelCase : Tuple = F.normalize(__lowercase , dim=-1 )
__UpperCAmelCase : Tuple = prompt_embeds
# forward
__UpperCAmelCase : Dict = audioldm_pipe(**__lowercase )
__UpperCAmelCase : str = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def UpperCAmelCase ( self : Union[str, Any] ) -> str:
__UpperCAmelCase : Tuple = self.get_dummy_components()
__UpperCAmelCase : Any = AudioLDMPipeline(**__lowercase )
__UpperCAmelCase : Dict = audioldm_pipe.to(__lowercase )
__UpperCAmelCase : int = audioldm_pipe.to(__lowercase )
audioldm_pipe.set_progress_bar_config(disable=__lowercase )
__UpperCAmelCase : Union[str, Any] = self.get_dummy_inputs(__lowercase )
__UpperCAmelCase : Optional[Any] = 3 * ["""this is a negative prompt"""]
__UpperCAmelCase : Optional[Any] = negative_prompt
__UpperCAmelCase : Tuple = 3 * [inputs["""prompt"""]]
# forward
__UpperCAmelCase : int = audioldm_pipe(**__lowercase )
__UpperCAmelCase : Any = output.audios[0]
__UpperCAmelCase : List[Any] = self.get_dummy_inputs(__lowercase )
__UpperCAmelCase : Tuple = 3 * [inputs.pop("""prompt""" )]
__UpperCAmelCase : List[Any] = []
for p in [prompt, negative_prompt]:
__UpperCAmelCase : List[str] = audioldm_pipe.tokenizer(
__lowercase , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=__lowercase , return_tensors="""pt""" , )
__UpperCAmelCase : Union[str, Any] = text_inputs["""input_ids"""].to(__lowercase )
__UpperCAmelCase : Optional[Any] = audioldm_pipe.text_encoder(
__lowercase , )
__UpperCAmelCase : Tuple = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
__UpperCAmelCase : Any = F.normalize(__lowercase , dim=-1 )
embeds.append(__lowercase )
__UpperCAmelCase , __UpperCAmelCase : Optional[int] = embeds
# forward
__UpperCAmelCase : str = audioldm_pipe(**__lowercase )
__UpperCAmelCase : str = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def UpperCAmelCase ( self : Dict ) -> Tuple:
__UpperCAmelCase : str = """cpu""" # ensure determinism for the device-dependent torch.Generator
__UpperCAmelCase : List[Any] = self.get_dummy_components()
__UpperCAmelCase : Union[str, Any] = PNDMScheduler(skip_prk_steps=__lowercase )
__UpperCAmelCase : Tuple = AudioLDMPipeline(**__lowercase )
__UpperCAmelCase : str = audioldm_pipe.to(__lowercase )
audioldm_pipe.set_progress_bar_config(disable=__lowercase )
__UpperCAmelCase : Union[str, Any] = self.get_dummy_inputs(__lowercase )
__UpperCAmelCase : Optional[Any] = """egg cracking"""
__UpperCAmelCase : Optional[Any] = audioldm_pipe(**__lowercase , negative_prompt=__lowercase )
__UpperCAmelCase : Tuple = output.audios[0]
assert audio.ndim == 1
assert len(__lowercase ) == 256
__UpperCAmelCase : Union[str, Any] = audio[:10]
__UpperCAmelCase : int = np.array(
[-0.0_051, 0.0_050, -0.0_060, 0.0_034, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_032] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def UpperCAmelCase ( self : str ) -> Any:
__UpperCAmelCase : str = """cpu""" # ensure determinism for the device-dependent torch.Generator
__UpperCAmelCase : List[Any] = self.get_dummy_components()
__UpperCAmelCase : str = PNDMScheduler(skip_prk_steps=__lowercase )
__UpperCAmelCase : Tuple = AudioLDMPipeline(**__lowercase )
__UpperCAmelCase : Tuple = audioldm_pipe.to(__lowercase )
audioldm_pipe.set_progress_bar_config(disable=__lowercase )
__UpperCAmelCase : str = """A hammer hitting a wooden surface"""
# test num_waveforms_per_prompt=1 (default)
__UpperCAmelCase : Union[str, Any] = audioldm_pipe(__lowercase , num_inference_steps=2 ).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
__UpperCAmelCase : Optional[Any] = 2
__UpperCAmelCase : int = audioldm_pipe([prompt] * batch_size , num_inference_steps=2 ).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
__UpperCAmelCase : int = 2
__UpperCAmelCase : str = audioldm_pipe(__lowercase , num_inference_steps=2 , num_waveforms_per_prompt=__lowercase ).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
__UpperCAmelCase : Any = 2
__UpperCAmelCase : Tuple = audioldm_pipe(
[prompt] * batch_size , num_inference_steps=2 , num_waveforms_per_prompt=__lowercase ).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def UpperCAmelCase ( self : List[str] ) -> str:
__UpperCAmelCase : Optional[Any] = """cpu""" # ensure determinism for the device-dependent torch.Generator
__UpperCAmelCase : Tuple = self.get_dummy_components()
__UpperCAmelCase : int = AudioLDMPipeline(**__lowercase )
__UpperCAmelCase : Dict = audioldm_pipe.to(__lowercase )
audioldm_pipe.set_progress_bar_config(disable=__lowercase )
__UpperCAmelCase : List[str] = audioldm_pipe.vocoder.config.sampling_rate
__UpperCAmelCase : Union[str, Any] = self.get_dummy_inputs(__lowercase )
__UpperCAmelCase : Optional[Any] = audioldm_pipe(audio_length_in_s=0.016 , **__lowercase )
__UpperCAmelCase : Tuple = output.audios[0]
assert audio.ndim == 1
assert len(__lowercase ) / vocoder_sampling_rate == 0.016
__UpperCAmelCase : Optional[Any] = audioldm_pipe(audio_length_in_s=0.032 , **__lowercase )
__UpperCAmelCase : Dict = output.audios[0]
assert audio.ndim == 1
assert len(__lowercase ) / vocoder_sampling_rate == 0.032
def UpperCAmelCase ( self : Any ) -> List[Any]:
__UpperCAmelCase : List[Any] = self.get_dummy_components()
__UpperCAmelCase : Any = AudioLDMPipeline(**__lowercase )
__UpperCAmelCase : Dict = audioldm_pipe.to(__lowercase )
audioldm_pipe.set_progress_bar_config(disable=__lowercase )
__UpperCAmelCase : List[str] = ["""hey"""]
__UpperCAmelCase : Dict = audioldm_pipe(__lowercase , num_inference_steps=1 )
__UpperCAmelCase : Tuple = output.audios.shape
assert audio_shape == (1, 256)
__UpperCAmelCase : Optional[Any] = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
__UpperCAmelCase : List[Any] = SpeechTaHifiGan(__lowercase ).to(__lowercase )
__UpperCAmelCase : Dict = audioldm_pipe(__lowercase , num_inference_steps=1 )
__UpperCAmelCase : int = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def UpperCAmelCase ( self : Dict ) -> Optional[int]:
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=__lowercase )
def UpperCAmelCase ( self : str ) -> Any:
self._test_inference_batch_single_identical(test_mean_pixel_difference=__lowercase )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def UpperCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__lowercase )
@slow
class a ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase ( self : Dict ) -> Tuple:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase ( self : Optional[Any] , __lowercase : Optional[int] , __lowercase : int="cpu" , __lowercase : List[Any]=torch.floataa , __lowercase : Tuple=0 ) -> Dict:
__UpperCAmelCase : int = torch.Generator(device=__lowercase ).manual_seed(__lowercase )
__UpperCAmelCase : Dict = np.random.RandomState(__lowercase ).standard_normal((1, 8, 128, 16) )
__UpperCAmelCase : Optional[Any] = torch.from_numpy(__lowercase ).to(device=__lowercase , dtype=__lowercase )
__UpperCAmelCase : int = {
"""prompt""": """A hammer hitting a wooden surface""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 2.5,
}
return inputs
def UpperCAmelCase ( self : int ) -> List[str]:
__UpperCAmelCase : Any = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
__UpperCAmelCase : Union[str, Any] = audioldm_pipe.to(__lowercase )
audioldm_pipe.set_progress_bar_config(disable=__lowercase )
__UpperCAmelCase : Tuple = self.get_inputs(__lowercase )
__UpperCAmelCase : str = 25
__UpperCAmelCase : Optional[int] = audioldm_pipe(**__lowercase ).audios[0]
assert audio.ndim == 1
assert len(__lowercase ) == 81920
__UpperCAmelCase : Dict = audio[77230:77240]
__UpperCAmelCase : Optional[Any] = np.array(
[-0.4_884, -0.4_607, 0.0_023, 0.5_007, 0.5_896, 0.5_151, 0.3_813, -0.0_208, -0.3_687, -0.4_315] )
__UpperCAmelCase : Optional[Any] = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 1e-2
def UpperCAmelCase ( self : str ) -> Tuple:
__UpperCAmelCase : Optional[Any] = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
__UpperCAmelCase : Any = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
__UpperCAmelCase : int = audioldm_pipe.to(__lowercase )
audioldm_pipe.set_progress_bar_config(disable=__lowercase )
__UpperCAmelCase : List[Any] = self.get_inputs(__lowercase )
__UpperCAmelCase : Optional[int] = audioldm_pipe(**__lowercase ).audios[0]
assert audio.ndim == 1
assert len(__lowercase ) == 81920
__UpperCAmelCase : int = audio[27780:27790]
__UpperCAmelCase : Optional[Any] = np.array([-0.2_131, -0.0_873, -0.0_124, -0.0_189, 0.0_569, 0.1_373, 0.1_883, 0.2_886, 0.3_297, 0.2_212] )
__UpperCAmelCase : Dict = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 3e-2
| 63 | import sys
from collections import defaultdict
class lowerCAmelCase_ :
def __init__( self : Optional[int] ):
_UpperCamelCase = []
def UpperCamelCase_ ( self : Any , _A : str ):
return self.node_position[vertex]
def UpperCamelCase_ ( self : Optional[Any] , _A : List[str] , _A : Union[str, Any] ):
_UpperCamelCase = pos
def UpperCamelCase_ ( self : Any , _A : List[str] , _A : int , _A : Optional[Any] , _A : Union[str, Any] ):
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
_UpperCamelCase = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
_UpperCamelCase = 2 * start + 1
else:
_UpperCamelCase = 2 * start + 2
if heap[smallest_child] < heap[start]:
_UpperCamelCase , _UpperCamelCase = heap[smallest_child], positions[smallest_child]
_UpperCamelCase , _UpperCamelCase = (
heap[start],
positions[start],
)
_UpperCamelCase , _UpperCamelCase = temp, tempa
_UpperCamelCase = self.get_position(positions[smallest_child] )
self.set_position(
positions[smallest_child] , self.get_position(positions[start] ) )
self.set_position(positions[start] , _A )
self.top_to_bottom(_A , _A , _A , _A )
def UpperCamelCase_ ( self : List[str] , _A : Tuple , _A : Optional[Any] , _A : int , _A : Optional[int] ):
_UpperCamelCase = position[index]
while index != 0:
_UpperCamelCase = int((index - 2) / 2 ) if index % 2 == 0 else int((index - 1) / 2 )
if val < heap[parent]:
_UpperCamelCase = heap[parent]
_UpperCamelCase = position[parent]
self.set_position(position[parent] , _A )
else:
_UpperCamelCase = val
_UpperCamelCase = temp
self.set_position(_A , _A )
break
_UpperCamelCase = parent
else:
_UpperCamelCase = val
_UpperCamelCase = temp
self.set_position(_A , 0 )
def UpperCamelCase_ ( self : int , _A : Tuple , _A : int ):
_UpperCamelCase = len(_A ) // 2 - 1
for i in range(_A , -1 , -1 ):
self.top_to_bottom(_A , _A , len(_A ) , _A )
def UpperCamelCase_ ( self : Any , _A : int , _A : List[str] ):
_UpperCamelCase = positions[0]
_UpperCamelCase = sys.maxsize
self.top_to_bottom(_A , 0 , len(_A ) , _A )
return temp
def _snake_case ( __snake_case ):
_UpperCamelCase = Heap()
_UpperCamelCase = [0] * len(__snake_case )
_UpperCamelCase = [-1] * len(__snake_case ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
_UpperCamelCase = [] # Heap of Distance of vertices from their neighboring vertex
_UpperCamelCase = []
for vertex in range(len(__snake_case ) ):
distance_tv.append(sys.maxsize )
positions.append(__snake_case )
heap.node_position.append(__snake_case )
_UpperCamelCase = []
_UpperCamelCase = 1
_UpperCamelCase = sys.maxsize
for neighbor, distance in adjacency_list[0]:
_UpperCamelCase = 0
_UpperCamelCase = distance
heap.heapify(__snake_case , __snake_case )
for _ in range(1 , len(__snake_case ) ):
_UpperCamelCase = heap.delete_minimum(__snake_case , __snake_case )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
_UpperCamelCase = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(__snake_case )]
):
_UpperCamelCase = distance
heap.bottom_to_top(
__snake_case , heap.get_position(__snake_case ) , __snake_case , __snake_case )
_UpperCamelCase = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
_lowerCAmelCase = int(input("Enter number of edges: ").strip())
_lowerCAmelCase = defaultdict(list)
for _ in range(edges_number):
_lowerCAmelCase = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 10 | 0 |
from __future__ import annotations
from collections.abc import Callable
lowercase_ : int = list[list[float | int]]
def A__ ( snake_case_ : Matrix , snake_case_ : Matrix ):
SCREAMING_SNAKE_CASE__: int= len(snake_case_ )
SCREAMING_SNAKE_CASE__: Matrix= [[0 for _ in range(size + 1 )] for _ in range(snake_case_ )]
SCREAMING_SNAKE_CASE__: int
SCREAMING_SNAKE_CASE__: int
SCREAMING_SNAKE_CASE__: int
SCREAMING_SNAKE_CASE__: int
SCREAMING_SNAKE_CASE__: int
SCREAMING_SNAKE_CASE__: float
for row in range(snake_case_ ):
for col in range(snake_case_ ):
SCREAMING_SNAKE_CASE__: Union[str, Any]= matrix[row][col]
SCREAMING_SNAKE_CASE__: int= vector[row][0]
SCREAMING_SNAKE_CASE__: int= 0
SCREAMING_SNAKE_CASE__: Union[str, Any]= 0
while row < size and col < size:
# pivoting
SCREAMING_SNAKE_CASE__: int= max((abs(augmented[rowa][col] ), rowa) for rowa in range(snake_case_ , snake_case_ ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__: str= augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , snake_case_ ):
SCREAMING_SNAKE_CASE__: Union[str, Any]= augmented[rowa][col] / augmented[row][col]
SCREAMING_SNAKE_CASE__: Optional[int]= 0
for cola in range(col + 1 , size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , snake_case_ ):
for row in range(snake_case_ ):
SCREAMING_SNAKE_CASE__: List[Any]= augmented[row][col] / augmented[col][col]
for cola in range(snake_case_ , size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 10 )] for row in range(snake_case_ )
]
def A__ ( snake_case_ : list[int] ):
SCREAMING_SNAKE_CASE__: int= len(snake_case_ )
SCREAMING_SNAKE_CASE__: Matrix= [[0 for _ in range(snake_case_ )] for _ in range(snake_case_ )]
SCREAMING_SNAKE_CASE__: Matrix= [[0] for _ in range(snake_case_ )]
SCREAMING_SNAKE_CASE__: Matrix
SCREAMING_SNAKE_CASE__: int
SCREAMING_SNAKE_CASE__: int
SCREAMING_SNAKE_CASE__: int
for x_val, y_val in enumerate(snake_case_ ):
for col in range(snake_case_ ):
SCREAMING_SNAKE_CASE__: Union[str, Any]= (x_val + 1) ** (size - col - 1)
SCREAMING_SNAKE_CASE__: List[Any]= y_val
SCREAMING_SNAKE_CASE__: int= solve(snake_case_ , snake_case_ )
def interpolated_func(snake_case_ : int ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(snake_case_ ) )
return interpolated_func
def A__ ( snake_case_ : int ):
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def A__ ( snake_case_ : Callable[[int], int] = question_function , snake_case_ : int = 10 ):
SCREAMING_SNAKE_CASE__: list[int]= [func(snake_case_ ) for x_val in range(1 , order + 1 )]
SCREAMING_SNAKE_CASE__: list[Callable[[int], int]]= [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
SCREAMING_SNAKE_CASE__: int= 0
SCREAMING_SNAKE_CASE__: Callable[[int], int]
SCREAMING_SNAKE_CASE__: int
for poly in polynomials:
SCREAMING_SNAKE_CASE__: Optional[Any]= 1
while func(snake_case_ ) == poly(snake_case_ ):
x_val += 1
ret += poly(snake_case_ )
return ret
if __name__ == "__main__":
print(f'''{solution() = }''')
| 64 | import logging
import os
from .state import PartialState
class lowerCAmelCase_ ( logging.LoggerAdapter ):
@staticmethod
def UpperCamelCase_ ( _A : Any ):
_UpperCamelCase = PartialState()
return not main_process_only or (main_process_only and state.is_main_process)
def UpperCamelCase_ ( self : Union[str, Any] , _A : Optional[Any] , _A : str , *_A : int , **_A : List[Any] ):
if PartialState._shared_state == {}:
raise RuntimeError(
'''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.''' )
_UpperCamelCase = kwargs.pop('''main_process_only''' , _A )
_UpperCamelCase = kwargs.pop('''in_order''' , _A )
if self.isEnabledFor(_A ):
if self._should_log(_A ):
_UpperCamelCase , _UpperCamelCase = self.process(_A , _A )
self.logger.log(_A , _A , *_A , **_A )
elif in_order:
_UpperCamelCase = PartialState()
for i in range(state.num_processes ):
if i == state.process_index:
_UpperCamelCase , _UpperCamelCase = self.process(_A , _A )
self.logger.log(_A , _A , *_A , **_A )
state.wait_for_everyone()
def _snake_case ( __snake_case , __snake_case = None ):
if log_level is None:
_UpperCamelCase = os.environ.get('''ACCELERATE_LOG_LEVEL''' , __snake_case )
_UpperCamelCase = logging.getLogger(__snake_case )
if log_level is not None:
logger.setLevel(log_level.upper() )
logger.root.setLevel(log_level.upper() )
return MultiProcessAdapter(__snake_case , {} )
| 10 | 0 |
"""simple docstring"""
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
if not isinstance(__UpperCamelCase , __UpperCamelCase ) or number < 0:
raise ValueError("""Input must be a non-negative integer""" )
UpperCAmelCase__ : Union[str, Any] = 0
while number:
# This way we arrive at next set bit (next 1) instead of looping
# through each bit and checking for 1s hence the
# loop won't run 32 times it will only run the number of `1` times
number &= number - 1
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 65 | import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCAmelCase = "▁"
_lowerCAmelCase = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
class lowerCAmelCase_ ( __lowercase, unittest.TestCase ):
UpperCAmelCase = BertGenerationTokenizer
UpperCAmelCase = False
UpperCAmelCase = True
def UpperCamelCase_ ( self : List[str] ):
super().setUp()
_UpperCamelCase = BertGenerationTokenizer(_A , keep_accents=_A )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = '''<s>'''
_UpperCamelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A ) , _A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A ) , _A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<unk>''' )
self.assertEqual(vocab_keys[1] , '''<s>''' )
self.assertEqual(vocab_keys[-1] , '''<pad>''' )
self.assertEqual(len(_A ) , 1002 )
def UpperCamelCase_ ( self : Dict ):
self.assertEqual(self.get_tokenizer().vocab_size , 1000 )
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = BertGenerationTokenizer(_A , keep_accents=_A )
_UpperCamelCase = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(_A , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_A ) , [285, 46, 10, 170, 382] , )
_UpperCamelCase = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
_A , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
_UpperCamelCase = tokenizer.convert_tokens_to_ids(_A )
self.assertListEqual(
_A , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(
_A , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
@cached_property
def UpperCamelCase_ ( self : Union[str, Any] ):
return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''' )
@slow
def UpperCamelCase_ ( self : Optional[Any] ):
_UpperCamelCase = '''Hello World!'''
_UpperCamelCase = [1_8536, 2260, 101]
self.assertListEqual(_A , self.big_tokenizer.encode(_A ) )
@slow
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
_UpperCamelCase = [
871,
419,
358,
946,
991,
2521,
452,
358,
1357,
387,
7751,
3536,
112,
985,
456,
126,
865,
938,
5400,
5734,
458,
1368,
467,
786,
2462,
5246,
1159,
633,
865,
4519,
457,
582,
852,
2557,
427,
916,
508,
405,
3_4324,
497,
391,
408,
1_1342,
1244,
385,
100,
938,
985,
456,
574,
362,
1_2597,
3200,
3129,
1172,
]
self.assertListEqual(_A , self.big_tokenizer.encode(_A ) )
@require_torch
@slow
def UpperCamelCase_ ( self : Dict ):
import torch
from transformers import BertGenerationConfig, BertGenerationEncoder
# Build sequence
_UpperCamelCase = list(self.big_tokenizer.get_vocab().keys() )[:10]
_UpperCamelCase = ''' '''.join(_A )
_UpperCamelCase = self.big_tokenizer.encode_plus(_A , return_tensors='''pt''' , return_token_type_ids=_A )
_UpperCamelCase = self.big_tokenizer.batch_encode_plus(
[sequence + ''' ''' + sequence] , return_tensors='''pt''' , return_token_type_ids=_A )
_UpperCamelCase = BertGenerationConfig()
_UpperCamelCase = BertGenerationEncoder(_A )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**_A )
model(**_A )
@slow
def UpperCamelCase_ ( self : Dict ):
# fmt: off
_UpperCamelCase = {'''input_ids''': [[3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114], [448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_A , model_name='''google/bert_for_seq_generation_L-24_bbc_encoder''' , revision='''c817d1fd1be2ffa69431227a1fe320544943d4db''' , )
| 10 | 0 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
UpperCamelCase = None
try:
import msvcrt
except ImportError:
UpperCamelCase = None
try:
import fcntl
except ImportError:
UpperCamelCase = None
# Backward compatibility
# ------------------------------------------------
try:
TimeoutError
except NameError:
UpperCamelCase = OSError
# Data
# ------------------------------------------------
UpperCamelCase = [
"Timeout",
"BaseFileLock",
"WindowsFileLock",
"UnixFileLock",
"SoftFileLock",
"FileLock",
]
UpperCamelCase = "3.0.12"
UpperCamelCase = None
def __magic_name__ ( ) -> List[Any]:
global _logger
_lowercase : Optional[int] = _logger or logging.getLogger(__name__ )
return _logger
class lowerCAmelCase_ ( __snake_case ):
def __init__( self , _lowerCAmelCase ):
_lowercase : List[Any] = lock_file
return None
def __str__( self ):
_lowercase : Tuple = F"""The file lock '{self.lock_file}' could not be acquired."""
return temp
class lowerCAmelCase_ :
def __init__( self , _lowerCAmelCase ):
_lowercase : Any = lock
return None
def __enter__( self ):
return self.lock
def __exit__( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
self.lock.release()
return None
class lowerCAmelCase_ :
def __init__( self , _lowerCAmelCase , _lowerCAmelCase=-1 , _lowerCAmelCase=None ):
_lowercase : Tuple = max_filename_length if max_filename_length is not None else 2_5_5
# Hash the filename if it's too long
_lowercase : Union[str, Any] = self.hash_filename_if_too_long(_lowerCAmelCase , _lowerCAmelCase )
# The path to the lock file.
_lowercase : int = lock_file
# The file descriptor for the *_lock_file* as it is returned by the
# os.open() function.
# This file lock is only NOT None, if the object currently holds the
# lock.
_lowercase : List[Any] = None
# The default timeout value.
_lowercase : Union[str, Any] = timeout
# We use this lock primarily for the lock counter.
_lowercase : Any = threading.Lock()
# The lock counter is used for implementing the nested locking
# mechanism. Whenever the lock is acquired, the counter is increased and
# the lock is only released, when this value is 0 again.
_lowercase : Optional[Any] = 0
return None
@property
def __a ( self ):
return self._lock_file
@property
def __a ( self ):
return self._timeout
@timeout.setter
def __a ( self , _lowerCAmelCase ):
_lowercase : Tuple = float(_lowerCAmelCase )
return None
def __a ( self ):
raise NotImplementedError()
def __a ( self ):
raise NotImplementedError()
@property
def __a ( self ):
return self._lock_file_fd is not None
def __a ( self , _lowerCAmelCase=None , _lowerCAmelCase=0.05 ):
# Use the default timeout, if no timeout is provided.
if timeout is None:
_lowercase : Any = self.timeout
# Increment the number right at the beginning.
# We can still undo it, if something fails.
with self._thread_lock:
self._lock_counter += 1
_lowercase : Tuple = id(self )
_lowercase : Union[str, Any] = self._lock_file
_lowercase : Tuple = time.time()
try:
while True:
with self._thread_lock:
if not self.is_locked:
logger().debug(F"""Attempting to acquire lock {lock_id} on {lock_filename}""" )
self._acquire()
if self.is_locked:
logger().debug(F"""Lock {lock_id} acquired on {lock_filename}""" )
break
elif timeout >= 0 and time.time() - start_time > timeout:
logger().debug(F"""Timeout on acquiring lock {lock_id} on {lock_filename}""" )
raise Timeout(self._lock_file )
else:
logger().debug(
F"""Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ...""" )
time.sleep(_lowerCAmelCase )
except: # noqa
# Something did go wrong, so decrement the counter.
with self._thread_lock:
_lowercase : Dict = max(0 , self._lock_counter - 1 )
raise
return _Acquire_ReturnProxy(lock=self )
def __a ( self , _lowerCAmelCase=False ):
with self._thread_lock:
if self.is_locked:
self._lock_counter -= 1
if self._lock_counter == 0 or force:
_lowercase : Tuple = id(self )
_lowercase : Optional[Any] = self._lock_file
logger().debug(F"""Attempting to release lock {lock_id} on {lock_filename}""" )
self._release()
_lowercase : Union[str, Any] = 0
logger().debug(F"""Lock {lock_id} released on {lock_filename}""" )
return None
def __enter__( self ):
self.acquire()
return self
def __exit__( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
self.release()
return None
def __del__( self ):
self.release(force=_lowerCAmelCase )
return None
def __a ( self , _lowerCAmelCase , _lowerCAmelCase ):
_lowercase : Optional[Any] = os.path.basename(_lowerCAmelCase )
if len(_lowerCAmelCase ) > max_length and max_length > 0:
_lowercase : Optional[int] = os.path.dirname(_lowerCAmelCase )
_lowercase : int = str(hash(_lowerCAmelCase ) )
_lowercase : Union[str, Any] = filename[: max_length - len(_lowerCAmelCase ) - 8] + '...' + hashed_filename + '.lock'
return os.path.join(_lowerCAmelCase , _lowerCAmelCase )
else:
return path
class lowerCAmelCase_ ( __snake_case ):
def __init__( self , _lowerCAmelCase , _lowerCAmelCase=-1 , _lowerCAmelCase=None ):
from .file_utils import relative_to_absolute_path
super().__init__(_lowerCAmelCase , timeout=_lowerCAmelCase , max_filename_length=_lowerCAmelCase )
_lowercase : Optional[Any] = '\\\\?\\' + relative_to_absolute_path(self.lock_file )
def __a ( self ):
_lowercase : str = os.O_RDWR | os.O_CREAT | os.O_TRUNC
try:
_lowercase : Dict = os.open(self._lock_file , _lowerCAmelCase )
except OSError:
pass
else:
try:
msvcrt.locking(_lowerCAmelCase , msvcrt.LK_NBLCK , 1 )
except OSError:
os.close(_lowerCAmelCase )
else:
_lowercase : List[str] = fd
return None
def __a ( self ):
_lowercase : Tuple = self._lock_file_fd
_lowercase : Union[str, Any] = None
msvcrt.locking(_lowerCAmelCase , msvcrt.LK_UNLCK , 1 )
os.close(_lowerCAmelCase )
try:
os.remove(self._lock_file )
# Probably another instance of the application
# that acquired the file lock.
except OSError:
pass
return None
class lowerCAmelCase_ ( __snake_case ):
def __init__( self , _lowerCAmelCase , _lowerCAmelCase=-1 , _lowerCAmelCase=None ):
_lowercase : List[str] = os.statvfs(os.path.dirname(_lowerCAmelCase ) ).f_namemax
super().__init__(_lowerCAmelCase , timeout=_lowerCAmelCase , max_filename_length=_lowerCAmelCase )
def __a ( self ):
_lowercase : List[str] = os.O_RDWR | os.O_CREAT | os.O_TRUNC
_lowercase : List[str] = os.open(self._lock_file , _lowerCAmelCase )
try:
fcntl.flock(_lowerCAmelCase , fcntl.LOCK_EX | fcntl.LOCK_NB )
except OSError:
os.close(_lowerCAmelCase )
else:
_lowercase : Optional[Any] = fd
return None
def __a ( self ):
# Do not remove the lockfile:
#
# https://github.com/benediktschmitt/py-filelock/issues/31
# https://stackoverflow.com/questions/17708885/flock-removing-locked-file-without-race-condition
_lowercase : Tuple = self._lock_file_fd
_lowercase : Optional[Any] = None
fcntl.flock(_lowerCAmelCase , fcntl.LOCK_UN )
os.close(_lowerCAmelCase )
return None
class lowerCAmelCase_ ( __snake_case ):
def __a ( self ):
_lowercase : str = os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
try:
_lowercase : int = os.open(self._lock_file , _lowerCAmelCase )
except OSError:
pass
else:
_lowercase : str = fd
return None
def __a ( self ):
os.close(self._lock_file_fd )
_lowercase : Union[str, Any] = None
try:
os.remove(self._lock_file )
# The file is already deleted and that's what we want.
except OSError:
pass
return None
UpperCamelCase = None
if msvcrt:
UpperCamelCase = WindowsFileLock
elif fcntl:
UpperCamelCase = UnixFileLock
else:
UpperCamelCase = SoftFileLock
if warnings is not None:
warnings.warn("only soft file lock is available")
| 66 | import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class lowerCAmelCase_ ( __lowercase, __lowercase, __lowercase, unittest.TestCase ):
UpperCAmelCase = StableUnCLIPPipeline
UpperCAmelCase = TEXT_TO_IMAGE_PARAMS
UpperCAmelCase = TEXT_TO_IMAGE_BATCH_PARAMS
UpperCAmelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
UpperCAmelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
UpperCAmelCase = False
def UpperCamelCase_ ( self : Optional[int] ):
_UpperCamelCase = 32
_UpperCamelCase = embedder_hidden_size
# prior components
torch.manual_seed(0 )
_UpperCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
torch.manual_seed(0 )
_UpperCamelCase = CLIPTextModelWithProjection(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=_A , projection_dim=_A , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , ) )
torch.manual_seed(0 )
_UpperCamelCase = PriorTransformer(
num_attention_heads=2 , attention_head_dim=12 , embedding_dim=_A , num_layers=1 , )
torch.manual_seed(0 )
_UpperCamelCase = DDPMScheduler(
variance_type='''fixed_small_log''' , prediction_type='''sample''' , num_train_timesteps=1000 , clip_sample=_A , clip_sample_range=5.0 , beta_schedule='''squaredcos_cap_v2''' , )
# regular denoising components
torch.manual_seed(0 )
_UpperCamelCase = StableUnCLIPImageNormalizer(embedding_dim=_A )
_UpperCamelCase = DDPMScheduler(beta_schedule='''squaredcos_cap_v2''' )
torch.manual_seed(0 )
_UpperCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
torch.manual_seed(0 )
_UpperCamelCase = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=_A , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , ) )
torch.manual_seed(0 )
_UpperCamelCase = UNetaDConditionModel(
sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''') , up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''') , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type='''projection''' , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=_A , layers_per_block=1 , upcast_attention=_A , use_linear_projection=_A , )
torch.manual_seed(0 )
_UpperCamelCase = DDIMScheduler(
beta_schedule='''scaled_linear''' , beta_start=0.0_0085 , beta_end=0.012 , prediction_type='''v_prediction''' , set_alpha_to_one=_A , steps_offset=1 , )
torch.manual_seed(0 )
_UpperCamelCase = AutoencoderKL()
_UpperCamelCase = {
# prior components
'''prior_tokenizer''': prior_tokenizer,
'''prior_text_encoder''': prior_text_encoder,
'''prior''': prior,
'''prior_scheduler''': prior_scheduler,
# image noising components
'''image_normalizer''': image_normalizer,
'''image_noising_scheduler''': image_noising_scheduler,
# regular denoising components
'''tokenizer''': tokenizer,
'''text_encoder''': text_encoder,
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
}
return components
def UpperCamelCase_ ( self : Dict , _A : Tuple , _A : Dict=0 ):
if str(_A ).startswith('''mps''' ):
_UpperCamelCase = torch.manual_seed(_A )
else:
_UpperCamelCase = torch.Generator(device=_A ).manual_seed(_A )
_UpperCamelCase = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''prior_num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = torch_device == '''cpu'''
self._test_attention_slicing_forward_pass(test_max_difference=_A )
def UpperCamelCase_ ( self : List[Any] ):
_UpperCamelCase = torch_device in ['''cpu''', '''mps''']
self._test_inference_batch_single_identical(test_max_difference=_A )
@slow
@require_torch_gpu
class lowerCAmelCase_ ( unittest.TestCase ):
def UpperCamelCase_ ( self : Optional[Any] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy''' )
_UpperCamelCase = StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l''' , torch_dtype=torch.floataa )
pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
_UpperCamelCase = torch.Generator(device='''cpu''' ).manual_seed(0 )
_UpperCamelCase = pipe('''anime turle''' , generator=_A , output_type='''np''' )
_UpperCamelCase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_A , _A )
def UpperCamelCase_ ( self : Optional[Any] ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
_UpperCamelCase = StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l''' , torch_dtype=torch.floataa )
_UpperCamelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
_UpperCamelCase = pipe(
'''anime turtle''' , prior_num_inference_steps=2 , num_inference_steps=2 , output_type='''np''' , )
_UpperCamelCase = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 10 | 0 |
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
snake_case = logging.get_logger(__name__)
class A_ ( UpperCAmelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = ['''pixel_values''']
def __init__( self : Union[str, Any] ,__A : bool = True ,__A : Dict[str, int] = None ,__A : PILImageResampling = PILImageResampling.BICUBIC ,__A : bool = True ,__A : Dict[str, int] = None ,__A : bool = True ,__A : Union[int, float] = 1 / 255 ,__A : bool = True ,__A : Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN ,__A : Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD ,**__A : Optional[int] ,) -> None:
super().__init__(**__A )
_lowercase = size if size is not None else {'shortest_edge': 224}
_lowercase = get_size_dict(__A ,default_to_square=__A )
_lowercase = crop_size if crop_size is not None else {'height': 224, 'width': 224}
_lowercase = get_size_dict(__A ,param_name='crop_size' )
_lowercase = do_resize
_lowercase = size
_lowercase = resample
_lowercase = do_center_crop
_lowercase = crop_size
_lowercase = do_rescale
_lowercase = rescale_factor
_lowercase = do_normalize
_lowercase = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
_lowercase = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def __UpperCAmelCase ( self : int ,__A : np.ndarray ,__A : Dict[str, int] ,__A : PILImageResampling = PILImageResampling.BICUBIC ,__A : Optional[Union[str, ChannelDimension]] = None ,**__A : List[str] ,) -> np.ndarray:
_lowercase = get_size_dict(__A ,default_to_square=__A )
# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
if "shortest_edge" in size:
_lowercase = int((256 / 224) * size['shortest_edge'] )
_lowercase = get_resize_output_image_size(__A ,size=__A ,default_to_square=__A )
_lowercase = {'height': output_size[0], 'width': output_size[1]}
if "height" not in size_dict or "width" not in size_dict:
raise ValueError(
F"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}""" )
return resize(
__A ,size=(size_dict['height'], size_dict['width']) ,resample=__A ,data_format=__A ,**__A )
def __UpperCAmelCase ( self : int ,__A : np.ndarray ,__A : Dict[str, int] ,__A : Optional[Union[str, ChannelDimension]] = None ,**__A : Optional[int] ,) -> np.ndarray:
_lowercase = get_size_dict(__A )
if "height" not in size or "width" not in size:
raise ValueError(F"""Size dict must have keys 'height' and 'width'. Got {size.keys()}""" )
return center_crop(__A ,size=(size['height'], size['width']) ,data_format=__A ,**__A )
def __UpperCAmelCase ( self : Optional[int] ,__A : np.ndarray ,__A : Union[int, float] ,__A : Optional[Union[str, ChannelDimension]] = None ,**__A : List[str] ,) -> np.ndarray:
return rescale(__A ,scale=__A ,data_format=__A ,**__A )
def __UpperCAmelCase ( self : str ,__A : np.ndarray ,__A : Union[float, List[float]] ,__A : Union[float, List[float]] ,__A : Optional[Union[str, ChannelDimension]] = None ,**__A : int ,) -> np.ndarray:
return normalize(__A ,mean=__A ,std=__A ,data_format=__A ,**__A )
def __UpperCAmelCase ( self : Dict ,__A : ImageInput ,__A : Optional[bool] = None ,__A : Optional[Dict[str, int]] = None ,__A : PILImageResampling = None ,__A : Optional[bool] = None ,__A : Optional[Dict[str, int]] = None ,__A : Optional[bool] = None ,__A : Optional[float] = None ,__A : Optional[bool] = None ,__A : Optional[Union[float, Iterable[float]]] = None ,__A : Optional[Union[float, Iterable[float]]] = None ,__A : Optional[TensorType] = None ,__A : ChannelDimension = ChannelDimension.FIRST ,**__A : Optional[Any] ,) -> BatchFeature:
_lowercase = do_resize if do_resize is not None else self.do_resize
_lowercase = resample if resample is not None else self.resample
_lowercase = do_center_crop if do_center_crop is not None else self.do_center_crop
_lowercase = do_rescale if do_rescale is not None else self.do_rescale
_lowercase = rescale_factor if rescale_factor is not None else self.rescale_factor
_lowercase = do_normalize if do_normalize is not None else self.do_normalize
_lowercase = image_mean if image_mean is not None else self.image_mean
_lowercase = image_std if image_std is not None else self.image_std
_lowercase = size if size is not None else self.size
_lowercase = get_size_dict(__A ,default_to_square=__A )
_lowercase = crop_size if crop_size is not None else self.crop_size
_lowercase = get_size_dict(__A ,param_name='crop_size' )
_lowercase = make_list_of_images(__A )
if not valid_images(__A ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
_lowercase = [to_numpy_array(__A ) for image in images]
if do_resize:
_lowercase = [self.resize(__A ,__A ,__A ) for image in images]
if do_center_crop:
_lowercase = [self.center_crop(__A ,__A ) for image in images]
if do_rescale:
_lowercase = [self.rescale(__A ,__A ) for image in images]
if do_normalize:
_lowercase = [self.normalize(__A ,__A ,__A ) for image in images]
_lowercase = [to_channel_dimension_format(__A ,__A ) for image in images]
_lowercase = {'pixel_values': images}
return BatchFeature(data=__A ,tensor_type=__A ) | 67 | from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _snake_case ( __snake_case , __snake_case ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(__snake_case , __snake_case ) ) )
def _snake_case ( __snake_case , __snake_case ):
if dataset.ndim != value_array.ndim:
_UpperCamelCase = (
'''Wrong input data\'s dimensions... '''
f"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
)
raise ValueError(__snake_case )
try:
if dataset.shape[1] != value_array.shape[1]:
_UpperCamelCase = (
'''Wrong input data\'s shape... '''
f"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
)
raise ValueError(__snake_case )
except IndexError:
if dataset.ndim != value_array.ndim:
raise TypeError('''Wrong shape''' )
if dataset.dtype != value_array.dtype:
_UpperCamelCase = (
'''Input data have different datatype... '''
f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
)
raise TypeError(__snake_case )
_UpperCamelCase = []
for value in value_array:
_UpperCamelCase = euclidean(__snake_case , dataset[0] )
_UpperCamelCase = dataset[0].tolist()
for dataset_value in dataset[1:]:
_UpperCamelCase = euclidean(__snake_case , __snake_case )
if dist > temp_dist:
_UpperCamelCase = temp_dist
_UpperCamelCase = dataset_value.tolist()
answer.append([vector, dist] )
return answer
def _snake_case ( __snake_case , __snake_case ):
return np.dot(__snake_case , __snake_case ) / (norm(__snake_case ) * norm(__snake_case ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 10 | 0 |
def lowercase__ ( A_: str , A_: str ) -> Optional[Any]:
"""simple docstring"""
assert x is not None
assert y is not None
__UpperCAmelCase =len(A_ )
__UpperCAmelCase =len(A_ )
# declaring the array for storing the dp values
__UpperCAmelCase =[[0] * (n + 1) for _ in range(m + 1 )] # noqa: E741
for i in range(1 , m + 1 ):
for j in range(1 , n + 1 ):
__UpperCAmelCase =1 if x[i - 1] == y[j - 1] else 0
__UpperCAmelCase =max(l[i - 1][j] , l[i][j - 1] , l[i - 1][j - 1] + match )
__UpperCAmelCase =""""""
__UpperCAmelCase , __UpperCAmelCase =m, n
while i > 0 and j > 0:
__UpperCAmelCase =1 if x[i - 1] == y[j - 1] else 0
if l[i][j] == l[i - 1][j - 1] + match:
if match == 1:
__UpperCAmelCase =x[i - 1] + seq
i -= 1
j -= 1
elif l[i][j] == l[i - 1][j]:
i -= 1
else:
j -= 1
return l[m][n], seq
if __name__ == "__main__":
__A = "AGGTAB"
__A = "GXTXAYB"
__A = 4
__A = "GTAB"
__A , __A = longest_common_subsequence(a, b)
print("len =", ln, ", sub-sequence =", subseq)
import doctest
doctest.testmod()
| 68 | import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class lowerCAmelCase_ ( __lowercase, unittest.TestCase ):
UpperCAmelCase = ShapEPipeline
UpperCAmelCase = ["prompt"]
UpperCAmelCase = ["prompt"]
UpperCAmelCase = [
"num_images_per_prompt",
"num_inference_steps",
"generator",
"latents",
"guidance_scale",
"frame_size",
"output_type",
"return_dict",
]
UpperCAmelCase = False
@property
def UpperCamelCase_ ( self : Union[str, Any] ):
return 32
@property
def UpperCamelCase_ ( self : int ):
return 32
@property
def UpperCamelCase_ ( self : List[str] ):
return self.time_input_dim * 4
@property
def UpperCamelCase_ ( self : Optional[Any] ):
return 8
@property
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
return tokenizer
@property
def UpperCamelCase_ ( self : List[Any] ):
torch.manual_seed(0 )
_UpperCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(_A )
@property
def UpperCamelCase_ ( self : int ):
torch.manual_seed(0 )
_UpperCamelCase = {
'''num_attention_heads''': 2,
'''attention_head_dim''': 16,
'''embedding_dim''': self.time_input_dim,
'''num_embeddings''': 32,
'''embedding_proj_dim''': self.text_embedder_hidden_size,
'''time_embed_dim''': self.time_embed_dim,
'''num_layers''': 1,
'''clip_embed_dim''': self.time_input_dim * 2,
'''additional_embeddings''': 0,
'''time_embed_act_fn''': '''gelu''',
'''norm_in_type''': '''layer''',
'''encoder_hid_proj_type''': None,
'''added_emb_type''': None,
}
_UpperCamelCase = PriorTransformer(**_A )
return model
@property
def UpperCamelCase_ ( self : Union[str, Any] ):
torch.manual_seed(0 )
_UpperCamelCase = {
'''param_shapes''': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'''d_latent''': self.time_input_dim,
'''d_hidden''': self.renderer_dim,
'''n_output''': 12,
'''background''': (
0.1,
0.1,
0.1,
),
}
_UpperCamelCase = ShapERenderer(**_A )
return model
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = self.dummy_prior
_UpperCamelCase = self.dummy_text_encoder
_UpperCamelCase = self.dummy_tokenizer
_UpperCamelCase = self.dummy_renderer
_UpperCamelCase = HeunDiscreteScheduler(
beta_schedule='''exp''' , num_train_timesteps=1024 , prediction_type='''sample''' , use_karras_sigmas=_A , clip_sample=_A , clip_sample_range=1.0 , )
_UpperCamelCase = {
'''prior''': prior,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''renderer''': renderer,
'''scheduler''': scheduler,
}
return components
def UpperCamelCase_ ( self : Tuple , _A : Tuple , _A : Optional[int]=0 ):
if str(_A ).startswith('''mps''' ):
_UpperCamelCase = torch.manual_seed(_A )
else:
_UpperCamelCase = torch.Generator(device=_A ).manual_seed(_A )
_UpperCamelCase = {
'''prompt''': '''horse''',
'''generator''': generator,
'''num_inference_steps''': 1,
'''frame_size''': 32,
'''output_type''': '''np''',
}
return inputs
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = '''cpu'''
_UpperCamelCase = self.get_dummy_components()
_UpperCamelCase = self.pipeline_class(**_A )
_UpperCamelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
_UpperCamelCase = pipe(**self.get_dummy_inputs(_A ) )
_UpperCamelCase = output.images[0]
_UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
_UpperCamelCase = np.array(
[
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def UpperCamelCase_ ( self : Any ):
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = torch_device == '''cpu'''
_UpperCamelCase = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=_A , relax_max_difference=_A , )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = self.get_dummy_components()
_UpperCamelCase = self.pipeline_class(**_A )
_UpperCamelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
_UpperCamelCase = 1
_UpperCamelCase = 2
_UpperCamelCase = self.get_dummy_inputs(_A )
for key in inputs.keys():
if key in self.batch_params:
_UpperCamelCase = batch_size * [inputs[key]]
_UpperCamelCase = pipe(**_A , num_images_per_prompt=_A )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class lowerCAmelCase_ ( unittest.TestCase ):
def UpperCamelCase_ ( self : str ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/shap_e/test_shap_e_np_out.npy''' )
_UpperCamelCase = ShapEPipeline.from_pretrained('''openai/shap-e''' )
_UpperCamelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
_UpperCamelCase = torch.Generator(device=_A ).manual_seed(0 )
_UpperCamelCase = pipe(
'''a shark''' , generator=_A , guidance_scale=15.0 , num_inference_steps=64 , frame_size=64 , output_type='''np''' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(_A , _A )
| 10 | 0 |
'''simple docstring'''
import functools
from typing import Any
def __UpperCAmelCase ( _UpperCAmelCase : str , _UpperCAmelCase : list[str] ) -> bool:
# Validation
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or len(_UpperCAmelCase ) == 0:
raise ValueError("the string should be not empty string" )
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or not all(
isinstance(_UpperCAmelCase , _UpperCAmelCase ) and len(_UpperCAmelCase ) > 0 for item in words ):
raise ValueError("the words should be a list of non-empty strings" )
# Build trie
__snake_case = {}
__snake_case = "WORD_KEEPER"
for word in words:
__snake_case = trie
for c in word:
if c not in trie_node:
__snake_case = {}
__snake_case = trie_node[c]
__snake_case = True
__snake_case = len(_UpperCAmelCase )
# Dynamic programming method
@functools.cache
def is_breakable(_UpperCAmelCase : int ) -> bool:
if index == len_string:
return True
__snake_case = trie
for i in range(_UpperCAmelCase , _UpperCAmelCase ):
__snake_case = trie_node.get(string[i] , _UpperCAmelCase )
if trie_node is None:
return False
if trie_node.get(_UpperCAmelCase , _UpperCAmelCase ) and is_breakable(i + 1 ):
return True
return False
return is_breakable(0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 69 | import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
_lowerCAmelCase = HfApi()
_lowerCAmelCase = {}
# fmt: off
_lowerCAmelCase = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189,
-1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839,
0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557
])
_lowerCAmelCase = torch.tensor([
-2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436,
1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208,
-2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948,
2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365
])
_lowerCAmelCase = torch.tensor([
-0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,
-0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304,
-0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925,
0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943
])
_lowerCAmelCase = torch.tensor([
0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172,
-0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309,
0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805,
-0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505
])
_lowerCAmelCase = torch.tensor([
0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133,
-0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395,
0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559,
-0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386
])
_lowerCAmelCase = torch.tensor([
0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078,
-0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330,
0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683,
-0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431
])
_lowerCAmelCase = torch.tensor([
0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042,
-0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398,
0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574,
-0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390
])
_lowerCAmelCase = torch.tensor([
0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042,
-0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290,
0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746,
-0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473
])
_lowerCAmelCase = torch.tensor([
-1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330,
1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243,
-2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810,
1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251])
_lowerCAmelCase = torch.tensor([
-1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324,
0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181,
-2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259,
1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266
])
_lowerCAmelCase = torch.tensor([
-1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212,
0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027,
-2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131,
1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355
])
_lowerCAmelCase = torch.tensor([
-2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959,
1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351,
-3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341,
3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066
])
_lowerCAmelCase = torch.tensor([
-2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740,
1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398,
-2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395,
2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243
])
_lowerCAmelCase = torch.tensor([
-2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336,
1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908,
-3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560,
3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343
])
_lowerCAmelCase = torch.tensor([
-1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344,
1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391,
-2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439,
1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219
])
# fmt: on
_lowerCAmelCase = api.list_models(filter="diffusers")
for mod in models:
if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
_lowerCAmelCase = "/home/patrick/google_checkpoints/" + mod.modelId.split("/")[-1]
print(f'Started running {mod.modelId}!!!')
if mod.modelId.startswith("CompVis"):
_lowerCAmelCase = UNetaDModel.from_pretrained(local_checkpoint, subfolder="unet")
else:
_lowerCAmelCase = UNetaDModel.from_pretrained(local_checkpoint)
torch.manual_seed(0)
random.seed(0)
_lowerCAmelCase = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
_lowerCAmelCase = torch.tensor([10] * noise.shape[0])
with torch.no_grad():
_lowerCAmelCase = model(noise, time_step).sample
assert torch.allclose(
logits[0, 0, 0, :30], results["_".join("_".join(mod.modelId.split("/")).split("-"))], atol=1E-3
)
print(f'{mod.modelId} has passed successfully!!!')
| 10 | 0 |
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
lowerCamelCase : Tuple = {
"<": operator.lt,
"<=": operator.le,
"==": operator.eq,
"!=": operator.ne,
">=": operator.ge,
">": operator.gt,
}
def _SCREAMING_SNAKE_CASE ( lowercase : int , lowercase : Tuple , lowercase : List[Any] , lowercase : Tuple , lowercase : Any , lowercase : Optional[int] ):
'''simple docstring'''
if got_ver is None or want_ver is None:
raise ValueError(
f"""Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"""
f""" reinstalling {pkg}.""" )
if not ops[op](version.parse(lowercase ) , version.parse(lowercase ) ):
raise ImportError(
f"""{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}""" )
def _SCREAMING_SNAKE_CASE ( lowercase : str , lowercase : Optional[str] = None ):
'''simple docstring'''
lowerCamelCase_ = f"""\n{hint}""" if hint is not None else ''
# non-versioned check
if re.match(r'^[\w_\-\d]+$' , lowercase ):
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = requirement, None, None
else:
lowerCamelCase_ = re.findall(r'^([^!=<>\s]+)([\s!=<>]{1,2}.+)' , lowercase )
if not match:
raise ValueError(
'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'
f""" got {requirement}""" )
lowerCamelCase_ , lowerCamelCase_ = match[0]
lowerCamelCase_ = want_full.split(',' ) # there could be multiple requirements
lowerCamelCase_ = {}
for w in want_range:
lowerCamelCase_ = re.findall(r'^([\s!=<>]{1,2})(.+)' , lowercase )
if not match:
raise ValueError(
'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'
f""" but got {requirement}""" )
lowerCamelCase_ , lowerCamelCase_ = match[0]
lowerCamelCase_ = want_ver
if op not in ops:
raise ValueError(f"""{requirement}: need one of {list(ops.keys() )}, but got {op}""" )
# special case
if pkg == "python":
lowerCamelCase_ = '.'.join([str(lowercase ) for x in sys.version_info[:3]] )
for op, want_ver in wanted.items():
_compare_versions(lowercase , lowercase , lowercase , lowercase , lowercase , lowercase )
return
# check if any version is installed
try:
lowerCamelCase_ = importlib.metadata.version(lowercase )
except importlib.metadata.PackageNotFoundError:
raise importlib.metadata.PackageNotFoundError(
f"""The '{requirement}' distribution was not found and is required by this application. {hint}""" )
# check that the right version is installed if version number or a range was provided
if want_ver is not None:
for op, want_ver in wanted.items():
_compare_versions(lowercase , lowercase , lowercase , lowercase , lowercase , lowercase )
def _SCREAMING_SNAKE_CASE ( lowercase : List[str] ):
'''simple docstring'''
lowerCamelCase_ = 'Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'
return require_version(lowercase , lowercase )
| 70 | from typing import List
from .keymap import KEYMAP, get_character
def _snake_case ( __snake_case ):
def decorator(__snake_case ):
_UpperCamelCase = getattr(__snake_case , '''handle_key''' , [] )
handle += [key]
setattr(__snake_case , '''handle_key''' , __snake_case )
return func
return decorator
def _snake_case ( *__snake_case ):
def decorator(__snake_case ):
_UpperCamelCase = getattr(__snake_case , '''handle_key''' , [] )
handle += keys
setattr(__snake_case , '''handle_key''' , __snake_case )
return func
return decorator
class lowerCAmelCase_ ( __lowercase ):
def __new__( cls : Optional[Any] , _A : Optional[Any] , _A : Optional[int] , _A : Union[str, Any] ):
_UpperCamelCase = super().__new__(cls , _A , _A , _A )
if not hasattr(_A , '''key_handler''' ):
setattr(_A , '''key_handler''' , {} )
setattr(_A , '''handle_input''' , KeyHandler.handle_input )
for value in attrs.values():
_UpperCamelCase = getattr(_A , '''handle_key''' , [] )
for key in handled_keys:
_UpperCamelCase = value
return new_cls
@staticmethod
def UpperCamelCase_ ( cls : str ):
_UpperCamelCase = get_character()
if char != KEYMAP["undefined"]:
_UpperCamelCase = ord(_A )
_UpperCamelCase = cls.key_handler.get(_A )
if handler:
_UpperCamelCase = char
return handler(cls )
else:
return None
def _snake_case ( cls ):
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 10 | 0 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int ) -> bool:
"""simple docstring"""
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("""Program to check whether a number is a Perfect number or not...""")
_lowerCamelCase = int(input("""Enter number: """).strip())
print(f"""{number} is {'' if perfect(number) else 'not '}a Perfect Number.""")
| 71 | import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
require_torch_gpu,
require_torch_or_tf,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
class lowerCAmelCase_ ( unittest.TestCase ):
UpperCAmelCase = MODEL_FOR_CAUSAL_LM_MAPPING
UpperCAmelCase = TF_MODEL_FOR_CAUSAL_LM_MAPPING
@require_torch
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = pipeline(task='''text-generation''' , model='''sshleifer/tiny-ctrl''' , framework='''pt''' )
# Using `do_sample=False` to force deterministic output
_UpperCamelCase = text_generator('''This is a test''' , do_sample=_A )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
''' oscope. FiliFili@@'''
)
}
] , )
_UpperCamelCase = text_generator(['''This is a test''', '''This is a second test'''] )
self.assertEqual(
_A , [
[
{
'''generated_text''': (
'''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
''' oscope. FiliFili@@'''
)
}
],
[
{
'''generated_text''': (
'''This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'''
''' oscope. oscope. FiliFili@@'''
)
}
],
] , )
_UpperCamelCase = text_generator('''This is a test''' , do_sample=_A , num_return_sequences=2 , return_tensors=_A )
self.assertEqual(
_A , [
{'''generated_token_ids''': ANY(_A )},
{'''generated_token_ids''': ANY(_A )},
] , )
_UpperCamelCase = text_generator.model.config.eos_token_id
_UpperCamelCase = '''<pad>'''
_UpperCamelCase = text_generator(
['''This is a test''', '''This is a second test'''] , do_sample=_A , num_return_sequences=2 , batch_size=2 , return_tensors=_A , )
self.assertEqual(
_A , [
[
{'''generated_token_ids''': ANY(_A )},
{'''generated_token_ids''': ANY(_A )},
],
[
{'''generated_token_ids''': ANY(_A )},
{'''generated_token_ids''': ANY(_A )},
],
] , )
@require_tf
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = pipeline(task='''text-generation''' , model='''sshleifer/tiny-ctrl''' , framework='''tf''' )
# Using `do_sample=False` to force deterministic output
_UpperCamelCase = text_generator('''This is a test''' , do_sample=_A )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
''' please,'''
)
}
] , )
_UpperCamelCase = text_generator(['''This is a test''', '''This is a second test'''] , do_sample=_A )
self.assertEqual(
_A , [
[
{
'''generated_text''': (
'''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
''' please,'''
)
}
],
[
{
'''generated_text''': (
'''This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'''
''' Cannes 閲閲Cannes Cannes Cannes 攵 please,'''
)
}
],
] , )
def UpperCamelCase_ ( self : int , _A : str , _A : Union[str, Any] , _A : Any ):
_UpperCamelCase = TextGenerationPipeline(model=_A , tokenizer=_A )
return text_generator, ["This is a test", "Another test"]
def UpperCamelCase_ ( self : Union[str, Any] ):
_UpperCamelCase = '''Hello I believe in'''
_UpperCamelCase = pipeline('''text-generation''' , model='''hf-internal-testing/tiny-random-gpt2''' )
_UpperCamelCase = text_generator(_A )
self.assertEqual(
_A , [{'''generated_text''': '''Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'''}] , )
_UpperCamelCase = text_generator(_A , stop_sequence=''' fe''' )
self.assertEqual(_A , [{'''generated_text''': '''Hello I believe in fe'''}] )
def UpperCamelCase_ ( self : Any , _A : List[Any] , _A : Union[str, Any] ):
_UpperCamelCase = text_generator.model
_UpperCamelCase = text_generator.tokenizer
_UpperCamelCase = text_generator('''This is a test''' )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )
_UpperCamelCase = text_generator('''This is a test''' , return_full_text=_A )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
self.assertNotIn('''This is a test''' , outputs[0]['''generated_text'''] )
_UpperCamelCase = pipeline(task='''text-generation''' , model=_A , tokenizer=_A , return_full_text=_A )
_UpperCamelCase = text_generator('''This is a test''' )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
self.assertNotIn('''This is a test''' , outputs[0]['''generated_text'''] )
_UpperCamelCase = text_generator('''This is a test''' , return_full_text=_A )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )
_UpperCamelCase = text_generator(['''This is great !''', '''Something else'''] , num_return_sequences=2 , do_sample=_A )
self.assertEqual(
_A , [
[{'''generated_text''': ANY(_A )}, {'''generated_text''': ANY(_A )}],
[{'''generated_text''': ANY(_A )}, {'''generated_text''': ANY(_A )}],
] , )
if text_generator.tokenizer.pad_token is not None:
_UpperCamelCase = text_generator(
['''This is great !''', '''Something else'''] , num_return_sequences=2 , batch_size=2 , do_sample=_A )
self.assertEqual(
_A , [
[{'''generated_text''': ANY(_A )}, {'''generated_text''': ANY(_A )}],
[{'''generated_text''': ANY(_A )}, {'''generated_text''': ANY(_A )}],
] , )
with self.assertRaises(_A ):
_UpperCamelCase = text_generator('''test''' , return_full_text=_A , return_text=_A )
with self.assertRaises(_A ):
_UpperCamelCase = text_generator('''test''' , return_full_text=_A , return_tensors=_A )
with self.assertRaises(_A ):
_UpperCamelCase = text_generator('''test''' , return_text=_A , return_tensors=_A )
# Empty prompt is slighly special
# it requires BOS token to exist.
# Special case for Pegasus which will always append EOS so will
# work even without BOS.
if (
text_generator.tokenizer.bos_token_id is not None
or "Pegasus" in tokenizer.__class__.__name__
or "Git" in model.__class__.__name__
):
_UpperCamelCase = text_generator('''''' )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
else:
with self.assertRaises((ValueError, AssertionError) ):
_UpperCamelCase = text_generator('''''' )
if text_generator.framework == "tf":
# TF generation does not support max_new_tokens, and it's impossible
# to control long generation with only max_length without
# fancy calculation, dismissing tests for now.
return
# We don't care about infinite range models.
# They already work.
# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
_UpperCamelCase = ['''RwkvForCausalLM''', '''XGLMForCausalLM''', '''GPTNeoXForCausalLM''']
if (
tokenizer.model_max_length < 1_0000
and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
):
# Handling of large generations
with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError) ):
text_generator('''This is a test''' * 500 , max_new_tokens=20 )
_UpperCamelCase = text_generator('''This is a test''' * 500 , handle_long_generation='''hole''' , max_new_tokens=20 )
# Hole strategy cannot work
with self.assertRaises(_A ):
text_generator(
'''This is a test''' * 500 , handle_long_generation='''hole''' , max_new_tokens=tokenizer.model_max_length + 10 , )
@require_torch
@require_accelerate
@require_torch_gpu
def UpperCamelCase_ ( self : Optional[int] ):
import torch
# Classic `model_kwargs`
_UpperCamelCase = pipeline(
model='''hf-internal-testing/tiny-random-bloom''' , model_kwargs={'''device_map''': '''auto''', '''torch_dtype''': torch.bfloataa} , )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
_UpperCamelCase = pipe('''This is a test''' )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
_UpperCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' , torch_dtype=torch.bfloataa )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
_UpperCamelCase = pipe('''This is a test''' )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
_UpperCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.floataa )
_UpperCamelCase = pipe('''This is a test''' )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
@require_torch
@require_torch_gpu
def UpperCamelCase_ ( self : Union[str, Any] ):
import torch
_UpperCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device=0 , torch_dtype=torch.floataa )
pipe('''This is a test''' )
@require_torch
@require_accelerate
@require_torch_gpu
def UpperCamelCase_ ( self : Optional[int] ):
import torch
_UpperCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' , torch_dtype=torch.floataa )
pipe('''This is a test''' , do_sample=_A , top_p=0.5 )
def UpperCamelCase_ ( self : Tuple ):
_UpperCamelCase = '''Hello world'''
_UpperCamelCase = pipeline('''text-generation''' , model='''hf-internal-testing/tiny-random-gpt2''' )
if text_generator.model.framework == "tf":
_UpperCamelCase = logging.get_logger('''transformers.generation.tf_utils''' )
else:
_UpperCamelCase = logging.get_logger('''transformers.generation.utils''' )
_UpperCamelCase = '''Both `max_new_tokens`''' # The beggining of the message to be checked in this test
# Both are set by the user -> log warning
with CaptureLogger(_A ) as cl:
_UpperCamelCase = text_generator(_A , max_length=10 , max_new_tokens=1 )
self.assertIn(_A , cl.out )
# The user only sets one -> no warning
with CaptureLogger(_A ) as cl:
_UpperCamelCase = text_generator(_A , max_new_tokens=1 )
self.assertNotIn(_A , cl.out )
with CaptureLogger(_A ) as cl:
_UpperCamelCase = text_generator(_A , max_length=10 )
self.assertNotIn(_A , cl.out )
| 10 | 0 |
'''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_UpperCAmelCase : List[Any] = datasets.load_iris()
_UpperCAmelCase : Dict = np.array(data['''data'''])
_UpperCAmelCase : Union[str, Any] = np.array(data['''target'''])
_UpperCAmelCase : int = data['''target_names''']
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = train_test_split(X, y)
def UpperCamelCase ( lowercase_ : str , lowercase_ : Optional[Any] ) -> int:
'''simple docstring'''
return np.linalg.norm(np.array(lowercase_ ) - np.array(lowercase_ ) )
def UpperCamelCase ( lowercase_ : Any , lowercase_ : Any , lowercase_ : int , lowercase_ : Tuple , lowercase_ : Tuple=5 ) -> List[Any]:
'''simple docstring'''
lowercase =zip(lowercase_ , lowercase_ )
# List of distances of all points from the point to be classified
lowercase =[]
for data_point in data:
lowercase =euclidean_distance(data_point[0] , lowercase_ )
distances.append((distance, data_point[1]) )
# Choosing 'k' points with the least distances.
lowercase =[i[1] for i in sorted(lowercase_ )[:k]]
# Most commonly occurring class among them
# is the class into which the point is classified
lowercase =Counter(lowercase_ ).most_common(1 )[0][0]
return classes[result]
if __name__ == "__main__":
print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
| 72 | def _snake_case ( __snake_case = 100 ):
_UpperCamelCase = (n * (n + 1) // 2) ** 2
_UpperCamelCase = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f'{solution() = }')
| 10 | 0 |
a_ : Optional[int] = 9.80_665
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = g):
if fluid_density <= 0:
raise ValueError('Impossible fluid density')
if volume < 0:
raise ValueError('Impossible Object volume')
if gravity <= 0:
raise ValueError('Impossible Gravity')
return fluid_density * gravity * volume
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
| 73 | import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
_lowerCAmelCase = logging.get_logger(__name__)
def _snake_case ( __snake_case , __snake_case , __snake_case , __snake_case ):
def constraint_to_multiple_of(__snake_case , __snake_case , __snake_case=0 , __snake_case=None ):
_UpperCamelCase = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
_UpperCamelCase = math.floor(val / multiple ) * multiple
if x < min_val:
_UpperCamelCase = math.ceil(val / multiple ) * multiple
return x
_UpperCamelCase = (output_size, output_size) if isinstance(__snake_case , __snake_case ) else output_size
_UpperCamelCase , _UpperCamelCase = get_image_size(__snake_case )
_UpperCamelCase , _UpperCamelCase = output_size
# determine new height and width
_UpperCamelCase = output_height / input_height
_UpperCamelCase = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
_UpperCamelCase = scale_width
else:
# fit height
_UpperCamelCase = scale_height
_UpperCamelCase = constraint_to_multiple_of(scale_height * input_height , multiple=__snake_case )
_UpperCamelCase = constraint_to_multiple_of(scale_width * input_width , multiple=__snake_case )
return (new_height, new_width)
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = ["pixel_values"]
def __init__( self : List[Any] , _A : bool = True , _A : Dict[str, int] = None , _A : PILImageResampling = PILImageResampling.BILINEAR , _A : bool = False , _A : int = 1 , _A : bool = True , _A : Union[int, float] = 1 / 255 , _A : bool = True , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[float, List[float]]] = None , **_A : List[str] , ):
super().__init__(**_A )
_UpperCamelCase = size if size is not None else {'''height''': 384, '''width''': 384}
_UpperCamelCase = get_size_dict(_A )
_UpperCamelCase = do_resize
_UpperCamelCase = size
_UpperCamelCase = keep_aspect_ratio
_UpperCamelCase = ensure_multiple_of
_UpperCamelCase = resample
_UpperCamelCase = do_rescale
_UpperCamelCase = rescale_factor
_UpperCamelCase = do_normalize
_UpperCamelCase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_UpperCamelCase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def UpperCamelCase_ ( self : List[str] , _A : np.ndarray , _A : Dict[str, int] , _A : bool = False , _A : int = 1 , _A : PILImageResampling = PILImageResampling.BICUBIC , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Any , ):
_UpperCamelCase = get_size_dict(_A )
if "height" not in size or "width" not in size:
raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
_UpperCamelCase = get_resize_output_image_size(
_A , output_size=(size['''height'''], size['''width''']) , keep_aspect_ratio=_A , multiple=_A , )
return resize(_A , size=_A , resample=_A , data_format=_A , **_A )
def UpperCamelCase_ ( self : str , _A : np.ndarray , _A : Union[int, float] , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Any , ):
return rescale(_A , scale=_A , data_format=_A , **_A )
def UpperCamelCase_ ( self : int , _A : np.ndarray , _A : Union[float, List[float]] , _A : Union[float, List[float]] , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Any , ):
return normalize(_A , mean=_A , std=_A , data_format=_A , **_A )
def UpperCamelCase_ ( self : Optional[int] , _A : ImageInput , _A : bool = None , _A : int = None , _A : bool = None , _A : int = None , _A : PILImageResampling = None , _A : bool = None , _A : float = None , _A : bool = None , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[str, TensorType]] = None , _A : ChannelDimension = ChannelDimension.FIRST , **_A : str , ):
_UpperCamelCase = do_resize if do_resize is not None else self.do_resize
_UpperCamelCase = size if size is not None else self.size
_UpperCamelCase = get_size_dict(_A )
_UpperCamelCase = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
_UpperCamelCase = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
_UpperCamelCase = resample if resample is not None else self.resample
_UpperCamelCase = do_rescale if do_rescale is not None else self.do_rescale
_UpperCamelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
_UpperCamelCase = do_normalize if do_normalize is not None else self.do_normalize
_UpperCamelCase = image_mean if image_mean is not None else self.image_mean
_UpperCamelCase = image_std if image_std is not None else self.image_std
_UpperCamelCase = make_list_of_images(_A )
if not valid_images(_A ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None or resample is None:
raise ValueError('''Size and resample must be specified if do_resize is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
_UpperCamelCase = [to_numpy_array(_A ) for image in images]
if do_resize:
_UpperCamelCase = [self.resize(image=_A , size=_A , resample=_A ) for image in images]
if do_rescale:
_UpperCamelCase = [self.rescale(image=_A , scale=_A ) for image in images]
if do_normalize:
_UpperCamelCase = [self.normalize(image=_A , mean=_A , std=_A ) for image in images]
_UpperCamelCase = [to_channel_dimension_format(_A , _A ) for image in images]
_UpperCamelCase = {'''pixel_values''': images}
return BatchFeature(data=_A , tensor_type=_A )
def UpperCamelCase_ ( self : Any , _A : Any , _A : List[Tuple] = None ):
_UpperCamelCase = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(_A ) != len(_A ):
raise ValueError(
'''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )
if is_torch_tensor(_A ):
_UpperCamelCase = target_sizes.numpy()
_UpperCamelCase = []
for idx in range(len(_A ) ):
_UpperCamelCase = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='''bilinear''' , align_corners=_A )
_UpperCamelCase = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(_A )
else:
_UpperCamelCase = logits.argmax(dim=1 )
_UpperCamelCase = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 10 | 0 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstandard
def a__ ( snake_case ):
"""simple docstring"""
assert "mock" in _fsspec_registry
assert "bz2" in _fsspec_registry
def a__ ( ):
"""simple docstring"""
assert "mock" not in _fsspec_registry
assert "bz2" in _fsspec_registry
def a__ ( ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = '''mock-s3-bucket'''
__SCREAMING_SNAKE_CASE : int = F'''s3://{mock_bucket}'''
__SCREAMING_SNAKE_CASE : Dict = extract_path_from_uri(snake_case )
assert dataset_path.startswith('''s3://''' ) is False
__SCREAMING_SNAKE_CASE : List[str] = '''./local/path'''
__SCREAMING_SNAKE_CASE : str = extract_path_from_uri(snake_case )
assert dataset_path == new_dataset_path
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : int = is_remote_filesystem(snake_case )
assert is_remote is True
__SCREAMING_SNAKE_CASE : int = fsspec.filesystem('''file''' )
__SCREAMING_SNAKE_CASE : str = is_remote_filesystem(snake_case )
assert is_remote is False
@pytest.mark.parametrize('''compression_fs_class''' , snake_case )
def a__ ( snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Tuple = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_file, '''bz2''': bza_file, '''lz4''': lza_file}
__SCREAMING_SNAKE_CASE : List[str] = input_paths[compression_fs_class.protocol]
if input_path is None:
__SCREAMING_SNAKE_CASE : Any = F'''for \'{compression_fs_class.protocol}\' compression protocol, '''
if compression_fs_class.protocol == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_fs_class.protocol == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(snake_case )
__SCREAMING_SNAKE_CASE : str = fsspec.filesystem(compression_fs_class.protocol , fo=snake_case )
assert isinstance(snake_case , snake_case )
__SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.basename(snake_case )
__SCREAMING_SNAKE_CASE : Optional[int] = expected_filename[: expected_filename.rindex('''.''' )]
assert fs.glob('''*''' ) == [expected_filename]
with fs.open(snake_case , '''r''' , encoding='''utf-8''' ) as f, open(snake_case , encoding='''utf-8''' ) as expected_file:
assert f.read() == expected_file.read()
@pytest.mark.parametrize('''protocol''' , ['''zip''', '''gzip'''] )
def a__ ( snake_case , snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = {'''zip''': zip_jsonl_path, '''gzip''': jsonl_gz_path}
__SCREAMING_SNAKE_CASE : Dict = compressed_file_paths[protocol]
__SCREAMING_SNAKE_CASE : int = '''dataset.jsonl'''
__SCREAMING_SNAKE_CASE : int = F'''{protocol}://{member_file_path}::{compressed_file_path}'''
__SCREAMING_SNAKE_CASE, *__SCREAMING_SNAKE_CASE : Dict = fsspec.get_fs_token_paths(snake_case )
assert fs.isfile(snake_case )
assert not fs.isfile('''non_existing_''' + member_file_path )
@pytest.mark.integration
def a__ ( snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = hf_api.dataset_info(snake_case , token=snake_case )
__SCREAMING_SNAKE_CASE : Optional[Any] = HfFileSystem(repo_info=snake_case , token=snake_case )
assert sorted(hffs.glob('''*''' ) ) == [".gitattributes", "data"]
assert hffs.isdir('''data''' )
assert hffs.isfile('''.gitattributes''' ) and hffs.isfile('''data/text_data.txt''' )
with open(snake_case ) as f:
assert hffs.open('''data/text_data.txt''' , '''r''' ).read() == f.read()
def a__ ( ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = '''bz2'''
# Import module
import datasets.filesystems
# Overwrite protocol and reload
register_implementation(snake_case , snake_case , clobber=snake_case )
with pytest.warns(snake_case ) as warning_info:
importlib.reload(datasets.filesystems )
assert len(snake_case ) == 1
assert (
str(warning_info[0].message )
== F'''A filesystem protocol was already set for {protocol} and will be overwritten.'''
)
| 74 | import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_lowerCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_lowerCAmelCase = " \"\"\"\n Output class for the scheduler's step function output.\n\n Args:\n prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.FloatTensor\n pred_original_sample: Optional[torch.FloatTensor] = None\n"
class lowerCAmelCase_ ( unittest.TestCase ):
def UpperCamelCase_ ( self : List[Any] ):
_UpperCamelCase = tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir , '''schedulers/''' ) )
_UpperCamelCase = self.diffusers_dir
shutil.copy(
os.path.join(_A , '''src/diffusers/schedulers/scheduling_ddpm.py''' ) , os.path.join(self.diffusers_dir , '''schedulers/scheduling_ddpm.py''' ) , )
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = '''src/diffusers'''
shutil.rmtree(self.diffusers_dir )
def UpperCamelCase_ ( self : str , _A : List[str] , _A : Optional[Any] , _A : List[str] , _A : Optional[int]=None ):
_UpperCamelCase = comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
if overwrite_result is not None:
_UpperCamelCase = comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
_UpperCamelCase = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 )
_UpperCamelCase = black.format_str(_A , mode=_A )
_UpperCamelCase = os.path.join(self.diffusers_dir , '''new_code.py''' )
with open(_A , '''w''' , newline='''\n''' ) as f:
f.write(_A )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(_A ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=_A )
with open(_A , '''r''' ) as f:
self.assertTrue(f.read() , _A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput''' )
self.assertEqual(_A , _A )
def UpperCamelCase_ ( self : List[str] ):
# Base copy consistency
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' , '''DDPMSchedulerOutput''' , REFERENCE_CODE + '''\n''' , )
# With no empty line at the end
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' , '''DDPMSchedulerOutput''' , _A , )
# Copy consistency with rename
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' , '''TestSchedulerOutput''' , re.sub('''DDPM''' , '''Test''' , _A ) , )
# Copy consistency with a really long name
_UpperCamelCase = '''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
self.check_copy_consistency(
F"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}""" , F"""{long_class_name}SchedulerOutput""" , re.sub('''Bert''' , _A , _A ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' , '''TestSchedulerOutput''' , _A , overwrite_result=re.sub('''DDPM''' , '''Test''' , _A ) , )
| 10 | 0 |
'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
UpperCamelCase__ = version.parse(importlib_metadata.version('''nltk'''))
if NLTK_VERSION >= version.Version('''3.6.4'''):
from nltk import word_tokenize
UpperCamelCase__ = '''\
@inproceedings{banarjee2005,
title = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},
author = {Banerjee, Satanjeev and Lavie, Alon},
booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},
month = jun,
year = {2005},
address = {Ann Arbor, Michigan},
publisher = {Association for Computational Linguistics},
url = {https://www.aclweb.org/anthology/W05-0909},
pages = {65--72},
}
'''
UpperCamelCase__ = '''\
METEOR, an automatic metric for machine translation evaluation
that is based on a generalized concept of unigram matching between the
machine-produced translation and human-produced reference translations.
Unigrams can be matched based on their surface forms, stemmed forms,
and meanings; furthermore, METEOR can be easily extended to include more
advanced matching strategies. Once all generalized unigram matches
between the two strings have been found, METEOR computes a score for
this matching using a combination of unigram-precision, unigram-recall, and
a measure of fragmentation that is designed to directly capture how
well-ordered the matched words in the machine translation are in relation
to the reference.
METEOR gets an R correlation value of 0.347 with human evaluation on the Arabic
data and 0.331 on the Chinese data. This is shown to be an improvement on
using simply unigram-precision, unigram-recall and their harmonic F1
combination.
'''
UpperCamelCase__ = '''
Computes METEOR score of translated segments against one or more references.
Args:
predictions: list of predictions to score. Each prediction
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
alpha: Parameter for controlling relative weights of precision and recall. default: 0.9
beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3
gamma: Relative weight assigned to fragmentation penalty. default: 0.5
Returns:
\'meteor\': meteor score.
Examples:
>>> meteor = datasets.load_metric(\'meteor\')
>>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
>>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
>>> results = meteor.compute(predictions=predictions, references=references)
>>> print(round(results["meteor"], 4))
0.6944
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCamelCase_ ( datasets.Metric ):
def lowercase_ ( self : List[str] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py'''] , reference_urls=[
'''https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score''',
'''https://en.wikipedia.org/wiki/METEOR''',
] , )
def lowercase_ ( self : str , _A : Optional[Any] ):
'''simple docstring'''
import nltk
nltk.download('''wordnet''' )
if NLTK_VERSION >= version.Version('''3.6.5''' ):
nltk.download('''punkt''' )
if NLTK_VERSION >= version.Version('''3.6.6''' ):
nltk.download('''omw-1.4''' )
def lowercase_ ( self : str , _A : Dict , _A : Optional[Any] , _A : Tuple=0.9 , _A : Optional[int]=3 , _A : Tuple=0.5 ):
'''simple docstring'''
if NLTK_VERSION >= version.Version('''3.6.5''' ):
UpperCAmelCase__ : Union[str, Any] = [
meteor_score.single_meteor_score(
word_tokenize(_A ) , word_tokenize(_A ) , alpha=_A , beta=_A , gamma=_A )
for ref, pred in zip(_A , _A )
]
else:
UpperCAmelCase__ : Tuple = [
meteor_score.single_meteor_score(_A , _A , alpha=_A , beta=_A , gamma=_A )
for ref, pred in zip(_A , _A )
]
return {"meteor": np.mean(_A )}
| 75 | import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaProcessor,
is_apex_available,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
_lowerCAmelCase = True
from torch.cuda.amp import autocast
_lowerCAmelCase = logging.getLogger(__name__)
def _snake_case ( __snake_case=None , __snake_case=None ):
return field(default_factory=lambda: default , metadata=__snake_case )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
UpperCAmelCase = field(
default=0.1, metadata={"help": "The dropout ratio for the attention probabilities."} )
UpperCAmelCase = field(
default=0.1, metadata={"help": "The dropout ratio for activations inside the fully connected layer."} )
UpperCAmelCase = field(
default=0.1, metadata={
"help": "The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."
}, )
UpperCAmelCase = field(
default=0.1, metadata={"help": "The dropout probabilitiy for all 1D convolutional layers in feature extractor."}, )
UpperCAmelCase = field(
default=0.0_5, metadata={
"help": (
"Propability of each feature vector along the time axis to be chosen as the start of the vector"
"span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"
"vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."
)
}, )
UpperCAmelCase = field(default=0.0, metadata={"help": "The LayerDrop probability."} )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
UpperCAmelCase = field(
default="train+validation", metadata={
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "The number of processes to use for the preprocessing."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
}, )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of validation examples to this "
"value if set."
)
}, )
UpperCAmelCase = list_field(
default=[",", "?", ".", "!", "-", ";", ":", "\"\"", "%", "'", "\"", "�"], metadata={"help": "A list of characters to remove from the transcripts."}, )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = 42
UpperCAmelCase = True
UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = None
def __call__( self : Union[str, Any] , _A : List[Dict[str, Union[List[int], torch.Tensor]]] ):
# split inputs and labels since they have to be of different lenghts and need
# different padding methods
_UpperCamelCase = [{'''input_values''': feature['''input_values''']} for feature in features]
_UpperCamelCase = [{'''input_ids''': feature['''labels''']} for feature in features]
_UpperCamelCase = self.processor.pad(
_A , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' , )
_UpperCamelCase = self.processor.pad(
labels=_A , padding=self.padding , max_length=self.max_length_labels , pad_to_multiple_of=self.pad_to_multiple_of_labels , return_tensors='''pt''' , )
# replace padding with -100 to ignore loss correctly
_UpperCamelCase = labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1 ) , -100 )
_UpperCamelCase = labels
return batch
class lowerCAmelCase_ ( __lowercase ):
def UpperCamelCase_ ( self : Dict , _A : nn.Module , _A : Dict[str, Union[torch.Tensor, Any]] ):
model.train()
_UpperCamelCase = self._prepare_inputs(_A )
if self.use_amp:
with autocast():
_UpperCamelCase = self.compute_loss(_A , _A )
else:
_UpperCamelCase = self.compute_loss(_A , _A )
if self.args.n_gpu > 1:
if model.module.config.ctc_loss_reduction == "mean":
_UpperCamelCase = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
_UpperCamelCase = loss.sum() / (inputs['''labels'''] >= 0).sum()
else:
raise ValueError(F"""{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']""" )
if self.args.gradient_accumulation_steps > 1:
_UpperCamelCase = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(_A ).backward()
elif self.use_apex:
with amp.scale_loss(_A , self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(_A )
else:
loss.backward()
return loss.detach()
def _snake_case ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_UpperCamelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
_UpperCamelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCamelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'''Use --overwrite_output_dir to overcome.''' )
elif last_checkpoint is not None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info('''Training/evaluation parameters %s''' , __snake_case )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets:
_UpperCamelCase = datasets.load_dataset(
'''common_voice''' , data_args.dataset_config_name , split=data_args.train_split_name )
_UpperCamelCase = datasets.load_dataset('''common_voice''' , data_args.dataset_config_name , split='''test''' )
# Create and save tokenizer
_UpperCamelCase = f"""[{"".join(data_args.chars_to_ignore )}]"""
def remove_special_characters(__snake_case ):
_UpperCamelCase = re.sub(__snake_case , '''''' , batch['''sentence'''] ).lower() + ''' '''
return batch
_UpperCamelCase = train_dataset.map(__snake_case , remove_columns=['''sentence'''] )
_UpperCamelCase = eval_dataset.map(__snake_case , remove_columns=['''sentence'''] )
def extract_all_chars(__snake_case ):
_UpperCamelCase = ''' '''.join(batch['''text'''] )
_UpperCamelCase = list(set(__snake_case ) )
return {"vocab": [vocab], "all_text": [all_text]}
_UpperCamelCase = train_dataset.map(
__snake_case , batched=__snake_case , batch_size=-1 , keep_in_memory=__snake_case , remove_columns=train_dataset.column_names , )
_UpperCamelCase = train_dataset.map(
__snake_case , batched=__snake_case , batch_size=-1 , keep_in_memory=__snake_case , remove_columns=eval_dataset.column_names , )
_UpperCamelCase = list(set(vocab_train['''vocab'''][0] ) | set(vocab_test['''vocab'''][0] ) )
_UpperCamelCase = {v: k for k, v in enumerate(__snake_case )}
_UpperCamelCase = vocab_dict[''' ''']
del vocab_dict[" "]
_UpperCamelCase = len(__snake_case )
_UpperCamelCase = len(__snake_case )
with open('''vocab.json''' , '''w''' ) as vocab_file:
json.dump(__snake_case , __snake_case )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCamelCase = WavaVecaCTCTokenizer(
'''vocab.json''' , unk_token='''[UNK]''' , pad_token='''[PAD]''' , word_delimiter_token='''|''' , )
_UpperCamelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0.0 , do_normalize=__snake_case , return_attention_mask=__snake_case )
_UpperCamelCase = WavaVecaProcessor(feature_extractor=__snake_case , tokenizer=__snake_case )
_UpperCamelCase = WavaVecaForCTC.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , activation_dropout=model_args.activation_dropout , attention_dropout=model_args.attention_dropout , hidden_dropout=model_args.hidden_dropout , feat_proj_dropout=model_args.feat_proj_dropout , mask_time_prob=model_args.mask_time_prob , gradient_checkpointing=training_args.gradient_checkpointing , layerdrop=model_args.layerdrop , ctc_loss_reduction='''mean''' , pad_token_id=processor.tokenizer.pad_token_id , vocab_size=len(processor.tokenizer ) , )
if data_args.max_train_samples is not None:
_UpperCamelCase = min(len(__snake_case ) , data_args.max_train_samples )
_UpperCamelCase = train_dataset.select(range(__snake_case ) )
if data_args.max_val_samples is not None:
_UpperCamelCase = eval_dataset.select(range(data_args.max_val_samples ) )
_UpperCamelCase = torchaudio.transforms.Resample(48000 , 16000 )
# Preprocessing the datasets.
# We need to read the aduio files as arrays and tokenize the targets.
def speech_file_to_array_fn(__snake_case ):
_UpperCamelCase , _UpperCamelCase = torchaudio.load(batch['''path'''] )
_UpperCamelCase = resampler(__snake_case ).squeeze().numpy()
_UpperCamelCase = 16000
_UpperCamelCase = batch['''text''']
return batch
_UpperCamelCase = train_dataset.map(
__snake_case , remove_columns=train_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
_UpperCamelCase = eval_dataset.map(
__snake_case , remove_columns=eval_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
def prepare_dataset(__snake_case ):
# check that all files have the correct sampling rate
assert (
len(set(batch['''sampling_rate'''] ) ) == 1
), f"""Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."""
_UpperCamelCase = processor(
audio=batch['''speech'''] , text=batch['''target_text'''] , sampling_rate=batch['''sampling_rate'''][0] )
batch.update(__snake_case )
return batch
_UpperCamelCase = train_dataset.map(
__snake_case , remove_columns=train_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=__snake_case , num_proc=data_args.preprocessing_num_workers , )
_UpperCamelCase = eval_dataset.map(
__snake_case , remove_columns=eval_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=__snake_case , num_proc=data_args.preprocessing_num_workers , )
# Metric
_UpperCamelCase = datasets.load_metric('''wer''' )
def compute_metrics(__snake_case ):
_UpperCamelCase = pred.predictions
_UpperCamelCase = np.argmax(__snake_case , axis=-1 )
_UpperCamelCase = processor.tokenizer.pad_token_id
_UpperCamelCase = processor.batch_decode(__snake_case )
# we do not want to group tokens when computing the metrics
_UpperCamelCase = processor.batch_decode(pred.label_ids , group_tokens=__snake_case )
_UpperCamelCase = wer_metric.compute(predictions=__snake_case , references=__snake_case )
return {"wer": wer}
if model_args.freeze_feature_extractor:
model.freeze_feature_extractor()
# Data collator
_UpperCamelCase = DataCollatorCTCWithPadding(processor=__snake_case , padding=__snake_case )
# Initialize our Trainer
_UpperCamelCase = CTCTrainer(
model=__snake_case , data_collator=__snake_case , args=__snake_case , compute_metrics=__snake_case , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=processor.feature_extractor , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
_UpperCamelCase = last_checkpoint
elif os.path.isdir(model_args.model_name_or_path ):
_UpperCamelCase = model_args.model_name_or_path
else:
_UpperCamelCase = None
# Save the feature_extractor and the tokenizer
if is_main_process(training_args.local_rank ):
processor.save_pretrained(training_args.output_dir )
_UpperCamelCase = trainer.train(resume_from_checkpoint=__snake_case )
trainer.save_model()
_UpperCamelCase = train_result.metrics
_UpperCamelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__snake_case )
)
_UpperCamelCase = min(__snake_case , len(__snake_case ) )
trainer.log_metrics('''train''' , __snake_case )
trainer.save_metrics('''train''' , __snake_case )
trainer.save_state()
# Evaluation
_UpperCamelCase = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
_UpperCamelCase = trainer.evaluate()
_UpperCamelCase = data_args.max_val_samples if data_args.max_val_samples is not None else len(__snake_case )
_UpperCamelCase = min(__snake_case , len(__snake_case ) )
trainer.log_metrics('''eval''' , __snake_case )
trainer.save_metrics('''eval''' , __snake_case )
return results
if __name__ == "__main__":
main()
| 10 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AutoformerConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'AutoformerForPrediction',
'AutoformerModel',
'AutoformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_autoformer import (
AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_autoformer import (
AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
AutoformerForPrediction,
AutoformerModel,
AutoformerPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 76 | import math
class lowerCAmelCase_ :
def __init__( self : Tuple , _A : int=0 ): # a graph with Node 0,1,...,N-1
_UpperCamelCase = n
_UpperCamelCase = [
[math.inf for j in range(0 , _A )] for i in range(0 , _A )
] # adjacency matrix for weight
_UpperCamelCase = [
[math.inf for j in range(0 , _A )] for i in range(0 , _A )
] # dp[i][j] stores minimum distance from i to j
def UpperCamelCase_ ( self : Dict , _A : str , _A : List[str] , _A : Optional[Any] ):
_UpperCamelCase = w
def UpperCamelCase_ ( self : Optional[int] ):
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
_UpperCamelCase = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def UpperCamelCase_ ( self : List[str] , _A : Optional[int] , _A : Optional[int] ):
return self.dp[u][v]
if __name__ == "__main__":
_lowerCAmelCase = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 10 | 0 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"
# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
A = """facebook/wmt19-en-de"""
A = FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
A = FSMTConfig.from_pretrained(mname)
config.update(
dict(
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
)
A = FSMTForConditionalGeneration(config)
print(f'''num of params {tiny_model.num_parameters()}''')
# Test
A = tokenizer(["""Making tiny model"""], return_tensors="""pt""")
A = tiny_model(**batch)
print("""test output:""", len(outputs.logits[0]))
# Save
A = """tiny-wmt19-en-de"""
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(f'''Generated {mname_tiny}''')
# Upload
# transformers-cli upload tiny-wmt19-en-de
| 77 | import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
def _snake_case ( __snake_case=None , __snake_case=None ):
return field(default_factory=lambda: default , metadata=__snake_case )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = list_field(
default=[], metadata={
"help": (
"Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
" of all available models"
)
}, )
UpperCAmelCase = list_field(
default=[8], metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"} )
UpperCAmelCase = list_field(
default=[8, 32, 128, 512], metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Use FP16 to accelerate inference."} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Benchmark training of model"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Verbose memory tracing"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
}, )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Trace memory line by line"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Save result to a CSV file"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Save all print statements in a log file"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Whether to print environment information"} )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
" multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
" for debugging / testing and on TPU."
)
}, )
UpperCAmelCase = field(
default=F"""inference_time_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving time results to csv."}, )
UpperCAmelCase = field(
default=F"""inference_memory_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving memory results to csv."}, )
UpperCAmelCase = field(
default=F"""train_time_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving time results to csv for training."}, )
UpperCAmelCase = field(
default=F"""train_memory_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving memory results to csv for training."}, )
UpperCAmelCase = field(
default=F"""env_info_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving environment information."}, )
UpperCAmelCase = field(
default=F"""log_{round(time() )}.csv""", metadata={"help": "Log filename used if print statements are saved in log."}, )
UpperCAmelCase = field(default=3, metadata={"help": "Times an experiment will be run."} )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
" model weights."
)
}, )
def UpperCamelCase_ ( self : Union[str, Any] ):
warnings.warn(
F"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
''' are deprecated in general and it is advised to use external Benchmarking libraries '''
''' to benchmark Transformer models.''' , _A , )
def UpperCamelCase_ ( self : str ):
return json.dumps(dataclasses.asdict(self ) , indent=2 )
@property
def UpperCamelCase_ ( self : List[Any] ):
if len(self.models ) <= 0:
raise ValueError(
'''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
''' bert-base-cased` or `args.models = [\'bert-base-cased\'].''' )
return self.models
@property
def UpperCamelCase_ ( self : Optional[int] ):
if not self.multi_process:
return False
elif self.is_tpu:
logger.info('''Multiprocessing is currently not possible on TPU.''' )
return False
else:
return True
| 10 | 0 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class __A ( unittest.TestCase ):
def _lowercase (self : List[str] ):
UpperCAmelCase_ = 0
def _lowercase (self : Tuple ):
UpperCAmelCase_ = AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch32" )
self.assertIsInstance(__a , __a )
def _lowercase (self : str ):
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase_ = Path(__a ) / "preprocessor_config.json"
UpperCAmelCase_ = Path(__a ) / "config.json"
json.dump(
{"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"} , open(__a , "w" ) , )
json.dump({"model_type": "clip"} , open(__a , "w" ) )
UpperCAmelCase_ = AutoImageProcessor.from_pretrained(__a )
self.assertIsInstance(__a , __a )
def _lowercase (self : Dict ):
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase_ = Path(__a ) / "preprocessor_config.json"
UpperCAmelCase_ = Path(__a ) / "config.json"
json.dump(
{"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"} , open(__a , "w" ) , )
json.dump({"model_type": "clip"} , open(__a , "w" ) )
UpperCAmelCase_ = AutoImageProcessor.from_pretrained(__a )
self.assertIsInstance(__a , __a )
def _lowercase (self : List[str] ):
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase_ = CLIPConfig()
# Create a dummy config file with image_proceesor_type
UpperCAmelCase_ = Path(__a ) / "preprocessor_config.json"
UpperCAmelCase_ = Path(__a ) / "config.json"
json.dump(
{"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"} , open(__a , "w" ) , )
json.dump({"model_type": "clip"} , open(__a , "w" ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
UpperCAmelCase_ = AutoImageProcessor.from_pretrained(__a ).to_dict()
config_dict.pop("image_processor_type" )
UpperCAmelCase_ = CLIPImageProcessor(**__a )
# save in new folder
model_config.save_pretrained(__a )
config.save_pretrained(__a )
UpperCAmelCase_ = AutoImageProcessor.from_pretrained(__a )
# make sure private variable is not incorrectly saved
UpperCAmelCase_ = json.loads(config.to_json_string() )
self.assertTrue("_processor_class" not in dict_as_saved )
self.assertIsInstance(__a , __a )
def _lowercase (self : int ):
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase_ = Path(__a ) / "preprocessor_config.json"
json.dump(
{"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"} , open(__a , "w" ) , )
UpperCAmelCase_ = AutoImageProcessor.from_pretrained(__a )
self.assertIsInstance(__a , __a )
def _lowercase (self : Tuple ):
with self.assertRaisesRegex(
__a , "clip-base is not a local folder and is not a valid model identifier" ):
UpperCAmelCase_ = AutoImageProcessor.from_pretrained("clip-base" )
def _lowercase (self : Optional[int] ):
with self.assertRaisesRegex(
__a , r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" ):
UpperCAmelCase_ = AutoImageProcessor.from_pretrained(__a , revision="aaaaaa" )
def _lowercase (self : Union[str, Any] ):
with self.assertRaisesRegex(
__a , "hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json." , ):
UpperCAmelCase_ = AutoImageProcessor.from_pretrained("hf-internal-testing/config-no-model" )
def _lowercase (self : List[Any] ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(__a ):
UpperCAmelCase_ = AutoImageProcessor.from_pretrained("hf-internal-testing/test_dynamic_image_processor" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(__a ):
UpperCAmelCase_ = AutoImageProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_image_processor" , trust_remote_code=__a )
UpperCAmelCase_ = AutoImageProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_image_processor" , trust_remote_code=__a )
self.assertEqual(image_processor.__class__.__name__ , "NewImageProcessor" )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(__a )
UpperCAmelCase_ = AutoImageProcessor.from_pretrained(__a , trust_remote_code=__a )
self.assertEqual(reloaded_image_processor.__class__.__name__ , "NewImageProcessor" )
def _lowercase (self : Optional[int] ):
try:
AutoConfig.register("custom" , __a )
AutoImageProcessor.register(__a , __a )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(__a ):
AutoImageProcessor.register(__a , __a )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase_ = Path(__a ) / "preprocessor_config.json"
UpperCAmelCase_ = Path(__a ) / "config.json"
json.dump(
{"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"} , open(__a , "w" ) , )
json.dump({"model_type": "clip"} , open(__a , "w" ) )
UpperCAmelCase_ = CustomImageProcessor.from_pretrained(__a )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(__a )
UpperCAmelCase_ = AutoImageProcessor.from_pretrained(__a )
self.assertIsInstance(__a , __a )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def _lowercase (self : Optional[int] ):
class __A ( UpperCamelCase__ ):
a__ : str = True
try:
AutoConfig.register("custom" , __a )
AutoImageProcessor.register(__a , __a )
# If remote code is not set, the default is to use local
UpperCAmelCase_ = AutoImageProcessor.from_pretrained("hf-internal-testing/test_dynamic_image_processor" )
self.assertEqual(image_processor.__class__.__name__ , "NewImageProcessor" )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
UpperCAmelCase_ = AutoImageProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_image_processor" , trust_remote_code=__a )
self.assertEqual(image_processor.__class__.__name__ , "NewImageProcessor" )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
UpperCAmelCase_ = AutoImageProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_image_processor" , trust_remote_code=__a )
self.assertEqual(image_processor.__class__.__name__ , "NewImageProcessor" )
self.assertTrue(not hasattr(__a , "is_local" ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 78 | import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _snake_case ( *__snake_case , __snake_case = None , __snake_case=True , __snake_case=2 ):
from .. import __version__
_UpperCamelCase = take_from
_UpperCamelCase = ()
if not isinstance(args[0] , __snake_case ):
_UpperCamelCase = (args,)
for attribute, version_name, message in args:
if version.parse(version.parse(__snake_case ).base_version ) >= version.parse(__snake_case ):
raise ValueError(
f"""The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"""
f""" version {__version__} is >= {version_name}""" )
_UpperCamelCase = None
if isinstance(__snake_case , __snake_case ) and attribute in deprecated_kwargs:
values += (deprecated_kwargs.pop(__snake_case ),)
_UpperCamelCase = f"""The `{attribute}` argument is deprecated and will be removed in version {version_name}."""
elif hasattr(__snake_case , __snake_case ):
values += (getattr(__snake_case , __snake_case ),)
_UpperCamelCase = f"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
elif deprecated_kwargs is None:
_UpperCamelCase = f"""`{attribute}` is deprecated and will be removed in version {version_name}."""
if warning is not None:
_UpperCamelCase = warning + ''' ''' if standard_warn else ''''''
warnings.warn(warning + message , __snake_case , stacklevel=__snake_case )
if isinstance(__snake_case , __snake_case ) and len(__snake_case ) > 0:
_UpperCamelCase = inspect.getouterframes(inspect.currentframe() )[1]
_UpperCamelCase = call_frame.filename
_UpperCamelCase = call_frame.lineno
_UpperCamelCase = call_frame.function
_UpperCamelCase , _UpperCamelCase = next(iter(deprecated_kwargs.items() ) )
raise TypeError(f"""{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`""" )
if len(__snake_case ) == 0:
return
elif len(__snake_case ) == 1:
return values[0]
return values
| 10 | 0 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel
@require_tf
class UpperCAmelCase_ :
__lowerCamelCase = BlenderbotConfig
__lowerCamelCase = {}
__lowerCamelCase = 'gelu'
def __init__( self , _lowerCAmelCase , _lowerCAmelCase=13 , _lowerCAmelCase=7 , _lowerCAmelCase=True , _lowerCAmelCase=False , _lowerCAmelCase=99 , _lowerCAmelCase=32 , _lowerCAmelCase=2 , _lowerCAmelCase=4 , _lowerCAmelCase=37 , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=20 , _lowerCAmelCase=2 , _lowerCAmelCase=1 , _lowerCAmelCase=0 , ):
UpperCAmelCase__ : List[str] = parent
UpperCAmelCase__ : str = batch_size
UpperCAmelCase__ : Optional[int] = seq_length
UpperCAmelCase__ : Dict = is_training
UpperCAmelCase__ : List[Any] = use_labels
UpperCAmelCase__ : List[Any] = vocab_size
UpperCAmelCase__ : Optional[int] = hidden_size
UpperCAmelCase__ : Optional[Any] = num_hidden_layers
UpperCAmelCase__ : int = num_attention_heads
UpperCAmelCase__ : Any = intermediate_size
UpperCAmelCase__ : Dict = hidden_dropout_prob
UpperCAmelCase__ : int = attention_probs_dropout_prob
UpperCAmelCase__ : Any = max_position_embeddings
UpperCAmelCase__ : Dict = eos_token_id
UpperCAmelCase__ : List[Any] = pad_token_id
UpperCAmelCase__ : Optional[int] = bos_token_id
def __UpperCAmelCase ( self ):
UpperCAmelCase__ : List[Any] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
UpperCAmelCase__ : List[str] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
UpperCAmelCase__ : Any = tf.concat([input_ids, eos_tensor] , axis=1 )
UpperCAmelCase__ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase__ : Dict = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
UpperCAmelCase__ : Tuple = prepare_blenderbot_inputs_dict(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return config, inputs_dict
def __UpperCAmelCase ( self , _lowerCAmelCase , _lowerCAmelCase ):
UpperCAmelCase__ : Union[str, Any] = TFBlenderbotModel(config=_lowerCAmelCase ).get_decoder()
UpperCAmelCase__ : Dict = inputs_dict["""input_ids"""]
UpperCAmelCase__ : Union[str, Any] = input_ids[:1, :]
UpperCAmelCase__ : Dict = inputs_dict["""attention_mask"""][:1, :]
UpperCAmelCase__ : List[Any] = inputs_dict["""head_mask"""]
UpperCAmelCase__ : Optional[Any] = 1
# first forward pass
UpperCAmelCase__ : List[str] = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase , head_mask=_lowerCAmelCase , use_cache=_lowerCAmelCase )
UpperCAmelCase__ , UpperCAmelCase__ : List[Any] = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
UpperCAmelCase__ : List[str] = ids_tensor((self.batch_size, 3) , config.vocab_size )
UpperCAmelCase__ : Tuple = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
UpperCAmelCase__ : str = tf.concat([input_ids, next_tokens] , axis=-1 )
UpperCAmelCase__ : int = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
UpperCAmelCase__ : List[str] = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase )[0]
UpperCAmelCase__ : Optional[Any] = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase , past_key_values=_lowerCAmelCase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
UpperCAmelCase__ : Any = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
UpperCAmelCase__ : Any = output_from_no_past[:, -3:, random_slice_idx]
UpperCAmelCase__ : Any = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(_lowerCAmelCase , _lowerCAmelCase , rtol=1e-3 )
def _lowerCamelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=None , __lowerCamelCase=None , __lowerCamelCase=None , __lowerCamelCase=None , __lowerCamelCase=None , ) -> List[Any]:
'''simple docstring'''
if attention_mask is None:
UpperCAmelCase__ : Tuple = tf.cast(tf.math.not_equal(__lowerCamelCase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
UpperCAmelCase__ : Tuple = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
UpperCAmelCase__ : Any = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
UpperCAmelCase__ : int = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
UpperCAmelCase__ : int = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class UpperCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
__lowerCamelCase = (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
__lowerCamelCase = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
__lowerCamelCase = (
{
'conversational': TFBlenderbotForConditionalGeneration,
'feature-extraction': TFBlenderbotModel,
'summarization': TFBlenderbotForConditionalGeneration,
'text2text-generation': TFBlenderbotForConditionalGeneration,
'translation': TFBlenderbotForConditionalGeneration,
}
if is_tf_available()
else {}
)
__lowerCamelCase = True
__lowerCamelCase = False
__lowerCamelCase = False
def __UpperCAmelCase ( self ):
UpperCAmelCase__ : Union[str, Any] = TFBlenderbotModelTester(self )
UpperCAmelCase__ : int = ConfigTester(self , config_class=_lowerCAmelCase )
def __UpperCAmelCase ( self ):
self.config_tester.run_common_tests()
def __UpperCAmelCase ( self ):
UpperCAmelCase__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*_lowerCAmelCase )
@require_tokenizers
@require_tf
class UpperCAmelCase_ ( unittest.TestCase ):
__lowerCamelCase = ['My friends are cool but they eat too many carbs.']
__lowerCamelCase = 'facebook/blenderbot-400M-distill'
@cached_property
def __UpperCAmelCase ( self ):
return BlenderbotTokenizer.from_pretrained(self.model_name )
@cached_property
def __UpperCAmelCase ( self ):
UpperCAmelCase__ : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
@slow
def __UpperCAmelCase ( self ):
UpperCAmelCase__ : Optional[int] = self.tokenizer(self.src_text , return_tensors="""tf""" )
UpperCAmelCase__ : Union[str, Any] = self.model.generate(
model_inputs.input_ids , )
UpperCAmelCase__ : List[str] = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=_lowerCAmelCase )[0]
assert (
generated_words
== " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
)
| 79 | import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
_lowerCAmelCase = logging.getLogger(__name__)
def _snake_case ( __snake_case , __snake_case ):
return (preds == labels).mean()
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Pretrained config name or path if not the same as model_name"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys() )} )
UpperCAmelCase = field(metadata={"help": "Should contain the data files for the task."} )
UpperCAmelCase = field(
default=128, metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Overwrite the cached training and evaluation sets"} )
def _snake_case ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_UpperCamelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
''' --overwrite_output_dir to overcome.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('''Training/evaluation parameters %s''' , __snake_case )
# Set seed
set_seed(training_args.seed )
try:
_UpperCamelCase = processors[data_args.task_name]()
_UpperCamelCase = processor.get_labels()
_UpperCamelCase = len(__snake_case )
except KeyError:
raise ValueError('''Task not found: %s''' % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCamelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__snake_case , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
_UpperCamelCase = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
_UpperCamelCase = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=__snake_case , cache_dir=model_args.cache_dir , )
# Get datasets
_UpperCamelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__snake_case , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
_UpperCamelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__snake_case , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def compute_metrics(__snake_case ) -> Dict:
_UpperCamelCase = np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(__snake_case , p.label_ids )}
# Data collator
_UpperCamelCase = DataCollatorWithPadding(__snake_case , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
_UpperCamelCase = Trainer(
model=__snake_case , args=__snake_case , train_dataset=__snake_case , eval_dataset=__snake_case , compute_metrics=__snake_case , data_collator=__snake_case , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
_UpperCamelCase = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
_UpperCamelCase = trainer.evaluate()
_UpperCamelCase = os.path.join(training_args.output_dir , '''eval_results.txt''' )
if trainer.is_world_master():
with open(__snake_case , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''' , __snake_case , __snake_case )
writer.write('''%s = %s\n''' % (key, value) )
results.update(__snake_case )
return results
def _snake_case ( __snake_case ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 10 | 0 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
__UpperCamelCase : List[str] = logging.getLogger(__name__)
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
return (preds == labels).mean()
@dataclass
class __UpperCamelCase :
__snake_case :str = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
@dataclass
class __UpperCamelCase :
__snake_case :str = field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys() )} )
__snake_case :str = field(metadata={'help': 'Should contain the data files for the task.'} )
__snake_case :int = field(
default=1_2_8 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__snake_case :bool = field(
default=_lowerCAmelCase , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def snake_case ( ):
'''simple docstring'''
__lowercase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
__lowercase , __lowercase , __lowercase = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
""" --overwrite_output_dir to overcome.""" )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"""Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("""Training/evaluation parameters %s""" , lowerCamelCase )
# Set seed
set_seed(training_args.seed )
try:
__lowercase = processors[data_args.task_name]()
__lowercase = processor.get_labels()
__lowercase = len(lowerCamelCase )
except KeyError:
raise ValueError("""Task not found: %s""" % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__lowercase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=lowerCamelCase , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
__lowercase = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
__lowercase = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=lowerCamelCase , cache_dir=model_args.cache_dir , )
# Get datasets
__lowercase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=lowerCamelCase , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
__lowercase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=lowerCamelCase , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def compute_metrics(lowerCamelCase ) -> Dict:
__lowercase = np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(lowerCamelCase , p.label_ids )}
# Data collator
__lowercase = DataCollatorWithPadding(lowerCamelCase , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
__lowercase = Trainer(
model=lowerCamelCase , args=lowerCamelCase , train_dataset=lowerCamelCase , eval_dataset=lowerCamelCase , compute_metrics=lowerCamelCase , data_collator=lowerCamelCase , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
__lowercase = {}
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
__lowercase = trainer.evaluate()
__lowercase = os.path.join(training_args.output_dir , """eval_results.txt""" )
if trainer.is_world_master():
with open(lowerCamelCase , """w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key, value in result.items():
logger.info(""" %s = %s""" , lowerCamelCase , lowerCamelCase )
writer.write("""%s = %s\n""" % (key, value) )
results.update(lowerCamelCase )
return results
def snake_case ( lowerCamelCase ):
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 80 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
),
# See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = "trocr"
UpperCAmelCase = ["past_key_values"]
UpperCAmelCase = {
"num_attention_heads": "decoder_attention_heads",
"hidden_size": "d_model",
"num_hidden_layers": "decoder_layers",
}
def __init__( self : List[str] , _A : Optional[Any]=5_0265 , _A : Optional[Any]=1024 , _A : Optional[Any]=12 , _A : Any=16 , _A : Any=4096 , _A : Optional[Any]="gelu" , _A : Union[str, Any]=512 , _A : Dict=0.1 , _A : List[str]=0.0 , _A : Optional[Any]=0.0 , _A : Union[str, Any]=2 , _A : Any=0.02 , _A : List[str]=0.0 , _A : List[str]=True , _A : str=False , _A : List[str]=True , _A : Optional[Any]=True , _A : Optional[int]=1 , _A : int=0 , _A : Any=2 , **_A : Optional[int] , ):
_UpperCamelCase = vocab_size
_UpperCamelCase = d_model
_UpperCamelCase = decoder_layers
_UpperCamelCase = decoder_attention_heads
_UpperCamelCase = decoder_ffn_dim
_UpperCamelCase = activation_function
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = activation_dropout
_UpperCamelCase = init_std
_UpperCamelCase = decoder_layerdrop
_UpperCamelCase = use_cache
_UpperCamelCase = scale_embedding
_UpperCamelCase = use_learned_position_embeddings
_UpperCamelCase = layernorm_embedding
super().__init__(
pad_token_id=_A , bos_token_id=_A , eos_token_id=_A , decoder_start_token_id=_A , **_A , )
| 10 | 0 |
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