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import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class lowercase ( a_ , unittest.TestCase ):
"""simple docstring"""
_UpperCamelCase : Union[str, Any] = MobileBertTokenizer
_UpperCamelCase : Tuple = MobileBertTokenizerFast
_UpperCamelCase : Union[str, Any] = True
_UpperCamelCase : Union[str, Any] = True
_UpperCamelCase : Dict = filter_non_english
_UpperCamelCase : Any = "google/mobilebert-uncased"
def __UpperCAmelCase ( self : int ):
'''simple docstring'''
super().setUp()
_snake_case : Optional[int] = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
_snake_case : int = 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 : Optional[Any] = [
(tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2]) # else the 'google/' prefix is stripped
for tokenizer_def in self.tokenizers_list
]
def __UpperCAmelCase ( self : Optional[int] , lowerCamelCase_ : List[str] ):
'''simple docstring'''
_snake_case : Any = 'UNwant\u00E9d,running'
_snake_case : Optional[Any] = 'unwanted, running'
return input_text, output_text
def __UpperCAmelCase ( self : List[str] ):
'''simple docstring'''
_snake_case : Dict = self.tokenizer_class(self.vocab_file )
_snake_case : int = tokenizer.tokenize('UNwant\u00E9d,running' )
self.assertListEqual(lowerCamelCase_ , ['un', '##want', '##ed', ',', 'runn', '##ing'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_ ) , [9, 6, 7, 12, 10, 11] )
def __UpperCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
if not self.test_rust_tokenizer:
return
_snake_case : str = self.get_tokenizer()
_snake_case : Optional[int] = self.get_rust_tokenizer()
_snake_case : str = 'UNwant\u00E9d,running'
_snake_case : str = tokenizer.tokenize(lowerCamelCase_ )
_snake_case : Union[str, Any] = rust_tokenizer.tokenize(lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
_snake_case : Tuple = tokenizer.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ )
_snake_case : Tuple = rust_tokenizer.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
_snake_case : Tuple = self.get_rust_tokenizer()
_snake_case : str = tokenizer.encode(lowerCamelCase_ )
_snake_case : Dict = rust_tokenizer.encode(lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
# With lower casing
_snake_case : str = self.get_tokenizer(do_lower_case=lowerCamelCase_ )
_snake_case : Tuple = self.get_rust_tokenizer(do_lower_case=lowerCamelCase_ )
_snake_case : Tuple = 'UNwant\u00E9d,running'
_snake_case : int = tokenizer.tokenize(lowerCamelCase_ )
_snake_case : Optional[int] = rust_tokenizer.tokenize(lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
_snake_case : List[Any] = tokenizer.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ )
_snake_case : str = rust_tokenizer.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
_snake_case : Any = self.get_rust_tokenizer()
_snake_case : Optional[int] = tokenizer.encode(lowerCamelCase_ )
_snake_case : int = rust_tokenizer.encode(lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
def __UpperCAmelCase ( self : Tuple ):
'''simple docstring'''
_snake_case : Tuple = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz' ) , ['ah', '\u535A', '\u63A8', 'zz'] )
def __UpperCAmelCase ( self : Union[str, Any] ):
'''simple docstring'''
_snake_case : List[Any] = BasicTokenizer(do_lower_case=lowerCamelCase_ )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['hello', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def __UpperCAmelCase ( self : str ):
'''simple docstring'''
_snake_case : str = BasicTokenizer(do_lower_case=lowerCamelCase_ , strip_accents=lowerCamelCase_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hällo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['h\u00E9llo'] )
def __UpperCAmelCase ( self : Dict ):
'''simple docstring'''
_snake_case : Optional[int] = BasicTokenizer(do_lower_case=lowerCamelCase_ , strip_accents=lowerCamelCase_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def __UpperCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
_snake_case : Any = BasicTokenizer(do_lower_case=lowerCamelCase_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def __UpperCAmelCase ( self : Any ):
'''simple docstring'''
_snake_case : Optional[int] = BasicTokenizer(do_lower_case=lowerCamelCase_ )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )
def __UpperCAmelCase ( self : Dict ):
'''simple docstring'''
_snake_case : List[Any] = BasicTokenizer(do_lower_case=lowerCamelCase_ , strip_accents=lowerCamelCase_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HäLLo', '!', 'how', 'Are', 'yoU', '?'] )
def __UpperCAmelCase ( self : int ):
'''simple docstring'''
_snake_case : Union[str, Any] = BasicTokenizer(do_lower_case=lowerCamelCase_ , strip_accents=lowerCamelCase_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HaLLo', '!', 'how', 'Are', 'yoU', '?'] )
def __UpperCAmelCase ( self : Dict ):
'''simple docstring'''
_snake_case : List[str] = BasicTokenizer(do_lower_case=lowerCamelCase_ , never_split=['[UNK]'] )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? [UNK]' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'] )
def __UpperCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
_snake_case : List[str] = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
_snake_case : Any = {}
for i, token in enumerate(lowerCamelCase_ ):
_snake_case : Optional[Any] = i
_snake_case : Any = WordpieceTokenizer(vocab=lowerCamelCase_ , unk_token='[UNK]' )
self.assertListEqual(tokenizer.tokenize('' ) , [] )
self.assertListEqual(tokenizer.tokenize('unwanted running' ) , ['un', '##want', '##ed', 'runn', '##ing'] )
self.assertListEqual(tokenizer.tokenize('unwantedX running' ) , ['[UNK]', 'runn', '##ing'] )
def __UpperCAmelCase ( self : Optional[int] ):
'''simple docstring'''
self.assertTrue(_is_whitespace(' ' ) )
self.assertTrue(_is_whitespace('\t' ) )
self.assertTrue(_is_whitespace('\r' ) )
self.assertTrue(_is_whitespace('\n' ) )
self.assertTrue(_is_whitespace('\u00A0' ) )
self.assertFalse(_is_whitespace('A' ) )
self.assertFalse(_is_whitespace('-' ) )
def __UpperCAmelCase ( self : Dict ):
'''simple docstring'''
self.assertTrue(_is_control('\u0005' ) )
self.assertFalse(_is_control('A' ) )
self.assertFalse(_is_control(' ' ) )
self.assertFalse(_is_control('\t' ) )
self.assertFalse(_is_control('\r' ) )
def __UpperCAmelCase ( self : List[str] ):
'''simple docstring'''
self.assertTrue(_is_punctuation('-' ) )
self.assertTrue(_is_punctuation('$' ) )
self.assertTrue(_is_punctuation('`' ) )
self.assertTrue(_is_punctuation('.' ) )
self.assertFalse(_is_punctuation('A' ) )
self.assertFalse(_is_punctuation(' ' ) )
def __UpperCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
_snake_case : List[Any] = self.get_tokenizer()
_snake_case : str = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(lowerCamelCase_ ) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']] )
self.assertListEqual(
[rust_tokenizer.tokenize(lowerCamelCase_ ) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']] )
@slow
def __UpperCAmelCase ( self : List[str] ):
'''simple docstring'''
_snake_case : int = self.tokenizer_class.from_pretrained('google/mobilebert-uncased' )
_snake_case : Union[str, Any] = tokenizer.encode('sequence builders' , add_special_tokens=lowerCamelCase_ )
_snake_case : Tuple = tokenizer.encode('multi-sequence build' , add_special_tokens=lowerCamelCase_ )
_snake_case : Union[str, Any] = tokenizer.build_inputs_with_special_tokens(lowerCamelCase_ )
_snake_case : List[str] = tokenizer.build_inputs_with_special_tokens(lowerCamelCase_ , lowerCamelCase_ )
assert encoded_sentence == [1_01] + text + [1_02]
assert encoded_pair == [1_01] + text + [1_02] + text_a + [1_02]
def __UpperCAmelCase ( self : str ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
_snake_case : List[str] = self.rust_tokenizer_class.from_pretrained(lowerCamelCase_ , **lowerCamelCase_ )
_snake_case : List[Any] = f'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
_snake_case : Optional[int] = tokenizer_r.encode_plus(
lowerCamelCase_ , return_attention_mask=lowerCamelCase_ , return_token_type_ids=lowerCamelCase_ , return_offsets_mapping=lowerCamelCase_ , add_special_tokens=lowerCamelCase_ , )
_snake_case : Optional[Any] = tokenizer_r.do_lower_case if hasattr(lowerCamelCase_ , 'do_lower_case' ) else False
_snake_case : List[str] = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), 'A'),
((1, 2), ','),
((3, 5), 'na'),
((5, 6), '##ï'),
((6, 8), '##ve'),
((9, 15), tokenizer_r.mask_token),
((16, 21), 'Allen'),
((21, 23), '##NL'),
((23, 24), '##P'),
((25, 33), 'sentence'),
((33, 34), '.'),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), 'a'),
((1, 2), ','),
((3, 8), 'naive'),
((9, 15), tokenizer_r.mask_token),
((16, 21), 'allen'),
((21, 23), '##nl'),
((23, 24), '##p'),
((25, 33), 'sentence'),
((33, 34), '.'),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens['input_ids'] ) )
self.assertEqual([e[0] for e in expected_results] , tokens['offset_mapping'] )
def __UpperCAmelCase ( self : str ):
'''simple docstring'''
_snake_case : str = ['的', '人', '有']
_snake_case : Optional[int] = ''.join(lowerCamelCase_ )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
_snake_case : Tuple = True
_snake_case : Tuple = self.tokenizer_class.from_pretrained(lowerCamelCase_ , **lowerCamelCase_ )
_snake_case : List[str] = self.rust_tokenizer_class.from_pretrained(lowerCamelCase_ , **lowerCamelCase_ )
_snake_case : Dict = tokenizer_p.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ )
_snake_case : int = tokenizer_r.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ )
_snake_case : Dict = tokenizer_r.convert_ids_to_tokens(lowerCamelCase_ )
_snake_case : Union[str, Any] = tokenizer_p.convert_ids_to_tokens(lowerCamelCase_ )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
_snake_case : List[str] = False
_snake_case : Any = self.rust_tokenizer_class.from_pretrained(lowerCamelCase_ , **lowerCamelCase_ )
_snake_case : Any = self.tokenizer_class.from_pretrained(lowerCamelCase_ , **lowerCamelCase_ )
_snake_case : Optional[int] = tokenizer_r.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ )
_snake_case : List[Any] = tokenizer_p.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ )
_snake_case : str = tokenizer_r.convert_ids_to_tokens(lowerCamelCase_ )
_snake_case : str = tokenizer_p.convert_ids_to_tokens(lowerCamelCase_ )
# it is expected that only the first Chinese character is not preceded by "##".
_snake_case : Optional[Any] = [
f'''##{token}''' if idx != 0 else token for idx, token in enumerate(lowerCamelCase_ )
]
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
| 714 |
import math
def A__( __lowerCAmelCase ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(__lowerCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def A__( __lowerCAmelCase = 1_00_01 ):
try:
_snake_case : int = int(__lowerCAmelCase )
except (TypeError, ValueError):
raise TypeError('Parameter nth must be int or castable to int.' ) from None
if nth <= 0:
raise ValueError('Parameter nth must be greater than or equal to one.' )
_snake_case : list[int] = []
_snake_case : List[Any] = 2
while len(__lowerCAmelCase ) < nth:
if is_prime(__lowerCAmelCase ):
primes.append(__lowerCAmelCase )
num += 1
else:
num += 1
return primes[len(__lowerCAmelCase ) - 1]
if __name__ == "__main__":
print(F'''{solution() = }''')
| 652 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ : Tuple = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : Dict = [
'''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''InformerForPrediction''',
'''InformerModel''',
'''InformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_informer import (
INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
InformerForPrediction,
InformerModel,
InformerPreTrainedModel,
)
else:
import sys
lowercase_ : int = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 715 |
import torch
from transformers import AutoModel
class lowercase ( torch.nn.Module ):
"""simple docstring"""
def __init__( self : Tuple , lowerCamelCase_ : Dict="sayef/fsner-bert-base-uncased" ):
'''simple docstring'''
super(lowerCamelCase_ , self ).__init__()
_snake_case : Optional[Any] = AutoModel.from_pretrained(lowerCamelCase_ , return_dict=lowerCamelCase_ )
_snake_case : str = torch.nn.CosineSimilarity(3 , 1e-08 )
_snake_case : str = torch.nn.Softmax(dim=1 )
def __UpperCAmelCase ( self : int , **lowerCamelCase_ : List[str] ):
'''simple docstring'''
return self.bert(**lowerCamelCase_ ).last_hidden_state
def __UpperCAmelCase ( self : List[Any] , lowerCamelCase_ : Union[str, Any] ):
'''simple docstring'''
return token_embeddings.sum(2 , keepdim=lowerCamelCase_ )
def __UpperCAmelCase ( self : Dict , lowerCamelCase_ : Dict , lowerCamelCase_ : Tuple , lowerCamelCase_ : Optional[Any]=1 ):
'''simple docstring'''
return self.softmax(T * self.cos(lowerCamelCase_ , lowerCamelCase_ ) )
def __UpperCAmelCase ( self : Optional[Any] , lowerCamelCase_ : Any , lowerCamelCase_ : Union[str, Any] ):
'''simple docstring'''
_snake_case : int = W_supports['sizes'].tolist()
_snake_case : int = W_supports['start_token_id'].item()
_snake_case : List[str] = W_supports['end_token_id'].item()
del W_supports["sizes"]
del W_supports["start_token_id"]
del W_supports["end_token_id"]
_snake_case : Optional[int] = self.BERT(**lowerCamelCase_ )
_snake_case : Optional[Any] = self.BERT(**lowerCamelCase_ )
_snake_case : Optional[int] = None
_snake_case : Optional[int] = None
_snake_case : List[str] = W_supports['input_ids'] == start_token_id
_snake_case : Union[str, Any] = W_supports['input_ids'] == end_token_id
for i, size in enumerate(lowerCamelCase_ ):
if i == 0:
_snake_case : str = 0
else:
_snake_case : Union[str, Any] = support_sizes[i - 1]
_snake_case : Tuple = S[s : s + size][start_token_masks[s : s + size]]
_snake_case : Optional[int] = S[s : s + size][end_token_masks[s : s + size]]
_snake_case : Tuple = torch.matmul(q[i] , s_start.T ).sum(1 ).softmax(0 )
_snake_case : Union[str, Any] = torch.matmul(q[i] , s_end.T ).sum(1 ).softmax(0 )
if p_starts is not None:
_snake_case : Optional[Any] = torch.vstack((p_starts, p_start) )
_snake_case : List[str] = torch.vstack((p_ends, p_end) )
else:
_snake_case : Union[str, Any] = p_start
_snake_case : Any = p_end
return p_starts, p_ends
| 652 | 0 |
def A__( __lowerCAmelCase , __lowerCAmelCase ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
_snake_case : Dict = (boundary[1] - boundary[0]) / steps
_snake_case : List[str] = boundary[0]
_snake_case : int = boundary[1]
_snake_case : List[str] = make_points(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
_snake_case : Optional[int] = 0.0
y += (h / 2.0) * f(__lowerCAmelCase )
for i in x_i:
# print(i)
y += h * f(__lowerCAmelCase )
y += (h / 2.0) * f(__lowerCAmelCase )
return y
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
_snake_case : Optional[int] = a + h
while x < (b - h):
yield x
_snake_case : Union[str, Any] = x + h
def A__( __lowerCAmelCase ): # enter your function here
_snake_case : Tuple = (x - 0) * (x - 0)
return y
def A__( ):
_snake_case : Optional[int] = 0.0 # Lower bound of integration
_snake_case : str = 1.0 # Upper bound of integration
_snake_case : int = 10.0 # define number of steps or resolution
_snake_case : List[str] = [a, b] # define boundary of integration
_snake_case : Any = method_a(__lowerCAmelCase , __lowerCAmelCase )
print(F'''y = {y}''' )
if __name__ == "__main__":
main()
| 716 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowercase ( a_ , a_ , a_ ):
"""simple docstring"""
@register_to_config
def __init__( self : Any , lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : float , lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : str , lowerCamelCase_ : bool = False , ):
'''simple docstring'''
super().__init__()
_snake_case : Optional[int] = nn.Embedding(lowerCamelCase_ , lowerCamelCase_ )
_snake_case : List[str] = nn.Embedding(lowerCamelCase_ , lowerCamelCase_ )
_snake_case : Union[str, Any] = False
_snake_case : Tuple = nn.Dropout(p=lowerCamelCase_ )
_snake_case : Union[str, Any] = TaConfig(
vocab_size=lowerCamelCase_ , d_model=lowerCamelCase_ , num_heads=lowerCamelCase_ , d_kv=lowerCamelCase_ , d_ff=lowerCamelCase_ , dropout_rate=lowerCamelCase_ , feed_forward_proj=lowerCamelCase_ , is_decoder=lowerCamelCase_ , is_encoder_decoder=lowerCamelCase_ , )
_snake_case : Union[str, Any] = nn.ModuleList()
for lyr_num in range(lowerCamelCase_ ):
_snake_case : Any = TaBlock(lowerCamelCase_ )
self.encoders.append(lowerCamelCase_ )
_snake_case : Tuple = TaLayerNorm(lowerCamelCase_ )
_snake_case : List[str] = nn.Dropout(p=lowerCamelCase_ )
def __UpperCAmelCase ( self : Optional[int] , lowerCamelCase_ : int , lowerCamelCase_ : Tuple ):
'''simple docstring'''
_snake_case : Any = self.token_embedder(lowerCamelCase_ )
_snake_case : List[Any] = encoder_input_tokens.shape[1]
_snake_case : Any = torch.arange(lowerCamelCase_ , device=encoder_input_tokens.device )
x += self.position_encoding(lowerCamelCase_ )
_snake_case : Tuple = self.dropout_pre(lowerCamelCase_ )
# inverted the attention mask
_snake_case : Dict = encoder_input_tokens.size()
_snake_case : Optional[int] = self.get_extended_attention_mask(lowerCamelCase_ , lowerCamelCase_ )
for lyr in self.encoders:
_snake_case : str = lyr(lowerCamelCase_ , lowerCamelCase_ )[0]
_snake_case : Any = self.layer_norm(lowerCamelCase_ )
return self.dropout_post(lowerCamelCase_ ), encoder_inputs_mask
| 652 | 0 |
'''simple docstring'''
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from ..utils import (
gather,
is_bnb_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_mps_available,
is_safetensors_available,
is_tensorboard_available,
is_torch_version,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
def A__( __lowerCAmelCase , __lowerCAmelCase=False ):
try:
_snake_case : int = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
_snake_case : str = default
else:
# KEY is set, convert it to True or False.
try:
_snake_case : Optional[int] = strtobool(__lowerCAmelCase )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(F'''If set, {key} must be yes or no.''' )
return _value
lowercase_ : str = parse_flag_from_env('''RUN_SLOW''', default=False)
def A__( __lowerCAmelCase ):
return unittest.skip('Test was skipped' )(__lowerCAmelCase )
def A__( __lowerCAmelCase ):
return unittest.skipUnless(_run_slow_tests , 'test is slow' )(__lowerCAmelCase )
def A__( __lowerCAmelCase ):
return unittest.skipUnless(not torch.cuda.is_available() , 'test requires only a CPU' )(__lowerCAmelCase )
def A__( __lowerCAmelCase ):
return unittest.skipUnless(torch.cuda.is_available() , 'test requires a GPU' )(__lowerCAmelCase )
def A__( __lowerCAmelCase ):
return unittest.skipUnless(is_xpu_available() , 'test requires a XPU' )(__lowerCAmelCase )
def A__( __lowerCAmelCase ):
return unittest.skipUnless(is_mps_available() , 'test requires a `mps` backend support in `torch`' )(__lowerCAmelCase )
def A__( __lowerCAmelCase ):
return unittest.skipUnless(
is_transformers_available() and is_datasets_available() , 'test requires the Hugging Face suite' )(__lowerCAmelCase )
def A__( __lowerCAmelCase ):
return unittest.skipUnless(is_bnb_available() , 'test requires the bitsandbytes library' )(__lowerCAmelCase )
def A__( __lowerCAmelCase ):
return unittest.skipUnless(is_tpu_available() , 'test requires TPU' )(__lowerCAmelCase )
def A__( __lowerCAmelCase ):
return unittest.skipUnless(torch.cuda.device_count() == 1 , 'test requires a GPU' )(__lowerCAmelCase )
def A__( __lowerCAmelCase ):
return unittest.skipUnless(torch.xpu.device_count() == 1 , 'test requires a XPU' )(__lowerCAmelCase )
def A__( __lowerCAmelCase ):
return unittest.skipUnless(torch.cuda.device_count() > 1 , 'test requires multiple GPUs' )(__lowerCAmelCase )
def A__( __lowerCAmelCase ):
return unittest.skipUnless(torch.xpu.device_count() > 1 , 'test requires multiple XPUs' )(__lowerCAmelCase )
def A__( __lowerCAmelCase ):
return unittest.skipUnless(is_safetensors_available() , 'test requires safetensors' )(__lowerCAmelCase )
def A__( __lowerCAmelCase ):
return unittest.skipUnless(is_deepspeed_available() , 'test requires DeepSpeed' )(__lowerCAmelCase )
def A__( __lowerCAmelCase ):
return unittest.skipUnless(is_torch_version('>=' , '1.12.0' ) , 'test requires torch version >= 1.12.0' )(__lowerCAmelCase )
def A__( __lowerCAmelCase=None , __lowerCAmelCase=None ):
if test_case is None:
return partial(__lowerCAmelCase , version=__lowerCAmelCase )
return unittest.skipUnless(is_torch_version('>=' , __lowerCAmelCase ) , F'''test requires torch version >= {version}''' )(__lowerCAmelCase )
def A__( __lowerCAmelCase ):
return unittest.skipUnless(is_tensorboard_available() , 'test requires Tensorboard' )(__lowerCAmelCase )
def A__( __lowerCAmelCase ):
return unittest.skipUnless(is_wandb_available() , 'test requires wandb' )(__lowerCAmelCase )
def A__( __lowerCAmelCase ):
return unittest.skipUnless(is_comet_ml_available() , 'test requires comet_ml' )(__lowerCAmelCase )
lowercase_ : Optional[Any] = (
any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def A__( __lowerCAmelCase ):
return unittest.skipUnless(
_atleast_one_tracker_available , 'test requires at least one tracker to be available and for `comet_ml` to not be installed' , )(__lowerCAmelCase )
class lowercase ( unittest.TestCase ):
"""simple docstring"""
_UpperCamelCase : List[Any] = True
@classmethod
def __UpperCAmelCase ( cls : List[Any] ):
'''simple docstring'''
_snake_case : str = tempfile.mkdtemp()
@classmethod
def __UpperCAmelCase ( cls : int ):
'''simple docstring'''
if os.path.exists(cls.tmpdir ):
shutil.rmtree(cls.tmpdir )
def __UpperCAmelCase ( self : Any ):
'''simple docstring'''
if self.clear_on_setup:
for path in Path(self.tmpdir ).glob('**/*' ):
if path.is_file():
path.unlink()
elif path.is_dir():
shutil.rmtree(lowerCamelCase_ )
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def __UpperCAmelCase ( self : str ):
'''simple docstring'''
super().tearDown()
# Reset the state of the AcceleratorState singleton.
AcceleratorState._reset_state()
PartialState._reset_state()
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def __UpperCAmelCase ( self : Optional[int] , lowerCamelCase_ : Union[mock.Mock, List[mock.Mock]] ):
'''simple docstring'''
_snake_case : Tuple = mocks if isinstance(lowerCamelCase_ , (tuple, list) ) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop )
def A__( __lowerCAmelCase ):
_snake_case : List[Any] = AcceleratorState()
_snake_case : Union[str, Any] = tensor[None].clone().to(state.device )
_snake_case : str = gather(__lowerCAmelCase ).cpu()
_snake_case : Optional[int] = tensor[0].cpu()
for i in range(tensors.shape[0] ):
if not torch.equal(tensors[i] , __lowerCAmelCase ):
return False
return True
class lowercase :
"""simple docstring"""
def __init__( self : str , lowerCamelCase_ : Any , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
_snake_case : List[str] = returncode
_snake_case : Optional[Any] = stdout
_snake_case : str = stderr
async def A__( __lowerCAmelCase , __lowerCAmelCase ):
while True:
_snake_case : Optional[Any] = await stream.readline()
if line:
callback(__lowerCAmelCase )
else:
break
async def A__( __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=False , __lowerCAmelCase=False ):
if echo:
print('\nRunning: ' , ' '.join(__lowerCAmelCase ) )
_snake_case : Dict = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=__lowerCAmelCase , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=__lowerCAmelCase , )
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
_snake_case : List[str] = []
_snake_case : Optional[int] = []
def tee(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase="" ):
_snake_case : Tuple = line.decode('utf-8' ).rstrip()
sink.append(__lowerCAmelCase )
if not quiet:
print(__lowerCAmelCase , __lowerCAmelCase , file=__lowerCAmelCase )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
asyncio.create_task(_read_stream(p.stdout , lambda __lowerCAmelCase : tee(__lowerCAmelCase , __lowerCAmelCase , sys.stdout , label='stdout:' ) ) ),
asyncio.create_task(_read_stream(p.stderr , lambda __lowerCAmelCase : tee(__lowerCAmelCase , __lowerCAmelCase , sys.stderr , label='stderr:' ) ) ),
] , timeout=__lowerCAmelCase , )
return _RunOutput(await p.wait() , __lowerCAmelCase , __lowerCAmelCase )
def A__( __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=1_80 , __lowerCAmelCase=False , __lowerCAmelCase=True ):
_snake_case : Optional[int] = asyncio.get_event_loop()
_snake_case : Tuple = loop.run_until_complete(
_stream_subprocess(__lowerCAmelCase , env=__lowerCAmelCase , stdin=__lowerCAmelCase , timeout=__lowerCAmelCase , quiet=__lowerCAmelCase , echo=__lowerCAmelCase ) )
_snake_case : Optional[int] = ' '.join(__lowerCAmelCase )
if result.returncode > 0:
_snake_case : Optional[int] = '\n'.join(result.stderr )
raise RuntimeError(
F'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n'''
F'''The combined stderr from workers follows:\n{stderr}''' )
return result
class lowercase ( a_ ):
"""simple docstring"""
pass
def A__( __lowerCAmelCase , __lowerCAmelCase=False ):
try:
_snake_case : Optional[Any] = subprocess.check_output(__lowerCAmelCase , stderr=subprocess.STDOUT )
if return_stdout:
if hasattr(__lowerCAmelCase , 'decode' ):
_snake_case : List[Any] = output.decode('utf-8' )
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
F'''Command `{' '.join(__lowerCAmelCase )}` failed with the following error:\n\n{e.output.decode()}''' ) from e
| 717 |
def A__( __lowerCAmelCase ):
assert column_title.isupper()
_snake_case : List[Any] = 0
_snake_case : List[str] = len(__lowerCAmelCase ) - 1
_snake_case : Dict = 0
while index >= 0:
_snake_case : List[str] = (ord(column_title[index] ) - 64) * pow(26 , __lowerCAmelCase )
answer += value
power += 1
index -= 1
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 652 | 0 |
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
lowercase_ : Optional[Any] = {
'''return_dict''': False,
'''output_hidden_states''': True,
'''output_attentions''': True,
'''torchscript''': True,
'''torch_dtype''': '''float16''',
'''use_bfloat16''': True,
'''tf_legacy_loss''': True,
'''pruned_heads''': {'''a''': 1},
'''tie_word_embeddings''': False,
'''is_decoder''': True,
'''cross_attention_hidden_size''': 128,
'''add_cross_attention''': True,
'''tie_encoder_decoder''': True,
'''max_length''': 50,
'''min_length''': 3,
'''do_sample''': True,
'''early_stopping''': True,
'''num_beams''': 3,
'''num_beam_groups''': 3,
'''diversity_penalty''': 0.5,
'''temperature''': 2.0,
'''top_k''': 10,
'''top_p''': 0.7,
'''typical_p''': 0.2,
'''repetition_penalty''': 0.8,
'''length_penalty''': 0.8,
'''no_repeat_ngram_size''': 5,
'''encoder_no_repeat_ngram_size''': 5,
'''bad_words_ids''': [1, 2, 3],
'''num_return_sequences''': 3,
'''chunk_size_feed_forward''': 5,
'''output_scores''': True,
'''return_dict_in_generate''': True,
'''forced_bos_token_id''': 2,
'''forced_eos_token_id''': 3,
'''remove_invalid_values''': True,
'''architectures''': ['''BertModel'''],
'''finetuning_task''': '''translation''',
'''id2label''': {0: '''label'''},
'''label2id''': {'''label''': '''0'''},
'''tokenizer_class''': '''BertTokenizerFast''',
'''prefix''': '''prefix''',
'''bos_token_id''': 6,
'''pad_token_id''': 7,
'''eos_token_id''': 8,
'''sep_token_id''': 9,
'''decoder_start_token_id''': 10,
'''exponential_decay_length_penalty''': (5, 1.01),
'''suppress_tokens''': [0, 1],
'''begin_suppress_tokens''': 2,
'''task_specific_params''': {'''translation''': '''some_params'''},
'''problem_type''': '''regression''',
}
@is_staging_test
class lowercase ( unittest.TestCase ):
"""simple docstring"""
@classmethod
def __UpperCAmelCase ( cls : int ):
'''simple docstring'''
_snake_case : Optional[Any] = TOKEN
HfFolder.save_token(lowerCamelCase_ )
@classmethod
def __UpperCAmelCase ( cls : Optional[Any] ):
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id='test-config' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-config-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-config' )
except HTTPError:
pass
def __UpperCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
_snake_case : Any = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub('test-config' , use_auth_token=self._token )
_snake_case : Dict = BertConfig.from_pretrained(f'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase_ , getattr(lowerCamelCase_ , lowerCamelCase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-config' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCamelCase_ , repo_id='test-config' , push_to_hub=lowerCamelCase_ , use_auth_token=self._token )
_snake_case : List[str] = BertConfig.from_pretrained(f'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase_ , getattr(lowerCamelCase_ , lowerCamelCase_ ) )
def __UpperCAmelCase ( self : List[Any] ):
'''simple docstring'''
_snake_case : List[Any] = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub('valid_org/test-config-org' , use_auth_token=self._token )
_snake_case : Optional[Any] = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase_ , getattr(lowerCamelCase_ , lowerCamelCase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-config-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowerCamelCase_ , repo_id='valid_org/test-config-org' , push_to_hub=lowerCamelCase_ , use_auth_token=self._token )
_snake_case : int = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase_ , getattr(lowerCamelCase_ , lowerCamelCase_ ) )
def __UpperCAmelCase ( self : str ):
'''simple docstring'''
CustomConfig.register_for_auto_class()
_snake_case : Union[str, Any] = CustomConfig(attribute=42 )
config.push_to_hub('test-dynamic-config' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map , {'AutoConfig': 'custom_configuration.CustomConfig'} )
_snake_case : int = AutoConfig.from_pretrained(f'''{USER}/test-dynamic-config''' , trust_remote_code=lowerCamelCase_ )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__ , 'CustomConfig' )
self.assertEqual(new_config.attribute , 42 )
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def __UpperCAmelCase ( self : List[Any] ):
'''simple docstring'''
_snake_case : Union[str, Any] = GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
_snake_case : Dict = c.n_embd + 1 # int
_snake_case : Union[str, Any] = c.resid_pdrop + 1.0 # float
_snake_case : int = not c.scale_attn_weights # bool
_snake_case : Dict = c.summary_type + 'foo' # str
c.update_from_string(
f'''n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}''' )
self.assertEqual(lowerCamelCase_ , c.n_embd , 'mismatch for key: n_embd' )
self.assertEqual(lowerCamelCase_ , c.resid_pdrop , 'mismatch for key: resid_pdrop' )
self.assertEqual(lowerCamelCase_ , c.scale_attn_weights , 'mismatch for key: scale_attn_weights' )
self.assertEqual(lowerCamelCase_ , c.summary_type , 'mismatch for key: summary_type' )
def __UpperCAmelCase ( self : Union[str, Any] ):
'''simple docstring'''
_snake_case : List[Any] = PretrainedConfig()
_snake_case : Dict = [key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
lowerCamelCase_ , ['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version'] )
_snake_case : int = [key for key, value in config_common_kwargs.items() if value == getattr(lowerCamelCase_ , lowerCamelCase_ )]
if len(lowerCamelCase_ ) > 0:
raise ValueError(
'The following keys are set with the default values in'
' `test_configuration_common.config_common_kwargs` pick another value for them:'
f''' {', '.join(lowerCamelCase_ )}.''' )
def __UpperCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
with self.assertRaises(lowerCamelCase_ ):
# config is in subfolder, the following should not work without specifying the subfolder
_snake_case : Optional[int] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' )
_snake_case : Tuple = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' , subfolder='bert' )
self.assertIsNotNone(lowerCamelCase_ )
def __UpperCAmelCase ( self : int ):
'''simple docstring'''
_snake_case : str = mock.Mock()
_snake_case : Optional[int] = 5_00
_snake_case : Any = {}
_snake_case : List[str] = HTTPError
_snake_case : Any = {}
# Download this model to make sure it's in the cache.
_snake_case : Union[str, Any] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request' , return_value=lowerCamelCase_ ) as mock_head:
_snake_case : List[str] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
def __UpperCAmelCase ( self : Any ):
'''simple docstring'''
_snake_case : Tuple = BertConfig.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json' )
def __UpperCAmelCase ( self : Any ):
'''simple docstring'''
_snake_case : Union[str, Any] = AutoConfig.from_pretrained('bert-base-cased' )
_snake_case : Union[str, Any] = ['config.4.0.0.json']
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(lowerCamelCase_ )
_snake_case : str = 2
json.dump(configuration.to_dict() , open(os.path.join(lowerCamelCase_ , 'config.4.0.0.json' ) , 'w' ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
_snake_case : int = AutoConfig.from_pretrained(lowerCamelCase_ )
self.assertEqual(new_configuration.hidden_size , 2 )
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
_snake_case : List[str] = ['config.42.0.0.json']
_snake_case : Dict = 7_68
configuration.save_pretrained(lowerCamelCase_ )
shutil.move(os.path.join(lowerCamelCase_ , 'config.4.0.0.json' ) , os.path.join(lowerCamelCase_ , 'config.42.0.0.json' ) )
_snake_case : Optional[int] = AutoConfig.from_pretrained(lowerCamelCase_ )
self.assertEqual(new_configuration.hidden_size , 7_68 )
def __UpperCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
_snake_case : List[Any] = 'hf-internal-testing/test-two-configs'
import transformers as new_transformers
_snake_case : Union[str, Any] = 'v4.0.0'
_snake_case : List[Any] = new_transformers.models.auto.AutoConfig.from_pretrained(
lowerCamelCase_ , return_unused_kwargs=lowerCamelCase_ )
self.assertEqual(new_configuration.hidden_size , 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(lowerCamelCase_ , {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
_snake_case : Tuple = 'v3.0.0'
_snake_case : Any = old_transformers.models.auto.AutoConfig.from_pretrained(lowerCamelCase_ )
self.assertEqual(old_configuration.hidden_size , 7_68 )
| 718 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
lowercase_ : List[str] = '''\
@inproceedings{snover-etal-2006-study,
title = "A Study of Translation Edit Rate with Targeted Human Annotation",
author = "Snover, Matthew and
Dorr, Bonnie and
Schwartz, Rich and
Micciulla, Linnea and
Makhoul, John",
booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",
month = aug # " 8-12",
year = "2006",
address = "Cambridge, Massachusetts, USA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2006.amta-papers.25",
pages = "223--231",
}
@inproceedings{post-2018-call,
title = "A Call for Clarity in Reporting {BLEU} Scores",
author = "Post, Matt",
booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
month = oct,
year = "2018",
address = "Belgium, Brussels",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W18-6319",
pages = "186--191",
}
'''
lowercase_ : Optional[int] = '''\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.
The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534
See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
'''
lowercase_ : Any = '''
Produces TER scores alongside the number of edits and reference length.
Args:
predictions (list of str): The system stream (a sequence of segments).
references (list of list of str): A list of one or more reference streams (each a sequence of segments).
normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
Only applies if `normalized = True`. Defaults to `False`.
case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.
Returns:
\'score\' (float): TER score (num_edits / sum_ref_lengths * 100)
\'num_edits\' (int): The cumulative number of edits
\'ref_length\' (float): The cumulative average reference length
Examples:
Example 1:
>>> predictions = ["does this sentence match??",
... "what about this sentence?",
... "What did the TER metric user say to the developer?"]
>>> references = [["does this sentence match", "does this sentence match!?!"],
... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
... ["Your jokes are...", "...TERrible"]]
>>> ter = datasets.load_metric("ter")
>>> results = ter.compute(predictions=predictions,
... references=references,
... case_sensitive=True)
>>> print(results)
{\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}
Example 2:
>>> predictions = ["does this sentence match??",
... "what about this sentence?"]
>>> references = [["does this sentence match", "does this sentence match!?!"],
... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
>>> ter = datasets.load_metric("ter")
>>> results = ter.compute(predictions=predictions,
... references=references,
... case_sensitive=True)
>>> print(results)
{\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}
Example 3:
>>> predictions = ["does this sentence match??",
... "what about this sentence?"]
>>> references = [["does this sentence match", "does this sentence match!?!"],
... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
>>> ter = datasets.load_metric("ter")
>>> results = ter.compute(predictions=predictions,
... references=references,
... normalized=True,
... case_sensitive=True)
>>> print(results)
{\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}
Example 4:
>>> predictions = ["does this sentence match??",
... "what about this sentence?"]
>>> references = [["does this sentence match", "does this sentence match!?!"],
... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
>>> ter = datasets.load_metric("ter")
>>> results = ter.compute(predictions=predictions,
... references=references,
... ignore_punct=True,
... case_sensitive=False)
>>> print(results)
{\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}
Example 5:
>>> predictions = ["does this sentence match??",
... "what about this sentence?",
... "What did the TER metric user say to the developer?"]
>>> references = [["does this sentence match", "does this sentence match!?!"],
... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
... ["Your jokes are...", "...TERrible"]]
>>> ter = datasets.load_metric("ter")
>>> results = ter.compute(predictions=predictions,
... references=references,
... ignore_punct=True,
... case_sensitive=False)
>>> print(results)
{\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowercase ( datasets.Metric ):
"""simple docstring"""
def __UpperCAmelCase ( self : Union[str, Any] ):
'''simple docstring'''
if version.parse(scb.__version__ ) < version.parse('1.4.12' ):
raise ImportWarning(
'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
'You can install it with `pip install "sacrebleu>=1.4.12"`.' )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage='http://www.cs.umd.edu/~snover/tercom/' , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Sequence(datasets.Value('string' , id='sequence' ) , id='references' ),
} ) , codebase_urls=['https://github.com/mjpost/sacreBLEU#ter'] , reference_urls=[
'https://github.com/jhclark/tercom',
] , )
def __UpperCAmelCase ( self : Dict , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : str , lowerCamelCase_ : bool = False , lowerCamelCase_ : bool = False , lowerCamelCase_ : bool = False , lowerCamelCase_ : bool = False , ):
'''simple docstring'''
_snake_case : str = len(references[0] )
if any(len(lowerCamelCase_ ) != references_per_prediction for refs in references ):
raise ValueError('Sacrebleu requires the same number of references for each prediction' )
_snake_case : int = [[refs[i] for refs in references] for i in range(lowerCamelCase_ )]
_snake_case : Optional[int] = TER(
normalized=lowerCamelCase_ , no_punct=lowerCamelCase_ , asian_support=lowerCamelCase_ , case_sensitive=lowerCamelCase_ , )
_snake_case : Optional[Any] = sb_ter.corpus_score(lowerCamelCase_ , lowerCamelCase_ )
return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}
| 652 | 0 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / '''utils'''))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
lowercase_ : str = get_tests_dir('''fixtures''')
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def __UpperCAmelCase ( self : List[str] ):
'''simple docstring'''
_snake_case : Optional[Any] = mock.Mock()
_snake_case : Dict = 5_00
_snake_case : Optional[int] = {}
_snake_case : Optional[Any] = HTTPError
_snake_case : str = {}
# Download this model to make sure it's in the cache.
_snake_case : List[Any] = ViTImageProcessor.from_pretrained('hf-internal-testing/tiny-random-vit' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request' , return_value=lowerCamelCase_ ) as mock_head:
_snake_case : Tuple = ViTImageProcessor.from_pretrained('hf-internal-testing/tiny-random-vit' )
# This check we did call the fake head request
mock_head.assert_called()
def __UpperCAmelCase ( self : Union[str, Any] ):
'''simple docstring'''
_snake_case : Optional[Any] = ViTImageProcessor.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json' )
def __UpperCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
with self.assertRaises(lowerCamelCase_ ):
# config is in subfolder, the following should not work without specifying the subfolder
_snake_case : List[Any] = AutoImageProcessor.from_pretrained('hf-internal-testing/stable-diffusion-all-variants' )
_snake_case : List[str] = AutoImageProcessor.from_pretrained(
'hf-internal-testing/stable-diffusion-all-variants' , subfolder='feature_extractor' )
self.assertIsNotNone(lowerCamelCase_ )
@is_staging_test
class lowercase ( unittest.TestCase ):
"""simple docstring"""
@classmethod
def __UpperCAmelCase ( cls : Tuple ):
'''simple docstring'''
_snake_case : Tuple = TOKEN
HfFolder.save_token(lowerCamelCase_ )
@classmethod
def __UpperCAmelCase ( cls : List[str] ):
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id='test-image-processor' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-image-processor-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-image-processor' )
except HTTPError:
pass
def __UpperCAmelCase ( self : Dict ):
'''simple docstring'''
_snake_case : Any = ViTImageProcessor.from_pretrained(lowerCamelCase_ )
image_processor.push_to_hub('test-image-processor' , use_auth_token=self._token )
_snake_case : int = ViTImageProcessor.from_pretrained(f'''{USER}/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(lowerCamelCase_ , getattr(lowerCamelCase_ , lowerCamelCase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-image-processor' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
lowerCamelCase_ , repo_id='test-image-processor' , push_to_hub=lowerCamelCase_ , use_auth_token=self._token )
_snake_case : Any = ViTImageProcessor.from_pretrained(f'''{USER}/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(lowerCamelCase_ , getattr(lowerCamelCase_ , lowerCamelCase_ ) )
def __UpperCAmelCase ( self : Union[str, Any] ):
'''simple docstring'''
_snake_case : Tuple = ViTImageProcessor.from_pretrained(lowerCamelCase_ )
image_processor.push_to_hub('valid_org/test-image-processor' , use_auth_token=self._token )
_snake_case : Any = ViTImageProcessor.from_pretrained('valid_org/test-image-processor' )
for k, v in image_processor.__dict__.items():
self.assertEqual(lowerCamelCase_ , getattr(lowerCamelCase_ , lowerCamelCase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-image-processor' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
lowerCamelCase_ , repo_id='valid_org/test-image-processor-org' , push_to_hub=lowerCamelCase_ , use_auth_token=self._token )
_snake_case : Optional[int] = ViTImageProcessor.from_pretrained('valid_org/test-image-processor-org' )
for k, v in image_processor.__dict__.items():
self.assertEqual(lowerCamelCase_ , getattr(lowerCamelCase_ , lowerCamelCase_ ) )
def __UpperCAmelCase ( self : List[Any] ):
'''simple docstring'''
CustomImageProcessor.register_for_auto_class()
_snake_case : Dict = CustomImageProcessor.from_pretrained(lowerCamelCase_ )
image_processor.push_to_hub('test-dynamic-image-processor' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map , {'AutoImageProcessor': 'custom_image_processing.CustomImageProcessor'} , )
_snake_case : Optional[Any] = AutoImageProcessor.from_pretrained(
f'''{USER}/test-dynamic-image-processor''' , trust_remote_code=lowerCamelCase_ )
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__ , 'CustomImageProcessor' )
| 719 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
lowercase_ : Optional[int] = object()
# For specifying empty leaf dict `{}`
lowercase_ : List[Any] = object()
def A__( __lowerCAmelCase , __lowerCAmelCase ):
_snake_case : Optional[Any] = tuple((re.compile(x + '$' ) for x in qs) )
for i in range(len(__lowerCAmelCase ) - len(__lowerCAmelCase ) + 1 ):
_snake_case : Tuple = [x.match(__lowerCAmelCase ) for x, y in zip(__lowerCAmelCase , ks[i:] )]
if matches and all(__lowerCAmelCase ):
return True
return False
def A__( __lowerCAmelCase ):
def replace(__lowerCAmelCase , __lowerCAmelCase ):
for rule, replacement in rules:
if _match(__lowerCAmelCase , __lowerCAmelCase ):
return replacement
return val
return replace
def A__( ):
return [
# embeddings
(("transformer", "wpe", "embedding"), P('mp' , __lowerCAmelCase )),
(("transformer", "wte", "embedding"), P('mp' , __lowerCAmelCase )),
# atention
(("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__lowerCAmelCase , 'mp' )),
(("attention", "out_proj", "kernel"), P('mp' , __lowerCAmelCase )),
(("attention", "out_proj", "bias"), None),
# mlp
(("mlp", "c_fc", "kernel"), P(__lowerCAmelCase , 'mp' )),
(("mlp", "c_fc", "bias"), P('mp' )),
(("mlp", "c_proj", "kernel"), P('mp' , __lowerCAmelCase )),
(("mlp", "c_proj", "bias"), None),
# layer norms
((r"ln_\d+", "bias"), None),
((r"\d+", r"ln_\d+", "scale"), None),
(("ln_f", "bias"), None),
(("ln_f", "scale"), None),
]
def A__( __lowerCAmelCase ):
_snake_case : Optional[Any] = _get_partition_rules()
_snake_case : Optional[Any] = _replacement_rules(__lowerCAmelCase )
_snake_case : str = {k: _unmatched for k in flatten_dict(__lowerCAmelCase )}
_snake_case : str = {k: replace(__lowerCAmelCase , __lowerCAmelCase ) for k, v in initd.items()}
assert _unmatched not in result.values(), "Incomplete partition spec."
return freeze(unflatten_dict(__lowerCAmelCase ) )
| 652 | 0 |
from math import factorial
def A__( __lowerCAmelCase = 20 ):
_snake_case : List[Any] = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
_snake_case : int = n // 2
return int(factorial(__lowerCAmelCase ) / (factorial(__lowerCAmelCase ) * factorial(n - k )) )
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution(20))
else:
try:
lowercase_ : Optional[Any] = int(sys.argv[1])
print(solution(n))
except ValueError:
print('''Invalid entry - please enter a number.''')
| 720 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
lowercase_ : Any = logging.getLogger(__name__)
def A__( __lowerCAmelCase , __lowerCAmelCase ):
# save results
if os.path.exists(__lowerCAmelCase ):
if os.path.exists(os.path.join(__lowerCAmelCase , 'config.json' ) ) and os.path.isfile(
os.path.join(__lowerCAmelCase , 'config.json' ) ):
os.remove(os.path.join(__lowerCAmelCase , 'config.json' ) )
if os.path.exists(os.path.join(__lowerCAmelCase , 'pytorch_model.bin' ) ) and os.path.isfile(
os.path.join(__lowerCAmelCase , 'pytorch_model.bin' ) ):
os.remove(os.path.join(__lowerCAmelCase , 'pytorch_model.bin' ) )
else:
os.makedirs(__lowerCAmelCase )
model.save_pretrained(__lowerCAmelCase )
def A__( __lowerCAmelCase , __lowerCAmelCase=False ):
_snake_case : int = 2
if unlogit:
_snake_case : Dict = torch.pow(__lowerCAmelCase , __lowerCAmelCase )
_snake_case : Optional[int] = p * torch.log(__lowerCAmelCase )
_snake_case : List[str] = 0
return -plogp.sum(dim=-1 )
def A__( __lowerCAmelCase ):
logger.info('lv, h >\t' + '\t'.join(F'''{x + 1}''' for x in range(len(__lowerCAmelCase ) ) ) )
for row in range(len(__lowerCAmelCase ) ):
if tensor.dtype != torch.long:
logger.info(F'''layer {row + 1}:\t''' + '\t'.join(F'''{x:.5f}''' for x in tensor[row].cpu().data ) )
else:
logger.info(F'''layer {row + 1}:\t''' + '\t'.join(F'''{x:d}''' for x in tensor[row].cpu().data ) )
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=True , __lowerCAmelCase=True , __lowerCAmelCase=None , __lowerCAmelCase=False ):
_snake_case , _snake_case : List[str] = model.config.num_hidden_layers, model.config.num_attention_heads
_snake_case : Optional[int] = torch.zeros(__lowerCAmelCase , __lowerCAmelCase ).to(args.device )
_snake_case : Union[str, Any] = torch.zeros(__lowerCAmelCase , __lowerCAmelCase ).to(args.device )
if head_mask is None:
_snake_case : List[str] = torch.ones(__lowerCAmelCase , __lowerCAmelCase ).to(args.device )
head_mask.requires_grad_(requires_grad=__lowerCAmelCase )
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
_snake_case : List[str] = None
_snake_case : str = 0.0
_snake_case : List[str] = 0.0
for step, inputs in enumerate(tqdm(__lowerCAmelCase , desc='Iteration' , disable=args.local_rank not in [-1, 0] ) ):
_snake_case : Dict = tuple(t.to(args.device ) for t in inputs )
((_snake_case) , ) : int = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
_snake_case : List[str] = model(__lowerCAmelCase , labels=__lowerCAmelCase , head_mask=__lowerCAmelCase )
# (loss), lm_logits, presents, (all hidden_states), (attentions)
_snake_case , _snake_case , _snake_case : Dict = (
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(__lowerCAmelCase ):
_snake_case : int = entropy(attn.detach() , __lowerCAmelCase )
attn_entropy[layer] += masked_entropy.sum(-1 ).sum(0 ).sum(0 ).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(__lowerCAmelCase ).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
_snake_case : List[str] = 2
_snake_case : Any = torch.pow(torch.pow(__lowerCAmelCase , __lowerCAmelCase ).sum(-1 ) , 1 / exponent )
head_importance /= norm_by_layer.unsqueeze(-1 ) + 1E-20
if not args.dont_normalize_global_importance:
_snake_case : Union[str, Any] = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info('Attention entropies' )
print_ad_tensor(__lowerCAmelCase )
if compute_importance:
logger.info('Head importance scores' )
print_ad_tensor(__lowerCAmelCase )
logger.info('Head ranked by importance scores' )
_snake_case : Tuple = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device )
_snake_case : Optional[Any] = torch.arange(
head_importance.numel() , device=args.device )
_snake_case : Union[str, Any] = head_ranks.view_as(__lowerCAmelCase )
print_ad_tensor(__lowerCAmelCase )
return attn_entropy, head_importance, total_loss
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
_snake_case , _snake_case , _snake_case : int = compute_heads_importance(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , compute_entropy=__lowerCAmelCase )
_snake_case : List[str] = 1 / loss # instead of downsteam score use the LM loss
logger.info('Pruning: original score: %f, threshold: %f' , __lowerCAmelCase , original_score * args.masking_threshold )
_snake_case : Optional[Any] = torch.ones_like(__lowerCAmelCase )
_snake_case : Tuple = max(1 , int(new_head_mask.numel() * args.masking_amount ) )
_snake_case : Dict = original_score
while current_score >= original_score * args.masking_threshold:
_snake_case : List[str] = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
_snake_case : Optional[Any] = float('Inf' )
_snake_case : Optional[int] = head_importance.view(-1 ).sort()[1]
if len(__lowerCAmelCase ) <= num_to_mask:
print('BREAK BY num_to_mask' )
break
# mask heads
_snake_case : List[Any] = current_heads_to_mask[:num_to_mask]
logger.info('Heads to mask: %s' , str(current_heads_to_mask.tolist() ) )
_snake_case : Tuple = new_head_mask.view(-1 )
_snake_case : List[str] = 0.0
_snake_case : int = new_head_mask.view_as(__lowerCAmelCase )
_snake_case : Optional[Any] = new_head_mask.clone().detach()
print_ad_tensor(__lowerCAmelCase )
# Compute metric and head importance again
_snake_case , _snake_case , _snake_case : List[str] = compute_heads_importance(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , compute_entropy=__lowerCAmelCase , head_mask=__lowerCAmelCase )
_snake_case : Union[str, Any] = 1 / loss
logger.info(
'Masking: current score: %f, remaining heads %d (%.1f percents)' , __lowerCAmelCase , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 1_00 , )
logger.info('Final head mask' )
print_ad_tensor(__lowerCAmelCase )
np.save(os.path.join(args.output_dir , 'head_mask.npy' ) , head_mask.detach().cpu().numpy() )
return head_mask
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
_snake_case : List[Any] = datetime.now()
_snake_case , _snake_case , _snake_case : List[Any] = compute_heads_importance(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , compute_entropy=__lowerCAmelCase , compute_importance=__lowerCAmelCase , head_mask=__lowerCAmelCase )
_snake_case : str = 1 / loss
_snake_case : Optional[int] = datetime.now() - before_time
_snake_case : Optional[int] = sum(p.numel() for p in model.parameters() )
_snake_case : Optional[Any] = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__lowerCAmelCase ) )
}
for k, v in heads_to_prune.items():
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
_snake_case : Optional[int] = [
v,
]
assert sum(len(__lowerCAmelCase ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
model.prune_heads(__lowerCAmelCase )
_snake_case : Optional[Any] = sum(p.numel() for p in model.parameters() )
_snake_case : List[str] = datetime.now()
_snake_case , _snake_case , _snake_case : Union[str, Any] = compute_heads_importance(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , compute_entropy=__lowerCAmelCase , compute_importance=__lowerCAmelCase , head_mask=__lowerCAmelCase , actually_pruned=__lowerCAmelCase , )
_snake_case : Dict = 1 / loss
_snake_case : str = datetime.now() - before_time
logger.info(
'Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)' , __lowerCAmelCase , __lowerCAmelCase , pruned_num_params / original_num_params * 1_00 , )
logger.info('Pruning: score with masking: %f score with pruning: %f' , __lowerCAmelCase , __lowerCAmelCase )
logger.info('Pruning: speed ratio (original timing / new timing): %f percents' , original_time / new_time * 1_00 )
save_model(__lowerCAmelCase , args.output_dir )
def A__( ):
_snake_case : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--data_dir' , default=__lowerCAmelCase , type=__lowerCAmelCase , required=__lowerCAmelCase , help='The input data dir. Should contain the .tsv files (or other data files) for the task.' , )
parser.add_argument(
'--model_name_or_path' , default=__lowerCAmelCase , type=__lowerCAmelCase , required=__lowerCAmelCase , help='Path to pretrained model or model identifier from huggingface.co/models' , )
parser.add_argument(
'--output_dir' , default=__lowerCAmelCase , type=__lowerCAmelCase , required=__lowerCAmelCase , help='The output directory where the model predictions and checkpoints will be written.' , )
# Other parameters
parser.add_argument(
'--config_name' , default='' , type=__lowerCAmelCase , help='Pretrained config name or path if not the same as model_name_or_path' , )
parser.add_argument(
'--tokenizer_name' , default='' , type=__lowerCAmelCase , help='Pretrained tokenizer name or path if not the same as model_name_or_path' , )
parser.add_argument(
'--cache_dir' , default=__lowerCAmelCase , type=__lowerCAmelCase , help='Where do you want to store the pre-trained models downloaded from s3' , )
parser.add_argument(
'--data_subset' , type=__lowerCAmelCase , default=-1 , help='If > 0: limit the data to a subset of data_subset instances.' )
parser.add_argument(
'--overwrite_output_dir' , action='store_true' , help='Whether to overwrite data in output directory' )
parser.add_argument(
'--overwrite_cache' , action='store_true' , help='Overwrite the cached training and evaluation sets' )
parser.add_argument(
'--dont_normalize_importance_by_layer' , action='store_true' , help='Don\'t normalize importance score by layers' )
parser.add_argument(
'--dont_normalize_global_importance' , action='store_true' , help='Don\'t normalize all importance scores between 0 and 1' , )
parser.add_argument(
'--try_masking' , action='store_true' , help='Whether to try to mask head until a threshold of accuracy.' )
parser.add_argument(
'--masking_threshold' , default=0.9 , type=__lowerCAmelCase , help='masking threshold in term of metrics (stop masking when metric < threshold * original metric value).' , )
parser.add_argument(
'--masking_amount' , default=0.1 , type=__lowerCAmelCase , help='Amount to heads to masking at each masking step.' )
parser.add_argument('--metric_name' , default='acc' , type=__lowerCAmelCase , help='Metric to use for head masking.' )
parser.add_argument(
'--max_seq_length' , default=1_28 , type=__lowerCAmelCase , help=(
'The maximum total input sequence length after WordPiece tokenization. \n'
'Sequences longer than this will be truncated, sequences shorter padded.'
) , )
parser.add_argument('--batch_size' , default=1 , type=__lowerCAmelCase , help='Batch size.' )
parser.add_argument('--seed' , type=__lowerCAmelCase , default=42 )
parser.add_argument('--local_rank' , type=__lowerCAmelCase , default=-1 , help='local_rank for distributed training on gpus' )
parser.add_argument('--no_cuda' , action='store_true' , help='Whether not to use CUDA when available' )
parser.add_argument('--server_ip' , type=__lowerCAmelCase , default='' , help='Can be used for distant debugging.' )
parser.add_argument('--server_port' , type=__lowerCAmelCase , default='' , help='Can be used for distant debugging.' )
_snake_case : List[str] = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('Waiting for debugger attach' )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=__lowerCAmelCase )
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
_snake_case : Optional[Any] = torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu' )
_snake_case : Any = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank )
_snake_case : Union[str, Any] = torch.device('cuda' , args.local_rank )
_snake_case : Dict = 1
torch.distributed.init_process_group(backend='nccl' ) # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN )
logger.info('device: {} n_gpu: {}, distributed: {}'.format(args.device , args.n_gpu , bool(args.local_rank != -1 ) ) )
_snake_case : Any = GPTaLMHeadModel.from_pretrained(args.model_name_or_path )
# Distributed and parallel training
model.to(args.device )
if args.local_rank != -1:
_snake_case : List[str] = nn.parallel.DistributedDataParallel(
__lowerCAmelCase , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=__lowerCAmelCase )
elif args.n_gpu > 1:
_snake_case : int = nn.DataParallel(__lowerCAmelCase )
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=__lowerCAmelCase )
torch.save(__lowerCAmelCase , os.path.join(args.output_dir , 'run_args.bin' ) )
logger.info('Training/evaluation parameters %s' , __lowerCAmelCase )
# Prepare dataset
_snake_case : str = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa ),
] )
_snake_case : Dict = (torch.from_numpy(__lowerCAmelCase ),)
_snake_case : List[Any] = TensorDataset(*__lowerCAmelCase )
_snake_case : List[str] = RandomSampler(__lowerCAmelCase )
_snake_case : List[str] = DataLoader(__lowerCAmelCase , sampler=__lowerCAmelCase , batch_size=args.batch_size )
# Compute head entropy and importance score
compute_heads_importance(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
_snake_case : Union[str, Any] = mask_heads(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
prune_heads(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
if __name__ == "__main__":
main()
| 652 | 0 |
def A__( __lowerCAmelCase , __lowerCAmelCase ):
return int((input_a, input_a).count(0 ) != 0 )
def A__( ):
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ) == 1
assert nand_gate(1 , 0 ) == 1
assert nand_gate(1 , 1 ) == 0
if __name__ == "__main__":
print(nand_gate(0, 0))
print(nand_gate(0, 1))
print(nand_gate(1, 0))
print(nand_gate(1, 1))
| 721 |
def A__( __lowerCAmelCase ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError('only integers accepted as input' )
else:
_snake_case : Any = str(abs(__lowerCAmelCase ) )
_snake_case : List[str] = [list(__lowerCAmelCase ) for char in range(len(__lowerCAmelCase ) )]
for index in range(len(__lowerCAmelCase ) ):
num_transpositions[index].pop(__lowerCAmelCase )
return max(
int(''.join(list(__lowerCAmelCase ) ) ) for transposition in num_transpositions )
if __name__ == "__main__":
__import__('''doctest''').testmod()
| 652 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowercase_ : Tuple = {
'''configuration_perceiver''': ['''PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PerceiverConfig''', '''PerceiverOnnxConfig'''],
'''tokenization_perceiver''': ['''PerceiverTokenizer'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : Union[str, Any] = ['''PerceiverFeatureExtractor''']
lowercase_ : List[Any] = ['''PerceiverImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : List[str] = [
'''PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''PerceiverForImageClassificationConvProcessing''',
'''PerceiverForImageClassificationFourier''',
'''PerceiverForImageClassificationLearned''',
'''PerceiverForMaskedLM''',
'''PerceiverForMultimodalAutoencoding''',
'''PerceiverForOpticalFlow''',
'''PerceiverForSequenceClassification''',
'''PerceiverLayer''',
'''PerceiverModel''',
'''PerceiverPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
from .tokenization_perceiver import PerceiverTokenizer
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_perceiver import PerceiverFeatureExtractor
from .image_processing_perceiver import PerceiverImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_perceiver import (
PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
PerceiverForImageClassificationConvProcessing,
PerceiverForImageClassificationFourier,
PerceiverForImageClassificationLearned,
PerceiverForMaskedLM,
PerceiverForMultimodalAutoencoding,
PerceiverForOpticalFlow,
PerceiverForSequenceClassification,
PerceiverLayer,
PerceiverModel,
PerceiverPreTrainedModel,
)
else:
import sys
lowercase_ : Union[str, Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 653 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
lowercase_ : Tuple = logging.getLogger(__name__)
@dataclass
class __UpperCamelCase :
__A = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''Whether tp freeze the encoder.'''} )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''Whether to freeze the embeddings.'''} )
@dataclass
class __UpperCamelCase :
__A = field(
metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''} )
__A = field(
default='''summarization''' , metadata={'''help''': '''Task name, summarization (or summarization_{dataset} for pegasus) or translation'''} , )
__A = field(
default=1024 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
__A = field(
default=128 , metadata={
'''help''': (
'''The maximum total sequence length for target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
__A = field(
default=142 , metadata={
'''help''': (
'''The maximum total sequence length for validation target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded. '''
'''This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '''
'''during ``evaluate`` and ``predict``.'''
)
} , )
__A = field(
default=142 , metadata={
'''help''': (
'''The maximum total sequence length for test target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
__A = field(default=-1 , metadata={'''help''': '''# training examples. -1 means use all.'''} )
__A = field(default=-1 , metadata={'''help''': '''# validation examples. -1 means use all.'''} )
__A = field(default=-1 , metadata={'''help''': '''# test examples. -1 means use all.'''} )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''Source language id for translation.'''} )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''Target language id for translation.'''} )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''# num_beams to use for evaluation.'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''} , )
def SCREAMING_SNAKE_CASE ( lowercase_ : List[Any] , lowercase_ : int , lowercase_ : List[Any] ):
logger.info(F"""***** {split} metrics *****""" )
for key in sorted(metrics.keys() ):
logger.info(F""" {key} = {metrics[key]}""" )
save_json(lowercase_ , os.path.join(lowercase_ , F"""{split}_results.json""" ) )
def SCREAMING_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.
lowercase = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
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.
lowercase , lowercase , lowercase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase , lowercase , lowercase = parser.parse_args_into_dataclasses()
check_output_dir(lowercase_ )
# 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.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# 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""" , lowercase_ )
# Set seed
set_seed(training_args.seed )
# 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 , cache_dir=model_args.cache_dir , )
lowercase = ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
for p in extra_model_params:
if getattr(lowercase_ , lowercase_ , lowercase_ ):
assert hasattr(lowercase_ , lowercase_ ), F"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
setattr(lowercase_ , lowercase_ , getattr(lowercase_ , lowercase_ ) )
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 = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf=""".ckpt""" in model_args.model_name_or_path , config=lowercase_ , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(lowercase_ , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
lowercase = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(lowercase_ , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(lowercase_ , lowercase_ ):
lowercase = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
lowercase = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(lowercase_ )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
lowercase = SeqaSeqDataset
# Get datasets
lowercase = (
dataset_class(
lowercase_ , type_path="""train""" , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_train
else None
)
lowercase = (
dataset_class(
lowercase_ , type_path="""val""" , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
lowercase = (
dataset_class(
lowercase_ , type_path="""test""" , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_predict
else None
)
# Initialize our Trainer
lowercase = (
build_compute_metrics_fn(data_args.task , lowercase_ ) if training_args.predict_with_generate else None
)
lowercase = SeqaSeqTrainer(
model=lowercase_ , args=lowercase_ , data_args=lowercase_ , train_dataset=lowercase_ , eval_dataset=lowercase_ , data_collator=SeqaSeqDataCollator(
lowercase_ , lowercase_ , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=lowercase_ , tokenizer=lowercase_ , )
lowercase = {}
# Training
if training_args.do_train:
logger.info("""*** Train ***""" )
lowercase = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
lowercase = train_result.metrics
lowercase = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics("""train""" , lowercase_ , training_args.output_dir )
all_metrics.update(lowercase_ )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , """trainer_state.json""" ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
lowercase = trainer.evaluate(metric_key_prefix="""val""" )
lowercase = data_args.n_val
lowercase = round(metrics["""val_loss"""] , 4 )
if trainer.is_world_process_zero():
handle_metrics("""val""" , lowercase_ , training_args.output_dir )
all_metrics.update(lowercase_ )
if training_args.do_predict:
logger.info("""*** Predict ***""" )
lowercase = trainer.predict(test_dataset=lowercase_ , metric_key_prefix="""test""" )
lowercase = test_output.metrics
lowercase = data_args.n_test
if trainer.is_world_process_zero():
lowercase = round(metrics["""test_loss"""] , 4 )
handle_metrics("""test""" , lowercase_ , training_args.output_dir )
all_metrics.update(lowercase_ )
if training_args.predict_with_generate:
lowercase = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=lowercase_ , clean_up_tokenization_spaces=lowercase_ )
lowercase = lmap(str.strip , lowercase_ )
write_txt_file(lowercase_ , os.path.join(training_args.output_dir , """test_generations.txt""" ) )
if trainer.is_world_process_zero():
save_json(lowercase_ , os.path.join(training_args.output_dir , """all_results.json""" ) )
return all_metrics
def SCREAMING_SNAKE_CASE ( lowercase_ : Dict ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 653 | 1 |
'''simple docstring'''
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,
CustomDtype,
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
DynamoBackend,
FPaRecipeKwargs,
FullyShardedDataParallelPlugin,
GradientAccumulationPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
KwargsHandler,
LoggerType,
MegatronLMPlugin,
PrecisionType,
ProjectConfiguration,
RNGType,
SageMakerDistributedType,
TensorInformation,
TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
get_ccl_version,
is_abit_bnb_available,
is_abit_bnb_available,
is_aim_available,
is_bfaa_available,
is_bnb_available,
is_botoa_available,
is_ccl_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_fpa_available,
is_ipex_available,
is_megatron_lm_available,
is_mlflow_available,
is_mps_available,
is_npu_available,
is_rich_available,
is_safetensors_available,
is_sagemaker_available,
is_tensorboard_available,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
from .modeling import (
check_device_map,
check_tied_parameters_in_config,
check_tied_parameters_on_same_device,
compute_module_sizes,
convert_file_size_to_int,
dtype_byte_size,
find_tied_parameters,
get_balanced_memory,
get_max_layer_size,
get_max_memory,
get_mixed_precision_context_manager,
id_tensor_storage,
infer_auto_device_map,
load_checkpoint_in_model,
load_offloaded_weights,
load_state_dict,
named_module_tensors,
retie_parameters,
set_module_tensor_to_device,
shard_checkpoint,
)
from .offload import (
OffloadedWeightsLoader,
PrefixedDataset,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
save_offload_index,
)
from .operations import (
broadcast,
broadcast_object_list,
concatenate,
convert_outputs_to_fpaa,
convert_to_fpaa,
find_batch_size,
find_device,
gather,
gather_object,
get_data_structure,
honor_type,
initialize_tensors,
is_namedtuple,
is_tensor_information,
is_torch_tensor,
listify,
pad_across_processes,
recursively_apply,
reduce,
send_to_device,
slice_tensors,
)
from .versions import compare_versions, is_torch_version
if is_deepspeed_available():
from .deepspeed import (
DeepSpeedEngineWrapper,
DeepSpeedOptimizerWrapper,
DeepSpeedSchedulerWrapper,
DummyOptim,
DummyScheduler,
HfDeepSpeedConfig,
)
from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
PrepareForLaunch,
_filter_args,
prepare_deepspeed_cmd_env,
prepare_multi_gpu_env,
prepare_sagemager_args_inputs,
prepare_simple_launcher_cmd_env,
prepare_tpu,
)
from .megatron_lm import (
AbstractTrainStep,
BertTrainStep,
GPTTrainStep,
MegatronEngine,
MegatronLMDummyDataLoader,
MegatronLMDummyScheduler,
MegatronLMOptimizerWrapper,
MegatronLMSchedulerWrapper,
TaTrainStep,
avg_losses_across_data_parallel_group,
gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
extract_model_from_parallel,
get_pretty_name,
is_port_in_use,
merge_dicts,
patch_environment,
save,
wait_for_everyone,
write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers
| 653 |
'''simple docstring'''
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_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
lowercase_ : Union[str, Any] = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase )
class __UpperCamelCase (_UpperCAmelCase ):
def __init__( self , *_lowerCAmelCase , **_lowerCAmelCase ) -> Dict:
'''simple docstring'''
super().__init__(*_lowerCAmelCase , **_lowerCAmelCase )
requires_backends(self , """vision""" )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING )
def _a ( self , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase=None ) -> str:
'''simple docstring'''
lowercase = {}
lowercase = {}
if prompt is not None:
lowercase = prompt
if generate_kwargs is not None:
lowercase = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
lowercase = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
"""'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
""" please use only one""" )
lowercase = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self , _lowerCAmelCase , **_lowerCAmelCase ) -> Any:
'''simple docstring'''
return super().__call__(_lowerCAmelCase , **_lowerCAmelCase )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase=None ) -> List[str]:
'''simple docstring'''
lowercase = load_image(_lowerCAmelCase )
if prompt is not None:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise ValueError(
F"""Received an invalid text input, got - {type(_lowerCAmelCase )} - but expected a single string. """
"""Note also that one single text can be provided for conditional image to text generation.""" )
lowercase = self.model.config.model_type
if model_type == "git":
lowercase = self.image_processor(images=_lowerCAmelCase , return_tensors=self.framework )
lowercase = self.tokenizer(text=_lowerCAmelCase , add_special_tokens=_lowerCAmelCase ).input_ids
lowercase = [self.tokenizer.cls_token_id] + input_ids
lowercase = torch.tensor(_lowerCAmelCase ).unsqueeze(0 )
model_inputs.update({"""input_ids""": input_ids} )
elif model_type == "pix2struct":
lowercase = self.image_processor(images=_lowerCAmelCase , header_text=_lowerCAmelCase , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
lowercase = self.image_processor(images=_lowerCAmelCase , return_tensors=self.framework )
lowercase = self.tokenizer(_lowerCAmelCase , return_tensors=self.framework )
model_inputs.update(_lowerCAmelCase )
else:
raise ValueError(F"""Model type {model_type} does not support conditional text generation""" )
else:
lowercase = self.image_processor(images=_lowerCAmelCase , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
lowercase = None
return model_inputs
def _a ( self , _lowerCAmelCase , _lowerCAmelCase=None ) -> Union[str, Any]:
'''simple docstring'''
if (
"input_ids" in model_inputs
and isinstance(model_inputs["""input_ids"""] , _lowerCAmelCase )
and all(x is None for x in model_inputs["""input_ids"""] )
):
lowercase = None
if generate_kwargs is None:
lowercase = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
lowercase = model_inputs.pop(self.model.main_input_name )
lowercase = self.model.generate(_lowerCAmelCase , **_lowerCAmelCase , **_lowerCAmelCase )
return model_outputs
def _a ( self , _lowerCAmelCase ) -> List[str]:
'''simple docstring'''
lowercase = []
for output_ids in model_outputs:
lowercase = {
"""generated_text""": self.tokenizer.decode(
_lowerCAmelCase , skip_special_tokens=_lowerCAmelCase , )
}
records.append(_lowerCAmelCase )
return records
| 653 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowercase_ : float , lowercase_ : float ):
if density <= 0:
raise ValueError("""Impossible fluid density""" )
if bulk_modulus <= 0:
raise ValueError("""Impossible bulk modulus""" )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import doctest
doctest.testmod()
| 653 |
'''simple docstring'''
from ... import PretrainedConfig
lowercase_ : int = {
'''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}
class __UpperCamelCase (_UpperCAmelCase ):
__A = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
__A = '''nezha'''
def __init__( self , _lowerCAmelCase=2_1128 , _lowerCAmelCase=768 , _lowerCAmelCase=12 , _lowerCAmelCase=12 , _lowerCAmelCase=3072 , _lowerCAmelCase="gelu" , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=512 , _lowerCAmelCase=64 , _lowerCAmelCase=2 , _lowerCAmelCase=0.02 , _lowerCAmelCase=1E-12 , _lowerCAmelCase=0.1 , _lowerCAmelCase=0 , _lowerCAmelCase=2 , _lowerCAmelCase=3 , _lowerCAmelCase=True , **_lowerCAmelCase , ) -> int:
'''simple docstring'''
super().__init__(pad_token_id=_lowerCAmelCase , bos_token_id=_lowerCAmelCase , eos_token_id=_lowerCAmelCase , **_lowerCAmelCase )
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = hidden_act
lowercase = intermediate_size
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = max_relative_position
lowercase = type_vocab_size
lowercase = initializer_range
lowercase = layer_norm_eps
lowercase = classifier_dropout
lowercase = use_cache
| 653 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. 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 torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class __UpperCamelCase (_UpperCAmelCase ):
__A = '''facebook/bart-large-mnli'''
__A = (
'''This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '''
'''should be the text to classify, and `labels`, which should be the list of labels to use for classification. '''
'''It returns the most likely label in the list of provided `labels` for the input text.'''
)
__A = '''text_classifier'''
__A = AutoTokenizer
__A = AutoModelForSequenceClassification
__A = ['''text''', ['''text''']]
__A = ['''text''']
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
super().setup()
lowercase = self.model.config
lowercase = -1
for idx, label in config.idalabel.items():
if label.lower().startswith("""entail""" ):
lowercase = int(_lowerCAmelCase )
if self.entailment_id == -1:
raise ValueError("""Could not determine the entailment ID from the model config, please pass it at init.""" )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase ) -> List[str]:
'''simple docstring'''
lowercase = labels
return self.pre_processor(
[text] * len(_lowerCAmelCase ) , [F"""This example is {label}""" for label in labels] , return_tensors="""pt""" , padding="""max_length""" , )
def _a ( self , _lowerCAmelCase ) -> Tuple:
'''simple docstring'''
lowercase = outputs.logits
lowercase = torch.argmax(logits[:, 2] ).item()
return self._labels[label_id]
| 653 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
lowercase_ : Tuple = logging.getLogger(__name__)
def SCREAMING_SNAKE_CASE ( lowercase_ : str ):
lowercase = git.Repo(search_parent_directories=lowercase_ )
lowercase = {
"""repo_id""": str(lowercase_ ),
"""repo_sha""": str(repo.head.object.hexsha ),
"""repo_branch""": str(repo.active_branch ),
}
with open(os.path.join(lowercase_ , """git_log.json""" ) , """w""" ) as f:
json.dump(lowercase_ , lowercase_ , indent=4 )
def SCREAMING_SNAKE_CASE ( lowercase_ : str ):
if params.n_gpu <= 0:
lowercase = 0
lowercase = -1
lowercase = True
lowercase = False
return
assert torch.cuda.is_available()
logger.info("""Initializing GPUs""" )
if params.n_gpu > 1:
assert params.local_rank != -1
lowercase = int(os.environ["""WORLD_SIZE"""] )
lowercase = int(os.environ["""N_GPU_NODE"""] )
lowercase = int(os.environ["""RANK"""] )
# number of nodes / node ID
lowercase = params.world_size // params.n_gpu_per_node
lowercase = params.global_rank // params.n_gpu_per_node
lowercase = True
assert params.n_nodes == int(os.environ["""N_NODES"""] )
assert params.node_id == int(os.environ["""NODE_RANK"""] )
# local job (single GPU)
else:
assert params.local_rank == -1
lowercase = 1
lowercase = 0
lowercase = 0
lowercase = 0
lowercase = 1
lowercase = 1
lowercase = False
# sanity checks
assert params.n_nodes >= 1
assert 0 <= params.node_id < params.n_nodes
assert 0 <= params.local_rank <= params.global_rank < params.world_size
assert params.world_size == params.n_nodes * params.n_gpu_per_node
# define whether this is the master process / if we are in multi-node distributed mode
lowercase = params.node_id == 0 and params.local_rank == 0
lowercase = params.n_nodes > 1
# summary
lowercase = F"""--- Global rank: {params.global_rank} - """
logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes )
logger.info(PREFIX + """Node ID : %i""" % params.node_id )
logger.info(PREFIX + """Local rank : %i""" % params.local_rank )
logger.info(PREFIX + """World size : %i""" % params.world_size )
logger.info(PREFIX + """GPUs per node : %i""" % params.n_gpu_per_node )
logger.info(PREFIX + """Master : %s""" % str(params.is_master ) )
logger.info(PREFIX + """Multi-node : %s""" % str(params.multi_node ) )
logger.info(PREFIX + """Multi-GPU : %s""" % str(params.multi_gpu ) )
logger.info(PREFIX + """Hostname : %s""" % socket.gethostname() )
# set GPU device
torch.cuda.set_device(params.local_rank )
# initialize multi-GPU
if params.multi_gpu:
logger.info("""Initializing PyTorch distributed""" )
torch.distributed.init_process_group(
init_method="""env://""" , backend="""nccl""" , )
def SCREAMING_SNAKE_CASE ( lowercase_ : Optional[Any] ):
np.random.seed(args.seed )
torch.manual_seed(args.seed )
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed )
| 653 | 1 |
'''simple docstring'''
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
PROCESSOR_MAPPING,
TOKENIZER_MAPPING,
AutoConfig,
AutoFeatureExtractor,
AutoProcessor,
AutoTokenizer,
BertTokenizer,
ProcessorMixin,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
from test_module.custom_processing import CustomProcessor # noqa E402
from test_module.custom_tokenization import CustomTokenizer # noqa E402
lowercase_ : Dict = get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
lowercase_ : Tuple = get_tests_dir('''fixtures/vocab.json''')
lowercase_ : Optional[int] = get_tests_dir('''fixtures''')
class __UpperCamelCase (unittest.TestCase ):
__A = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']
def _a ( self ) -> Optional[int]:
'''simple docstring'''
lowercase = 0
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase = AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h""" )
self.assertIsInstance(_lowerCAmelCase , _lowerCAmelCase )
def _a ( self ) -> int:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase = WavaVecaConfig()
lowercase = AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h""" )
# save in new folder
model_config.save_pretrained(_lowerCAmelCase )
processor.save_pretrained(_lowerCAmelCase )
lowercase = AutoProcessor.from_pretrained(_lowerCAmelCase )
self.assertIsInstance(_lowerCAmelCase , _lowerCAmelCase )
def _a ( self ) -> str:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
# copy relevant files
copyfile(_lowerCAmelCase , os.path.join(_lowerCAmelCase , _lowerCAmelCase ) )
copyfile(_lowerCAmelCase , os.path.join(_lowerCAmelCase , """vocab.json""" ) )
lowercase = AutoProcessor.from_pretrained(_lowerCAmelCase )
self.assertIsInstance(_lowerCAmelCase , _lowerCAmelCase )
def _a ( self ) -> List[str]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase = WavaVecaFeatureExtractor()
lowercase = AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h""" )
lowercase = WavaVecaProcessor(_lowerCAmelCase , _lowerCAmelCase )
# save in new folder
processor.save_pretrained(_lowerCAmelCase )
# drop `processor_class` in tokenizer
with open(os.path.join(_lowerCAmelCase , _lowerCAmelCase ) , """r""" ) as f:
lowercase = json.load(_lowerCAmelCase )
config_dict.pop("""processor_class""" )
with open(os.path.join(_lowerCAmelCase , _lowerCAmelCase ) , """w""" ) as f:
f.write(json.dumps(_lowerCAmelCase ) )
lowercase = AutoProcessor.from_pretrained(_lowerCAmelCase )
self.assertIsInstance(_lowerCAmelCase , _lowerCAmelCase )
def _a ( self ) -> Tuple:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase = WavaVecaFeatureExtractor()
lowercase = AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h""" )
lowercase = WavaVecaProcessor(_lowerCAmelCase , _lowerCAmelCase )
# save in new folder
processor.save_pretrained(_lowerCAmelCase )
# drop `processor_class` in feature extractor
with open(os.path.join(_lowerCAmelCase , _lowerCAmelCase ) , """r""" ) as f:
lowercase = json.load(_lowerCAmelCase )
config_dict.pop("""processor_class""" )
with open(os.path.join(_lowerCAmelCase , _lowerCAmelCase ) , """w""" ) as f:
f.write(json.dumps(_lowerCAmelCase ) )
lowercase = AutoProcessor.from_pretrained(_lowerCAmelCase )
self.assertIsInstance(_lowerCAmelCase , _lowerCAmelCase )
def _a ( self ) -> List[str]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase = WavaVecaConfig(processor_class="""Wav2Vec2Processor""" )
model_config.save_pretrained(_lowerCAmelCase )
# copy relevant files
copyfile(_lowerCAmelCase , os.path.join(_lowerCAmelCase , """vocab.json""" ) )
# create emtpy sample processor
with open(os.path.join(_lowerCAmelCase , _lowerCAmelCase ) , """w""" ) as f:
f.write("""{}""" )
lowercase = AutoProcessor.from_pretrained(_lowerCAmelCase )
self.assertIsInstance(_lowerCAmelCase , _lowerCAmelCase )
def _a ( self ) -> int:
'''simple docstring'''
with self.assertRaises(_lowerCAmelCase ):
lowercase = AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_lowerCAmelCase ):
lowercase = AutoProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_processor""" , trust_remote_code=_lowerCAmelCase )
lowercase = AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor""" , trust_remote_code=_lowerCAmelCase )
self.assertTrue(processor.special_attribute_present )
self.assertEqual(processor.__class__.__name__ , """NewProcessor""" )
lowercase = processor.feature_extractor
self.assertTrue(feature_extractor.special_attribute_present )
self.assertEqual(feature_extractor.__class__.__name__ , """NewFeatureExtractor""" )
lowercase = processor.tokenizer
self.assertTrue(tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , """NewTokenizerFast""" )
# Test we can also load the slow version
lowercase = AutoProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_processor""" , trust_remote_code=_lowerCAmelCase , use_fast=_lowerCAmelCase )
lowercase = new_processor.tokenizer
self.assertTrue(new_tokenizer.special_attribute_present )
self.assertEqual(new_tokenizer.__class__.__name__ , """NewTokenizer""" )
else:
self.assertEqual(tokenizer.__class__.__name__ , """NewTokenizer""" )
def _a ( self ) -> Any:
'''simple docstring'''
try:
AutoConfig.register("""custom""" , _lowerCAmelCase )
AutoFeatureExtractor.register(_lowerCAmelCase , _lowerCAmelCase )
AutoTokenizer.register(_lowerCAmelCase , slow_tokenizer_class=_lowerCAmelCase )
AutoProcessor.register(_lowerCAmelCase , _lowerCAmelCase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_lowerCAmelCase ):
AutoProcessor.register(_lowerCAmelCase , _lowerCAmelCase )
# Now that the config is registered, it can be used as any other config with the auto-API
lowercase = CustomFeatureExtractor.from_pretrained(_lowerCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase = os.path.join(_lowerCAmelCase , """vocab.txt""" )
with open(_lowerCAmelCase , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens] ) )
lowercase = CustomTokenizer(_lowerCAmelCase )
lowercase = CustomProcessor(_lowerCAmelCase , _lowerCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(_lowerCAmelCase )
lowercase = AutoProcessor.from_pretrained(_lowerCAmelCase )
self.assertIsInstance(_lowerCAmelCase , _lowerCAmelCase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def _a ( self ) -> int:
'''simple docstring'''
class __UpperCamelCase (_UpperCAmelCase ):
__A = False
class __UpperCamelCase (_UpperCAmelCase ):
__A = False
class __UpperCamelCase (_UpperCAmelCase ):
__A = '''AutoFeatureExtractor'''
__A = '''AutoTokenizer'''
__A = False
try:
AutoConfig.register("""custom""" , _lowerCAmelCase )
AutoFeatureExtractor.register(_lowerCAmelCase , _lowerCAmelCase )
AutoTokenizer.register(_lowerCAmelCase , slow_tokenizer_class=_lowerCAmelCase )
AutoProcessor.register(_lowerCAmelCase , _lowerCAmelCase )
# If remote code is not set, the default is to use local classes.
lowercase = AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor""" )
self.assertEqual(processor.__class__.__name__ , """NewProcessor""" )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote code is disabled, we load the local ones.
lowercase = AutoProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_processor""" , trust_remote_code=_lowerCAmelCase )
self.assertEqual(processor.__class__.__name__ , """NewProcessor""" )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote is enabled, we load from the Hub.
lowercase = AutoProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_processor""" , trust_remote_code=_lowerCAmelCase )
self.assertEqual(processor.__class__.__name__ , """NewProcessor""" )
self.assertTrue(processor.special_attribute_present )
self.assertTrue(processor.feature_extractor.special_attribute_present )
self.assertTrue(processor.tokenizer.special_attribute_present )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def _a ( self ) -> str:
'''simple docstring'''
lowercase = AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-bert""" )
self.assertEqual(processor.__class__.__name__ , """BertTokenizerFast""" )
def _a ( self ) -> List[str]:
'''simple docstring'''
lowercase = AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-convnext""" )
self.assertEqual(processor.__class__.__name__ , """ConvNextImageProcessor""" )
@is_staging_test
class __UpperCamelCase (unittest.TestCase ):
__A = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']
@classmethod
def _a ( cls ) -> Union[str, Any]:
'''simple docstring'''
lowercase = TOKEN
HfFolder.save_token(_lowerCAmelCase )
@classmethod
def _a ( cls ) -> Optional[Any]:
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id="""test-processor""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""valid_org/test-processor-org""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""test-dynamic-processor""" )
except HTTPError:
pass
def _a ( self ) -> str:
'''simple docstring'''
lowercase = WavaVecaProcessor.from_pretrained(_lowerCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(_lowerCAmelCase , """test-processor""" ) , push_to_hub=_lowerCAmelCase , use_auth_token=self._token )
lowercase = WavaVecaProcessor.from_pretrained(F"""{USER}/test-processor""" )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(_lowerCAmelCase , getattr(new_processor.feature_extractor , _lowerCAmelCase ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() )
def _a ( self ) -> List[str]:
'''simple docstring'''
lowercase = WavaVecaProcessor.from_pretrained(_lowerCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(_lowerCAmelCase , """test-processor-org""" ) , push_to_hub=_lowerCAmelCase , use_auth_token=self._token , organization="""valid_org""" , )
lowercase = WavaVecaProcessor.from_pretrained("""valid_org/test-processor-org""" )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(_lowerCAmelCase , getattr(new_processor.feature_extractor , _lowerCAmelCase ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() )
def _a ( self ) -> Tuple:
'''simple docstring'''
CustomFeatureExtractor.register_for_auto_class()
CustomTokenizer.register_for_auto_class()
CustomProcessor.register_for_auto_class()
lowercase = CustomFeatureExtractor.from_pretrained(_lowerCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase = os.path.join(_lowerCAmelCase , """vocab.txt""" )
with open(_lowerCAmelCase , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens] ) )
lowercase = CustomTokenizer(_lowerCAmelCase )
lowercase = CustomProcessor(_lowerCAmelCase , _lowerCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
create_repo(F"""{USER}/test-dynamic-processor""" , token=self._token )
lowercase = Repository(_lowerCAmelCase , clone_from=F"""{USER}/test-dynamic-processor""" , token=self._token )
processor.save_pretrained(_lowerCAmelCase )
# This has added the proper auto_map field to the feature extractor config
self.assertDictEqual(
processor.feature_extractor.auto_map , {
"""AutoFeatureExtractor""": """custom_feature_extraction.CustomFeatureExtractor""",
"""AutoProcessor""": """custom_processing.CustomProcessor""",
} , )
# This has added the proper auto_map field to the tokenizer config
with open(os.path.join(_lowerCAmelCase , """tokenizer_config.json""" ) ) as f:
lowercase = json.load(_lowerCAmelCase )
self.assertDictEqual(
tokenizer_config["""auto_map"""] , {
"""AutoTokenizer""": ["""custom_tokenization.CustomTokenizer""", None],
"""AutoProcessor""": """custom_processing.CustomProcessor""",
} , )
# The code has been copied from fixtures
self.assertTrue(os.path.isfile(os.path.join(_lowerCAmelCase , """custom_feature_extraction.py""" ) ) )
self.assertTrue(os.path.isfile(os.path.join(_lowerCAmelCase , """custom_tokenization.py""" ) ) )
self.assertTrue(os.path.isfile(os.path.join(_lowerCAmelCase , """custom_processing.py""" ) ) )
repo.push_to_hub()
lowercase = AutoProcessor.from_pretrained(F"""{USER}/test-dynamic-processor""" , trust_remote_code=_lowerCAmelCase )
# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
self.assertEqual(new_processor.__class__.__name__ , """CustomProcessor""" )
| 653 |
'''simple docstring'''
from __future__ import annotations
import os
from typing import Any
import requests
lowercase_ : List[str] = '''https://api.github.com'''
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
lowercase_ : Any = BASE_URL + '''/user'''
# https://github.com/settings/tokens
lowercase_ : Union[str, Any] = os.environ.get('''USER_TOKEN''', '''''')
def SCREAMING_SNAKE_CASE ( lowercase_ : str ):
lowercase = {
"""Authorization""": F"""token {auth_token}""",
"""Accept""": """application/vnd.github.v3+json""",
}
return requests.get(lowercase_ , headers=lowercase_ ).json()
if __name__ == "__main__": # pragma: no cover
if USER_TOKEN:
for key, value in fetch_github_info(USER_TOKEN).items():
print(f'''{key}: {value}''')
else:
raise ValueError('''\'USER_TOKEN\' field cannot be empty.''')
| 653 | 1 |
'''simple docstring'''
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
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 SwiftFormerForImageClassification, SwiftFormerModel
from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __UpperCamelCase :
def __init__( self , _lowerCAmelCase , _lowerCAmelCase=13 , _lowerCAmelCase=3 , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=224 , _lowerCAmelCase=1000 , _lowerCAmelCase=[3, 3, 6, 4] , _lowerCAmelCase=[48, 56, 112, 220] , ) -> List[str]:
'''simple docstring'''
lowercase = parent
lowercase = batch_size
lowercase = num_channels
lowercase = is_training
lowercase = use_labels
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = num_labels
lowercase = image_size
lowercase = layer_depths
lowercase = embed_dims
def _a ( self ) -> Tuple:
'''simple docstring'''
lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase = None
if self.use_labels:
lowercase = ids_tensor([self.batch_size] , self.num_labels )
lowercase = self.get_config()
return config, pixel_values, labels
def _a ( self ) -> int:
'''simple docstring'''
return SwiftFormerConfig(
depths=self.layer_depths , embed_dims=self.embed_dims , mlp_ratio=4 , downsamples=[True, True, True, True] , hidden_act="""gelu""" , num_labels=self.num_labels , down_patch_size=3 , down_stride=2 , down_pad=1 , drop_rate=0.0 , drop_path_rate=0.0 , use_layer_scale=_lowerCAmelCase , layer_scale_init_value=1E-5 , )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowercase = SwiftFormerModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
lowercase = model(_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dims[-1], 7, 7) )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowercase = self.num_labels
lowercase = SwiftFormerForImageClassification(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
lowercase = model(_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
lowercase = SwiftFormerForImageClassification(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase = model(_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
((lowercase) , (lowercase) , (lowercase)) = self.prepare_config_and_inputs()
lowercase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __UpperCamelCase (_UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
__A = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
__A = (
{'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
if is_torch_available()
else {}
)
__A = False
__A = False
__A = False
__A = False
__A = False
def _a ( self ) -> Dict:
'''simple docstring'''
lowercase = SwiftFormerModelTester(self )
lowercase = ConfigTester(
self , config_class=_lowerCAmelCase , has_text_modality=_lowerCAmelCase , hidden_size=37 , num_attention_heads=12 , num_hidden_layers=12 , )
def _a ( self ) -> List[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="""SwiftFormer does not use inputs_embeds""" )
def _a ( self ) -> List[str]:
'''simple docstring'''
pass
def _a ( self ) -> Dict:
'''simple docstring'''
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase = model_class(_lowerCAmelCase )
lowercase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowerCAmelCase , nn.Linear ) )
def _a ( self ) -> int:
'''simple docstring'''
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase = model_class(_lowerCAmelCase )
lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase = [*signature.parameters.keys()]
lowercase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
def _a ( self ) -> List[str]:
'''simple docstring'''
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase )
@slow
def _a ( self ) -> Any:
'''simple docstring'''
for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase = SwiftFormerModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
@unittest.skip(reason="""SwiftFormer does not output attentions""" )
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
pass
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
def check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
with torch.no_grad():
lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
lowercase = outputs.hidden_states
lowercase = 8
self.assertEqual(len(_lowerCAmelCase ) , _lowerCAmelCase ) # TODO
# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
# with the width and height being successively divided by 2, after every 2 blocks
for i in range(len(_lowerCAmelCase ) ):
self.assertEqual(
hidden_states[i].shape , torch.Size(
[
self.model_tester.batch_size,
self.model_tester.embed_dims[i // 2],
(self.model_tester.image_size // 4) // 2 ** (i // 2),
(self.model_tester.image_size // 4) // 2 ** (i // 2),
] ) , )
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def _a ( self ) -> Dict:
'''simple docstring'''
def _config_zero_init(_lowerCAmelCase ):
lowercase = copy.deepcopy(_lowerCAmelCase )
for key in configs_no_init.__dict__.keys():
if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
setattr(_lowerCAmelCase , _lowerCAmelCase , 1E-10 )
if isinstance(getattr(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) , _lowerCAmelCase ):
lowercase = _config_zero_init(getattr(_lowerCAmelCase , _lowerCAmelCase ) )
setattr(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return configs_no_init
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = _config_zero_init(_lowerCAmelCase )
for model_class in self.all_model_classes:
lowercase = model_class(config=_lowerCAmelCase )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9) / 1E9).round().item() , [0.0, 1.0] , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def _a ( self ) -> Any:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE ( ):
lowercase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class __UpperCamelCase (unittest.TestCase ):
@cached_property
def _a ( self ) -> List[str]:
'''simple docstring'''
return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs""" ) if is_vision_available() else None
@slow
def _a ( self ) -> List[Any]:
'''simple docstring'''
lowercase = SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs""" ).to(_lowerCAmelCase )
lowercase = self.default_image_processor
lowercase = prepare_img()
lowercase = image_processor(images=_lowerCAmelCase , return_tensors="""pt""" ).to(_lowerCAmelCase )
# forward pass
with torch.no_grad():
lowercase = model(**_lowerCAmelCase )
# verify the logits
lowercase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
lowercase = torch.tensor([[-2.17_03E00, 2.11_07E00, -2.08_11E00]] ).to(_lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowerCAmelCase , atol=1E-4 ) )
| 653 |
'''simple docstring'''
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowercase_ : Union[str, Any] = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
require_version('''datasets>=1.14.0''', '''To fix: pip install -r examples/pytorch/audio-classification/requirements.txt''')
def SCREAMING_SNAKE_CASE ( lowercase_ : np.ndarray , lowercase_ : float , lowercase_ : int = 1_6000 ):
lowercase = int(round(sample_rate * max_length ) )
if len(lowercase_ ) <= sample_length:
return wav
lowercase = randint(0 , len(lowercase_ ) - sample_length - 1 )
return wav[random_offset : random_offset + sample_length]
@dataclass
class __UpperCamelCase :
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''Name of a dataset from the datasets package'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''A file containing the training audio paths and labels.'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''A file containing the validation audio paths and labels.'''} )
__A = field(
default='''train''' , metadata={
'''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
} , )
__A = field(
default='''validation''' , metadata={
'''help''': (
'''The name of the training data set split to use (via the datasets library). Defaults to \'validation\''''
)
} , )
__A = field(
default='''audio''' , metadata={'''help''': '''The name of the dataset column containing the audio data. Defaults to \'audio\''''} , )
__A = field(
default='''label''' , metadata={'''help''': '''The name of the dataset column containing the labels. Defaults to \'label\''''} )
__A = field(
default=_UpperCAmelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
__A = field(
default=_UpperCAmelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
__A = field(
default=20 , metadata={'''help''': '''Audio clips will be randomly cut to this length during training if the value is set.'''} , )
@dataclass
class __UpperCamelCase :
__A = field(
default='''facebook/wav2vec2-base''' , metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} , )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from the Hub'''} )
__A = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Name or path of preprocessor config.'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Whether to freeze the feature encoder layers of the model.'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Whether to generate an attention mask in the feature extractor.'''} )
__A = field(
default=_UpperCAmelCase , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''} , )
def _a ( self ) -> List[Any]:
'''simple docstring'''
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
"""The argument `--freeze_feature_extractor` is deprecated and """
"""will be removed in a future version. Use `--freeze_feature_encoder`"""
"""instead. Setting `freeze_feature_encoder==True`.""" , _lowerCAmelCase , )
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
"""The argument `--freeze_feature_extractor` is deprecated and """
"""should not be used in combination with `--freeze_feature_encoder`."""
"""Only make use of `--freeze_feature_encoder`.""" )
def SCREAMING_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.
lowercase = 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.
lowercase , lowercase , lowercase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase , lowercase , lowercase = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_audio_classification""" , lowercase_ , lowercase_ )
# 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 )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
lowercase = training_args.get_process_log_level()
logger.setLevel(lowercase_ )
transformers.utils.logging.set_verbosity(lowercase_ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# 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}""" )
logger.info(F"""Training/evaluation parameters {training_args}""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Detecting last checkpoint.
lowercase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowercase = 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 train from scratch.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is 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.""" )
# Initialize our dataset and prepare it for the audio classification task.
lowercase = DatasetDict()
lowercase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.train_split_name , use_auth_token=True if model_args.use_auth_token else None , )
lowercase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.eval_split_name , use_auth_token=True if model_args.use_auth_token else None , )
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
F"""--audio_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. """
"""Make sure to set `--audio_column_name` to the correct audio column - one of """
F"""{', '.join(raw_datasets['train'].column_names )}.""" )
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
F"""--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. """
"""Make sure to set `--label_column_name` to the correct text column - one of """
F"""{', '.join(raw_datasets['train'].column_names )}.""" )
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
lowercase = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path , return_attention_mask=model_args.attention_mask , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
lowercase = raw_datasets.cast_column(
data_args.audio_column_name , datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )
lowercase = feature_extractor.model_input_names[0]
def train_transforms(lowercase_ : int ):
lowercase = []
for audio in batch[data_args.audio_column_name]:
lowercase = random_subsample(
audio["""array"""] , max_length=data_args.max_length_seconds , sample_rate=feature_extractor.sampling_rate )
subsampled_wavs.append(lowercase_ )
lowercase = feature_extractor(lowercase_ , sampling_rate=feature_extractor.sampling_rate )
lowercase = {model_input_name: inputs.get(lowercase_ )}
lowercase = list(batch[data_args.label_column_name] )
return output_batch
def val_transforms(lowercase_ : Dict ):
lowercase = [audio["""array"""] for audio in batch[data_args.audio_column_name]]
lowercase = feature_extractor(lowercase_ , sampling_rate=feature_extractor.sampling_rate )
lowercase = {model_input_name: inputs.get(lowercase_ )}
lowercase = list(batch[data_args.label_column_name] )
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
lowercase = raw_datasets["""train"""].features[data_args.label_column_name].names
lowercase , lowercase = {}, {}
for i, label in enumerate(lowercase_ ):
lowercase = str(lowercase_ )
lowercase = label
# Load the accuracy metric from the datasets package
lowercase = evaluate.load("""accuracy""" )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(lowercase_ : Tuple ):
lowercase = np.argmax(eval_pred.predictions , axis=1 )
return metric.compute(predictions=lowercase_ , references=eval_pred.label_ids )
lowercase = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(lowercase_ ) , labelaid=lowercase_ , idalabel=lowercase_ , finetuning_task="""audio-classification""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
lowercase = AutoModelForAudioClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=lowercase_ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
lowercase = (
raw_datasets["""train"""].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
raw_datasets["train"].set_transform(lowercase_ , output_all_columns=lowercase_ )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
lowercase = (
raw_datasets["""eval"""].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
raw_datasets["eval"].set_transform(lowercase_ , output_all_columns=lowercase_ )
# Initialize our trainer
lowercase = Trainer(
model=lowercase_ , args=lowercase_ , train_dataset=raw_datasets["""train"""] if training_args.do_train else None , eval_dataset=raw_datasets["""eval"""] if training_args.do_eval else None , compute_metrics=lowercase_ , tokenizer=lowercase_ , )
# Training
if training_args.do_train:
lowercase = None
if training_args.resume_from_checkpoint is not None:
lowercase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowercase = last_checkpoint
lowercase = trainer.train(resume_from_checkpoint=lowercase_ )
trainer.save_model()
trainer.log_metrics("""train""" , train_result.metrics )
trainer.save_metrics("""train""" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
lowercase = trainer.evaluate()
trainer.log_metrics("""eval""" , lowercase_ )
trainer.save_metrics("""eval""" , lowercase_ )
# Write model card and (optionally) push to hub
lowercase = {
"""finetuned_from""": model_args.model_name_or_path,
"""tasks""": """audio-classification""",
"""dataset""": data_args.dataset_name,
"""tags""": ["""audio-classification"""],
}
if training_args.push_to_hub:
trainer.push_to_hub(**lowercase_ )
else:
trainer.create_model_card(**lowercase_ )
if __name__ == "__main__":
main()
| 653 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : Optional[Any] = logging.get_logger(__name__)
lowercase_ : int = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}
class __UpperCamelCase (_UpperCAmelCase ):
__A = '''gpt_bigcode'''
__A = ['''past_key_values''']
__A = {
'''hidden_size''': '''n_embd''',
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self , _lowerCAmelCase=5_0257 , _lowerCAmelCase=1024 , _lowerCAmelCase=768 , _lowerCAmelCase=12 , _lowerCAmelCase=12 , _lowerCAmelCase=None , _lowerCAmelCase="gelu_pytorch_tanh" , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=1E-5 , _lowerCAmelCase=0.02 , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=5_0256 , _lowerCAmelCase=5_0256 , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=True , **_lowerCAmelCase , ) -> Optional[int]:
'''simple docstring'''
lowercase = vocab_size
lowercase = n_positions
lowercase = n_embd
lowercase = n_layer
lowercase = n_head
lowercase = n_inner
lowercase = activation_function
lowercase = resid_pdrop
lowercase = embd_pdrop
lowercase = attn_pdrop
lowercase = layer_norm_epsilon
lowercase = initializer_range
lowercase = scale_attn_weights
lowercase = use_cache
lowercase = attention_softmax_in_fpaa
lowercase = scale_attention_softmax_in_fpaa
lowercase = multi_query
lowercase = bos_token_id
lowercase = eos_token_id
super().__init__(bos_token_id=_lowerCAmelCase , eos_token_id=_lowerCAmelCase , **_lowerCAmelCase )
| 653 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
lowercase_ : Union[str, Any] = logging.get_logger(__name__)
@dataclass
class __UpperCamelCase (_UpperCAmelCase ):
__A = [
'''no_inference''',
'''no_cuda''',
'''no_tpu''',
'''no_speed''',
'''no_memory''',
'''no_env_print''',
'''no_multi_process''',
]
def __init__( self , **_lowerCAmelCase ) -> Optional[int]:
'''simple docstring'''
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
lowercase = deprecated_arg[3:]
lowercase = not kwargs.pop(_lowerCAmelCase )
logger.warning(
F"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
F""" {positive_arg}={kwargs[positive_arg]}""" )
lowercase = kwargs.pop("""tpu_name""" , self.tpu_name )
lowercase = kwargs.pop("""device_idx""" , self.device_idx )
lowercase = kwargs.pop("""eager_mode""" , self.eager_mode )
lowercase = kwargs.pop("""use_xla""" , self.use_xla )
super().__init__(**_lowerCAmelCase )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Name of TPU'''} , )
__A = field(
default=0 , metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''} , )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''Benchmark models in eager model.'''} )
__A = field(
default=_UpperCAmelCase , metadata={
'''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
} , )
@cached_property
def _a ( self ) -> Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
'''simple docstring'''
requires_backends(self , ["""tf"""] )
lowercase = None
if self.tpu:
try:
if self.tpu_name:
lowercase = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
else:
lowercase = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
lowercase = None
return tpu
@cached_property
def _a ( self ) -> Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
'''simple docstring'''
requires_backends(self , ["""tf"""] )
if self.is_tpu:
tf.config.experimental_connect_to_cluster(self._setup_tpu )
tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )
lowercase = tf.distribute.TPUStrategy(self._setup_tpu )
else:
# currently no multi gpu is allowed
if self.is_gpu:
# TODO: Currently only single GPU is supported
tf.config.set_visible_devices(self.gpu_list[self.device_idx] , """GPU""" )
lowercase = tf.distribute.OneDeviceStrategy(device=F"""/gpu:{self.device_idx}""" )
else:
tf.config.set_visible_devices([] , """GPU""" ) # disable GPU
lowercase = tf.distribute.OneDeviceStrategy(device=F"""/cpu:{self.device_idx}""" )
return strategy
@property
def _a ( self ) -> bool:
'''simple docstring'''
requires_backends(self , ["""tf"""] )
return self._setup_tpu is not None
@property
def _a ( self ) -> "tf.distribute.Strategy":
'''simple docstring'''
requires_backends(self , ["""tf"""] )
return self._setup_strategy
@property
def _a ( self ) -> Tuple:
'''simple docstring'''
requires_backends(self , ["""tf"""] )
return tf.config.list_physical_devices("""GPU""" )
@property
def _a ( self ) -> int:
'''simple docstring'''
requires_backends(self , ["""tf"""] )
if self.cuda:
return len(self.gpu_list )
return 0
@property
def _a ( self ) -> bool:
'''simple docstring'''
return self.n_gpu > 0
| 653 | 1 |
'''simple docstring'''
from random import shuffle
import tensorflow as tf
from numpy import array
def SCREAMING_SNAKE_CASE ( lowercase_ : List[str] , lowercase_ : Optional[int] ):
lowercase = int(lowercase_ )
assert noofclusters < len(lowercase_ )
# Find out the dimensionality
lowercase = len(vectors[0] )
# Will help select random centroids from among the available vectors
lowercase = list(range(len(lowercase_ ) ) )
shuffle(lowercase_ )
# GRAPH OF COMPUTATION
# We initialize a new graph and set it as the default during each run
# of this algorithm. This ensures that as this function is called
# multiple times, the default graph doesn't keep getting crowded with
# unused ops and Variables from previous function calls.
lowercase = tf.Graph()
with graph.as_default():
# SESSION OF COMPUTATION
lowercase = tf.Session()
##CONSTRUCTING THE ELEMENTS OF COMPUTATION
##First lets ensure we have a Variable vector for each centroid,
##initialized to one of the vectors from the available data points
lowercase = [
tf.Variable(vectors[vector_indices[i]] ) for i in range(lowercase_ )
]
##These nodes will assign the centroid Variables the appropriate
##values
lowercase = tf.placeholder("""float64""" , [dim] )
lowercase = []
for centroid in centroids:
cent_assigns.append(tf.assign(lowercase_ , lowercase_ ) )
##Variables for cluster assignments of individual vectors(initialized
##to 0 at first)
lowercase = [tf.Variable(0 ) for i in range(len(lowercase_ ) )]
##These nodes will assign an assignment Variable the appropriate
##value
lowercase = tf.placeholder("""int32""" )
lowercase = []
for assignment in assignments:
cluster_assigns.append(tf.assign(lowercase_ , lowercase_ ) )
##Now lets construct the node that will compute the mean
# The placeholder for the input
lowercase = tf.placeholder("""float""" , [None, dim] )
# The Node/op takes the input and computes a mean along the 0th
# dimension, i.e. the list of input vectors
lowercase = tf.reduce_mean(lowercase_ , 0 )
##Node for computing Euclidean distances
# Placeholders for input
lowercase = tf.placeholder("""float""" , [dim] )
lowercase = tf.placeholder("""float""" , [dim] )
lowercase = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(lowercase_ , lowercase_ ) , 2 ) ) )
##This node will figure out which cluster to assign a vector to,
##based on Euclidean distances of the vector from the centroids.
# Placeholder for input
lowercase = tf.placeholder("""float""" , [noofclusters] )
lowercase = tf.argmin(lowercase_ , 0 )
##INITIALIZING STATE VARIABLES
##This will help initialization of all Variables defined with respect
##to the graph. The Variable-initializer should be defined after
##all the Variables have been constructed, so that each of them
##will be included in the initialization.
lowercase = tf.initialize_all_variables()
# Initialize all variables
sess.run(lowercase_ )
##CLUSTERING ITERATIONS
# Now perform the Expectation-Maximization steps of K-Means clustering
# iterations. To keep things simple, we will only do a set number of
# iterations, instead of using a Stopping Criterion.
lowercase = 100
for _ in range(lowercase_ ):
##EXPECTATION STEP
##Based on the centroid locations till last iteration, compute
##the _expected_ centroid assignments.
# Iterate over each vector
for vector_n in range(len(lowercase_ ) ):
lowercase = vectors[vector_n]
# Compute Euclidean distance between this vector and each
# centroid. Remember that this list cannot be named
#'centroid_distances', since that is the input to the
# cluster assignment node.
lowercase = [
sess.run(lowercase_ , feed_dict={va: vect, va: sess.run(lowercase_ )} )
for centroid in centroids
]
# Now use the cluster assignment node, with the distances
# as the input
lowercase = sess.run(
lowercase_ , feed_dict={centroid_distances: distances} )
# Now assign the value to the appropriate state variable
sess.run(
cluster_assigns[vector_n] , feed_dict={assignment_value: assignment} )
##MAXIMIZATION STEP
# Based on the expected state computed from the Expectation Step,
# compute the locations of the centroids so as to maximize the
# overall objective of minimizing within-cluster Sum-of-Squares
for cluster_n in range(lowercase_ ):
# Collect all the vectors assigned to this cluster
lowercase = [
vectors[i]
for i in range(len(lowercase_ ) )
if sess.run(assignments[i] ) == cluster_n
]
# Compute new centroid location
lowercase = sess.run(
lowercase_ , feed_dict={mean_input: array(lowercase_ )} )
# Assign value to appropriate variable
sess.run(
cent_assigns[cluster_n] , feed_dict={centroid_value: new_location} )
# Return centroids and assignments
lowercase = sess.run(lowercase_ )
lowercase = sess.run(lowercase_ )
return centroids, assignments
| 653 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : Any = logging.get_logger(__name__)
lowercase_ : str = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class __UpperCamelCase (_UpperCAmelCase ):
__A = '''vit_msn'''
def __init__( self , _lowerCAmelCase=768 , _lowerCAmelCase=12 , _lowerCAmelCase=12 , _lowerCAmelCase=3072 , _lowerCAmelCase="gelu" , _lowerCAmelCase=0.0 , _lowerCAmelCase=0.0 , _lowerCAmelCase=0.02 , _lowerCAmelCase=1E-06 , _lowerCAmelCase=224 , _lowerCAmelCase=16 , _lowerCAmelCase=3 , _lowerCAmelCase=True , **_lowerCAmelCase , ) -> List[Any]:
'''simple docstring'''
super().__init__(**_lowerCAmelCase )
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = intermediate_size
lowercase = hidden_act
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = initializer_range
lowercase = layer_norm_eps
lowercase = image_size
lowercase = patch_size
lowercase = num_channels
lowercase = qkv_bias
| 653 | 1 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
lowercase_ : Optional[Any] = logging.get_logger(__name__)
lowercase_ : Optional[int] = {'''vocab_file''': '''spiece.model'''}
lowercase_ : Optional[Any] = {
'''vocab_file''': {
'''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
}
}
class __UpperCamelCase (_UpperCAmelCase ):
def __init__( self , _lowerCAmelCase , _lowerCAmelCase=False , _lowerCAmelCase=True , _lowerCAmelCase=False , _lowerCAmelCase="<s>" , _lowerCAmelCase="</s>" , _lowerCAmelCase="<unk>" , _lowerCAmelCase="<sep>" , _lowerCAmelCase="<pad>" , _lowerCAmelCase="<cls>" , _lowerCAmelCase="<mask>" , _lowerCAmelCase=["<eop>", "<eod>"] , _lowerCAmelCase = None , **_lowerCAmelCase , ) -> None:
'''simple docstring'''
lowercase = AddedToken(_lowerCAmelCase , lstrip=_lowerCAmelCase , rstrip=_lowerCAmelCase ) if isinstance(_lowerCAmelCase , _lowerCAmelCase ) else mask_token
lowercase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=_lowerCAmelCase , remove_space=_lowerCAmelCase , keep_accents=_lowerCAmelCase , bos_token=_lowerCAmelCase , eos_token=_lowerCAmelCase , unk_token=_lowerCAmelCase , sep_token=_lowerCAmelCase , pad_token=_lowerCAmelCase , cls_token=_lowerCAmelCase , mask_token=_lowerCAmelCase , additional_special_tokens=_lowerCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **_lowerCAmelCase , )
lowercase = 3
lowercase = do_lower_case
lowercase = remove_space
lowercase = keep_accents
lowercase = vocab_file
lowercase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowerCAmelCase )
try:
import jieba
except ModuleNotFoundError as error:
raise error.__class__(
"""You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
"""See https://pypi.org/project/jieba/ for installation.""" )
lowercase = jieba
lowercase = str.maketrans(""" \n""" , """\u2582\u2583""" )
@property
# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
def _a ( self ) -> Dict:
'''simple docstring'''
return len(self.sp_model )
def _a ( self ) -> Dict:
'''simple docstring'''
lowercase = {self.convert_ids_to_tokens(_lowerCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> str:
'''simple docstring'''
lowercase = self.__dict__.copy()
lowercase = None
return state
def __setstate__( self , _lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
lowercase = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
lowercase = {}
lowercase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _a ( self , _lowerCAmelCase ) -> Tuple:
'''simple docstring'''
if self.remove_space:
lowercase = """ """.join(inputs.strip().split() )
else:
lowercase = inputs
lowercase = outputs.replace("""``""" , """\"""" ).replace("""''""" , """\"""" )
if not self.keep_accents:
lowercase = unicodedata.normalize("""NFKD""" , _lowerCAmelCase )
lowercase = """""".join([c for c in outputs if not unicodedata.combining(_lowerCAmelCase )] )
if self.do_lower_case:
lowercase = outputs.lower()
return outputs
def _a ( self , _lowerCAmelCase ) -> List[str]:
'''simple docstring'''
lowercase = self.preprocess_text(_lowerCAmelCase )
lowercase = self.sp_model.encode(_lowerCAmelCase , out_type=_lowerCAmelCase )
lowercase = []
for piece in pieces:
if len(_lowerCAmelCase ) > 1 and piece[-1] == str(""",""" ) and piece[-2].isdigit():
lowercase = self.sp_model.EncodeAsPieces(piece[:-1].replace(_lowerCAmelCase , """""" ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
lowercase = cur_pieces[1:]
else:
lowercase = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(_lowerCAmelCase )
else:
new_pieces.append(_lowerCAmelCase )
return new_pieces
def _a ( self , _lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
return self.sp_model.PieceToId(_lowerCAmelCase )
def _a ( self , _lowerCAmelCase ) -> Any:
'''simple docstring'''
return self.sp_model.IdToPiece(_lowerCAmelCase )
def _a ( self , _lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase = """""".join(_lowerCAmelCase ).replace(_lowerCAmelCase , """ """ ).strip()
return out_string
def _a ( self , _lowerCAmelCase , _lowerCAmelCase = None ) -> List[int]:
'''simple docstring'''
lowercase = [self.sep_token_id]
lowercase = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def _a ( self , _lowerCAmelCase , _lowerCAmelCase = None , _lowerCAmelCase = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCAmelCase , token_ids_a=_lowerCAmelCase , already_has_special_tokens=_lowerCAmelCase )
if token_ids_a is not None:
return ([0] * len(_lowerCAmelCase )) + [1] + ([0] * len(_lowerCAmelCase )) + [1, 1]
return ([0] * len(_lowerCAmelCase )) + [1, 1]
def _a ( self , _lowerCAmelCase , _lowerCAmelCase = None ) -> List[int]:
'''simple docstring'''
lowercase = [self.sep_token_id]
lowercase = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def _a ( self , _lowerCAmelCase , _lowerCAmelCase = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(_lowerCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowercase = os.path.join(
_lowerCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowerCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCAmelCase , """wb""" ) as fi:
lowercase = self.sp_model.serialized_model_proto()
fi.write(_lowerCAmelCase )
return (out_vocab_file,)
def _a ( self , *_lowerCAmelCase , **_lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowercase = super()._decode(*_lowerCAmelCase , **_lowerCAmelCase )
lowercase = text.replace(""" """ , """""" ).replace("""\u2582""" , """ """ ).replace("""\u2583""" , """\n""" )
return text
| 653 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowercase_ : Union[str, Any] , lowercase_ : str ):
lowercase = """"""
for i in table:
res += inp[i - 1]
return res
def SCREAMING_SNAKE_CASE ( lowercase_ : List[Any] ):
return data[1:] + data[0]
def SCREAMING_SNAKE_CASE ( lowercase_ : Tuple , lowercase_ : Dict ):
lowercase = """"""
for i in range(len(lowercase_ ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def SCREAMING_SNAKE_CASE ( lowercase_ : str , lowercase_ : str ):
lowercase = int("""0b""" + data[0] + data[-1] , 2 )
lowercase = int("""0b""" + data[1:3] , 2 )
return bin(s[row][col] )[2:]
def SCREAMING_SNAKE_CASE ( lowercase_ : Dict , lowercase_ : Tuple , lowercase_ : Tuple , lowercase_ : str , lowercase_ : Any ):
lowercase = message[:4]
lowercase = message[4:]
lowercase = apply_table(lowercase_ , lowercase_ )
lowercase = xor(lowercase_ , lowercase_ )
lowercase = apply_sbox(lowercase_ , temp[:4] ) # noqa: E741
lowercase = apply_sbox(lowercase_ , temp[4:] )
lowercase = """0""" * (2 - len(lowercase_ )) + l # noqa: E741
lowercase = """0""" * (2 - len(lowercase_ )) + r
lowercase = apply_table(l + r , lowercase_ )
lowercase = xor(lowercase_ , lowercase_ )
return temp + right
if __name__ == "__main__":
lowercase_ : Tuple = input('''Enter 10 bit key: ''')
lowercase_ : Any = input('''Enter 8 bit message: ''')
lowercase_ : Dict = [6, 3, 7, 4, 8, 5, 10, 9]
lowercase_ : str = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
lowercase_ : List[Any] = [2, 4, 3, 1]
lowercase_ : List[str] = [2, 6, 3, 1, 4, 8, 5, 7]
lowercase_ : Tuple = [4, 1, 3, 5, 7, 2, 8, 6]
lowercase_ : Optional[Any] = [4, 1, 2, 3, 2, 3, 4, 1]
lowercase_ : List[str] = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
lowercase_ : List[Any] = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
lowercase_ : Union[str, Any] = apply_table(key, paa_table)
lowercase_ : Optional[Any] = temp[:5]
lowercase_ : int = temp[5:]
lowercase_ : List[str] = left_shift(left)
lowercase_ : int = left_shift(right)
lowercase_ : Tuple = apply_table(left + right, pa_table)
lowercase_ : List[str] = left_shift(left)
lowercase_ : Optional[Any] = left_shift(right)
lowercase_ : Union[str, Any] = left_shift(left)
lowercase_ : Union[str, Any] = left_shift(right)
lowercase_ : Optional[int] = apply_table(left + right, pa_table)
# encryption
lowercase_ : int = apply_table(message, IP)
lowercase_ : Dict = function(expansion, sa, sa, keya, temp)
lowercase_ : Any = temp[4:] + temp[:4]
lowercase_ : List[Any] = function(expansion, sa, sa, keya, temp)
lowercase_ : Tuple = apply_table(temp, IP_inv)
print('''Cipher text is:''', CT)
# decryption
lowercase_ : List[str] = apply_table(CT, IP)
lowercase_ : Optional[int] = function(expansion, sa, sa, keya, temp)
lowercase_ : Optional[Any] = temp[4:] + temp[:4]
lowercase_ : Optional[int] = function(expansion, sa, sa, keya, temp)
lowercase_ : Optional[Any] = apply_table(temp, IP_inv)
print('''Plain text after decypting is:''', PT)
| 653 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : str = logging.get_logger(__name__)
lowercase_ : Optional[int] = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class __UpperCamelCase (_UpperCAmelCase ):
__A = '''roc_bert'''
def __init__( self , _lowerCAmelCase=3_0522 , _lowerCAmelCase=768 , _lowerCAmelCase=12 , _lowerCAmelCase=12 , _lowerCAmelCase=3072 , _lowerCAmelCase="gelu" , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=512 , _lowerCAmelCase=2 , _lowerCAmelCase=0.02 , _lowerCAmelCase=1E-12 , _lowerCAmelCase=True , _lowerCAmelCase=0 , _lowerCAmelCase="absolute" , _lowerCAmelCase=None , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=768 , _lowerCAmelCase=910 , _lowerCAmelCase=512 , _lowerCAmelCase=2_4858 , _lowerCAmelCase=True , **_lowerCAmelCase , ) -> List[str]:
'''simple docstring'''
lowercase = vocab_size
lowercase = max_position_embeddings
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = intermediate_size
lowercase = hidden_act
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = initializer_range
lowercase = type_vocab_size
lowercase = layer_norm_eps
lowercase = use_cache
lowercase = enable_pronunciation
lowercase = enable_shape
lowercase = pronunciation_embed_dim
lowercase = pronunciation_vocab_size
lowercase = shape_embed_dim
lowercase = shape_vocab_size
lowercase = concat_input
lowercase = position_embedding_type
lowercase = classifier_dropout
super().__init__(pad_token_id=_lowerCAmelCase , **_lowerCAmelCase )
| 653 |
'''simple docstring'''
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
lowercase_ : int = 50_0000
lowercase_ , lowercase_ : Union[str, Any] = os.path.split(__file__)
lowercase_ : Optional[Any] = os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))
@get_duration
def SCREAMING_SNAKE_CASE ( lowercase_ : datasets.Dataset , **lowercase_ : Dict ):
lowercase = dataset.map(**lowercase_ )
@get_duration
def SCREAMING_SNAKE_CASE ( lowercase_ : datasets.Dataset , **lowercase_ : Optional[int] ):
lowercase = dataset.filter(**lowercase_ )
def SCREAMING_SNAKE_CASE ( ):
lowercase = {"""num examples""": SPEED_TEST_N_EXAMPLES}
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase = datasets.Features({"""text""": datasets.Value("""string""" ), """numbers""": datasets.Value("""float32""" )} )
lowercase = generate_example_dataset(
os.path.join(lowercase_ , """dataset.arrow""" ) , lowercase_ , num_examples=lowercase_ )
lowercase = transformers.AutoTokenizer.from_pretrained("""bert-base-cased""" , use_fast=lowercase_ )
def tokenize(lowercase_ : Dict ):
return tokenizer(examples["""text"""] )
lowercase = map(lowercase_ )
lowercase = map(lowercase_ , batched=lowercase_ )
lowercase = map(lowercase_ , function=lambda lowercase_ : None , batched=lowercase_ )
with dataset.formatted_as(type="""numpy""" ):
lowercase = map(lowercase_ , function=lambda lowercase_ : None , batched=lowercase_ )
with dataset.formatted_as(type="""pandas""" ):
lowercase = map(lowercase_ , function=lambda lowercase_ : None , batched=lowercase_ )
with dataset.formatted_as(type="""torch""" , columns="""numbers""" ):
lowercase = map(lowercase_ , function=lambda lowercase_ : None , batched=lowercase_ )
with dataset.formatted_as(type="""tensorflow""" , columns="""numbers""" ):
lowercase = map(lowercase_ , function=lambda lowercase_ : None , batched=lowercase_ )
lowercase = map(lowercase_ , function=lowercase_ , batched=lowercase_ )
lowercase = filter(lowercase_ )
# Activate later when tokenizer support batched inputs
# with dataset.formatted_as(type='numpy'):
# times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)
with open(lowercase_ , """wb""" ) as f:
f.write(json.dumps(lowercase_ ).encode("""utf-8""" ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_map_filter()
| 653 | 1 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, 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 MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class __UpperCamelCase :
def __init__( self , _lowerCAmelCase , _lowerCAmelCase=2 , _lowerCAmelCase=True , _lowerCAmelCase=False , _lowerCAmelCase=10 , _lowerCAmelCase=3 , _lowerCAmelCase=32 * 4 , _lowerCAmelCase=32 * 6 , _lowerCAmelCase=4 , _lowerCAmelCase=32 , ) -> Optional[int]:
'''simple docstring'''
lowercase = parent
lowercase = batch_size
lowercase = is_training
lowercase = use_auxiliary_loss
lowercase = num_queries
lowercase = num_channels
lowercase = min_size
lowercase = max_size
lowercase = num_labels
lowercase = mask_feature_size
def _a ( self ) -> List[Any]:
'''simple docstring'''
lowercase = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
_lowerCAmelCase )
lowercase = torch.ones([self.batch_size, self.min_size, self.max_size] , device=_lowerCAmelCase )
lowercase = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=_lowerCAmelCase ) > 0.5
).float()
lowercase = (torch.rand((self.batch_size, self.num_labels) , device=_lowerCAmelCase ) > 0.5).long()
lowercase = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def _a ( self ) -> List[Any]:
'''simple docstring'''
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=128 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def _a ( self ) -> List[str]:
'''simple docstring'''
lowercase , lowercase , lowercase , lowercase , lowercase = self.prepare_config_and_inputs()
lowercase = {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
return config, inputs_dict
def _a ( self , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowercase = output.encoder_hidden_states
lowercase = output.pixel_decoder_hidden_states
lowercase = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(_lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCAmelCase ) , config.decoder_config.decoder_layers )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase=False ) -> Optional[Any]:
'''simple docstring'''
with torch.no_grad():
lowercase = MaskFormerModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
lowercase = model(pixel_values=_lowerCAmelCase , pixel_mask=_lowerCAmelCase )
lowercase = model(_lowerCAmelCase , output_hidden_states=_lowerCAmelCase )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# 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(_lowerCAmelCase , _lowerCAmelCase )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
lowercase = MaskFormerForInstanceSegmentation(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
def comm_check_on_output(_lowerCAmelCase ):
# 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():
lowercase = model(pixel_values=_lowerCAmelCase , pixel_mask=_lowerCAmelCase )
lowercase = model(_lowerCAmelCase )
comm_check_on_output(_lowerCAmelCase )
lowercase = model(
pixel_values=_lowerCAmelCase , pixel_mask=_lowerCAmelCase , mask_labels=_lowerCAmelCase , class_labels=_lowerCAmelCase )
comm_check_on_output(_lowerCAmelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class __UpperCamelCase (_UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
__A = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
__A = (
{'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
__A = False
__A = False
__A = False
__A = False
def _a ( self ) -> int:
'''simple docstring'''
lowercase = MaskFormerModelTester(self )
lowercase = ConfigTester(self , config_class=_lowerCAmelCase , has_text_modality=_lowerCAmelCase )
def _a ( self ) -> str:
'''simple docstring'''
self.config_tester.run_common_tests()
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_lowerCAmelCase , **_lowerCAmelCase , output_hidden_states=_lowerCAmelCase )
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_lowerCAmelCase )
@unittest.skip(reason="""MaskFormer does not use inputs_embeds""" )
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
pass
@unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method""" )
def _a ( self ) -> str:
'''simple docstring'''
pass
@unittest.skip(reason="""MaskFormer is not a generative model""" )
def _a ( self ) -> int:
'''simple docstring'''
pass
@unittest.skip(reason="""MaskFormer does not use token embeddings""" )
def _a ( self ) -> Any:
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(
reason="""MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" )
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def _a ( self ) -> int:
'''simple docstring'''
pass
def _a ( self ) -> str:
'''simple docstring'''
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase = model_class(_lowerCAmelCase )
lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase = [*signature.parameters.keys()]
lowercase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
@slow
def _a ( self ) -> int:
'''simple docstring'''
for model_name in ["facebook/maskformer-swin-small-coco"]:
lowercase = MaskFormerModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
def _a ( self ) -> Dict:
'''simple docstring'''
lowercase = (self.model_tester.min_size,) * 2
lowercase = {
"""pixel_values""": torch.randn((2, 3, *size) , device=_lowerCAmelCase ),
"""mask_labels""": torch.randn((2, 10, *size) , device=_lowerCAmelCase ),
"""class_labels""": torch.zeros(2 , 10 , device=_lowerCAmelCase ).long(),
}
lowercase = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(_lowerCAmelCase )
lowercase = model(**_lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
def _a ( self ) -> str:
'''simple docstring'''
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_lowerCAmelCase , **_lowerCAmelCase , output_hidden_states=_lowerCAmelCase )
def _a ( self ) -> Any:
'''simple docstring'''
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase = model_class(_lowerCAmelCase ).to(_lowerCAmelCase )
lowercase = model(**_lowerCAmelCase , output_attentions=_lowerCAmelCase )
self.assertTrue(outputs.attentions is not None )
def _a ( self ) -> int:
'''simple docstring'''
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
lowercase = self.all_model_classes[1]
lowercase , lowercase , lowercase , lowercase , lowercase = self.model_tester.prepare_config_and_inputs()
lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.train()
lowercase = model(_lowerCAmelCase , mask_labels=_lowerCAmelCase , class_labels=_lowerCAmelCase ).loss
loss.backward()
def _a ( self ) -> Optional[int]:
'''simple docstring'''
lowercase = self.all_model_classes[1]
lowercase , lowercase , lowercase , lowercase , lowercase = self.model_tester.prepare_config_and_inputs()
lowercase = True
lowercase = True
lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.train()
lowercase = model(_lowerCAmelCase , mask_labels=_lowerCAmelCase , class_labels=_lowerCAmelCase )
lowercase = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
lowercase = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
lowercase = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
lowercase = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=_lowerCAmelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
lowercase_ : Dict = 1e-4
def SCREAMING_SNAKE_CASE ( ):
lowercase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_vision
@slow
class __UpperCamelCase (unittest.TestCase ):
@cached_property
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
return (
MaskFormerImageProcessor.from_pretrained("""facebook/maskformer-swin-small-coco""" )
if is_vision_available()
else None
)
def _a ( self ) -> Any:
'''simple docstring'''
lowercase = MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco""" ).to(_lowerCAmelCase )
lowercase = self.default_image_processor
lowercase = prepare_img()
lowercase = image_processor(_lowerCAmelCase , return_tensors="""pt""" ).to(_lowerCAmelCase )
lowercase = 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(_lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
lowercase = model(**_lowerCAmelCase )
lowercase = torch.tensor(
[[-0.0482, 0.9228, 0.4951], [-0.2547, 0.8017, 0.8527], [-0.0069, 0.3385, -0.0089]] ).to(_lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , _lowerCAmelCase , atol=_lowerCAmelCase ) )
lowercase = torch.tensor(
[[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]] ).to(_lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , _lowerCAmelCase , atol=_lowerCAmelCase ) )
lowercase = torch.tensor(
[[0.2852, -0.0159, 0.9735], [0.6254, 0.1858, 0.8529], [-0.0680, -0.4116, 1.8413]] ).to(_lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , _lowerCAmelCase , atol=_lowerCAmelCase ) )
def _a ( self ) -> Tuple:
'''simple docstring'''
lowercase = (
MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco""" )
.to(_lowerCAmelCase )
.eval()
)
lowercase = self.default_image_processor
lowercase = prepare_img()
lowercase = image_processor(_lowerCAmelCase , return_tensors="""pt""" ).to(_lowerCAmelCase )
lowercase = 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(_lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
lowercase = model(**_lowerCAmelCase )
# masks_queries_logits
lowercase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
lowercase = [
[-1.373_7124, -1.772_4937, -1.936_4233],
[-1.597_7281, -1.986_7939, -2.152_3695],
[-1.579_5398, -1.926_9832, -2.09_3942],
]
lowercase = torch.tensor(_lowerCAmelCase ).to(_lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _lowerCAmelCase , atol=_lowerCAmelCase ) )
# class_queries_logits
lowercase = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
lowercase = torch.tensor(
[
[1.65_12E00, -5.25_72E00, -3.35_19E00],
[3.61_69E-02, -5.90_25E00, -2.93_13E00],
[1.07_66E-04, -7.76_30E00, -5.12_63E00],
] ).to(_lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCAmelCase , atol=_lowerCAmelCase ) )
def _a ( self ) -> str:
'''simple docstring'''
lowercase = (
MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff""" )
.to(_lowerCAmelCase )
.eval()
)
lowercase = self.default_image_processor
lowercase = prepare_img()
lowercase = image_processor(_lowerCAmelCase , return_tensors="""pt""" ).to(_lowerCAmelCase )
lowercase = 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(_lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
lowercase = model(**_lowerCAmelCase )
# masks_queries_logits
lowercase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
lowercase = [[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
lowercase = torch.tensor(_lowerCAmelCase ).to(_lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _lowerCAmelCase , atol=_lowerCAmelCase ) )
# class_queries_logits
lowercase = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
lowercase = torch.tensor(
[[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]] ).to(_lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCAmelCase , atol=_lowerCAmelCase ) )
def _a ( self ) -> Any:
'''simple docstring'''
lowercase = (
MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco""" )
.to(_lowerCAmelCase )
.eval()
)
lowercase = self.default_image_processor
lowercase = 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""" , )
lowercase = inputs["""pixel_values"""].to(_lowerCAmelCase )
lowercase = [el.to(_lowerCAmelCase ) for el in inputs["""mask_labels"""]]
lowercase = [el.to(_lowerCAmelCase ) for el in inputs["""class_labels"""]]
with torch.no_grad():
lowercase = model(**_lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
| 653 |
'''simple docstring'''
from random import shuffle
import tensorflow as tf
from numpy import array
def SCREAMING_SNAKE_CASE ( lowercase_ : List[str] , lowercase_ : Optional[int] ):
lowercase = int(lowercase_ )
assert noofclusters < len(lowercase_ )
# Find out the dimensionality
lowercase = len(vectors[0] )
# Will help select random centroids from among the available vectors
lowercase = list(range(len(lowercase_ ) ) )
shuffle(lowercase_ )
# GRAPH OF COMPUTATION
# We initialize a new graph and set it as the default during each run
# of this algorithm. This ensures that as this function is called
# multiple times, the default graph doesn't keep getting crowded with
# unused ops and Variables from previous function calls.
lowercase = tf.Graph()
with graph.as_default():
# SESSION OF COMPUTATION
lowercase = tf.Session()
##CONSTRUCTING THE ELEMENTS OF COMPUTATION
##First lets ensure we have a Variable vector for each centroid,
##initialized to one of the vectors from the available data points
lowercase = [
tf.Variable(vectors[vector_indices[i]] ) for i in range(lowercase_ )
]
##These nodes will assign the centroid Variables the appropriate
##values
lowercase = tf.placeholder("""float64""" , [dim] )
lowercase = []
for centroid in centroids:
cent_assigns.append(tf.assign(lowercase_ , lowercase_ ) )
##Variables for cluster assignments of individual vectors(initialized
##to 0 at first)
lowercase = [tf.Variable(0 ) for i in range(len(lowercase_ ) )]
##These nodes will assign an assignment Variable the appropriate
##value
lowercase = tf.placeholder("""int32""" )
lowercase = []
for assignment in assignments:
cluster_assigns.append(tf.assign(lowercase_ , lowercase_ ) )
##Now lets construct the node that will compute the mean
# The placeholder for the input
lowercase = tf.placeholder("""float""" , [None, dim] )
# The Node/op takes the input and computes a mean along the 0th
# dimension, i.e. the list of input vectors
lowercase = tf.reduce_mean(lowercase_ , 0 )
##Node for computing Euclidean distances
# Placeholders for input
lowercase = tf.placeholder("""float""" , [dim] )
lowercase = tf.placeholder("""float""" , [dim] )
lowercase = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(lowercase_ , lowercase_ ) , 2 ) ) )
##This node will figure out which cluster to assign a vector to,
##based on Euclidean distances of the vector from the centroids.
# Placeholder for input
lowercase = tf.placeholder("""float""" , [noofclusters] )
lowercase = tf.argmin(lowercase_ , 0 )
##INITIALIZING STATE VARIABLES
##This will help initialization of all Variables defined with respect
##to the graph. The Variable-initializer should be defined after
##all the Variables have been constructed, so that each of them
##will be included in the initialization.
lowercase = tf.initialize_all_variables()
# Initialize all variables
sess.run(lowercase_ )
##CLUSTERING ITERATIONS
# Now perform the Expectation-Maximization steps of K-Means clustering
# iterations. To keep things simple, we will only do a set number of
# iterations, instead of using a Stopping Criterion.
lowercase = 100
for _ in range(lowercase_ ):
##EXPECTATION STEP
##Based on the centroid locations till last iteration, compute
##the _expected_ centroid assignments.
# Iterate over each vector
for vector_n in range(len(lowercase_ ) ):
lowercase = vectors[vector_n]
# Compute Euclidean distance between this vector and each
# centroid. Remember that this list cannot be named
#'centroid_distances', since that is the input to the
# cluster assignment node.
lowercase = [
sess.run(lowercase_ , feed_dict={va: vect, va: sess.run(lowercase_ )} )
for centroid in centroids
]
# Now use the cluster assignment node, with the distances
# as the input
lowercase = sess.run(
lowercase_ , feed_dict={centroid_distances: distances} )
# Now assign the value to the appropriate state variable
sess.run(
cluster_assigns[vector_n] , feed_dict={assignment_value: assignment} )
##MAXIMIZATION STEP
# Based on the expected state computed from the Expectation Step,
# compute the locations of the centroids so as to maximize the
# overall objective of minimizing within-cluster Sum-of-Squares
for cluster_n in range(lowercase_ ):
# Collect all the vectors assigned to this cluster
lowercase = [
vectors[i]
for i in range(len(lowercase_ ) )
if sess.run(assignments[i] ) == cluster_n
]
# Compute new centroid location
lowercase = sess.run(
lowercase_ , feed_dict={mean_input: array(lowercase_ )} )
# Assign value to appropriate variable
sess.run(
cent_assigns[cluster_n] , feed_dict={centroid_value: new_location} )
# Return centroids and assignments
lowercase = sess.run(lowercase_ )
lowercase = sess.run(lowercase_ )
return centroids, assignments
| 653 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowercase_ : str , lowercase_ : str ):
def get_matched_characters(lowercase_ : str , lowercase_ : str ) -> str:
lowercase = []
lowercase = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
lowercase = int(max(0 , i - limit ) )
lowercase = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(lowercase_ )
lowercase = F"""{_stra[0:_stra.index(lowercase_ )]} {_stra[_stra.index(lowercase_ ) + 1:]}"""
return "".join(lowercase_ )
# matching characters
lowercase = get_matched_characters(lowercase_ , lowercase_ )
lowercase = get_matched_characters(lowercase_ , lowercase_ )
lowercase = len(lowercase_ )
# transposition
lowercase = (
len([(ca, ca) for ca, ca in zip(lowercase_ , lowercase_ ) if ca != ca] ) // 2
)
if not match_count:
lowercase = 0.0
else:
lowercase = (
1
/ 3
* (
match_count / len(lowercase_ )
+ match_count / len(lowercase_ )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
lowercase = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler('''hello''', '''world'''))
| 653 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : int , lowercase_ : list[list[int]] ):
def update_area_of_max_square(lowercase_ : int , lowercase_ : int ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
lowercase = update_area_of_max_square(lowercase_ , col + 1 )
lowercase = update_area_of_max_square(row + 1 , col + 1 )
lowercase = update_area_of_max_square(row + 1 , lowercase_ )
if mat[row][col]:
lowercase = 1 + min([right, diagonal, down] )
lowercase = max(largest_square_area[0] , lowercase_ )
return sub_problem_sol
else:
return 0
lowercase = [0]
update_area_of_max_square(0 , 0 )
return largest_square_area[0]
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : int , lowercase_ : list[list[int]] ):
def update_area_of_max_square_using_dp_array(
lowercase_ : int , lowercase_ : int , lowercase_ : list[list[int]] ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
lowercase = update_area_of_max_square_using_dp_array(lowercase_ , col + 1 , lowercase_ )
lowercase = update_area_of_max_square_using_dp_array(row + 1 , col + 1 , lowercase_ )
lowercase = update_area_of_max_square_using_dp_array(row + 1 , lowercase_ , lowercase_ )
if mat[row][col]:
lowercase = 1 + min([right, diagonal, down] )
lowercase = max(largest_square_area[0] , lowercase_ )
lowercase = sub_problem_sol
return sub_problem_sol
else:
return 0
lowercase = [0]
lowercase = [[-1] * cols for _ in range(lowercase_ )]
update_area_of_max_square_using_dp_array(0 , 0 , lowercase_ )
return largest_square_area[0]
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : int , lowercase_ : list[list[int]] ):
lowercase = [[0] * (cols + 1) for _ in range(rows + 1 )]
lowercase = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
lowercase = dp_array[row][col + 1]
lowercase = dp_array[row + 1][col + 1]
lowercase = dp_array[row + 1][col]
if mat[row][col] == 1:
lowercase = 1 + min(lowercase_ , lowercase_ , lowercase_ )
lowercase = max(dp_array[row][col] , lowercase_ )
else:
lowercase = 0
return largest_square_area
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : int , lowercase_ : list[list[int]] ):
lowercase = [0] * (cols + 1)
lowercase = [0] * (cols + 1)
lowercase = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
lowercase = current_row[col + 1]
lowercase = next_row[col + 1]
lowercase = next_row[col]
if mat[row][col] == 1:
lowercase = 1 + min(lowercase_ , lowercase_ , lowercase_ )
lowercase = max(current_row[col] , lowercase_ )
else:
lowercase = 0
lowercase = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 653 | 1 |
'''simple docstring'''
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def SCREAMING_SNAKE_CASE ( lowercase_ : List[str] ): # picklable for multiprocessing
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def SCREAMING_SNAKE_CASE ( ):
with parallel_backend("""spark""" ):
assert ParallelBackendConfig.backend_name == "spark"
lowercase = [1, 2, 3]
with pytest.raises(lowercase_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(lowercase_ , lowercase_ , num_proc=2 )
with pytest.raises(lowercase_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(lowercase_ , lowercase_ , num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc""" , [2, -1] )
def SCREAMING_SNAKE_CASE ( lowercase_ : Tuple ):
lowercase = [1, 2]
lowercase = {"""a""": 1, """b""": 2}
lowercase = {"""a""": [1, 2], """b""": [3, 4]}
lowercase = {"""a""": {"""1""": 1}, """b""": 2}
lowercase = {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
lowercase = [2, 3]
lowercase = {"""a""": 2, """b""": 3}
lowercase = {"""a""": [2, 3], """b""": [4, 5]}
lowercase = {"""a""": {"""1""": 2}, """b""": 3}
lowercase = {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
with parallel_backend("""spark""" ):
assert map_nested(lowercase_ , lowercase_ , num_proc=lowercase_ ) == expected_map_nested_sa
assert map_nested(lowercase_ , lowercase_ , num_proc=lowercase_ ) == expected_map_nested_sa
assert map_nested(lowercase_ , lowercase_ , num_proc=lowercase_ ) == expected_map_nested_sa
assert map_nested(lowercase_ , lowercase_ , num_proc=lowercase_ ) == expected_map_nested_sa
assert map_nested(lowercase_ , lowercase_ , num_proc=lowercase_ ) == expected_map_nested_sa
| 653 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : Optional[Any] = logging.get_logger(__name__)
lowercase_ : int = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}
class __UpperCamelCase (_UpperCAmelCase ):
__A = '''gpt_bigcode'''
__A = ['''past_key_values''']
__A = {
'''hidden_size''': '''n_embd''',
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self , _lowerCAmelCase=5_0257 , _lowerCAmelCase=1024 , _lowerCAmelCase=768 , _lowerCAmelCase=12 , _lowerCAmelCase=12 , _lowerCAmelCase=None , _lowerCAmelCase="gelu_pytorch_tanh" , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=1E-5 , _lowerCAmelCase=0.02 , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=5_0256 , _lowerCAmelCase=5_0256 , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=True , **_lowerCAmelCase , ) -> Optional[int]:
'''simple docstring'''
lowercase = vocab_size
lowercase = n_positions
lowercase = n_embd
lowercase = n_layer
lowercase = n_head
lowercase = n_inner
lowercase = activation_function
lowercase = resid_pdrop
lowercase = embd_pdrop
lowercase = attn_pdrop
lowercase = layer_norm_epsilon
lowercase = initializer_range
lowercase = scale_attn_weights
lowercase = use_cache
lowercase = attention_softmax_in_fpaa
lowercase = scale_attention_softmax_in_fpaa
lowercase = multi_query
lowercase = bos_token_id
lowercase = eos_token_id
super().__init__(bos_token_id=_lowerCAmelCase , eos_token_id=_lowerCAmelCase , **_lowerCAmelCase )
| 653 | 1 |
'''simple docstring'''
import itertools
import string
from collections.abc import Generator, Iterable
def SCREAMING_SNAKE_CASE ( lowercase_ : Iterable[str] , lowercase_ : int ):
lowercase = iter(lowercase_ )
while True:
lowercase = tuple(itertools.islice(lowercase_ , lowercase_ ) )
if not chunk:
return
yield chunk
def SCREAMING_SNAKE_CASE ( lowercase_ : str ):
lowercase = """""".join([c.upper() for c in dirty if c in string.ascii_letters] )
lowercase = """"""
if len(lowercase_ ) < 2:
return dirty
for i in range(len(lowercase_ ) - 1 ):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(lowercase_ ) & 1:
clean += "X"
return clean
def SCREAMING_SNAKE_CASE ( lowercase_ : str ):
# I and J are used interchangeably to allow
# us to use a 5x5 table (25 letters)
lowercase = """ABCDEFGHIKLMNOPQRSTUVWXYZ"""
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
lowercase = []
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(lowercase_ )
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(lowercase_ )
return table
def SCREAMING_SNAKE_CASE ( lowercase_ : str , lowercase_ : str ):
lowercase = generate_table(lowercase_ )
lowercase = prepare_input(lowercase_ )
lowercase = """"""
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(lowercase_ , 2 ):
lowercase , lowercase = divmod(table.index(lowercase_ ) , 5 )
lowercase , lowercase = divmod(table.index(lowercase_ ) , 5 )
if rowa == rowa:
ciphertext += table[rowa * 5 + (cola + 1) % 5]
ciphertext += table[rowa * 5 + (cola + 1) % 5]
elif cola == cola:
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
else: # rectangle
ciphertext += table[rowa * 5 + cola]
ciphertext += table[rowa * 5 + cola]
return ciphertext
def SCREAMING_SNAKE_CASE ( lowercase_ : str , lowercase_ : str ):
lowercase = generate_table(lowercase_ )
lowercase = """"""
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(lowercase_ , 2 ):
lowercase , lowercase = divmod(table.index(lowercase_ ) , 5 )
lowercase , lowercase = divmod(table.index(lowercase_ ) , 5 )
if rowa == rowa:
plaintext += table[rowa * 5 + (cola - 1) % 5]
plaintext += table[rowa * 5 + (cola - 1) % 5]
elif cola == cola:
plaintext += table[((rowa - 1) % 5) * 5 + cola]
plaintext += table[((rowa - 1) % 5) * 5 + cola]
else: # rectangle
plaintext += table[rowa * 5 + cola]
plaintext += table[rowa * 5 + cola]
return plaintext
| 653 |
'''simple docstring'''
import requests
def SCREAMING_SNAKE_CASE ( lowercase_ : str , lowercase_ : str ):
lowercase = {"""Content-Type""": """application/json"""}
lowercase = requests.post(lowercase_ , json={"""text""": message_body} , headers=lowercase_ )
if response.status_code != 200:
lowercase = (
"""Request to slack returned an error """
F"""{response.status_code}, the response is:\n{response.text}"""
)
raise ValueError(lowercase_ )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message('''<YOUR MESSAGE BODY>''', '''<SLACK CHANNEL URL>''')
| 653 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase_ : Dict = {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''ViTOnnxConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : Dict = ['''ViTFeatureExtractor''']
lowercase_ : Optional[int] = ['''ViTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : str = [
'''VIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ViTForImageClassification''',
'''ViTForMaskedImageModeling''',
'''ViTModel''',
'''ViTPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : Dict = [
'''TFViTForImageClassification''',
'''TFViTModel''',
'''TFViTPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : Any = [
'''FlaxViTForImageClassification''',
'''FlaxViTModel''',
'''FlaxViTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_vit import ViTFeatureExtractor
from .image_processing_vit import ViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit import (
VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTForImageClassification,
ViTForMaskedImageModeling,
ViTModel,
ViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel
else:
import sys
lowercase_ : int = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 653 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
lowercase_ : List[str] = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE ( lowercase_ : int ):
lowercase = MobileViTConfig()
# size of the architecture
if "mobilevit_s" in mobilevit_name:
lowercase = [144, 192, 240]
lowercase = [16, 32, 64, 96, 128, 160, 640]
elif "mobilevit_xs" in mobilevit_name:
lowercase = [96, 120, 144]
lowercase = [16, 32, 48, 64, 80, 96, 384]
elif "mobilevit_xxs" in mobilevit_name:
lowercase = [64, 80, 96]
lowercase = [16, 16, 24, 48, 64, 80, 320]
lowercase = 0.05
lowercase = 2.0
if mobilevit_name.startswith("""deeplabv3_""" ):
lowercase = 512
lowercase = 16
lowercase = 21
lowercase = """pascal-voc-id2label.json"""
else:
lowercase = 1000
lowercase = """imagenet-1k-id2label.json"""
lowercase = """huggingface/label-files"""
lowercase = json.load(open(hf_hub_download(lowercase_ , lowercase_ , repo_type="""dataset""" ) , """r""" ) )
lowercase = {int(lowercase_ ): v for k, v in idalabel.items()}
lowercase = idalabel
lowercase = {v: k for k, v in idalabel.items()}
return config
def SCREAMING_SNAKE_CASE ( lowercase_ : Any , lowercase_ : Any=False ):
for i in range(1 , 6 ):
if F"""layer_{i}.""" in name:
lowercase = name.replace(F"""layer_{i}.""" , F"""encoder.layer.{i - 1}.""" )
if "conv_1." in name:
lowercase = name.replace("""conv_1.""" , """conv_stem.""" )
if ".block." in name:
lowercase = name.replace(""".block.""" , """.""" )
if "exp_1x1" in name:
lowercase = name.replace("""exp_1x1""" , """expand_1x1""" )
if "red_1x1" in name:
lowercase = name.replace("""red_1x1""" , """reduce_1x1""" )
if ".local_rep.conv_3x3." in name:
lowercase = name.replace(""".local_rep.conv_3x3.""" , """.conv_kxk.""" )
if ".local_rep.conv_1x1." in name:
lowercase = name.replace(""".local_rep.conv_1x1.""" , """.conv_1x1.""" )
if ".norm." in name:
lowercase = name.replace(""".norm.""" , """.normalization.""" )
if ".conv." in name:
lowercase = name.replace(""".conv.""" , """.convolution.""" )
if ".conv_proj." in name:
lowercase = name.replace(""".conv_proj.""" , """.conv_projection.""" )
for i in range(0 , 2 ):
for j in range(0 , 4 ):
if F""".{i}.{j}.""" in name:
lowercase = name.replace(F""".{i}.{j}.""" , F""".{i}.layer.{j}.""" )
for i in range(2 , 6 ):
for j in range(0 , 4 ):
if F""".{i}.{j}.""" in name:
lowercase = name.replace(F""".{i}.{j}.""" , F""".{i}.""" )
if "expand_1x1" in name:
lowercase = name.replace("""expand_1x1""" , """downsampling_layer.expand_1x1""" )
if "conv_3x3" in name:
lowercase = name.replace("""conv_3x3""" , """downsampling_layer.conv_3x3""" )
if "reduce_1x1" in name:
lowercase = name.replace("""reduce_1x1""" , """downsampling_layer.reduce_1x1""" )
for i in range(2 , 5 ):
if F""".global_rep.{i}.weight""" in name:
lowercase = name.replace(F""".global_rep.{i}.weight""" , """.layernorm.weight""" )
if F""".global_rep.{i}.bias""" in name:
lowercase = name.replace(F""".global_rep.{i}.bias""" , """.layernorm.bias""" )
if ".global_rep." in name:
lowercase = name.replace(""".global_rep.""" , """.transformer.""" )
if ".pre_norm_mha.0." in name:
lowercase = name.replace(""".pre_norm_mha.0.""" , """.layernorm_before.""" )
if ".pre_norm_mha.1.out_proj." in name:
lowercase = name.replace(""".pre_norm_mha.1.out_proj.""" , """.attention.output.dense.""" )
if ".pre_norm_ffn.0." in name:
lowercase = name.replace(""".pre_norm_ffn.0.""" , """.layernorm_after.""" )
if ".pre_norm_ffn.1." in name:
lowercase = name.replace(""".pre_norm_ffn.1.""" , """.intermediate.dense.""" )
if ".pre_norm_ffn.4." in name:
lowercase = name.replace(""".pre_norm_ffn.4.""" , """.output.dense.""" )
if ".transformer." in name:
lowercase = name.replace(""".transformer.""" , """.transformer.layer.""" )
if ".aspp_layer." in name:
lowercase = name.replace(""".aspp_layer.""" , """.""" )
if ".aspp_pool." in name:
lowercase = name.replace(""".aspp_pool.""" , """.""" )
if "seg_head." in name:
lowercase = name.replace("""seg_head.""" , """segmentation_head.""" )
if "segmentation_head.classifier.classifier." in name:
lowercase = name.replace("""segmentation_head.classifier.classifier.""" , """segmentation_head.classifier.""" )
if "classifier.fc." in name:
lowercase = name.replace("""classifier.fc.""" , """classifier.""" )
elif (not base_model) and ("segmentation_head." not in name):
lowercase = """mobilevit.""" + name
return name
def SCREAMING_SNAKE_CASE ( lowercase_ : Optional[Any] , lowercase_ : List[Any] , lowercase_ : str=False ):
if base_model:
lowercase = """"""
else:
lowercase = """mobilevit."""
for key in orig_state_dict.copy().keys():
lowercase = orig_state_dict.pop(lowercase_ )
if key[:8] == "encoder.":
lowercase = key[8:]
if "qkv" in key:
lowercase = key.split(""".""" )
lowercase = int(key_split[0][6:] ) - 1
lowercase = int(key_split[3] )
lowercase = model.get_submodule(F"""{model_prefix}encoder.layer.{layer_num}""" )
lowercase = layer.transformer.layer[transformer_num].attention.attention.all_head_size
lowercase = (
F"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
)
if "weight" in key:
lowercase = val[:dim, :]
lowercase = val[dim : dim * 2, :]
lowercase = val[-dim:, :]
else:
lowercase = val[:dim]
lowercase = val[dim : dim * 2]
lowercase = val[-dim:]
else:
lowercase = val
return orig_state_dict
def SCREAMING_SNAKE_CASE ( ):
lowercase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowercase = Image.open(requests.get(lowercase_ , stream=lowercase_ ).raw )
return im
@torch.no_grad()
def SCREAMING_SNAKE_CASE ( lowercase_ : Dict , lowercase_ : List[Any] , lowercase_ : Any , lowercase_ : List[str]=False ):
lowercase = get_mobilevit_config(lowercase_ )
# load original state_dict
lowercase = torch.load(lowercase_ , map_location="""cpu""" )
# load 🤗 model
if mobilevit_name.startswith("""deeplabv3_""" ):
lowercase = MobileViTForSemanticSegmentation(lowercase_ ).eval()
else:
lowercase = MobileViTForImageClassification(lowercase_ ).eval()
lowercase = convert_state_dict(lowercase_ , lowercase_ )
model.load_state_dict(lowercase_ )
# Check outputs on an image, prepared by MobileViTImageProcessor
lowercase = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
lowercase = image_processor(images=prepare_img() , return_tensors="""pt""" )
lowercase = model(**lowercase_ )
lowercase = outputs.logits
if mobilevit_name.startswith("""deeplabv3_""" ):
assert logits.shape == (1, 21, 32, 32)
if mobilevit_name == "deeplabv3_mobilevit_s":
lowercase = torch.tensor(
[
[[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
[[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
[[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xs":
lowercase = torch.tensor(
[
[[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
[[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
[[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xxs":
lowercase = torch.tensor(
[
[[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
[[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
[[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
] )
else:
raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )
assert torch.allclose(logits[0, :3, :3, :3] , lowercase_ , atol=1E-4 )
else:
assert logits.shape == (1, 1000)
if mobilevit_name == "mobilevit_s":
lowercase = torch.tensor([-0.9_866, 0.2_392, -1.1_241] )
elif mobilevit_name == "mobilevit_xs":
lowercase = torch.tensor([-2.4_761, -0.9_399, -1.9_587] )
elif mobilevit_name == "mobilevit_xxs":
lowercase = torch.tensor([-1.9_364, -1.2_327, -0.4_653] )
else:
raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )
assert torch.allclose(logits[0, :3] , lowercase_ , atol=1E-4 )
Path(lowercase_ ).mkdir(exist_ok=lowercase_ )
print(F"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(lowercase_ )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(lowercase_ )
if push_to_hub:
lowercase = {
"""mobilevit_s""": """mobilevit-small""",
"""mobilevit_xs""": """mobilevit-x-small""",
"""mobilevit_xxs""": """mobilevit-xx-small""",
"""deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
"""deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
"""deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
}
print("""Pushing to the hub...""" )
lowercase = model_mapping[mobilevit_name]
image_processor.push_to_hub(lowercase_ , organization="""apple""" )
model.push_to_hub(lowercase_ , organization="""apple""" )
if __name__ == "__main__":
lowercase_ : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--mobilevit_name''',
default='''mobilevit_s''',
type=str,
help=(
'''Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','''
''' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'''
),
)
parser.add_argument(
'''--checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
lowercase_ : List[str] = parser.parse_args()
convert_movilevit_checkpoint(
args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 653 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowercase_ : str , lowercase_ : str ):
lowercase = len(lowercase_ ) + 1
lowercase = len(lowercase_ ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
lowercase = [[0 for i in range(lowercase_ )] for j in range(lowercase_ )]
# since string of zero length match pattern of zero length
lowercase = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , lowercase_ ):
lowercase = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , lowercase_ ):
lowercase = dp[0][j - 2] if pattern[j - 1] == """*""" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , lowercase_ ):
for j in range(1 , lowercase_ ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
lowercase = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
lowercase = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
lowercase = dp[i - 1][j]
else:
lowercase = 0
else:
lowercase = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
lowercase_ : Any = '''aab'''
lowercase_ : Union[str, Any] = '''c*a*b'''
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(f'''{input_string} matches the given pattern {pattern}''')
else:
print(f'''{input_string} does not match with the given pattern {pattern}''')
| 653 |
'''simple docstring'''
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
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 SwiftFormerForImageClassification, SwiftFormerModel
from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __UpperCamelCase :
def __init__( self , _lowerCAmelCase , _lowerCAmelCase=13 , _lowerCAmelCase=3 , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=224 , _lowerCAmelCase=1000 , _lowerCAmelCase=[3, 3, 6, 4] , _lowerCAmelCase=[48, 56, 112, 220] , ) -> List[str]:
'''simple docstring'''
lowercase = parent
lowercase = batch_size
lowercase = num_channels
lowercase = is_training
lowercase = use_labels
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = num_labels
lowercase = image_size
lowercase = layer_depths
lowercase = embed_dims
def _a ( self ) -> Tuple:
'''simple docstring'''
lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase = None
if self.use_labels:
lowercase = ids_tensor([self.batch_size] , self.num_labels )
lowercase = self.get_config()
return config, pixel_values, labels
def _a ( self ) -> int:
'''simple docstring'''
return SwiftFormerConfig(
depths=self.layer_depths , embed_dims=self.embed_dims , mlp_ratio=4 , downsamples=[True, True, True, True] , hidden_act="""gelu""" , num_labels=self.num_labels , down_patch_size=3 , down_stride=2 , down_pad=1 , drop_rate=0.0 , drop_path_rate=0.0 , use_layer_scale=_lowerCAmelCase , layer_scale_init_value=1E-5 , )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowercase = SwiftFormerModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
lowercase = model(_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dims[-1], 7, 7) )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowercase = self.num_labels
lowercase = SwiftFormerForImageClassification(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
lowercase = model(_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
lowercase = SwiftFormerForImageClassification(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase = model(_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
((lowercase) , (lowercase) , (lowercase)) = self.prepare_config_and_inputs()
lowercase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __UpperCamelCase (_UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
__A = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
__A = (
{'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
if is_torch_available()
else {}
)
__A = False
__A = False
__A = False
__A = False
__A = False
def _a ( self ) -> Dict:
'''simple docstring'''
lowercase = SwiftFormerModelTester(self )
lowercase = ConfigTester(
self , config_class=_lowerCAmelCase , has_text_modality=_lowerCAmelCase , hidden_size=37 , num_attention_heads=12 , num_hidden_layers=12 , )
def _a ( self ) -> List[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="""SwiftFormer does not use inputs_embeds""" )
def _a ( self ) -> List[str]:
'''simple docstring'''
pass
def _a ( self ) -> Dict:
'''simple docstring'''
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase = model_class(_lowerCAmelCase )
lowercase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowerCAmelCase , nn.Linear ) )
def _a ( self ) -> int:
'''simple docstring'''
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase = model_class(_lowerCAmelCase )
lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase = [*signature.parameters.keys()]
lowercase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
def _a ( self ) -> List[str]:
'''simple docstring'''
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase )
@slow
def _a ( self ) -> Any:
'''simple docstring'''
for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase = SwiftFormerModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
@unittest.skip(reason="""SwiftFormer does not output attentions""" )
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
pass
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
def check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
with torch.no_grad():
lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
lowercase = outputs.hidden_states
lowercase = 8
self.assertEqual(len(_lowerCAmelCase ) , _lowerCAmelCase ) # TODO
# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
# with the width and height being successively divided by 2, after every 2 blocks
for i in range(len(_lowerCAmelCase ) ):
self.assertEqual(
hidden_states[i].shape , torch.Size(
[
self.model_tester.batch_size,
self.model_tester.embed_dims[i // 2],
(self.model_tester.image_size // 4) // 2 ** (i // 2),
(self.model_tester.image_size // 4) // 2 ** (i // 2),
] ) , )
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def _a ( self ) -> Dict:
'''simple docstring'''
def _config_zero_init(_lowerCAmelCase ):
lowercase = copy.deepcopy(_lowerCAmelCase )
for key in configs_no_init.__dict__.keys():
if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
setattr(_lowerCAmelCase , _lowerCAmelCase , 1E-10 )
if isinstance(getattr(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) , _lowerCAmelCase ):
lowercase = _config_zero_init(getattr(_lowerCAmelCase , _lowerCAmelCase ) )
setattr(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return configs_no_init
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = _config_zero_init(_lowerCAmelCase )
for model_class in self.all_model_classes:
lowercase = model_class(config=_lowerCAmelCase )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9) / 1E9).round().item() , [0.0, 1.0] , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def _a ( self ) -> Any:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE ( ):
lowercase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class __UpperCamelCase (unittest.TestCase ):
@cached_property
def _a ( self ) -> List[str]:
'''simple docstring'''
return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs""" ) if is_vision_available() else None
@slow
def _a ( self ) -> List[Any]:
'''simple docstring'''
lowercase = SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs""" ).to(_lowerCAmelCase )
lowercase = self.default_image_processor
lowercase = prepare_img()
lowercase = image_processor(images=_lowerCAmelCase , return_tensors="""pt""" ).to(_lowerCAmelCase )
# forward pass
with torch.no_grad():
lowercase = model(**_lowerCAmelCase )
# verify the logits
lowercase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
lowercase = torch.tensor([[-2.17_03E00, 2.11_07E00, -2.08_11E00]] ).to(_lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowerCAmelCase , atol=1E-4 ) )
| 653 | 1 |
'''simple docstring'''
import qiskit
def SCREAMING_SNAKE_CASE ( lowercase_ : int = 2 ):
lowercase = qubits
# Using Aer's simulator
lowercase = qiskit.Aer.get_backend("""aer_simulator""" )
# Creating a Quantum Circuit acting on the q register
lowercase = qiskit.QuantumCircuit(lowercase_ , lowercase_ )
# Adding a H gate on qubit 0 (now q0 in superposition)
circuit.h(0 )
for i in range(1 , lowercase_ ):
# Adding CX (CNOT) gate
circuit.cx(i - 1 , lowercase_ )
# Mapping the quantum measurement to the classical bits
circuit.measure(list(range(lowercase_ ) ) , list(range(lowercase_ ) ) )
# Now measuring any one qubit would affect other qubits to collapse
# their super position and have same state as the measured one.
# Executing the circuit on the simulator
lowercase = qiskit.execute(lowercase_ , lowercase_ , shots=1000 )
return job.result().get_counts(lowercase_ )
if __name__ == "__main__":
print(f'''Total count for various states are: {quantum_entanglement(3)}''')
| 653 |
'''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def SCREAMING_SNAKE_CASE ( ):
lowercase = HfArgumentParser(lowercase_ )
lowercase = parser.parse_args_into_dataclasses()[0]
lowercase = TensorFlowBenchmark(args=lowercase_ )
try:
lowercase = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
lowercase = """Arg --no_{0} is no longer used, please use --no-{0} instead."""
lowercase = """ """.join(str(lowercase_ ).split(""" """ )[:-1] )
lowercase = """"""
lowercase = eval(str(lowercase_ ).split(""" """ )[-1] )
lowercase = []
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(lowercase_ )
if len(lowercase_ ) > 0:
lowercase = full_error_msg + begin_error_msg + str(lowercase_ )
raise ValueError(lowercase_ )
benchmark.run()
if __name__ == "__main__":
main()
| 653 | 1 |
'''simple docstring'''
import math
def SCREAMING_SNAKE_CASE ( lowercase_ : int ):
lowercase = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(lowercase_ )
def SCREAMING_SNAKE_CASE ( lowercase_ : float = 1 / 1_2345 ):
lowercase = 0
lowercase = 0
lowercase = 3
while True:
lowercase = (integer**2 - 1) / 4
# if candidate is an integer, then there is a partition for k
if partition_candidate == int(lowercase_ ):
lowercase = int(lowercase_ )
total_partitions += 1
if check_partition_perfect(lowercase_ ):
perfect_partitions += 1
if perfect_partitions > 0:
if perfect_partitions / total_partitions < max_proportion:
return int(lowercase_ )
integer += 1
if __name__ == "__main__":
print(f'''{solution() = }''')
| 653 |
'''simple docstring'''
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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.
# this script dumps information about the environment
import os
import platform
import sys
lowercase_ : List[str] = '''3'''
print('''Python version:''', sys.version)
print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())
try:
import torch
print('''Torch version:''', torch.__version__)
print('''Cuda available:''', torch.cuda.is_available())
print('''Cuda version:''', torch.version.cuda)
print('''CuDNN version:''', torch.backends.cudnn.version())
print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
print('''Torch version:''', None)
try:
import transformers
print('''transformers version:''', transformers.__version__)
except ImportError:
print('''transformers version:''', None)
| 653 | 1 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class __UpperCamelCase :
@staticmethod
def _a ( *_lowerCAmelCase , **_lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE ( lowercase_ : List[Any] ):
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
lowercase_ : str = (
'''https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'''
)
@is_pipeline_test
@require_torch
@require_vision
class __UpperCamelCase (unittest.TestCase ):
__A = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Dict:
'''simple docstring'''
lowercase = pipeline(
"""document-question-answering""" , model=_lowerCAmelCase , tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
lowercase = INVOICE_URL
lowercase = list(zip(*apply_tesseract(load_image(_lowerCAmelCase ) , _lowerCAmelCase , """""" ) ) )
lowercase = """What is the placebo?"""
lowercase = [
{
"""image""": load_image(_lowerCAmelCase ),
"""question""": question,
},
{
"""image""": image,
"""question""": question,
},
{
"""image""": image,
"""question""": question,
"""word_boxes""": word_boxes,
},
]
return dqa_pipeline, examples
def _a ( self , _lowerCAmelCase , _lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
lowercase = dqa_pipeline(_lowerCAmelCase , top_k=2 )
self.assertEqual(
_lowerCAmelCase , [
[
{"""score""": ANY(_lowerCAmelCase ), """answer""": ANY(_lowerCAmelCase ), """start""": ANY(_lowerCAmelCase ), """end""": ANY(_lowerCAmelCase )},
{"""score""": ANY(_lowerCAmelCase ), """answer""": ANY(_lowerCAmelCase ), """start""": ANY(_lowerCAmelCase ), """end""": ANY(_lowerCAmelCase )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def _a ( self ) -> str:
'''simple docstring'''
lowercase = pipeline("""document-question-answering""" , model="""hf-internal-testing/tiny-random-layoutlmv2""" )
lowercase = INVOICE_URL
lowercase = """How many cats are there?"""
lowercase = [
{"""score""": 0.0001, """answer""": """oy 2312/2019""", """start""": 38, """end""": 39},
{"""score""": 0.0001, """answer""": """oy 2312/2019 DUE""", """start""": 38, """end""": 40},
]
lowercase = dqa_pipeline(image=_lowerCAmelCase , question=_lowerCAmelCase , top_k=2 )
self.assertEqual(nested_simplify(_lowerCAmelCase , decimals=4 ) , _lowerCAmelCase )
lowercase = dqa_pipeline({"""image""": image, """question""": question} , top_k=2 )
self.assertEqual(nested_simplify(_lowerCAmelCase , decimals=4 ) , _lowerCAmelCase )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
lowercase = """./tests/fixtures/tests_samples/COCO/000000039769.png"""
lowercase = dqa_pipeline(image=_lowerCAmelCase , question=_lowerCAmelCase , top_k=2 )
self.assertEqual(_lowerCAmelCase , [] )
# We can optionnally pass directly the words and bounding boxes
lowercase = """./tests/fixtures/tests_samples/COCO/000000039769.png"""
lowercase = []
lowercase = []
lowercase = dqa_pipeline(image=_lowerCAmelCase , question=_lowerCAmelCase , words=_lowerCAmelCase , boxes=_lowerCAmelCase , top_k=2 )
self.assertEqual(_lowerCAmelCase , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def _a ( self ) -> str:
'''simple docstring'''
lowercase = pipeline(
"""document-question-answering""" , model="""tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa""" , revision="""9977165""" , )
lowercase = INVOICE_URL
lowercase = """What is the invoice number?"""
lowercase = dqa_pipeline(image=_lowerCAmelCase , question=_lowerCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4 ) , [
{"""score""": 0.9944, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.0009, """answer""": """us-001""", """start""": 16, """end""": 16},
] , )
lowercase = dqa_pipeline({"""image""": image, """question""": question} , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4 ) , [
{"""score""": 0.9944, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.0009, """answer""": """us-001""", """start""": 16, """end""": 16},
] , )
lowercase = dqa_pipeline(
[{"""image""": image, """question""": question}, {"""image""": image, """question""": question}] , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4 ) , [
[
{"""score""": 0.9944, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.0009, """answer""": """us-001""", """start""": 16, """end""": 16},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def _a ( self ) -> Dict:
'''simple docstring'''
lowercase = pipeline(
"""document-question-answering""" , model="""tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa""" , revision="""9977165""" , max_seq_len=50 , )
lowercase = INVOICE_URL
lowercase = """What is the invoice number?"""
lowercase = dqa_pipeline(image=_lowerCAmelCase , question=_lowerCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4 ) , [
{"""score""": 0.9974, """answer""": """1110212019""", """start""": 23, """end""": 23},
{"""score""": 0.9948, """answer""": """us-001""", """start""": 16, """end""": 16},
] , )
lowercase = dqa_pipeline({"""image""": image, """question""": question} , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4 ) , [
{"""score""": 0.9974, """answer""": """1110212019""", """start""": 23, """end""": 23},
{"""score""": 0.9948, """answer""": """us-001""", """start""": 16, """end""": 16},
] , )
lowercase = dqa_pipeline(
[{"""image""": image, """question""": question}, {"""image""": image, """question""": question}] , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4 ) , [
[
{"""score""": 0.9974, """answer""": """1110212019""", """start""": 23, """end""": 23},
{"""score""": 0.9948, """answer""": """us-001""", """start""": 16, """end""": 16},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase = AutoTokenizer.from_pretrained(
"""impira/layoutlm-document-qa""" , revision="""3dc6de3""" , add_prefix_space=_lowerCAmelCase )
lowercase = pipeline(
"""document-question-answering""" , model="""impira/layoutlm-document-qa""" , tokenizer=_lowerCAmelCase , revision="""3dc6de3""" , )
lowercase = INVOICE_URL
lowercase = """What is the invoice number?"""
lowercase = dqa_pipeline(image=_lowerCAmelCase , question=_lowerCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4 ) , [
{"""score""": 0.4251, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.0819, """answer""": """1110212019""", """start""": 23, """end""": 23},
] , )
lowercase = dqa_pipeline({"""image""": image, """question""": question} , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4 ) , [
{"""score""": 0.4251, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.0819, """answer""": """1110212019""", """start""": 23, """end""": 23},
] , )
lowercase = dqa_pipeline(
[{"""image""": image, """question""": question}, {"""image""": image, """question""": question}] , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4 ) , [
[
{"""score""": 0.4251, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.0819, """answer""": """1110212019""", """start""": 23, """end""": 23},
]
]
* 2 , )
lowercase = list(zip(*apply_tesseract(load_image(_lowerCAmelCase ) , _lowerCAmelCase , """""" ) ) )
# This model should also work if `image` is set to None
lowercase = dqa_pipeline({"""image""": None, """word_boxes""": word_boxes, """question""": question} , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4 ) , [
{"""score""": 0.4251, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.0819, """answer""": """1110212019""", """start""": 23, """end""": 23},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def _a ( self ) -> Any:
'''simple docstring'''
lowercase = AutoTokenizer.from_pretrained(
"""impira/layoutlm-document-qa""" , revision="""3dc6de3""" , add_prefix_space=_lowerCAmelCase )
lowercase = pipeline(
"""document-question-answering""" , model="""impira/layoutlm-document-qa""" , tokenizer=_lowerCAmelCase , revision="""3dc6de3""" , max_seq_len=50 , )
lowercase = INVOICE_URL
lowercase = """What is the invoice number?"""
lowercase = dqa_pipeline(image=_lowerCAmelCase , question=_lowerCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4 ) , [
{"""score""": 0.9999, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.9998, """answer""": """us-001""", """start""": 16, """end""": 16},
] , )
lowercase = dqa_pipeline(
[{"""image""": image, """question""": question}, {"""image""": image, """question""": question}] , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4 ) , [
[
{"""score""": 0.9999, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.9998, """answer""": """us-001""", """start""": 16, """end""": 16},
]
]
* 2 , )
lowercase = list(zip(*apply_tesseract(load_image(_lowerCAmelCase ) , _lowerCAmelCase , """""" ) ) )
# This model should also work if `image` is set to None
lowercase = dqa_pipeline({"""image""": None, """word_boxes""": word_boxes, """question""": question} , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4 ) , [
{"""score""": 0.9999, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.9998, """answer""": """us-001""", """start""": 16, """end""": 16},
] , )
@slow
@require_torch
def _a ( self ) -> str:
'''simple docstring'''
lowercase = pipeline(
"""document-question-answering""" , model="""naver-clova-ix/donut-base-finetuned-docvqa""" , tokenizer=AutoTokenizer.from_pretrained("""naver-clova-ix/donut-base-finetuned-docvqa""" ) , feature_extractor="""naver-clova-ix/donut-base-finetuned-docvqa""" , )
lowercase = INVOICE_URL
lowercase = """What is the invoice number?"""
lowercase = dqa_pipeline(image=_lowerCAmelCase , question=_lowerCAmelCase , top_k=2 )
self.assertEqual(nested_simplify(_lowerCAmelCase , decimals=4 ) , [{"""answer""": """us-001"""}] )
@require_tf
@unittest.skip("""Document question answering not implemented in TF""" )
def _a ( self ) -> Dict:
'''simple docstring'''
pass
| 653 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase_ : Optional[Any] = logging.get_logger(__name__)
lowercase_ : int = {'''vocab_file''': '''spm_char.model'''}
lowercase_ : int = {
'''vocab_file''': {
'''microsoft/speecht5_asr''': '''https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model''',
'''microsoft/speecht5_tts''': '''https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model''',
'''microsoft/speecht5_vc''': '''https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model''',
}
}
lowercase_ : Optional[Any] = {
'''microsoft/speecht5_asr''': 1024,
'''microsoft/speecht5_tts''': 1024,
'''microsoft/speecht5_vc''': 1024,
}
class __UpperCamelCase (_UpperCAmelCase ):
__A = VOCAB_FILES_NAMES
__A = PRETRAINED_VOCAB_FILES_MAP
__A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A = ['''input_ids''', '''attention_mask''']
def __init__( self , _lowerCAmelCase , _lowerCAmelCase="<s>" , _lowerCAmelCase="</s>" , _lowerCAmelCase="<unk>" , _lowerCAmelCase="<pad>" , _lowerCAmelCase = None , **_lowerCAmelCase , ) -> None:
'''simple docstring'''
lowercase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_lowerCAmelCase , eos_token=_lowerCAmelCase , unk_token=_lowerCAmelCase , pad_token=_lowerCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **_lowerCAmelCase , )
lowercase = vocab_file
lowercase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowerCAmelCase )
@property
def _a ( self ) -> List[Any]:
'''simple docstring'''
return self.sp_model.get_piece_size()
def _a ( self ) -> str:
'''simple docstring'''
lowercase = {self.convert_ids_to_tokens(_lowerCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase = self.__dict__.copy()
lowercase = None
return state
def __setstate__( self , _lowerCAmelCase ) -> Optional[int]:
'''simple docstring'''
lowercase = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
lowercase = {}
lowercase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _a ( self , _lowerCAmelCase ) -> List[str]:
'''simple docstring'''
return self.sp_model.encode(_lowerCAmelCase , out_type=_lowerCAmelCase )
def _a ( self , _lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
return self.sp_model.piece_to_id(_lowerCAmelCase )
def _a ( self , _lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase = self.sp_model.IdToPiece(_lowerCAmelCase )
return token
def _a ( self , _lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowercase = []
lowercase = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(_lowerCAmelCase ) + token
lowercase = []
else:
current_sub_tokens.append(_lowerCAmelCase )
out_string += self.sp_model.decode(_lowerCAmelCase )
return out_string.strip()
def _a ( self , _lowerCAmelCase , _lowerCAmelCase=None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def _a ( self , _lowerCAmelCase , _lowerCAmelCase = None , _lowerCAmelCase = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCAmelCase , token_ids_a=_lowerCAmelCase , already_has_special_tokens=_lowerCAmelCase )
lowercase = [1]
if token_ids_a is None:
return ([0] * len(_lowerCAmelCase )) + suffix_ones
return ([0] * len(_lowerCAmelCase )) + ([0] * len(_lowerCAmelCase )) + suffix_ones
def _a ( self , _lowerCAmelCase , _lowerCAmelCase = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(_lowerCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowercase = os.path.join(
_lowerCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowerCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCAmelCase , """wb""" ) as fi:
lowercase = self.sp_model.serialized_model_proto()
fi.write(_lowerCAmelCase )
return (out_vocab_file,)
| 653 | 1 |
'''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __UpperCamelCase (_UpperCAmelCase ):
__A = DistilBertTokenizer
__A = DistilBertTokenizerFast
__A = True
@slow
def _a ( self ) -> Optional[int]:
'''simple docstring'''
lowercase = DistilBertTokenizer.from_pretrained("""distilbert-base-uncased""" )
lowercase = tokenizer.encode("""sequence builders""" , add_special_tokens=_lowerCAmelCase )
lowercase = tokenizer.encode("""multi-sequence build""" , add_special_tokens=_lowerCAmelCase )
lowercase = tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase )
lowercase = tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase , _lowerCAmelCase )
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
]
| 653 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( ):
lowercase = []
lowercase = 1
while len(lowercase_ ) < 1E6:
constant.append(str(lowercase_ ) )
i += 1
lowercase = """""".join(lowercase_ )
return (
int(constant[0] )
* int(constant[9] )
* int(constant[99] )
* int(constant[999] )
* int(constant[9999] )
* int(constant[9_9999] )
* int(constant[99_9999] )
)
if __name__ == "__main__":
print(solution())
| 653 | 1 |
'''simple docstring'''
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
lowercase_ : List[str] = logging.get_logger(__name__)
class __UpperCamelCase :
__A = None
@experimental
def SCREAMING_SNAKE_CASE ( lowercase_ : Union[str, Any] , lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : Any , lowercase_ : int , lowercase_ : List[str] , lowercase_ : List[Any] ):
if ParallelBackendConfig.backend_name is None:
return _map_with_multiprocessing_pool(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
return _map_with_joblib(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE ( lowercase_ : Any , lowercase_ : Dict , lowercase_ : Dict , lowercase_ : Tuple , lowercase_ : int , lowercase_ : int , lowercase_ : Dict ):
lowercase = num_proc if num_proc <= len(lowercase_ ) else len(lowercase_ )
lowercase = [] # We organize the splits ourselve (contiguous splits)
for index in range(lowercase_ ):
lowercase = len(lowercase_ ) // num_proc
lowercase = len(lowercase_ ) % num_proc
lowercase = div * index + min(lowercase_ , lowercase_ )
lowercase = start + div + (1 if index < mod else 0)
split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc) )
if len(lowercase_ ) != sum(len(i[1] ) for i in split_kwds ):
raise ValueError(
F"""Error dividing inputs iterable among processes. """
F"""Total number of objects {len(lowercase_ )}, """
F"""length: {sum(len(i[1] ) for i in split_kwds )}""" )
logger.info(
F"""Spawning {num_proc} processes for {len(lowercase_ )} objects in slices of {[len(i[1] ) for i in split_kwds]}""" )
lowercase , lowercase = None, None
if not disable_tqdm:
lowercase , lowercase = (RLock(),), tqdm.set_lock
with Pool(lowercase_ , initargs=lowercase_ , initializer=lowercase_ ) as pool:
lowercase = pool.map(lowercase_ , lowercase_ )
logger.info(F"""Finished {num_proc} processes""" )
lowercase = [obj for proc_res in mapped for obj in proc_res]
logger.info(F"""Unpacked {len(lowercase_ )} objects""" )
return mapped
def SCREAMING_SNAKE_CASE ( lowercase_ : List[str] , lowercase_ : str , lowercase_ : int , lowercase_ : Optional[Any] , lowercase_ : Any , lowercase_ : Dict , lowercase_ : Tuple ):
# progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
# and it requires monkey-patching joblib internal classes which is subject to change
import joblib
with joblib.parallel_backend(ParallelBackendConfig.backend_name , n_jobs=lowercase_ ):
return joblib.Parallel()(
joblib.delayed(lowercase_ )((function, obj, types, None, True, None) ) for obj in iterable )
@experimental
@contextlib.contextmanager
def SCREAMING_SNAKE_CASE ( lowercase_ : str ):
lowercase = backend_name
if backend_name == "spark":
from joblibspark import register_spark
register_spark()
# TODO: call create_cache_and_write_probe if "download" in steps
# TODO: raise NotImplementedError when Dataset.map etc is called
try:
yield
finally:
lowercase = None
| 653 |
'''simple docstring'''
import os
def SCREAMING_SNAKE_CASE ( ):
lowercase = os.path.join(os.path.dirname(lowercase_ ) , """num.txt""" )
with open(lowercase_ ) as file_hand:
return str(sum(int(lowercase_ ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 653 | 1 |
'''simple docstring'''
import os
def SCREAMING_SNAKE_CASE ( ):
lowercase = os.path.join(os.path.dirname(lowercase_ ) , """num.txt""" )
with open(lowercase_ ) as file_hand:
return str(sum(int(lowercase_ ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 653 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
@skip_mps
class __UpperCamelCase (_UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
__A = StableDiffusionPanoramaPipeline
__A = TEXT_TO_IMAGE_PARAMS
__A = TEXT_TO_IMAGE_BATCH_PARAMS
__A = TEXT_TO_IMAGE_IMAGE_PARAMS
__A = TEXT_TO_IMAGE_IMAGE_PARAMS
def _a ( self ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
lowercase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=1 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
lowercase = DDIMScheduler()
torch.manual_seed(0 )
lowercase = 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 , )
torch.manual_seed(0 )
lowercase = 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=1000 , )
lowercase = CLIPTextModel(_lowerCAmelCase )
lowercase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowercase = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def _a ( self , _lowerCAmelCase , _lowerCAmelCase=0 ) -> Optional[int]:
'''simple docstring'''
lowercase = torch.manual_seed(_lowerCAmelCase )
lowercase = {
"""prompt""": """a photo of the dolomites""",
"""generator""": generator,
# Setting height and width to None to prevent OOMs on CPU.
"""height""": None,
"""width""": None,
"""num_inference_steps""": 1,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def _a ( self ) -> int:
'''simple docstring'''
lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase = self.get_dummy_components()
lowercase = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase = self.get_dummy_inputs(_lowerCAmelCase )
lowercase = sd_pipe(**_lowerCAmelCase ).images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase = np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
super().test_inference_batch_consistent(batch_sizes=[1, 2] )
def _a ( self ) -> str:
'''simple docstring'''
super().test_inference_batch_single_identical(batch_size=2 , expected_max_diff=3.25E-3 )
def _a ( self ) -> List[Any]:
'''simple docstring'''
lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase = self.get_dummy_components()
lowercase = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase = self.get_dummy_inputs(_lowerCAmelCase )
lowercase = """french fries"""
lowercase = sd_pipe(**_lowerCAmelCase , negative_prompt=_lowerCAmelCase )
lowercase = output.images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase = np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> Tuple:
'''simple docstring'''
lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase = self.get_dummy_components()
lowercase = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase = self.get_dummy_inputs(_lowerCAmelCase )
lowercase = sd_pipe(**_lowerCAmelCase , view_batch_size=2 )
lowercase = output.images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase = np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> Any:
'''simple docstring'''
lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase = self.get_dummy_components()
lowercase = EulerAncestralDiscreteScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" )
lowercase = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase = self.get_dummy_inputs(_lowerCAmelCase )
lowercase = sd_pipe(**_lowerCAmelCase ).images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase = np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> Dict:
'''simple docstring'''
lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase = self.get_dummy_components()
lowercase = PNDMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , skip_prk_steps=_lowerCAmelCase )
lowercase = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase = self.get_dummy_inputs(_lowerCAmelCase )
lowercase = sd_pipe(**_lowerCAmelCase ).images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase = np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class __UpperCamelCase (unittest.TestCase ):
def _a ( self ) -> List[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self , _lowerCAmelCase=0 ) -> Optional[int]:
'''simple docstring'''
lowercase = torch.manual_seed(_lowerCAmelCase )
lowercase = {
"""prompt""": """a photo of the dolomites""",
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase = """stabilityai/stable-diffusion-2-base"""
lowercase = DDIMScheduler.from_pretrained(_lowerCAmelCase , subfolder="""scheduler""" )
lowercase = StableDiffusionPanoramaPipeline.from_pretrained(_lowerCAmelCase , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing()
lowercase = self.get_inputs()
lowercase = pipe(**_lowerCAmelCase ).images
lowercase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2048, 3)
lowercase = np.array(
[
0.3696_8392,
0.2702_5372,
0.3244_6766,
0.2837_9387,
0.3636_3274,
0.3073_3347,
0.2710_0027,
0.2705_4125,
0.2553_6096,
] )
assert np.abs(expected_slice - image_slice ).max() < 1E-2
def _a ( self ) -> str:
'''simple docstring'''
lowercase = StableDiffusionPanoramaPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2-base""" , safety_checker=_lowerCAmelCase )
lowercase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing()
lowercase = self.get_inputs()
lowercase = pipe(**_lowerCAmelCase ).images
lowercase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2048, 3)
lowercase = np.array(
[
[
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
] )
assert np.abs(expected_slice - image_slice ).max() < 1E-3
def _a ( self ) -> Any:
'''simple docstring'''
lowercase = 0
def callback_fn(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> None:
lowercase = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
lowercase = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
lowercase = latents[0, -3:, -3:, -1]
lowercase = np.array(
[
0.1868_1869,
0.3390_7816,
0.536_1276,
0.1443_2865,
-0.0285_6611,
-0.7394_1123,
0.2339_7987,
0.4732_2682,
-0.3782_3164,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
elif step == 2:
lowercase = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
lowercase = latents[0, -3:, -3:, -1]
lowercase = np.array(
[
0.1853_9645,
0.3398_7248,
0.537_8559,
0.1443_7142,
-0.0245_5261,
-0.733_8317,
0.2399_0755,
0.4735_6272,
-0.378_6505,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
lowercase = False
lowercase = """stabilityai/stable-diffusion-2-base"""
lowercase = DDIMScheduler.from_pretrained(_lowerCAmelCase , subfolder="""scheduler""" )
lowercase = StableDiffusionPanoramaPipeline.from_pretrained(_lowerCAmelCase , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase )
lowercase = pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing()
lowercase = self.get_inputs()
pipe(**_lowerCAmelCase , callback=_lowerCAmelCase , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def _a ( self ) -> int:
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
lowercase = """stabilityai/stable-diffusion-2-base"""
lowercase = DDIMScheduler.from_pretrained(_lowerCAmelCase , subfolder="""scheduler""" )
lowercase = StableDiffusionPanoramaPipeline.from_pretrained(_lowerCAmelCase , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase )
lowercase = pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
lowercase = self.get_inputs()
lowercase = pipe(**_lowerCAmelCase )
lowercase = torch.cuda.max_memory_allocated()
# make sure that less than 5.2 GB is allocated
assert mem_bytes < 5.5 * 10**9
| 653 | 1 |
'''simple docstring'''
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowercase_ : Any = '''src/transformers'''
# This is to make sure the transformers module imported is the one in the repo.
lowercase_ : List[str] = importlib.util.spec_from_file_location(
'''transformers''',
os.path.join(PATH_TO_TRANSFORMERS, '''__init__.py'''),
submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
lowercase_ : Any = spec.loader.load_module()
lowercase_ : Tuple = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
lowercase_ : List[Any] = re.compile('''\[(.+?)\]\((https://huggingface\.co/.+?)\)''')
lowercase_ : Any = {
'''CLIPConfigMixin''',
'''DecisionTransformerConfigMixin''',
'''EncoderDecoderConfigMixin''',
'''RagConfigMixin''',
'''SpeechEncoderDecoderConfigMixin''',
'''VisionEncoderDecoderConfigMixin''',
'''VisionTextDualEncoderConfigMixin''',
}
def SCREAMING_SNAKE_CASE ( ):
lowercase = []
for config_class in list(CONFIG_MAPPING.values() ):
lowercase = False
# source code of `config_class`
lowercase = inspect.getsource(lowercase_ )
lowercase = _re_checkpoint.findall(lowercase_ )
for checkpoint in checkpoints:
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
lowercase , lowercase = checkpoint
# verify the checkpoint name corresponds to the checkpoint link
lowercase = F"""https://huggingface.co/{ckpt_name}"""
if ckpt_link == ckpt_link_from_name:
lowercase = True
break
lowercase = config_class.__name__
if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(lowercase_ )
if len(lowercase_ ) > 0:
lowercase = """\n""".join(sorted(lowercase_ ) )
raise ValueError(F"""The following configurations don't contain any valid checkpoint:\n{message}""" )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 653 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
lowercase_ : Tuple = logging.getLogger(__name__)
@dataclass
class __UpperCamelCase :
__A = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''Whether tp freeze the encoder.'''} )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''Whether to freeze the embeddings.'''} )
@dataclass
class __UpperCamelCase :
__A = field(
metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''} )
__A = field(
default='''summarization''' , metadata={'''help''': '''Task name, summarization (or summarization_{dataset} for pegasus) or translation'''} , )
__A = field(
default=1024 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
__A = field(
default=128 , metadata={
'''help''': (
'''The maximum total sequence length for target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
__A = field(
default=142 , metadata={
'''help''': (
'''The maximum total sequence length for validation target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded. '''
'''This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '''
'''during ``evaluate`` and ``predict``.'''
)
} , )
__A = field(
default=142 , metadata={
'''help''': (
'''The maximum total sequence length for test target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
__A = field(default=-1 , metadata={'''help''': '''# training examples. -1 means use all.'''} )
__A = field(default=-1 , metadata={'''help''': '''# validation examples. -1 means use all.'''} )
__A = field(default=-1 , metadata={'''help''': '''# test examples. -1 means use all.'''} )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''Source language id for translation.'''} )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''Target language id for translation.'''} )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''# num_beams to use for evaluation.'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''} , )
def SCREAMING_SNAKE_CASE ( lowercase_ : List[Any] , lowercase_ : int , lowercase_ : List[Any] ):
logger.info(F"""***** {split} metrics *****""" )
for key in sorted(metrics.keys() ):
logger.info(F""" {key} = {metrics[key]}""" )
save_json(lowercase_ , os.path.join(lowercase_ , F"""{split}_results.json""" ) )
def SCREAMING_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.
lowercase = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
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.
lowercase , lowercase , lowercase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase , lowercase , lowercase = parser.parse_args_into_dataclasses()
check_output_dir(lowercase_ )
# 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.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# 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""" , lowercase_ )
# Set seed
set_seed(training_args.seed )
# 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 , cache_dir=model_args.cache_dir , )
lowercase = ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
for p in extra_model_params:
if getattr(lowercase_ , lowercase_ , lowercase_ ):
assert hasattr(lowercase_ , lowercase_ ), F"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
setattr(lowercase_ , lowercase_ , getattr(lowercase_ , lowercase_ ) )
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 = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf=""".ckpt""" in model_args.model_name_or_path , config=lowercase_ , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(lowercase_ , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
lowercase = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(lowercase_ , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(lowercase_ , lowercase_ ):
lowercase = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
lowercase = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(lowercase_ )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
lowercase = SeqaSeqDataset
# Get datasets
lowercase = (
dataset_class(
lowercase_ , type_path="""train""" , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_train
else None
)
lowercase = (
dataset_class(
lowercase_ , type_path="""val""" , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
lowercase = (
dataset_class(
lowercase_ , type_path="""test""" , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_predict
else None
)
# Initialize our Trainer
lowercase = (
build_compute_metrics_fn(data_args.task , lowercase_ ) if training_args.predict_with_generate else None
)
lowercase = SeqaSeqTrainer(
model=lowercase_ , args=lowercase_ , data_args=lowercase_ , train_dataset=lowercase_ , eval_dataset=lowercase_ , data_collator=SeqaSeqDataCollator(
lowercase_ , lowercase_ , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=lowercase_ , tokenizer=lowercase_ , )
lowercase = {}
# Training
if training_args.do_train:
logger.info("""*** Train ***""" )
lowercase = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
lowercase = train_result.metrics
lowercase = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics("""train""" , lowercase_ , training_args.output_dir )
all_metrics.update(lowercase_ )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , """trainer_state.json""" ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
lowercase = trainer.evaluate(metric_key_prefix="""val""" )
lowercase = data_args.n_val
lowercase = round(metrics["""val_loss"""] , 4 )
if trainer.is_world_process_zero():
handle_metrics("""val""" , lowercase_ , training_args.output_dir )
all_metrics.update(lowercase_ )
if training_args.do_predict:
logger.info("""*** Predict ***""" )
lowercase = trainer.predict(test_dataset=lowercase_ , metric_key_prefix="""test""" )
lowercase = test_output.metrics
lowercase = data_args.n_test
if trainer.is_world_process_zero():
lowercase = round(metrics["""test_loss"""] , 4 )
handle_metrics("""test""" , lowercase_ , training_args.output_dir )
all_metrics.update(lowercase_ )
if training_args.predict_with_generate:
lowercase = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=lowercase_ , clean_up_tokenization_spaces=lowercase_ )
lowercase = lmap(str.strip , lowercase_ )
write_txt_file(lowercase_ , os.path.join(training_args.output_dir , """test_generations.txt""" ) )
if trainer.is_world_process_zero():
save_json(lowercase_ , os.path.join(training_args.output_dir , """all_results.json""" ) )
return all_metrics
def SCREAMING_SNAKE_CASE ( lowercase_ : Dict ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 653 | 1 |
'''simple docstring'''
import numpy as np
def SCREAMING_SNAKE_CASE ( lowercase_ : np.ndarray ):
return 1 / (1 + np.exp(-vector ))
def SCREAMING_SNAKE_CASE ( lowercase_ : np.ndarray ):
return vector * sigmoid(lowercase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 653 |
'''simple docstring'''
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_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
lowercase_ : Union[str, Any] = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase )
class __UpperCamelCase (_UpperCAmelCase ):
def __init__( self , *_lowerCAmelCase , **_lowerCAmelCase ) -> Dict:
'''simple docstring'''
super().__init__(*_lowerCAmelCase , **_lowerCAmelCase )
requires_backends(self , """vision""" )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING )
def _a ( self , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase=None ) -> str:
'''simple docstring'''
lowercase = {}
lowercase = {}
if prompt is not None:
lowercase = prompt
if generate_kwargs is not None:
lowercase = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
lowercase = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
"""'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
""" please use only one""" )
lowercase = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self , _lowerCAmelCase , **_lowerCAmelCase ) -> Any:
'''simple docstring'''
return super().__call__(_lowerCAmelCase , **_lowerCAmelCase )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase=None ) -> List[str]:
'''simple docstring'''
lowercase = load_image(_lowerCAmelCase )
if prompt is not None:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise ValueError(
F"""Received an invalid text input, got - {type(_lowerCAmelCase )} - but expected a single string. """
"""Note also that one single text can be provided for conditional image to text generation.""" )
lowercase = self.model.config.model_type
if model_type == "git":
lowercase = self.image_processor(images=_lowerCAmelCase , return_tensors=self.framework )
lowercase = self.tokenizer(text=_lowerCAmelCase , add_special_tokens=_lowerCAmelCase ).input_ids
lowercase = [self.tokenizer.cls_token_id] + input_ids
lowercase = torch.tensor(_lowerCAmelCase ).unsqueeze(0 )
model_inputs.update({"""input_ids""": input_ids} )
elif model_type == "pix2struct":
lowercase = self.image_processor(images=_lowerCAmelCase , header_text=_lowerCAmelCase , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
lowercase = self.image_processor(images=_lowerCAmelCase , return_tensors=self.framework )
lowercase = self.tokenizer(_lowerCAmelCase , return_tensors=self.framework )
model_inputs.update(_lowerCAmelCase )
else:
raise ValueError(F"""Model type {model_type} does not support conditional text generation""" )
else:
lowercase = self.image_processor(images=_lowerCAmelCase , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
lowercase = None
return model_inputs
def _a ( self , _lowerCAmelCase , _lowerCAmelCase=None ) -> Union[str, Any]:
'''simple docstring'''
if (
"input_ids" in model_inputs
and isinstance(model_inputs["""input_ids"""] , _lowerCAmelCase )
and all(x is None for x in model_inputs["""input_ids"""] )
):
lowercase = None
if generate_kwargs is None:
lowercase = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
lowercase = model_inputs.pop(self.model.main_input_name )
lowercase = self.model.generate(_lowerCAmelCase , **_lowerCAmelCase , **_lowerCAmelCase )
return model_outputs
def _a ( self , _lowerCAmelCase ) -> List[str]:
'''simple docstring'''
lowercase = []
for output_ids in model_outputs:
lowercase = {
"""generated_text""": self.tokenizer.decode(
_lowerCAmelCase , skip_special_tokens=_lowerCAmelCase , )
}
records.append(_lowerCAmelCase )
return records
| 653 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification
logging.set_verbosity_info()
lowercase_ : int = logging.get_logger(__name__)
lowercase_ : str = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
'''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
'''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
'''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
'''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''adapter_layer''': '''encoder.layers.*.adapter_layer''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
'''pooling_layer.linear''': '''projector''',
'''pooling_layer.projection''': '''classifier''',
}
lowercase_ : Dict = [
'''lm_head''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
'''projector''',
'''classifier''',
]
def SCREAMING_SNAKE_CASE ( lowercase_ : List[Any] ):
lowercase = {}
with open(lowercase_ , """r""" ) as file:
for line_number, line in enumerate(lowercase_ ):
lowercase = line.strip()
if line:
lowercase = line.split()
lowercase = line_number
lowercase = words[0]
lowercase = value
return result
def SCREAMING_SNAKE_CASE ( lowercase_ : Optional[Any] , lowercase_ : str , lowercase_ : Tuple , lowercase_ : Dict , lowercase_ : List[str] ):
for attribute in key.split(""".""" ):
lowercase = getattr(lowercase_ , lowercase_ )
lowercase = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(lowercase_ ):
lowercase = PARAM_MAPPING[full_name.split(""".""" )[-1]]
lowercase = """param"""
if weight_type is not None and weight_type != "param":
lowercase = getattr(lowercase_ , lowercase_ ).shape
elif weight_type is not None and weight_type == "param":
lowercase = hf_pointer
for attribute in hf_param_name.split(""".""" ):
lowercase = getattr(lowercase_ , lowercase_ )
lowercase = shape_pointer.shape
# let's reduce dimension
lowercase = value[0]
else:
lowercase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}""" )
if weight_type == "weight":
lowercase = value
elif weight_type == "weight_g":
lowercase = value
elif weight_type == "weight_v":
lowercase = value
elif weight_type == "bias":
lowercase = value
elif weight_type == "param":
for attribute in hf_param_name.split(""".""" ):
lowercase = getattr(lowercase_ , lowercase_ )
lowercase = value
else:
lowercase = value
logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def SCREAMING_SNAKE_CASE ( lowercase_ : List[str] , lowercase_ : List[Any] , lowercase_ : Dict , lowercase_ : str , lowercase_ : Optional[Any] ):
lowercase = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(lowercase_ ):
lowercase = PARAM_MAPPING[full_name.split(""".""" )[-1]]
lowercase = """param"""
if weight_type is not None and weight_type != "param":
lowercase = """.""".join([key, weight_type] )
elif weight_type is not None and weight_type == "param":
lowercase = """.""".join([key, hf_param_name] )
else:
lowercase = key
lowercase = value if """lm_head""" in full_key else value[0]
lowercase_ : Tuple = {
'''W_a''': '''linear_1.weight''',
'''W_b''': '''linear_2.weight''',
'''b_a''': '''linear_1.bias''',
'''b_b''': '''linear_2.bias''',
'''ln_W''': '''norm.weight''',
'''ln_b''': '''norm.bias''',
}
def SCREAMING_SNAKE_CASE ( lowercase_ : Optional[int] , lowercase_ : str , lowercase_ : List[str]=None , lowercase_ : Optional[int]=None ):
lowercase = False
for key, mapped_key in MAPPING.items():
lowercase = """wav2vec2.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
lowercase = True
if "*" in mapped_key:
lowercase = name.split(lowercase_ )[0].split(""".""" )[-2]
lowercase = mapped_key.replace("""*""" , lowercase_ )
if "weight_g" in name:
lowercase = """weight_g"""
elif "weight_v" in name:
lowercase = """weight_v"""
elif "bias" in name:
lowercase = """bias"""
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
lowercase = """weight"""
else:
lowercase = None
if hf_dict is not None:
rename_dict(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
else:
set_recursively(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
return is_used
return is_used
def SCREAMING_SNAKE_CASE ( lowercase_ : List[Any] , lowercase_ : int , lowercase_ : Optional[int] ):
lowercase = []
lowercase = fairseq_model.state_dict()
lowercase = hf_model.wavaveca.feature_extractor
for name, value in fairseq_dict.items():
lowercase = False
if "conv_layers" in name:
load_conv_layer(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , hf_model.config.feat_extract_norm == """group""" , )
lowercase = True
else:
lowercase = load_wavaveca_layer(lowercase_ , lowercase_ , lowercase_ )
if not is_used:
unused_weights.append(lowercase_ )
logger.warning(F"""Unused weights: {unused_weights}""" )
def SCREAMING_SNAKE_CASE ( lowercase_ : Optional[int] , lowercase_ : Union[str, Any] , lowercase_ : Dict , lowercase_ : Optional[int] , lowercase_ : Optional[Any] ):
lowercase = full_name.split("""conv_layers.""" )[-1]
lowercase = name.split(""".""" )
lowercase = int(items[0] )
lowercase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
lowercase = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
lowercase = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
lowercase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
lowercase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(lowercase_ )
@torch.no_grad()
def SCREAMING_SNAKE_CASE ( lowercase_ : Tuple , lowercase_ : List[Any] , lowercase_ : List[str]=None , lowercase_ : str=None , lowercase_ : List[str]=True , lowercase_ : str=False ):
if config_path is not None:
lowercase = WavaVecaConfig.from_pretrained(lowercase_ )
else:
lowercase = WavaVecaConfig()
if is_seq_class:
lowercase = read_txt_into_dict(lowercase_ )
lowercase = idalabel
lowercase = WavaVecaForSequenceClassification(lowercase_ )
lowercase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6000 , padding_value=0 , do_normalize=lowercase_ , return_attention_mask=lowercase_ , )
feature_extractor.save_pretrained(lowercase_ )
elif is_finetuned:
if dict_path:
lowercase = Dictionary.load(lowercase_ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
lowercase = target_dict.pad_index
lowercase = target_dict.bos_index
lowercase = target_dict.eos_index
lowercase = len(target_dict.symbols )
lowercase = os.path.join(lowercase_ , """vocab.json""" )
if not os.path.isdir(lowercase_ ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowercase_ ) )
return
os.makedirs(lowercase_ , exist_ok=lowercase_ )
lowercase = target_dict.indices
# fairseq has the <pad> and <s> switched
lowercase = 0
lowercase = 1
with open(lowercase_ , """w""" , encoding="""utf-8""" ) as vocab_handle:
json.dump(lowercase_ , lowercase_ )
lowercase = WavaVecaCTCTokenizer(
lowercase_ , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=lowercase_ , )
lowercase = True if config.feat_extract_norm == """layer""" else False
lowercase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6000 , padding_value=0 , do_normalize=lowercase_ , return_attention_mask=lowercase_ , )
lowercase = WavaVecaProcessor(feature_extractor=lowercase_ , tokenizer=lowercase_ )
processor.save_pretrained(lowercase_ )
lowercase = WavaVecaForCTC(lowercase_ )
else:
lowercase = WavaVecaForPreTraining(lowercase_ )
if is_finetuned or is_seq_class:
lowercase , lowercase , lowercase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
else:
lowercase = argparse.Namespace(task="""audio_pretraining""" )
lowercase = fairseq.tasks.setup_task(lowercase_ )
lowercase , lowercase , lowercase = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=lowercase_ )
lowercase = model[0].eval()
recursively_load_weights(lowercase_ , lowercase_ , not is_finetuned )
hf_wavavec.save_pretrained(lowercase_ )
if __name__ == "__main__":
lowercase_ : Dict = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
)
parser.add_argument(
'''--is_seq_class''',
action='''store_true''',
help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
)
lowercase_ : Union[str, Any] = parser.parse_args()
lowercase_ : Optional[int] = not args.not_finetuned and not args.is_seq_class
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.dict_path,
is_finetuned,
args.is_seq_class,
)
| 653 |
'''simple docstring'''
from ... import PretrainedConfig
lowercase_ : int = {
'''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}
class __UpperCamelCase (_UpperCAmelCase ):
__A = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
__A = '''nezha'''
def __init__( self , _lowerCAmelCase=2_1128 , _lowerCAmelCase=768 , _lowerCAmelCase=12 , _lowerCAmelCase=12 , _lowerCAmelCase=3072 , _lowerCAmelCase="gelu" , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=512 , _lowerCAmelCase=64 , _lowerCAmelCase=2 , _lowerCAmelCase=0.02 , _lowerCAmelCase=1E-12 , _lowerCAmelCase=0.1 , _lowerCAmelCase=0 , _lowerCAmelCase=2 , _lowerCAmelCase=3 , _lowerCAmelCase=True , **_lowerCAmelCase , ) -> int:
'''simple docstring'''
super().__init__(pad_token_id=_lowerCAmelCase , bos_token_id=_lowerCAmelCase , eos_token_id=_lowerCAmelCase , **_lowerCAmelCase )
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = hidden_act
lowercase = intermediate_size
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = max_relative_position
lowercase = type_vocab_size
lowercase = initializer_range
lowercase = layer_norm_eps
lowercase = classifier_dropout
lowercase = use_cache
| 653 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ : List[Any] = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : str = [
'''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''OPTForCausalLM''',
'''OPTModel''',
'''OPTPreTrainedModel''',
'''OPTForSequenceClassification''',
'''OPTForQuestionAnswering''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : Optional[Any] = ['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : int = [
'''FlaxOPTForCausalLM''',
'''FlaxOPTModel''',
'''FlaxOPTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_opt import (
OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OPTForCausalLM,
OPTForQuestionAnswering,
OPTForSequenceClassification,
OPTModel,
OPTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel
else:
import sys
lowercase_ : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 653 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
lowercase_ : Tuple = logging.getLogger(__name__)
def SCREAMING_SNAKE_CASE ( lowercase_ : str ):
lowercase = git.Repo(search_parent_directories=lowercase_ )
lowercase = {
"""repo_id""": str(lowercase_ ),
"""repo_sha""": str(repo.head.object.hexsha ),
"""repo_branch""": str(repo.active_branch ),
}
with open(os.path.join(lowercase_ , """git_log.json""" ) , """w""" ) as f:
json.dump(lowercase_ , lowercase_ , indent=4 )
def SCREAMING_SNAKE_CASE ( lowercase_ : str ):
if params.n_gpu <= 0:
lowercase = 0
lowercase = -1
lowercase = True
lowercase = False
return
assert torch.cuda.is_available()
logger.info("""Initializing GPUs""" )
if params.n_gpu > 1:
assert params.local_rank != -1
lowercase = int(os.environ["""WORLD_SIZE"""] )
lowercase = int(os.environ["""N_GPU_NODE"""] )
lowercase = int(os.environ["""RANK"""] )
# number of nodes / node ID
lowercase = params.world_size // params.n_gpu_per_node
lowercase = params.global_rank // params.n_gpu_per_node
lowercase = True
assert params.n_nodes == int(os.environ["""N_NODES"""] )
assert params.node_id == int(os.environ["""NODE_RANK"""] )
# local job (single GPU)
else:
assert params.local_rank == -1
lowercase = 1
lowercase = 0
lowercase = 0
lowercase = 0
lowercase = 1
lowercase = 1
lowercase = False
# sanity checks
assert params.n_nodes >= 1
assert 0 <= params.node_id < params.n_nodes
assert 0 <= params.local_rank <= params.global_rank < params.world_size
assert params.world_size == params.n_nodes * params.n_gpu_per_node
# define whether this is the master process / if we are in multi-node distributed mode
lowercase = params.node_id == 0 and params.local_rank == 0
lowercase = params.n_nodes > 1
# summary
lowercase = F"""--- Global rank: {params.global_rank} - """
logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes )
logger.info(PREFIX + """Node ID : %i""" % params.node_id )
logger.info(PREFIX + """Local rank : %i""" % params.local_rank )
logger.info(PREFIX + """World size : %i""" % params.world_size )
logger.info(PREFIX + """GPUs per node : %i""" % params.n_gpu_per_node )
logger.info(PREFIX + """Master : %s""" % str(params.is_master ) )
logger.info(PREFIX + """Multi-node : %s""" % str(params.multi_node ) )
logger.info(PREFIX + """Multi-GPU : %s""" % str(params.multi_gpu ) )
logger.info(PREFIX + """Hostname : %s""" % socket.gethostname() )
# set GPU device
torch.cuda.set_device(params.local_rank )
# initialize multi-GPU
if params.multi_gpu:
logger.info("""Initializing PyTorch distributed""" )
torch.distributed.init_process_group(
init_method="""env://""" , backend="""nccl""" , )
def SCREAMING_SNAKE_CASE ( lowercase_ : Optional[Any] ):
np.random.seed(args.seed )
torch.manual_seed(args.seed )
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed )
| 653 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : int ):
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
lowercase = str(bin(lowercase_ ) )[2:] # remove the leading "0b"
lowercase = str(bin(lowercase_ ) )[2:]
lowercase = max(len(lowercase_ ) , len(lowercase_ ) )
return "0b" + "".join(
str(int("""1""" in (char_a, char_b) ) )
for char_a, char_b in zip(a_binary.zfill(lowercase_ ) , b_binary.zfill(lowercase_ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 653 |
'''simple docstring'''
from __future__ import annotations
import os
from typing import Any
import requests
lowercase_ : List[str] = '''https://api.github.com'''
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
lowercase_ : Any = BASE_URL + '''/user'''
# https://github.com/settings/tokens
lowercase_ : Union[str, Any] = os.environ.get('''USER_TOKEN''', '''''')
def SCREAMING_SNAKE_CASE ( lowercase_ : str ):
lowercase = {
"""Authorization""": F"""token {auth_token}""",
"""Accept""": """application/vnd.github.v3+json""",
}
return requests.get(lowercase_ , headers=lowercase_ ).json()
if __name__ == "__main__": # pragma: no cover
if USER_TOKEN:
for key, value in fetch_github_info(USER_TOKEN).items():
print(f'''{key}: {value}''')
else:
raise ValueError('''\'USER_TOKEN\' field cannot be empty.''')
| 653 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase_ : Union[str, Any] = {
'''configuration_mask2former''': [
'''MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Mask2FormerConfig''',
],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : Optional[Any] = ['''Mask2FormerImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : Optional[Any] = [
'''MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Mask2FormerForUniversalSegmentation''',
'''Mask2FormerModel''',
'''Mask2FormerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_maskaformer import MaskaFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_maskaformer import (
MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
MaskaFormerForUniversalSegmentation,
MaskaFormerModel,
MaskaFormerPreTrainedModel,
)
else:
import sys
lowercase_ : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 653 |
'''simple docstring'''
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowercase_ : Union[str, Any] = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
require_version('''datasets>=1.14.0''', '''To fix: pip install -r examples/pytorch/audio-classification/requirements.txt''')
def SCREAMING_SNAKE_CASE ( lowercase_ : np.ndarray , lowercase_ : float , lowercase_ : int = 1_6000 ):
lowercase = int(round(sample_rate * max_length ) )
if len(lowercase_ ) <= sample_length:
return wav
lowercase = randint(0 , len(lowercase_ ) - sample_length - 1 )
return wav[random_offset : random_offset + sample_length]
@dataclass
class __UpperCamelCase :
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''Name of a dataset from the datasets package'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''A file containing the training audio paths and labels.'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''A file containing the validation audio paths and labels.'''} )
__A = field(
default='''train''' , metadata={
'''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
} , )
__A = field(
default='''validation''' , metadata={
'''help''': (
'''The name of the training data set split to use (via the datasets library). Defaults to \'validation\''''
)
} , )
__A = field(
default='''audio''' , metadata={'''help''': '''The name of the dataset column containing the audio data. Defaults to \'audio\''''} , )
__A = field(
default='''label''' , metadata={'''help''': '''The name of the dataset column containing the labels. Defaults to \'label\''''} )
__A = field(
default=_UpperCAmelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
__A = field(
default=_UpperCAmelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
__A = field(
default=20 , metadata={'''help''': '''Audio clips will be randomly cut to this length during training if the value is set.'''} , )
@dataclass
class __UpperCamelCase :
__A = field(
default='''facebook/wav2vec2-base''' , metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} , )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from the Hub'''} )
__A = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Name or path of preprocessor config.'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Whether to freeze the feature encoder layers of the model.'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Whether to generate an attention mask in the feature extractor.'''} )
__A = field(
default=_UpperCAmelCase , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''} , )
def _a ( self ) -> List[Any]:
'''simple docstring'''
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
"""The argument `--freeze_feature_extractor` is deprecated and """
"""will be removed in a future version. Use `--freeze_feature_encoder`"""
"""instead. Setting `freeze_feature_encoder==True`.""" , _lowerCAmelCase , )
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
"""The argument `--freeze_feature_extractor` is deprecated and """
"""should not be used in combination with `--freeze_feature_encoder`."""
"""Only make use of `--freeze_feature_encoder`.""" )
def SCREAMING_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.
lowercase = 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.
lowercase , lowercase , lowercase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase , lowercase , lowercase = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_audio_classification""" , lowercase_ , lowercase_ )
# 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 )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
lowercase = training_args.get_process_log_level()
logger.setLevel(lowercase_ )
transformers.utils.logging.set_verbosity(lowercase_ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# 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}""" )
logger.info(F"""Training/evaluation parameters {training_args}""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Detecting last checkpoint.
lowercase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowercase = 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 train from scratch.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is 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.""" )
# Initialize our dataset and prepare it for the audio classification task.
lowercase = DatasetDict()
lowercase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.train_split_name , use_auth_token=True if model_args.use_auth_token else None , )
lowercase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.eval_split_name , use_auth_token=True if model_args.use_auth_token else None , )
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
F"""--audio_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. """
"""Make sure to set `--audio_column_name` to the correct audio column - one of """
F"""{', '.join(raw_datasets['train'].column_names )}.""" )
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
F"""--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. """
"""Make sure to set `--label_column_name` to the correct text column - one of """
F"""{', '.join(raw_datasets['train'].column_names )}.""" )
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
lowercase = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path , return_attention_mask=model_args.attention_mask , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
lowercase = raw_datasets.cast_column(
data_args.audio_column_name , datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )
lowercase = feature_extractor.model_input_names[0]
def train_transforms(lowercase_ : int ):
lowercase = []
for audio in batch[data_args.audio_column_name]:
lowercase = random_subsample(
audio["""array"""] , max_length=data_args.max_length_seconds , sample_rate=feature_extractor.sampling_rate )
subsampled_wavs.append(lowercase_ )
lowercase = feature_extractor(lowercase_ , sampling_rate=feature_extractor.sampling_rate )
lowercase = {model_input_name: inputs.get(lowercase_ )}
lowercase = list(batch[data_args.label_column_name] )
return output_batch
def val_transforms(lowercase_ : Dict ):
lowercase = [audio["""array"""] for audio in batch[data_args.audio_column_name]]
lowercase = feature_extractor(lowercase_ , sampling_rate=feature_extractor.sampling_rate )
lowercase = {model_input_name: inputs.get(lowercase_ )}
lowercase = list(batch[data_args.label_column_name] )
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
lowercase = raw_datasets["""train"""].features[data_args.label_column_name].names
lowercase , lowercase = {}, {}
for i, label in enumerate(lowercase_ ):
lowercase = str(lowercase_ )
lowercase = label
# Load the accuracy metric from the datasets package
lowercase = evaluate.load("""accuracy""" )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(lowercase_ : Tuple ):
lowercase = np.argmax(eval_pred.predictions , axis=1 )
return metric.compute(predictions=lowercase_ , references=eval_pred.label_ids )
lowercase = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(lowercase_ ) , labelaid=lowercase_ , idalabel=lowercase_ , finetuning_task="""audio-classification""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
lowercase = AutoModelForAudioClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=lowercase_ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
lowercase = (
raw_datasets["""train"""].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
raw_datasets["train"].set_transform(lowercase_ , output_all_columns=lowercase_ )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
lowercase = (
raw_datasets["""eval"""].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
raw_datasets["eval"].set_transform(lowercase_ , output_all_columns=lowercase_ )
# Initialize our trainer
lowercase = Trainer(
model=lowercase_ , args=lowercase_ , train_dataset=raw_datasets["""train"""] if training_args.do_train else None , eval_dataset=raw_datasets["""eval"""] if training_args.do_eval else None , compute_metrics=lowercase_ , tokenizer=lowercase_ , )
# Training
if training_args.do_train:
lowercase = None
if training_args.resume_from_checkpoint is not None:
lowercase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowercase = last_checkpoint
lowercase = trainer.train(resume_from_checkpoint=lowercase_ )
trainer.save_model()
trainer.log_metrics("""train""" , train_result.metrics )
trainer.save_metrics("""train""" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
lowercase = trainer.evaluate()
trainer.log_metrics("""eval""" , lowercase_ )
trainer.save_metrics("""eval""" , lowercase_ )
# Write model card and (optionally) push to hub
lowercase = {
"""finetuned_from""": model_args.model_name_or_path,
"""tasks""": """audio-classification""",
"""dataset""": data_args.dataset_name,
"""tags""": ["""audio-classification"""],
}
if training_args.push_to_hub:
trainer.push_to_hub(**lowercase_ )
else:
trainer.create_model_card(**lowercase_ )
if __name__ == "__main__":
main()
| 653 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase_ : Union[str, Any] = {
'''configuration_transfo_xl''': ['''TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TransfoXLConfig'''],
'''tokenization_transfo_xl''': ['''TransfoXLCorpus''', '''TransfoXLTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : List[str] = [
'''TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''AdaptiveEmbedding''',
'''TransfoXLForSequenceClassification''',
'''TransfoXLLMHeadModel''',
'''TransfoXLModel''',
'''TransfoXLPreTrainedModel''',
'''load_tf_weights_in_transfo_xl''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : str = [
'''TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFAdaptiveEmbedding''',
'''TFTransfoXLForSequenceClassification''',
'''TFTransfoXLLMHeadModel''',
'''TFTransfoXLMainLayer''',
'''TFTransfoXLModel''',
'''TFTransfoXLPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_transfo_xl import (
TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
AdaptiveEmbedding,
TransfoXLForSequenceClassification,
TransfoXLLMHeadModel,
TransfoXLModel,
TransfoXLPreTrainedModel,
load_tf_weights_in_transfo_xl,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_transfo_xl import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAdaptiveEmbedding,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLMainLayer,
TFTransfoXLModel,
TFTransfoXLPreTrainedModel,
)
else:
import sys
lowercase_ : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 653 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
lowercase_ : Union[str, Any] = logging.get_logger(__name__)
@dataclass
class __UpperCamelCase (_UpperCAmelCase ):
__A = [
'''no_inference''',
'''no_cuda''',
'''no_tpu''',
'''no_speed''',
'''no_memory''',
'''no_env_print''',
'''no_multi_process''',
]
def __init__( self , **_lowerCAmelCase ) -> Optional[int]:
'''simple docstring'''
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
lowercase = deprecated_arg[3:]
lowercase = not kwargs.pop(_lowerCAmelCase )
logger.warning(
F"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
F""" {positive_arg}={kwargs[positive_arg]}""" )
lowercase = kwargs.pop("""tpu_name""" , self.tpu_name )
lowercase = kwargs.pop("""device_idx""" , self.device_idx )
lowercase = kwargs.pop("""eager_mode""" , self.eager_mode )
lowercase = kwargs.pop("""use_xla""" , self.use_xla )
super().__init__(**_lowerCAmelCase )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Name of TPU'''} , )
__A = field(
default=0 , metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''} , )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''Benchmark models in eager model.'''} )
__A = field(
default=_UpperCAmelCase , metadata={
'''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
} , )
@cached_property
def _a ( self ) -> Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
'''simple docstring'''
requires_backends(self , ["""tf"""] )
lowercase = None
if self.tpu:
try:
if self.tpu_name:
lowercase = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
else:
lowercase = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
lowercase = None
return tpu
@cached_property
def _a ( self ) -> Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
'''simple docstring'''
requires_backends(self , ["""tf"""] )
if self.is_tpu:
tf.config.experimental_connect_to_cluster(self._setup_tpu )
tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )
lowercase = tf.distribute.TPUStrategy(self._setup_tpu )
else:
# currently no multi gpu is allowed
if self.is_gpu:
# TODO: Currently only single GPU is supported
tf.config.set_visible_devices(self.gpu_list[self.device_idx] , """GPU""" )
lowercase = tf.distribute.OneDeviceStrategy(device=F"""/gpu:{self.device_idx}""" )
else:
tf.config.set_visible_devices([] , """GPU""" ) # disable GPU
lowercase = tf.distribute.OneDeviceStrategy(device=F"""/cpu:{self.device_idx}""" )
return strategy
@property
def _a ( self ) -> bool:
'''simple docstring'''
requires_backends(self , ["""tf"""] )
return self._setup_tpu is not None
@property
def _a ( self ) -> "tf.distribute.Strategy":
'''simple docstring'''
requires_backends(self , ["""tf"""] )
return self._setup_strategy
@property
def _a ( self ) -> Tuple:
'''simple docstring'''
requires_backends(self , ["""tf"""] )
return tf.config.list_physical_devices("""GPU""" )
@property
def _a ( self ) -> int:
'''simple docstring'''
requires_backends(self , ["""tf"""] )
if self.cuda:
return len(self.gpu_list )
return 0
@property
def _a ( self ) -> bool:
'''simple docstring'''
return self.n_gpu > 0
| 653 | 1 |
'''simple docstring'''
import torch
from diffusers import DiffusionPipeline
class __UpperCamelCase (_UpperCAmelCase ):
def __init__( self , _lowerCAmelCase , _lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
super().__init__()
self.register_modules(unet=_lowerCAmelCase , scheduler=_lowerCAmelCase )
def __call__( self ) -> Optional[int]:
'''simple docstring'''
lowercase = torch.randn(
(1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , )
lowercase = 1
lowercase = self.unet(_lowerCAmelCase , _lowerCAmelCase ).sample
lowercase = self.scheduler.step(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ).prev_sample
lowercase = scheduler_output - scheduler_output + torch.ones_like(_lowerCAmelCase )
return result
| 653 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : Any = logging.get_logger(__name__)
lowercase_ : str = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class __UpperCamelCase (_UpperCAmelCase ):
__A = '''vit_msn'''
def __init__( self , _lowerCAmelCase=768 , _lowerCAmelCase=12 , _lowerCAmelCase=12 , _lowerCAmelCase=3072 , _lowerCAmelCase="gelu" , _lowerCAmelCase=0.0 , _lowerCAmelCase=0.0 , _lowerCAmelCase=0.02 , _lowerCAmelCase=1E-06 , _lowerCAmelCase=224 , _lowerCAmelCase=16 , _lowerCAmelCase=3 , _lowerCAmelCase=True , **_lowerCAmelCase , ) -> List[Any]:
'''simple docstring'''
super().__init__(**_lowerCAmelCase )
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = intermediate_size
lowercase = hidden_act
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = initializer_range
lowercase = layer_norm_eps
lowercase = image_size
lowercase = patch_size
lowercase = num_channels
lowercase = qkv_bias
| 653 | 1 |
'''simple docstring'''
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
def SCREAMING_SNAKE_CASE ( lowercase_ : int ):
lowercase = model.config
lowercase = DonutSwinConfig(
image_size=original_config.input_size , patch_size=4 , depths=original_config.encoder_layer , num_heads=[4, 8, 16, 32] , window_size=original_config.window_size , embed_dim=128 , )
lowercase = MBartConfig(
is_decoder=lowercase_ , is_encoder_decoder=lowercase_ , add_cross_attention=lowercase_ , decoder_layers=original_config.decoder_layer , max_position_embeddings=original_config.max_position_embeddings , vocab_size=len(
model.decoder.tokenizer ) , scale_embedding=lowercase_ , add_final_layer_norm=lowercase_ , )
return encoder_config, decoder_config
def SCREAMING_SNAKE_CASE ( lowercase_ : Optional[Any] ):
if "encoder.model" in name:
lowercase = name.replace("""encoder.model""" , """encoder""" )
if "decoder.model" in name:
lowercase = name.replace("""decoder.model""" , """decoder""" )
if "patch_embed.proj" in name:
lowercase = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
lowercase = name.replace("""patch_embed.norm""" , """embeddings.norm""" )
if name.startswith("""encoder""" ):
if "layers" in name:
lowercase = """encoder.""" + name
if "attn.proj" in name:
lowercase = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name and "mask" not in name:
lowercase = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
lowercase = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
lowercase = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
lowercase = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
lowercase = name.replace("""mlp.fc2""" , """output.dense""" )
if name == "encoder.norm.weight":
lowercase = """encoder.layernorm.weight"""
if name == "encoder.norm.bias":
lowercase = """encoder.layernorm.bias"""
return name
def SCREAMING_SNAKE_CASE ( lowercase_ : Optional[Any] , lowercase_ : List[Any] ):
for key in orig_state_dict.copy().keys():
lowercase = orig_state_dict.pop(lowercase_ )
if "qkv" in key:
lowercase = key.split(""".""" )
lowercase = int(key_split[3] )
lowercase = int(key_split[5] )
lowercase = model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
lowercase = val[:dim, :]
lowercase = val[dim : dim * 2, :]
lowercase = val[-dim:, :]
else:
lowercase = val[:dim]
lowercase = val[dim : dim * 2]
lowercase = val[-dim:]
elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
# HuggingFace implementation doesn't use attn_mask buffer
# and model doesn't use final LayerNorms for the encoder
pass
else:
lowercase = val
return orig_state_dict
def SCREAMING_SNAKE_CASE ( lowercase_ : Optional[Any] , lowercase_ : Optional[int]=None , lowercase_ : Dict=False ):
# load original model
lowercase = DonutModel.from_pretrained(lowercase_ ).eval()
# load HuggingFace model
lowercase , lowercase = get_configs(lowercase_ )
lowercase = DonutSwinModel(lowercase_ )
lowercase = MBartForCausalLM(lowercase_ )
lowercase = VisionEncoderDecoderModel(encoder=lowercase_ , decoder=lowercase_ )
model.eval()
lowercase = original_model.state_dict()
lowercase = convert_state_dict(lowercase_ , lowercase_ )
model.load_state_dict(lowercase_ )
# verify results on scanned document
lowercase = load_dataset("""hf-internal-testing/example-documents""" )
lowercase = dataset["""test"""][0]["""image"""].convert("""RGB""" )
lowercase = XLMRobertaTokenizerFast.from_pretrained(lowercase_ , from_slow=lowercase_ )
lowercase = DonutImageProcessor(
do_align_long_axis=original_model.config.align_long_axis , size=original_model.config.input_size[::-1] )
lowercase = DonutProcessor(lowercase_ , lowercase_ )
lowercase = processor(lowercase_ , return_tensors="""pt""" ).pixel_values
if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
lowercase = """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
lowercase = """When is the coffee break?"""
lowercase = task_prompt.replace("""{user_input}""" , lowercase_ )
elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
lowercase = """<s_rvlcdip>"""
elif model_name in [
"naver-clova-ix/donut-base-finetuned-cord-v1",
"naver-clova-ix/donut-base-finetuned-cord-v1-2560",
]:
lowercase = """<s_cord>"""
elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
lowercase = """s_cord-v2>"""
elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
lowercase = """<s_zhtrainticket>"""
elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
# use a random prompt
lowercase = """hello world"""
else:
raise ValueError("""Model name not supported""" )
lowercase = original_model.decoder.tokenizer(lowercase_ , add_special_tokens=lowercase_ , return_tensors="""pt""" )[
"""input_ids"""
]
lowercase = original_model.encoder.model.patch_embed(lowercase_ )
lowercase , lowercase = model.encoder.embeddings(lowercase_ )
assert torch.allclose(lowercase_ , lowercase_ , atol=1E-3 )
# verify encoder hidden states
lowercase = original_model.encoder(lowercase_ )
lowercase = model.encoder(lowercase_ ).last_hidden_state
assert torch.allclose(lowercase_ , lowercase_ , atol=1E-2 )
# verify decoder hidden states
lowercase = original_model(lowercase_ , lowercase_ , lowercase_ ).logits
lowercase = model(lowercase_ , decoder_input_ids=lowercase_ ).logits
assert torch.allclose(lowercase_ , lowercase_ , atol=1E-3 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(F"""Saving model and processor to {pytorch_dump_folder_path}""" )
model.save_pretrained(lowercase_ )
processor.save_pretrained(lowercase_ )
if push_to_hub:
model.push_to_hub("""nielsr/""" + model_name.split("""/""" )[-1] , commit_message="""Update model""" )
processor.push_to_hub("""nielsr/""" + model_name.split("""/""" )[-1] , commit_message="""Update model""" )
if __name__ == "__main__":
lowercase_ : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''naver-clova-ix/donut-base-finetuned-docvqa''',
required=False,
type=str,
help='''Name of the original model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
required=False,
type=str,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
help='''Whether or not to push the converted model and processor to the 🤗 hub.''',
)
lowercase_ : Optional[Any] = parser.parse_args()
convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 653 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowercase_ : Union[str, Any] , lowercase_ : str ):
lowercase = """"""
for i in table:
res += inp[i - 1]
return res
def SCREAMING_SNAKE_CASE ( lowercase_ : List[Any] ):
return data[1:] + data[0]
def SCREAMING_SNAKE_CASE ( lowercase_ : Tuple , lowercase_ : Dict ):
lowercase = """"""
for i in range(len(lowercase_ ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def SCREAMING_SNAKE_CASE ( lowercase_ : str , lowercase_ : str ):
lowercase = int("""0b""" + data[0] + data[-1] , 2 )
lowercase = int("""0b""" + data[1:3] , 2 )
return bin(s[row][col] )[2:]
def SCREAMING_SNAKE_CASE ( lowercase_ : Dict , lowercase_ : Tuple , lowercase_ : Tuple , lowercase_ : str , lowercase_ : Any ):
lowercase = message[:4]
lowercase = message[4:]
lowercase = apply_table(lowercase_ , lowercase_ )
lowercase = xor(lowercase_ , lowercase_ )
lowercase = apply_sbox(lowercase_ , temp[:4] ) # noqa: E741
lowercase = apply_sbox(lowercase_ , temp[4:] )
lowercase = """0""" * (2 - len(lowercase_ )) + l # noqa: E741
lowercase = """0""" * (2 - len(lowercase_ )) + r
lowercase = apply_table(l + r , lowercase_ )
lowercase = xor(lowercase_ , lowercase_ )
return temp + right
if __name__ == "__main__":
lowercase_ : Tuple = input('''Enter 10 bit key: ''')
lowercase_ : Any = input('''Enter 8 bit message: ''')
lowercase_ : Dict = [6, 3, 7, 4, 8, 5, 10, 9]
lowercase_ : str = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
lowercase_ : List[Any] = [2, 4, 3, 1]
lowercase_ : List[str] = [2, 6, 3, 1, 4, 8, 5, 7]
lowercase_ : Tuple = [4, 1, 3, 5, 7, 2, 8, 6]
lowercase_ : Optional[Any] = [4, 1, 2, 3, 2, 3, 4, 1]
lowercase_ : List[str] = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
lowercase_ : List[Any] = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
lowercase_ : Union[str, Any] = apply_table(key, paa_table)
lowercase_ : Optional[Any] = temp[:5]
lowercase_ : int = temp[5:]
lowercase_ : List[str] = left_shift(left)
lowercase_ : int = left_shift(right)
lowercase_ : Tuple = apply_table(left + right, pa_table)
lowercase_ : List[str] = left_shift(left)
lowercase_ : Optional[Any] = left_shift(right)
lowercase_ : Union[str, Any] = left_shift(left)
lowercase_ : Union[str, Any] = left_shift(right)
lowercase_ : Optional[int] = apply_table(left + right, pa_table)
# encryption
lowercase_ : int = apply_table(message, IP)
lowercase_ : Dict = function(expansion, sa, sa, keya, temp)
lowercase_ : Any = temp[4:] + temp[:4]
lowercase_ : List[Any] = function(expansion, sa, sa, keya, temp)
lowercase_ : Tuple = apply_table(temp, IP_inv)
print('''Cipher text is:''', CT)
# decryption
lowercase_ : List[str] = apply_table(CT, IP)
lowercase_ : Optional[int] = function(expansion, sa, sa, keya, temp)
lowercase_ : Optional[Any] = temp[4:] + temp[:4]
lowercase_ : Optional[int] = function(expansion, sa, sa, keya, temp)
lowercase_ : Optional[Any] = apply_table(temp, IP_inv)
print('''Plain text after decypting is:''', PT)
| 653 | 1 |
'''simple docstring'''
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize("""repo_id""" , ["""canonical_dataset_name""", """org-name/dataset-name"""] )
@pytest.mark.parametrize("""path""" , ["""filename.csv""", """filename with blanks.csv"""] )
@pytest.mark.parametrize("""revision""" , [None, """v2"""] )
def SCREAMING_SNAKE_CASE ( lowercase_ : Optional[Any] , lowercase_ : Any , lowercase_ : str ):
lowercase = hf_hub_url(repo_id=lowercase_ , path=lowercase_ , revision=lowercase_ )
assert url == F"""https://huggingface.co/datasets/{repo_id}/resolve/{revision or 'main'}/{quote(lowercase_ )}"""
| 653 |
'''simple docstring'''
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
lowercase_ : int = 50_0000
lowercase_ , lowercase_ : Union[str, Any] = os.path.split(__file__)
lowercase_ : Optional[Any] = os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))
@get_duration
def SCREAMING_SNAKE_CASE ( lowercase_ : datasets.Dataset , **lowercase_ : Dict ):
lowercase = dataset.map(**lowercase_ )
@get_duration
def SCREAMING_SNAKE_CASE ( lowercase_ : datasets.Dataset , **lowercase_ : Optional[int] ):
lowercase = dataset.filter(**lowercase_ )
def SCREAMING_SNAKE_CASE ( ):
lowercase = {"""num examples""": SPEED_TEST_N_EXAMPLES}
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase = datasets.Features({"""text""": datasets.Value("""string""" ), """numbers""": datasets.Value("""float32""" )} )
lowercase = generate_example_dataset(
os.path.join(lowercase_ , """dataset.arrow""" ) , lowercase_ , num_examples=lowercase_ )
lowercase = transformers.AutoTokenizer.from_pretrained("""bert-base-cased""" , use_fast=lowercase_ )
def tokenize(lowercase_ : Dict ):
return tokenizer(examples["""text"""] )
lowercase = map(lowercase_ )
lowercase = map(lowercase_ , batched=lowercase_ )
lowercase = map(lowercase_ , function=lambda lowercase_ : None , batched=lowercase_ )
with dataset.formatted_as(type="""numpy""" ):
lowercase = map(lowercase_ , function=lambda lowercase_ : None , batched=lowercase_ )
with dataset.formatted_as(type="""pandas""" ):
lowercase = map(lowercase_ , function=lambda lowercase_ : None , batched=lowercase_ )
with dataset.formatted_as(type="""torch""" , columns="""numbers""" ):
lowercase = map(lowercase_ , function=lambda lowercase_ : None , batched=lowercase_ )
with dataset.formatted_as(type="""tensorflow""" , columns="""numbers""" ):
lowercase = map(lowercase_ , function=lambda lowercase_ : None , batched=lowercase_ )
lowercase = map(lowercase_ , function=lowercase_ , batched=lowercase_ )
lowercase = filter(lowercase_ )
# Activate later when tokenizer support batched inputs
# with dataset.formatted_as(type='numpy'):
# times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)
with open(lowercase_ , """wb""" ) as f:
f.write(json.dumps(lowercase_ ).encode("""utf-8""" ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_map_filter()
| 653 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowercase_ : int ):
if not isinstance(lowercase_ , lowercase_ ):
lowercase = F"""Input value of [number={number}] must be an integer"""
raise TypeError(lowercase_ )
if number < 0:
return False
lowercase = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 653 |
'''simple docstring'''
from random import shuffle
import tensorflow as tf
from numpy import array
def SCREAMING_SNAKE_CASE ( lowercase_ : List[str] , lowercase_ : Optional[int] ):
lowercase = int(lowercase_ )
assert noofclusters < len(lowercase_ )
# Find out the dimensionality
lowercase = len(vectors[0] )
# Will help select random centroids from among the available vectors
lowercase = list(range(len(lowercase_ ) ) )
shuffle(lowercase_ )
# GRAPH OF COMPUTATION
# We initialize a new graph and set it as the default during each run
# of this algorithm. This ensures that as this function is called
# multiple times, the default graph doesn't keep getting crowded with
# unused ops and Variables from previous function calls.
lowercase = tf.Graph()
with graph.as_default():
# SESSION OF COMPUTATION
lowercase = tf.Session()
##CONSTRUCTING THE ELEMENTS OF COMPUTATION
##First lets ensure we have a Variable vector for each centroid,
##initialized to one of the vectors from the available data points
lowercase = [
tf.Variable(vectors[vector_indices[i]] ) for i in range(lowercase_ )
]
##These nodes will assign the centroid Variables the appropriate
##values
lowercase = tf.placeholder("""float64""" , [dim] )
lowercase = []
for centroid in centroids:
cent_assigns.append(tf.assign(lowercase_ , lowercase_ ) )
##Variables for cluster assignments of individual vectors(initialized
##to 0 at first)
lowercase = [tf.Variable(0 ) for i in range(len(lowercase_ ) )]
##These nodes will assign an assignment Variable the appropriate
##value
lowercase = tf.placeholder("""int32""" )
lowercase = []
for assignment in assignments:
cluster_assigns.append(tf.assign(lowercase_ , lowercase_ ) )
##Now lets construct the node that will compute the mean
# The placeholder for the input
lowercase = tf.placeholder("""float""" , [None, dim] )
# The Node/op takes the input and computes a mean along the 0th
# dimension, i.e. the list of input vectors
lowercase = tf.reduce_mean(lowercase_ , 0 )
##Node for computing Euclidean distances
# Placeholders for input
lowercase = tf.placeholder("""float""" , [dim] )
lowercase = tf.placeholder("""float""" , [dim] )
lowercase = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(lowercase_ , lowercase_ ) , 2 ) ) )
##This node will figure out which cluster to assign a vector to,
##based on Euclidean distances of the vector from the centroids.
# Placeholder for input
lowercase = tf.placeholder("""float""" , [noofclusters] )
lowercase = tf.argmin(lowercase_ , 0 )
##INITIALIZING STATE VARIABLES
##This will help initialization of all Variables defined with respect
##to the graph. The Variable-initializer should be defined after
##all the Variables have been constructed, so that each of them
##will be included in the initialization.
lowercase = tf.initialize_all_variables()
# Initialize all variables
sess.run(lowercase_ )
##CLUSTERING ITERATIONS
# Now perform the Expectation-Maximization steps of K-Means clustering
# iterations. To keep things simple, we will only do a set number of
# iterations, instead of using a Stopping Criterion.
lowercase = 100
for _ in range(lowercase_ ):
##EXPECTATION STEP
##Based on the centroid locations till last iteration, compute
##the _expected_ centroid assignments.
# Iterate over each vector
for vector_n in range(len(lowercase_ ) ):
lowercase = vectors[vector_n]
# Compute Euclidean distance between this vector and each
# centroid. Remember that this list cannot be named
#'centroid_distances', since that is the input to the
# cluster assignment node.
lowercase = [
sess.run(lowercase_ , feed_dict={va: vect, va: sess.run(lowercase_ )} )
for centroid in centroids
]
# Now use the cluster assignment node, with the distances
# as the input
lowercase = sess.run(
lowercase_ , feed_dict={centroid_distances: distances} )
# Now assign the value to the appropriate state variable
sess.run(
cluster_assigns[vector_n] , feed_dict={assignment_value: assignment} )
##MAXIMIZATION STEP
# Based on the expected state computed from the Expectation Step,
# compute the locations of the centroids so as to maximize the
# overall objective of minimizing within-cluster Sum-of-Squares
for cluster_n in range(lowercase_ ):
# Collect all the vectors assigned to this cluster
lowercase = [
vectors[i]
for i in range(len(lowercase_ ) )
if sess.run(assignments[i] ) == cluster_n
]
# Compute new centroid location
lowercase = sess.run(
lowercase_ , feed_dict={mean_input: array(lowercase_ )} )
# Assign value to appropriate variable
sess.run(
cent_assigns[cluster_n] , feed_dict={centroid_value: new_location} )
# Return centroids and assignments
lowercase = sess.run(lowercase_ )
lowercase = sess.run(lowercase_ )
return centroids, assignments
| 653 | 1 |
'''simple docstring'''
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
lowercase_ : int = logging.getLogger()
def SCREAMING_SNAKE_CASE ( ):
lowercase = argparse.ArgumentParser()
parser.add_argument("""-f""" )
lowercase = parser.parse_args()
return args.f
class __UpperCamelCase (_UpperCAmelCase ):
def _a ( self ) -> None:
'''simple docstring'''
lowercase = logging.StreamHandler(sys.stdout )
logger.addHandler(_lowerCAmelCase )
def _a ( self , _lowerCAmelCase ) -> Any:
'''simple docstring'''
lowercase = get_gpu_count()
if n_gpu > 1:
pass
# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
# cmd = [sys.executable] + distributed_args + args
# execute_subprocess_async(cmd, env=self.get_env())
# XXX: test the results - need to save them first into .json file
else:
args.insert(0 , """run_glue_deebert.py""" )
with patch.object(_lowerCAmelCase , """argv""" , _lowerCAmelCase ):
lowercase = run_glue_deebert.main()
for value in result.values():
self.assertGreaterEqual(_lowerCAmelCase , 0.666 )
@slow
@require_torch_non_multi_gpu
def _a ( self ) -> Tuple:
'''simple docstring'''
lowercase = """
--model_type roberta
--model_name_or_path roberta-base
--task_name MRPC
--do_train
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--max_seq_length 128
--per_gpu_eval_batch_size=1
--per_gpu_train_batch_size=8
--learning_rate 2e-4
--num_train_epochs 3
--overwrite_output_dir
--seed 42
--output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--save_steps 0
--overwrite_cache
--eval_after_first_stage
""".split()
self.run_and_check(_lowerCAmelCase )
lowercase = """
--model_type roberta
--model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--task_name MRPC
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--max_seq_length 128
--eval_each_highway
--eval_highway
--overwrite_cache
--per_gpu_eval_batch_size=1
""".split()
self.run_and_check(_lowerCAmelCase )
lowercase = """
--model_type roberta
--model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--task_name MRPC
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--max_seq_length 128
--early_exit_entropy 0.1
--eval_highway
--overwrite_cache
--per_gpu_eval_batch_size=1
""".split()
self.run_and_check(_lowerCAmelCase )
| 653 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : int , lowercase_ : list[list[int]] ):
def update_area_of_max_square(lowercase_ : int , lowercase_ : int ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
lowercase = update_area_of_max_square(lowercase_ , col + 1 )
lowercase = update_area_of_max_square(row + 1 , col + 1 )
lowercase = update_area_of_max_square(row + 1 , lowercase_ )
if mat[row][col]:
lowercase = 1 + min([right, diagonal, down] )
lowercase = max(largest_square_area[0] , lowercase_ )
return sub_problem_sol
else:
return 0
lowercase = [0]
update_area_of_max_square(0 , 0 )
return largest_square_area[0]
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : int , lowercase_ : list[list[int]] ):
def update_area_of_max_square_using_dp_array(
lowercase_ : int , lowercase_ : int , lowercase_ : list[list[int]] ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
lowercase = update_area_of_max_square_using_dp_array(lowercase_ , col + 1 , lowercase_ )
lowercase = update_area_of_max_square_using_dp_array(row + 1 , col + 1 , lowercase_ )
lowercase = update_area_of_max_square_using_dp_array(row + 1 , lowercase_ , lowercase_ )
if mat[row][col]:
lowercase = 1 + min([right, diagonal, down] )
lowercase = max(largest_square_area[0] , lowercase_ )
lowercase = sub_problem_sol
return sub_problem_sol
else:
return 0
lowercase = [0]
lowercase = [[-1] * cols for _ in range(lowercase_ )]
update_area_of_max_square_using_dp_array(0 , 0 , lowercase_ )
return largest_square_area[0]
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : int , lowercase_ : list[list[int]] ):
lowercase = [[0] * (cols + 1) for _ in range(rows + 1 )]
lowercase = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
lowercase = dp_array[row][col + 1]
lowercase = dp_array[row + 1][col + 1]
lowercase = dp_array[row + 1][col]
if mat[row][col] == 1:
lowercase = 1 + min(lowercase_ , lowercase_ , lowercase_ )
lowercase = max(dp_array[row][col] , lowercase_ )
else:
lowercase = 0
return largest_square_area
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : int , lowercase_ : list[list[int]] ):
lowercase = [0] * (cols + 1)
lowercase = [0] * (cols + 1)
lowercase = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
lowercase = current_row[col + 1]
lowercase = next_row[col + 1]
lowercase = next_row[col]
if mat[row][col] == 1:
lowercase = 1 + min(lowercase_ , lowercase_ , lowercase_ )
lowercase = max(current_row[col] , lowercase_ )
else:
lowercase = 0
lowercase = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 653 | 1 |
'''simple docstring'''
from PIL import Image
def SCREAMING_SNAKE_CASE ( lowercase_ : Image , lowercase_ : float ):
def brightness(lowercase_ : int ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("""level must be between -255.0 (black) and 255.0 (white)""" )
return img.point(lowercase_ )
if __name__ == "__main__":
# Load image
with Image.open('''image_data/lena.jpg''') as img:
# Change brightness to 100
lowercase_ : Dict = change_brightness(img, 100)
brigt_img.save('''image_data/lena_brightness.png''', format='''png''')
| 653 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : Optional[Any] = logging.get_logger(__name__)
lowercase_ : int = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}
class __UpperCamelCase (_UpperCAmelCase ):
__A = '''gpt_bigcode'''
__A = ['''past_key_values''']
__A = {
'''hidden_size''': '''n_embd''',
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self , _lowerCAmelCase=5_0257 , _lowerCAmelCase=1024 , _lowerCAmelCase=768 , _lowerCAmelCase=12 , _lowerCAmelCase=12 , _lowerCAmelCase=None , _lowerCAmelCase="gelu_pytorch_tanh" , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=1E-5 , _lowerCAmelCase=0.02 , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=5_0256 , _lowerCAmelCase=5_0256 , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=True , **_lowerCAmelCase , ) -> Optional[int]:
'''simple docstring'''
lowercase = vocab_size
lowercase = n_positions
lowercase = n_embd
lowercase = n_layer
lowercase = n_head
lowercase = n_inner
lowercase = activation_function
lowercase = resid_pdrop
lowercase = embd_pdrop
lowercase = attn_pdrop
lowercase = layer_norm_epsilon
lowercase = initializer_range
lowercase = scale_attn_weights
lowercase = use_cache
lowercase = attention_softmax_in_fpaa
lowercase = scale_attention_softmax_in_fpaa
lowercase = multi_query
lowercase = bos_token_id
lowercase = eos_token_id
super().__init__(bos_token_id=_lowerCAmelCase , eos_token_id=_lowerCAmelCase , **_lowerCAmelCase )
| 653 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : int , lowercase_ : list[list[int]] ):
def update_area_of_max_square(lowercase_ : int , lowercase_ : int ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
lowercase = update_area_of_max_square(lowercase_ , col + 1 )
lowercase = update_area_of_max_square(row + 1 , col + 1 )
lowercase = update_area_of_max_square(row + 1 , lowercase_ )
if mat[row][col]:
lowercase = 1 + min([right, diagonal, down] )
lowercase = max(largest_square_area[0] , lowercase_ )
return sub_problem_sol
else:
return 0
lowercase = [0]
update_area_of_max_square(0 , 0 )
return largest_square_area[0]
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : int , lowercase_ : list[list[int]] ):
def update_area_of_max_square_using_dp_array(
lowercase_ : int , lowercase_ : int , lowercase_ : list[list[int]] ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
lowercase = update_area_of_max_square_using_dp_array(lowercase_ , col + 1 , lowercase_ )
lowercase = update_area_of_max_square_using_dp_array(row + 1 , col + 1 , lowercase_ )
lowercase = update_area_of_max_square_using_dp_array(row + 1 , lowercase_ , lowercase_ )
if mat[row][col]:
lowercase = 1 + min([right, diagonal, down] )
lowercase = max(largest_square_area[0] , lowercase_ )
lowercase = sub_problem_sol
return sub_problem_sol
else:
return 0
lowercase = [0]
lowercase = [[-1] * cols for _ in range(lowercase_ )]
update_area_of_max_square_using_dp_array(0 , 0 , lowercase_ )
return largest_square_area[0]
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : int , lowercase_ : list[list[int]] ):
lowercase = [[0] * (cols + 1) for _ in range(rows + 1 )]
lowercase = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
lowercase = dp_array[row][col + 1]
lowercase = dp_array[row + 1][col + 1]
lowercase = dp_array[row + 1][col]
if mat[row][col] == 1:
lowercase = 1 + min(lowercase_ , lowercase_ , lowercase_ )
lowercase = max(dp_array[row][col] , lowercase_ )
else:
lowercase = 0
return largest_square_area
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : int , lowercase_ : list[list[int]] ):
lowercase = [0] * (cols + 1)
lowercase = [0] * (cols + 1)
lowercase = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
lowercase = current_row[col + 1]
lowercase = next_row[col + 1]
lowercase = next_row[col]
if mat[row][col] == 1:
lowercase = 1 + min(lowercase_ , lowercase_ , lowercase_ )
lowercase = max(current_row[col] , lowercase_ )
else:
lowercase = 0
lowercase = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 653 |
'''simple docstring'''
import requests
def SCREAMING_SNAKE_CASE ( lowercase_ : str , lowercase_ : str ):
lowercase = {"""Content-Type""": """application/json"""}
lowercase = requests.post(lowercase_ , json={"""text""": message_body} , headers=lowercase_ )
if response.status_code != 200:
lowercase = (
"""Request to slack returned an error """
F"""{response.status_code}, the response is:\n{response.text}"""
)
raise ValueError(lowercase_ )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message('''<YOUR MESSAGE BODY>''', '''<SLACK CHANNEL URL>''')
| 653 | 1 |
'''simple docstring'''
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
lowercase_ : Optional[Any] = Path(__file__).resolve().parents[3] / '''src'''
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itertools # noqa
import json # noqa
import os # noqa
import unittest # noqa
from copy import deepcopy # noqa
from parameterized import parameterized # noqa
from transformers import TrainingArguments, is_torch_available # noqa
from transformers.deepspeed import is_deepspeed_available # noqa
from transformers.file_utils import WEIGHTS_NAME # noqa
from transformers.testing_utils import ( # noqa
CaptureLogger,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
mockenv_context,
require_deepspeed,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
from transformers.trainer_utils import set_seed # noqa
set_seed(42)
lowercase_ : Union[str, Any] = {'''base''': '''patrickvonplaten/wav2vec2_tiny_random''', '''robust''': '''patrickvonplaten/wav2vec2_tiny_random_robust'''}
lowercase_ : Dict = '''zero2'''
lowercase_ : Dict = '''zero3'''
lowercase_ : int = [ZEROa, ZEROa]
def SCREAMING_SNAKE_CASE ( lowercase_ : Union[str, Any] , lowercase_ : Optional[Any] , lowercase_ : Union[str, Any] ):
# customize the test name generator function as we want both params to appear in the sub-test
# name, as by default it shows only the first param
lowercase = parameterized.to_safe_name("""_""".join(str(lowercase_ ) for x in param.args ) )
return F"""{func.__name__}_{param_based_name}"""
# Cartesian-product of zero stages with models to test
lowercase_ : int = list(itertools.product(stages, models.keys()))
@slow
@require_deepspeed
@require_torch_gpu
class __UpperCamelCase (_UpperCAmelCase ):
@parameterized.expand(_lowerCAmelCase , name_func=_lowerCAmelCase )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase ) -> int:
'''simple docstring'''
self.run_and_check(
stage=_lowerCAmelCase , model=_lowerCAmelCase , distributed=_lowerCAmelCase , fpaa=_lowerCAmelCase , )
@require_torch_multi_gpu
@parameterized.expand(_lowerCAmelCase , name_func=_lowerCAmelCase )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[int]:
'''simple docstring'''
self.run_and_check(
stage=_lowerCAmelCase , model=_lowerCAmelCase , distributed=_lowerCAmelCase , fpaa=_lowerCAmelCase , )
@parameterized.expand(_lowerCAmelCase , name_func=_lowerCAmelCase )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase ) -> Any:
'''simple docstring'''
self.run_and_check(
stage=_lowerCAmelCase , model=_lowerCAmelCase , distributed=_lowerCAmelCase , fpaa=_lowerCAmelCase , )
@require_torch_multi_gpu
@parameterized.expand(_lowerCAmelCase , name_func=_lowerCAmelCase )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase ) -> Tuple:
'''simple docstring'''
self.run_and_check(
stage=_lowerCAmelCase , model=_lowerCAmelCase , distributed=_lowerCAmelCase , fpaa=_lowerCAmelCase , )
def _a ( self , _lowerCAmelCase ) -> Dict:
'''simple docstring'''
pass
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 10 , _lowerCAmelCase = True , _lowerCAmelCase = True , _lowerCAmelCase = True , ) -> Dict:
'''simple docstring'''
lowercase = models[model]
lowercase = self.run_trainer(
stage=_lowerCAmelCase , model_name=_lowerCAmelCase , eval_steps=_lowerCAmelCase , num_train_epochs=1 , distributed=_lowerCAmelCase , fpaa=_lowerCAmelCase , )
self.do_checks(_lowerCAmelCase )
return output_dir
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 10 , _lowerCAmelCase = 1 , _lowerCAmelCase = True , _lowerCAmelCase = True , ) -> Optional[int]:
'''simple docstring'''
lowercase = self.get_auto_remove_tmp_dir("""./xxx""" , after=_lowerCAmelCase )
lowercase = F"""
--model_name_or_path {model_name}
--dataset_name hf-internal-testing/librispeech_asr_dummy
--dataset_config_name clean
--train_split_name validation
--validation_split_name validation
--output_dir {output_dir}
--num_train_epochs {str(_lowerCAmelCase )}
--per_device_train_batch_size 2
--per_device_eval_batch_size 2
--evaluation_strategy steps
--learning_rate 5e-4
--warmup_steps 8
--orthography timit
--preprocessing_num_workers 1
--group_by_length
--freeze_feature_extractor
--report_to none
--save_steps 0
--eval_steps {eval_steps}
--report_to none
""".split()
if fpaa:
args.extend(["""--fp16"""] )
# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
# hence the separate config files
lowercase = F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
lowercase = [F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
lowercase = self.get_launcher(_lowerCAmelCase )
lowercase = launcher + script + args + ds_args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(_lowerCAmelCase , env=self.get_env() )
return output_dir
def _a ( self , _lowerCAmelCase=False ) -> Union[str, Any]:
'''simple docstring'''
lowercase = min(2 , get_gpu_count() ) if distributed else 1
return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()
| 653 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
lowercase_ : List[str] = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE ( lowercase_ : int ):
lowercase = MobileViTConfig()
# size of the architecture
if "mobilevit_s" in mobilevit_name:
lowercase = [144, 192, 240]
lowercase = [16, 32, 64, 96, 128, 160, 640]
elif "mobilevit_xs" in mobilevit_name:
lowercase = [96, 120, 144]
lowercase = [16, 32, 48, 64, 80, 96, 384]
elif "mobilevit_xxs" in mobilevit_name:
lowercase = [64, 80, 96]
lowercase = [16, 16, 24, 48, 64, 80, 320]
lowercase = 0.05
lowercase = 2.0
if mobilevit_name.startswith("""deeplabv3_""" ):
lowercase = 512
lowercase = 16
lowercase = 21
lowercase = """pascal-voc-id2label.json"""
else:
lowercase = 1000
lowercase = """imagenet-1k-id2label.json"""
lowercase = """huggingface/label-files"""
lowercase = json.load(open(hf_hub_download(lowercase_ , lowercase_ , repo_type="""dataset""" ) , """r""" ) )
lowercase = {int(lowercase_ ): v for k, v in idalabel.items()}
lowercase = idalabel
lowercase = {v: k for k, v in idalabel.items()}
return config
def SCREAMING_SNAKE_CASE ( lowercase_ : Any , lowercase_ : Any=False ):
for i in range(1 , 6 ):
if F"""layer_{i}.""" in name:
lowercase = name.replace(F"""layer_{i}.""" , F"""encoder.layer.{i - 1}.""" )
if "conv_1." in name:
lowercase = name.replace("""conv_1.""" , """conv_stem.""" )
if ".block." in name:
lowercase = name.replace(""".block.""" , """.""" )
if "exp_1x1" in name:
lowercase = name.replace("""exp_1x1""" , """expand_1x1""" )
if "red_1x1" in name:
lowercase = name.replace("""red_1x1""" , """reduce_1x1""" )
if ".local_rep.conv_3x3." in name:
lowercase = name.replace(""".local_rep.conv_3x3.""" , """.conv_kxk.""" )
if ".local_rep.conv_1x1." in name:
lowercase = name.replace(""".local_rep.conv_1x1.""" , """.conv_1x1.""" )
if ".norm." in name:
lowercase = name.replace(""".norm.""" , """.normalization.""" )
if ".conv." in name:
lowercase = name.replace(""".conv.""" , """.convolution.""" )
if ".conv_proj." in name:
lowercase = name.replace(""".conv_proj.""" , """.conv_projection.""" )
for i in range(0 , 2 ):
for j in range(0 , 4 ):
if F""".{i}.{j}.""" in name:
lowercase = name.replace(F""".{i}.{j}.""" , F""".{i}.layer.{j}.""" )
for i in range(2 , 6 ):
for j in range(0 , 4 ):
if F""".{i}.{j}.""" in name:
lowercase = name.replace(F""".{i}.{j}.""" , F""".{i}.""" )
if "expand_1x1" in name:
lowercase = name.replace("""expand_1x1""" , """downsampling_layer.expand_1x1""" )
if "conv_3x3" in name:
lowercase = name.replace("""conv_3x3""" , """downsampling_layer.conv_3x3""" )
if "reduce_1x1" in name:
lowercase = name.replace("""reduce_1x1""" , """downsampling_layer.reduce_1x1""" )
for i in range(2 , 5 ):
if F""".global_rep.{i}.weight""" in name:
lowercase = name.replace(F""".global_rep.{i}.weight""" , """.layernorm.weight""" )
if F""".global_rep.{i}.bias""" in name:
lowercase = name.replace(F""".global_rep.{i}.bias""" , """.layernorm.bias""" )
if ".global_rep." in name:
lowercase = name.replace(""".global_rep.""" , """.transformer.""" )
if ".pre_norm_mha.0." in name:
lowercase = name.replace(""".pre_norm_mha.0.""" , """.layernorm_before.""" )
if ".pre_norm_mha.1.out_proj." in name:
lowercase = name.replace(""".pre_norm_mha.1.out_proj.""" , """.attention.output.dense.""" )
if ".pre_norm_ffn.0." in name:
lowercase = name.replace(""".pre_norm_ffn.0.""" , """.layernorm_after.""" )
if ".pre_norm_ffn.1." in name:
lowercase = name.replace(""".pre_norm_ffn.1.""" , """.intermediate.dense.""" )
if ".pre_norm_ffn.4." in name:
lowercase = name.replace(""".pre_norm_ffn.4.""" , """.output.dense.""" )
if ".transformer." in name:
lowercase = name.replace(""".transformer.""" , """.transformer.layer.""" )
if ".aspp_layer." in name:
lowercase = name.replace(""".aspp_layer.""" , """.""" )
if ".aspp_pool." in name:
lowercase = name.replace(""".aspp_pool.""" , """.""" )
if "seg_head." in name:
lowercase = name.replace("""seg_head.""" , """segmentation_head.""" )
if "segmentation_head.classifier.classifier." in name:
lowercase = name.replace("""segmentation_head.classifier.classifier.""" , """segmentation_head.classifier.""" )
if "classifier.fc." in name:
lowercase = name.replace("""classifier.fc.""" , """classifier.""" )
elif (not base_model) and ("segmentation_head." not in name):
lowercase = """mobilevit.""" + name
return name
def SCREAMING_SNAKE_CASE ( lowercase_ : Optional[Any] , lowercase_ : List[Any] , lowercase_ : str=False ):
if base_model:
lowercase = """"""
else:
lowercase = """mobilevit."""
for key in orig_state_dict.copy().keys():
lowercase = orig_state_dict.pop(lowercase_ )
if key[:8] == "encoder.":
lowercase = key[8:]
if "qkv" in key:
lowercase = key.split(""".""" )
lowercase = int(key_split[0][6:] ) - 1
lowercase = int(key_split[3] )
lowercase = model.get_submodule(F"""{model_prefix}encoder.layer.{layer_num}""" )
lowercase = layer.transformer.layer[transformer_num].attention.attention.all_head_size
lowercase = (
F"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
)
if "weight" in key:
lowercase = val[:dim, :]
lowercase = val[dim : dim * 2, :]
lowercase = val[-dim:, :]
else:
lowercase = val[:dim]
lowercase = val[dim : dim * 2]
lowercase = val[-dim:]
else:
lowercase = val
return orig_state_dict
def SCREAMING_SNAKE_CASE ( ):
lowercase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowercase = Image.open(requests.get(lowercase_ , stream=lowercase_ ).raw )
return im
@torch.no_grad()
def SCREAMING_SNAKE_CASE ( lowercase_ : Dict , lowercase_ : List[Any] , lowercase_ : Any , lowercase_ : List[str]=False ):
lowercase = get_mobilevit_config(lowercase_ )
# load original state_dict
lowercase = torch.load(lowercase_ , map_location="""cpu""" )
# load 🤗 model
if mobilevit_name.startswith("""deeplabv3_""" ):
lowercase = MobileViTForSemanticSegmentation(lowercase_ ).eval()
else:
lowercase = MobileViTForImageClassification(lowercase_ ).eval()
lowercase = convert_state_dict(lowercase_ , lowercase_ )
model.load_state_dict(lowercase_ )
# Check outputs on an image, prepared by MobileViTImageProcessor
lowercase = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
lowercase = image_processor(images=prepare_img() , return_tensors="""pt""" )
lowercase = model(**lowercase_ )
lowercase = outputs.logits
if mobilevit_name.startswith("""deeplabv3_""" ):
assert logits.shape == (1, 21, 32, 32)
if mobilevit_name == "deeplabv3_mobilevit_s":
lowercase = torch.tensor(
[
[[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
[[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
[[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xs":
lowercase = torch.tensor(
[
[[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
[[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
[[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xxs":
lowercase = torch.tensor(
[
[[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
[[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
[[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
] )
else:
raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )
assert torch.allclose(logits[0, :3, :3, :3] , lowercase_ , atol=1E-4 )
else:
assert logits.shape == (1, 1000)
if mobilevit_name == "mobilevit_s":
lowercase = torch.tensor([-0.9_866, 0.2_392, -1.1_241] )
elif mobilevit_name == "mobilevit_xs":
lowercase = torch.tensor([-2.4_761, -0.9_399, -1.9_587] )
elif mobilevit_name == "mobilevit_xxs":
lowercase = torch.tensor([-1.9_364, -1.2_327, -0.4_653] )
else:
raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )
assert torch.allclose(logits[0, :3] , lowercase_ , atol=1E-4 )
Path(lowercase_ ).mkdir(exist_ok=lowercase_ )
print(F"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(lowercase_ )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(lowercase_ )
if push_to_hub:
lowercase = {
"""mobilevit_s""": """mobilevit-small""",
"""mobilevit_xs""": """mobilevit-x-small""",
"""mobilevit_xxs""": """mobilevit-xx-small""",
"""deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
"""deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
"""deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
}
print("""Pushing to the hub...""" )
lowercase = model_mapping[mobilevit_name]
image_processor.push_to_hub(lowercase_ , organization="""apple""" )
model.push_to_hub(lowercase_ , organization="""apple""" )
if __name__ == "__main__":
lowercase_ : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--mobilevit_name''',
default='''mobilevit_s''',
type=str,
help=(
'''Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','''
''' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'''
),
)
parser.add_argument(
'''--checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
lowercase_ : List[str] = parser.parse_args()
convert_movilevit_checkpoint(
args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 653 | 1 |
'''simple docstring'''
import numpy as np
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : Optional[Any] , lowercase_ : int , lowercase_ : List[Any] , lowercase_ : str ):
lowercase = int(np.ceil((x_end - xa) / h ) )
lowercase = np.zeros((n + 1,) )
lowercase = ya
lowercase = xa
for k in range(lowercase_ ):
lowercase = f(lowercase_ , y[k] )
lowercase = f(x + 0.5 * h , y[k] + 0.5 * h * ka )
lowercase = f(x + 0.5 * h , y[k] + 0.5 * h * ka )
lowercase = f(x + h , y[k] + h * ka )
lowercase = y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
x += h
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 653 |
'''simple docstring'''
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
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 SwiftFormerForImageClassification, SwiftFormerModel
from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __UpperCamelCase :
def __init__( self , _lowerCAmelCase , _lowerCAmelCase=13 , _lowerCAmelCase=3 , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=224 , _lowerCAmelCase=1000 , _lowerCAmelCase=[3, 3, 6, 4] , _lowerCAmelCase=[48, 56, 112, 220] , ) -> List[str]:
'''simple docstring'''
lowercase = parent
lowercase = batch_size
lowercase = num_channels
lowercase = is_training
lowercase = use_labels
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = num_labels
lowercase = image_size
lowercase = layer_depths
lowercase = embed_dims
def _a ( self ) -> Tuple:
'''simple docstring'''
lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase = None
if self.use_labels:
lowercase = ids_tensor([self.batch_size] , self.num_labels )
lowercase = self.get_config()
return config, pixel_values, labels
def _a ( self ) -> int:
'''simple docstring'''
return SwiftFormerConfig(
depths=self.layer_depths , embed_dims=self.embed_dims , mlp_ratio=4 , downsamples=[True, True, True, True] , hidden_act="""gelu""" , num_labels=self.num_labels , down_patch_size=3 , down_stride=2 , down_pad=1 , drop_rate=0.0 , drop_path_rate=0.0 , use_layer_scale=_lowerCAmelCase , layer_scale_init_value=1E-5 , )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowercase = SwiftFormerModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
lowercase = model(_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dims[-1], 7, 7) )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowercase = self.num_labels
lowercase = SwiftFormerForImageClassification(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
lowercase = model(_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
lowercase = SwiftFormerForImageClassification(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase = model(_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
((lowercase) , (lowercase) , (lowercase)) = self.prepare_config_and_inputs()
lowercase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __UpperCamelCase (_UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
__A = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
__A = (
{'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
if is_torch_available()
else {}
)
__A = False
__A = False
__A = False
__A = False
__A = False
def _a ( self ) -> Dict:
'''simple docstring'''
lowercase = SwiftFormerModelTester(self )
lowercase = ConfigTester(
self , config_class=_lowerCAmelCase , has_text_modality=_lowerCAmelCase , hidden_size=37 , num_attention_heads=12 , num_hidden_layers=12 , )
def _a ( self ) -> List[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="""SwiftFormer does not use inputs_embeds""" )
def _a ( self ) -> List[str]:
'''simple docstring'''
pass
def _a ( self ) -> Dict:
'''simple docstring'''
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase = model_class(_lowerCAmelCase )
lowercase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowerCAmelCase , nn.Linear ) )
def _a ( self ) -> int:
'''simple docstring'''
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase = model_class(_lowerCAmelCase )
lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase = [*signature.parameters.keys()]
lowercase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
def _a ( self ) -> List[str]:
'''simple docstring'''
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase )
@slow
def _a ( self ) -> Any:
'''simple docstring'''
for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase = SwiftFormerModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
@unittest.skip(reason="""SwiftFormer does not output attentions""" )
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
pass
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
def check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
with torch.no_grad():
lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
lowercase = outputs.hidden_states
lowercase = 8
self.assertEqual(len(_lowerCAmelCase ) , _lowerCAmelCase ) # TODO
# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
# with the width and height being successively divided by 2, after every 2 blocks
for i in range(len(_lowerCAmelCase ) ):
self.assertEqual(
hidden_states[i].shape , torch.Size(
[
self.model_tester.batch_size,
self.model_tester.embed_dims[i // 2],
(self.model_tester.image_size // 4) // 2 ** (i // 2),
(self.model_tester.image_size // 4) // 2 ** (i // 2),
] ) , )
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def _a ( self ) -> Dict:
'''simple docstring'''
def _config_zero_init(_lowerCAmelCase ):
lowercase = copy.deepcopy(_lowerCAmelCase )
for key in configs_no_init.__dict__.keys():
if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
setattr(_lowerCAmelCase , _lowerCAmelCase , 1E-10 )
if isinstance(getattr(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) , _lowerCAmelCase ):
lowercase = _config_zero_init(getattr(_lowerCAmelCase , _lowerCAmelCase ) )
setattr(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return configs_no_init
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = _config_zero_init(_lowerCAmelCase )
for model_class in self.all_model_classes:
lowercase = model_class(config=_lowerCAmelCase )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9) / 1E9).round().item() , [0.0, 1.0] , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def _a ( self ) -> Any:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE ( ):
lowercase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class __UpperCamelCase (unittest.TestCase ):
@cached_property
def _a ( self ) -> List[str]:
'''simple docstring'''
return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs""" ) if is_vision_available() else None
@slow
def _a ( self ) -> List[Any]:
'''simple docstring'''
lowercase = SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs""" ).to(_lowerCAmelCase )
lowercase = self.default_image_processor
lowercase = prepare_img()
lowercase = image_processor(images=_lowerCAmelCase , return_tensors="""pt""" ).to(_lowerCAmelCase )
# forward pass
with torch.no_grad():
lowercase = model(**_lowerCAmelCase )
# verify the logits
lowercase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
lowercase = torch.tensor([[-2.17_03E00, 2.11_07E00, -2.08_11E00]] ).to(_lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowerCAmelCase , atol=1E-4 ) )
| 653 | 1 |
'''simple docstring'''
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
lowercase_ : List[Any] = get_tests_dir('''fixtures/dummy-config.json''')
class __UpperCamelCase (unittest.TestCase ):
def _a ( self ) -> int:
'''simple docstring'''
lowercase = 0
def _a ( self ) -> List[Any]:
'''simple docstring'''
self.assertIsNotNone(transformers.models.auto.__spec__ )
self.assertIsNotNone(importlib.util.find_spec("""transformers.models.auto""" ) )
def _a ( self ) -> Optional[int]:
'''simple docstring'''
lowercase = AutoConfig.from_pretrained("""bert-base-uncased""" )
self.assertIsInstance(_lowerCAmelCase , _lowerCAmelCase )
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase = AutoConfig.from_pretrained(_lowerCAmelCase )
self.assertIsInstance(_lowerCAmelCase , _lowerCAmelCase )
def _a ( self ) -> List[str]:
'''simple docstring'''
lowercase = AutoConfig.from_pretrained(_lowerCAmelCase )
self.assertIsInstance(_lowerCAmelCase , _lowerCAmelCase )
def _a ( self ) -> List[Any]:
'''simple docstring'''
lowercase = AutoConfig.for_model("""roberta""" )
self.assertIsInstance(_lowerCAmelCase , _lowerCAmelCase )
def _a ( self ) -> List[Any]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
# This model name contains bert and roberta, but roberta ends up being picked.
lowercase = os.path.join(_lowerCAmelCase , """fake-roberta""" )
os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase )
with open(os.path.join(_lowerCAmelCase , """config.json""" ) , """w""" ) as f:
f.write(json.dumps({} ) )
lowercase = AutoConfig.from_pretrained(_lowerCAmelCase )
self.assertEqual(type(_lowerCAmelCase ) , _lowerCAmelCase )
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
try:
AutoConfig.register("""custom""" , _lowerCAmelCase )
# Wrong model type will raise an error
with self.assertRaises(_lowerCAmelCase ):
AutoConfig.register("""model""" , _lowerCAmelCase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_lowerCAmelCase ):
AutoConfig.register("""bert""" , _lowerCAmelCase )
# Now that the config is registered, it can be used as any other config with the auto-API
lowercase = CustomConfig()
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(_lowerCAmelCase )
lowercase = AutoConfig.from_pretrained(_lowerCAmelCase )
self.assertIsInstance(_lowerCAmelCase , _lowerCAmelCase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
def _a ( self ) -> List[str]:
'''simple docstring'''
with self.assertRaisesRegex(
_lowerCAmelCase , """bert-base is not a local folder and is not a valid model identifier""" ):
lowercase = AutoConfig.from_pretrained("""bert-base""" )
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
with self.assertRaisesRegex(
_lowerCAmelCase , r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
lowercase = AutoConfig.from_pretrained(_lowerCAmelCase , revision="""aaaaaa""" )
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
with self.assertRaisesRegex(
_lowerCAmelCase , """hf-internal-testing/no-config-test-repo does not appear to have a file named config.json.""" , ):
lowercase = AutoConfig.from_pretrained("""hf-internal-testing/no-config-test-repo""" )
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
with self.assertRaises(_lowerCAmelCase ):
lowercase = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_lowerCAmelCase ):
lowercase = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" , trust_remote_code=_lowerCAmelCase )
lowercase = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" , trust_remote_code=_lowerCAmelCase )
self.assertEqual(config.__class__.__name__ , """NewModelConfig""" )
# Test config can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(_lowerCAmelCase )
lowercase = AutoConfig.from_pretrained(_lowerCAmelCase , trust_remote_code=_lowerCAmelCase )
self.assertEqual(reloaded_config.__class__.__name__ , """NewModelConfig""" )
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
class __UpperCamelCase (_UpperCAmelCase ):
__A = '''new-model'''
try:
AutoConfig.register("""new-model""" , _lowerCAmelCase )
# If remote code is not set, the default is to use local
lowercase = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" )
self.assertEqual(config.__class__.__name__ , """NewModelConfigLocal""" )
# If remote code is disabled, we load the local one.
lowercase = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" , trust_remote_code=_lowerCAmelCase )
self.assertEqual(config.__class__.__name__ , """NewModelConfigLocal""" )
# If remote is enabled, we load from the Hub
lowercase = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" , trust_remote_code=_lowerCAmelCase )
self.assertEqual(config.__class__.__name__ , """NewModelConfig""" )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
| 653 |
'''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def SCREAMING_SNAKE_CASE ( ):
lowercase = HfArgumentParser(lowercase_ )
lowercase = parser.parse_args_into_dataclasses()[0]
lowercase = TensorFlowBenchmark(args=lowercase_ )
try:
lowercase = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
lowercase = """Arg --no_{0} is no longer used, please use --no-{0} instead."""
lowercase = """ """.join(str(lowercase_ ).split(""" """ )[:-1] )
lowercase = """"""
lowercase = eval(str(lowercase_ ).split(""" """ )[-1] )
lowercase = []
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(lowercase_ )
if len(lowercase_ ) > 0:
lowercase = full_error_msg + begin_error_msg + str(lowercase_ )
raise ValueError(lowercase_ )
benchmark.run()
if __name__ == "__main__":
main()
| 653 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
lowercase_ : Optional[Any] = logging.get_logger(__name__)
class __UpperCamelCase (_UpperCAmelCase ):
def __init__( self , *_lowerCAmelCase , **_lowerCAmelCase ) -> None:
'''simple docstring'''
warnings.warn(
"""The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use VideoMAEImageProcessor instead.""" , _lowerCAmelCase , )
super().__init__(*_lowerCAmelCase , **_lowerCAmelCase )
| 653 |
'''simple docstring'''
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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.
# this script dumps information about the environment
import os
import platform
import sys
lowercase_ : List[str] = '''3'''
print('''Python version:''', sys.version)
print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())
try:
import torch
print('''Torch version:''', torch.__version__)
print('''Cuda available:''', torch.cuda.is_available())
print('''Cuda version:''', torch.version.cuda)
print('''CuDNN version:''', torch.backends.cudnn.version())
print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
print('''Torch version:''', None)
try:
import transformers
print('''transformers version:''', transformers.__version__)
except ImportError:
print('''transformers version:''', None)
| 653 | 1 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
lowercase_ : List[str] = logging.get_logger()
@dataclass
class __UpperCamelCase :
__A = 42
__A = field(default_factory=_UpperCAmelCase )
__A = field(default_factory=_UpperCAmelCase )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> List[str]:
'''simple docstring'''
lowercase = len(list(m.modules() ) ) == 1 or isinstance(_lowerCAmelCase , nn.Convad ) or isinstance(_lowerCAmelCase , nn.BatchNormad )
if has_not_submodules:
self.traced.append(_lowerCAmelCase )
def __call__( self , _lowerCAmelCase ) -> Tuple:
'''simple docstring'''
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(_lowerCAmelCase )
[x.remove() for x in self.handles]
return self
@property
def _a ( self ) -> Any:
'''simple docstring'''
return list(filter(lambda _lowerCAmelCase : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class __UpperCamelCase :
__A = 42
__A = 42
__A = 0
__A = field(default_factory=_UpperCAmelCase )
__A = field(default_factory=_UpperCAmelCase )
def __call__( self , _lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
lowercase = Tracker(self.dest )(_lowerCAmelCase ).parametrized
lowercase = Tracker(self.src )(_lowerCAmelCase ).parametrized
lowercase = list(filter(lambda _lowerCAmelCase : type(_lowerCAmelCase ) not in self.src_skip , _lowerCAmelCase ) )
lowercase = list(filter(lambda _lowerCAmelCase : type(_lowerCAmelCase ) not in self.dest_skip , _lowerCAmelCase ) )
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
raise Exception(
F"""Numbers of operations are different. Source module has {len(_lowerCAmelCase )} operations while"""
F""" destination module has {len(_lowerCAmelCase )}.""" )
for dest_m, src_m in zip(_lowerCAmelCase , _lowerCAmelCase ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F"""Transfered from={src_m} to={dest_m}""" )
def SCREAMING_SNAKE_CASE ( lowercase_ : str , lowercase_ : ResNetConfig , lowercase_ : Path , lowercase_ : bool = True ):
print(F"""Converting {name}...""" )
with torch.no_grad():
lowercase = timm.create_model(lowercase_ , pretrained=lowercase_ ).eval()
lowercase = ResNetForImageClassification(lowercase_ ).eval()
lowercase = ModuleTransfer(src=lowercase_ , dest=lowercase_ )
lowercase = torch.randn((1, 3, 224, 224) )
module_transfer(lowercase_ )
assert torch.allclose(from_model(lowercase_ ) , our_model(lowercase_ ).logits ), "The model logits don't match the original one."
lowercase = F"""resnet{'-'.join(name.split('resnet' ) )}"""
print(lowercase_ )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message="""Add model""" , use_temp_dir=lowercase_ , )
# we can use the convnext one
lowercase = AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message="""Add image processor""" , use_temp_dir=lowercase_ , )
print(F"""Pushed {checkpoint_name}""" )
def SCREAMING_SNAKE_CASE ( lowercase_ : Path , lowercase_ : str = None , lowercase_ : bool = True ):
lowercase = """imagenet-1k-id2label.json"""
lowercase = 1000
lowercase = (1, num_labels)
lowercase = """huggingface/label-files"""
lowercase = num_labels
lowercase = json.load(open(hf_hub_download(lowercase_ , lowercase_ , repo_type="""dataset""" ) , """r""" ) )
lowercase = {int(lowercase_ ): v for k, v in idalabel.items()}
lowercase = idalabel
lowercase = {v: k for k, v in idalabel.items()}
lowercase = partial(lowercase_ , num_labels=lowercase_ , idalabel=lowercase_ , labelaid=lowercase_ )
lowercase = {
"""resnet18""": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[64, 128, 256, 512] , layer_type="""basic""" ),
"""resnet26""": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[256, 512, 1024, 2048] , layer_type="""bottleneck""" ),
"""resnet34""": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[64, 128, 256, 512] , layer_type="""basic""" ),
"""resnet50""": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[256, 512, 1024, 2048] , layer_type="""bottleneck""" ),
"""resnet101""": ImageNetPreTrainedConfig(
depths=[3, 4, 23, 3] , hidden_sizes=[256, 512, 1024, 2048] , layer_type="""bottleneck""" ),
"""resnet152""": ImageNetPreTrainedConfig(
depths=[3, 8, 36, 3] , hidden_sizes=[256, 512, 1024, 2048] , layer_type="""bottleneck""" ),
}
if model_name:
convert_weight_and_push(lowercase_ , names_to_config[model_name] , lowercase_ , lowercase_ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
return config, expected_shape
if __name__ == "__main__":
lowercase_ : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default=None,
type=str,
help=(
'''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
''' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'''
),
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=Path,
required=True,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument(
'''--push_to_hub''',
default=True,
type=bool,
required=False,
help='''If True, push model and image processor to the hub.''',
)
lowercase_ : int = parser.parse_args()
lowercase_ : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 653 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase_ : Optional[Any] = logging.get_logger(__name__)
lowercase_ : int = {'''vocab_file''': '''spm_char.model'''}
lowercase_ : int = {
'''vocab_file''': {
'''microsoft/speecht5_asr''': '''https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model''',
'''microsoft/speecht5_tts''': '''https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model''',
'''microsoft/speecht5_vc''': '''https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model''',
}
}
lowercase_ : Optional[Any] = {
'''microsoft/speecht5_asr''': 1024,
'''microsoft/speecht5_tts''': 1024,
'''microsoft/speecht5_vc''': 1024,
}
class __UpperCamelCase (_UpperCAmelCase ):
__A = VOCAB_FILES_NAMES
__A = PRETRAINED_VOCAB_FILES_MAP
__A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A = ['''input_ids''', '''attention_mask''']
def __init__( self , _lowerCAmelCase , _lowerCAmelCase="<s>" , _lowerCAmelCase="</s>" , _lowerCAmelCase="<unk>" , _lowerCAmelCase="<pad>" , _lowerCAmelCase = None , **_lowerCAmelCase , ) -> None:
'''simple docstring'''
lowercase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_lowerCAmelCase , eos_token=_lowerCAmelCase , unk_token=_lowerCAmelCase , pad_token=_lowerCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **_lowerCAmelCase , )
lowercase = vocab_file
lowercase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowerCAmelCase )
@property
def _a ( self ) -> List[Any]:
'''simple docstring'''
return self.sp_model.get_piece_size()
def _a ( self ) -> str:
'''simple docstring'''
lowercase = {self.convert_ids_to_tokens(_lowerCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase = self.__dict__.copy()
lowercase = None
return state
def __setstate__( self , _lowerCAmelCase ) -> Optional[int]:
'''simple docstring'''
lowercase = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
lowercase = {}
lowercase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _a ( self , _lowerCAmelCase ) -> List[str]:
'''simple docstring'''
return self.sp_model.encode(_lowerCAmelCase , out_type=_lowerCAmelCase )
def _a ( self , _lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
return self.sp_model.piece_to_id(_lowerCAmelCase )
def _a ( self , _lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase = self.sp_model.IdToPiece(_lowerCAmelCase )
return token
def _a ( self , _lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowercase = []
lowercase = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(_lowerCAmelCase ) + token
lowercase = []
else:
current_sub_tokens.append(_lowerCAmelCase )
out_string += self.sp_model.decode(_lowerCAmelCase )
return out_string.strip()
def _a ( self , _lowerCAmelCase , _lowerCAmelCase=None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def _a ( self , _lowerCAmelCase , _lowerCAmelCase = None , _lowerCAmelCase = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCAmelCase , token_ids_a=_lowerCAmelCase , already_has_special_tokens=_lowerCAmelCase )
lowercase = [1]
if token_ids_a is None:
return ([0] * len(_lowerCAmelCase )) + suffix_ones
return ([0] * len(_lowerCAmelCase )) + ([0] * len(_lowerCAmelCase )) + suffix_ones
def _a ( self , _lowerCAmelCase , _lowerCAmelCase = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(_lowerCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowercase = os.path.join(
_lowerCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowerCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCAmelCase , """wb""" ) as fi:
lowercase = self.sp_model.serialized_model_proto()
fi.write(_lowerCAmelCase )
return (out_vocab_file,)
| 653 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowercase_ : str = {
'''configuration_layoutlmv3''': [
'''LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''LayoutLMv3Config''',
'''LayoutLMv3OnnxConfig''',
],
'''processing_layoutlmv3''': ['''LayoutLMv3Processor'''],
'''tokenization_layoutlmv3''': ['''LayoutLMv3Tokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : Tuple = ['''LayoutLMv3TokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : Optional[Any] = [
'''LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LayoutLMv3ForQuestionAnswering''',
'''LayoutLMv3ForSequenceClassification''',
'''LayoutLMv3ForTokenClassification''',
'''LayoutLMv3Model''',
'''LayoutLMv3PreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : str = [
'''TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFLayoutLMv3ForQuestionAnswering''',
'''TFLayoutLMv3ForSequenceClassification''',
'''TFLayoutLMv3ForTokenClassification''',
'''TFLayoutLMv3Model''',
'''TFLayoutLMv3PreTrainedModel''',
]
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : Optional[int] = ['''LayoutLMv3FeatureExtractor''']
lowercase_ : int = ['''LayoutLMv3ImageProcessor''']
if TYPE_CHECKING:
from .configuration_layoutlmva import (
LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
LayoutLMvaConfig,
LayoutLMvaOnnxConfig,
)
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_layoutlmva import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
TFLayoutLMvaPreTrainedModel,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
else:
import sys
lowercase_ : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 653 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( ):
lowercase = []
lowercase = 1
while len(lowercase_ ) < 1E6:
constant.append(str(lowercase_ ) )
i += 1
lowercase = """""".join(lowercase_ )
return (
int(constant[0] )
* int(constant[9] )
* int(constant[99] )
* int(constant[999] )
* int(constant[9999] )
* int(constant[9_9999] )
* int(constant[99_9999] )
)
if __name__ == "__main__":
print(solution())
| 653 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class __UpperCamelCase (unittest.TestCase ):
def __init__( self , _lowerCAmelCase , _lowerCAmelCase=13 , _lowerCAmelCase=7 , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=99 , _lowerCAmelCase=32 , _lowerCAmelCase=5 , _lowerCAmelCase=4 , _lowerCAmelCase=37 , _lowerCAmelCase="gelu" , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=512 , _lowerCAmelCase=16 , _lowerCAmelCase=2 , _lowerCAmelCase=0.02 , _lowerCAmelCase=4 , ) -> Any:
'''simple docstring'''
lowercase = parent
lowercase = batch_size
lowercase = seq_length
lowercase = is_training
lowercase = use_attention_mask
lowercase = use_token_type_ids
lowercase = use_labels
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = intermediate_size
lowercase = hidden_act
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = type_vocab_size
lowercase = type_sequence_label_size
lowercase = initializer_range
lowercase = num_choices
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase = None
if self.use_attention_mask:
lowercase = random_attention_mask([self.batch_size, self.seq_length] )
lowercase = None
if self.use_token_type_ids:
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase = RoFormerConfig(
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 , )
return config, input_ids, token_type_ids, attention_mask
def _a ( self ) -> List[Any]:
'''simple docstring'''
lowercase = self.prepare_config_and_inputs()
lowercase , lowercase , lowercase , lowercase = config_and_inputs
lowercase = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class __UpperCamelCase (_UpperCAmelCase , unittest.TestCase ):
__A = True
__A = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _a ( self ) -> str:
'''simple docstring'''
lowercase = FlaxRoFormerModelTester(self )
@slow
def _a ( self ) -> str:
'''simple docstring'''
for model_class_name in self.all_model_classes:
lowercase = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" , from_pt=_lowerCAmelCase )
lowercase = model(np.ones((1, 1) ) )
self.assertIsNotNone(_lowerCAmelCase )
@require_flax
class __UpperCamelCase (unittest.TestCase ):
@slow
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
lowercase = jnp.array([[0, 1, 2, 3, 4, 5]] )
lowercase = model(_lowerCAmelCase )[0]
lowercase = 5_0000
lowercase = (1, 6, vocab_size)
self.assertEqual(output.shape , _lowerCAmelCase )
lowercase = jnp.array(
[[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , _lowerCAmelCase , atol=1E-4 ) )
| 653 |
'''simple docstring'''
import os
def SCREAMING_SNAKE_CASE ( ):
lowercase = os.path.join(os.path.dirname(lowercase_ ) , """num.txt""" )
with open(lowercase_ ) as file_hand:
return str(sum(int(lowercase_ ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 653 | 1 |
'''simple docstring'''
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
AutoTokenizer,
TFAutoModelForCausalLM,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSpeechSeqaSeq,
TFAutoModelForVisionaSeq,
TFBartForConditionalGeneration,
TFLogitsProcessorList,
TFMinLengthLogitsProcessor,
tf_top_k_top_p_filtering,
)
if is_tensorflow_text_available():
import tensorflow_text as text
@require_tf
class __UpperCamelCase (unittest.TestCase ):
def _a ( self ) -> Optional[int]:
'''simple docstring'''
lowercase = tf.convert_to_tensor(
[
[
8.222_0991, # 3rd highest value; idx. 0
-0.562_0044,
5.2322_9752,
4.038_6393,
-6.879_8378,
-0.5478_5802,
-3.201_2153,
2.9277_7176,
1.8817_1953,
7.3534_1276, # 5th highest value; idx. 9
8.4320_7833, # 2nd highest value; idx. 10
-9.8571_1836,
-5.9620_9236,
-1.1303_9161,
-7.111_5294,
-0.836_9633,
-5.318_6408,
7.0642_7407,
0.8136_9344,
-0.8202_3817,
-5.917_9796,
0.5881_3443,
-6.9977_8438,
4.7155_1189,
-0.1877_1637,
7.4402_0759, # 4th highest value; idx. 25
9.3845_0987, # 1st highest value; idx. 26
2.1266_2941,
-9.3256_2038,
2.3565_2522,
], # cummulative prob of 5 highest values <= 0.6
[
0.5842_5518,
4.5313_9238,
-5.5751_0464,
-6.2803_0699,
-7.1952_9503,
-4.0212_2551,
1.3933_7037,
-6.0670_7057,
1.5948_0517,
-9.64_3119,
0.0390_7799,
0.6723_1762,
-8.8820_6726,
6.2711_5922, # 4th highest value; idx. 13
2.2852_0723,
4.8276_7506,
4.3042_1368,
8.827_5313, # 2nd highest value; idx. 17
5.4402_9958, # 5th highest value; idx. 18
-4.473_5794,
7.3857_9536, # 3rd highest value; idx. 20
-2.9105_1663,
2.6194_6077,
-2.567_4762,
-9.4895_9302,
-4.0292_2645,
-1.3541_6918,
9.6770_2323, # 1st highest value; idx. 27
-5.8947_8553,
1.8537_0467,
], # cummulative prob of 5 highest values <= 0.6
] , dtype=tf.floataa , )
lowercase = tf.convert_to_tensor(
[[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]] , dtype=tf.intaa , ) # expected non filtered idx as noted above
lowercase = tf.convert_to_tensor(
[8.22_2099, 7.353_4126, 8.43_2078, 7.440_2075, 9.3_8451, 6.27_1159, 8.82_7531, 5.440_2995, 7.385_7956, 9.67_7023] , dtype=tf.floataa , ) # expected non filtered values as noted above
lowercase = tf_top_k_top_p_filtering(_lowerCAmelCase , top_k=10 , top_p=0.6 , min_tokens_to_keep=4 )
lowercase = output[output != -float("""inf""" )]
lowercase = tf.cast(
tf.where(tf.not_equal(_lowerCAmelCase , tf.constant(-float("""inf""" ) , dtype=tf.floataa ) ) ) , dtype=tf.intaa , )
tf.debugging.assert_near(_lowerCAmelCase , _lowerCAmelCase , rtol=1E-12 )
tf.debugging.assert_equal(_lowerCAmelCase , _lowerCAmelCase )
@require_tf
class __UpperCamelCase (unittest.TestCase , _UpperCAmelCase ):
# setting framework_dependent_parameters needs to be gated, just like its contents' imports
if is_tf_available():
__A = {
'''AutoModelForCausalLM''': TFAutoModelForCausalLM,
'''AutoModelForSpeechSeq2Seq''': TFAutoModelForSpeechSeqaSeq,
'''AutoModelForSeq2SeqLM''': TFAutoModelForSeqaSeqLM,
'''AutoModelForVision2Seq''': TFAutoModelForVisionaSeq,
'''LogitsProcessorList''': TFLogitsProcessorList,
'''MinLengthLogitsProcessor''': TFMinLengthLogitsProcessor,
'''create_tensor_fn''': tf.convert_to_tensor,
'''floats_tensor''': floats_tensor,
'''return_tensors''': '''tf''',
}
@slow
def _a ( self ) -> List[str]:
'''simple docstring'''
lowercase = TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
lowercase = 2
lowercase = 2
class __UpperCamelCase (tf.Module ):
def __init__( self , _lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
super(_lowerCAmelCase , self ).__init__()
lowercase = model
@tf.function(
input_signature=(
tf.TensorSpec((None, input_length) , tf.intaa , name="""input_ids""" ),
tf.TensorSpec((None, input_length) , tf.intaa , name="""attention_mask""" ),
) , jit_compile=_lowerCAmelCase , )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase ) -> int:
'''simple docstring'''
lowercase = self.model.generate(
input_ids=_lowerCAmelCase , attention_mask=_lowerCAmelCase , max_new_tokens=_lowerCAmelCase , return_dict_in_generate=_lowerCAmelCase , )
return {"sequences": outputs["sequences"]}
lowercase = [[2, 0], [102, 103]]
lowercase = [[1, 0], [1, 1]]
lowercase = DummyModel(model=_lowerCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(_lowerCAmelCase , _lowerCAmelCase , signatures={"""serving_default""": dummy_model.serving} )
lowercase = tf.saved_model.load(_lowerCAmelCase ).signatures["""serving_default"""]
for batch_size in range(1 , len(_lowerCAmelCase ) + 1 ):
lowercase = {
"""input_ids""": tf.constant(dummy_input_ids[:batch_size] ),
"""attention_mask""": tf.constant(dummy_attention_masks[:batch_size] ),
}
lowercase = serving_func(**_lowerCAmelCase )["""sequences"""]
lowercase = test_model.generate(**_lowerCAmelCase , max_new_tokens=_lowerCAmelCase )
tf.debugging.assert_equal(_lowerCAmelCase , _lowerCAmelCase )
@slow
def _a ( self ) -> List[str]:
'''simple docstring'''
lowercase = TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
lowercase = 1
lowercase = 2
class __UpperCamelCase (tf.Module ):
def __init__( self , _lowerCAmelCase ) -> Optional[int]:
'''simple docstring'''
super(_lowerCAmelCase , self ).__init__()
lowercase = model
@tf.function(
input_signature=(
tf.TensorSpec((batch_size, None) , tf.intaa , name="""input_ids""" ),
tf.TensorSpec((batch_size, None) , tf.intaa , name="""attention_mask""" ),
) , jit_compile=_lowerCAmelCase , )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase ) -> Tuple:
'''simple docstring'''
lowercase = self.model.generate(
input_ids=_lowerCAmelCase , attention_mask=_lowerCAmelCase , max_new_tokens=_lowerCAmelCase , return_dict_in_generate=_lowerCAmelCase , )
return {"sequences": outputs["sequences"]}
lowercase = [[2], [102, 103]]
lowercase = [[1], [1, 1]]
lowercase = DummyModel(model=_lowerCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(_lowerCAmelCase , _lowerCAmelCase , signatures={"""serving_default""": dummy_model.serving} )
lowercase = tf.saved_model.load(_lowerCAmelCase ).signatures["""serving_default"""]
for input_row in range(len(_lowerCAmelCase ) ):
lowercase = {
"""input_ids""": tf.constant([dummy_input_ids[input_row]] ),
"""attention_mask""": tf.constant([dummy_attention_masks[input_row]] ),
}
lowercase = serving_func(**_lowerCAmelCase )["""sequences"""]
lowercase = test_model.generate(**_lowerCAmelCase , max_new_tokens=_lowerCAmelCase )
tf.debugging.assert_equal(_lowerCAmelCase , _lowerCAmelCase )
@slow
@require_tensorflow_text
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
# file needed to load the TF tokenizer
hf_hub_download(repo_id="""google/flan-t5-small""" , filename="""spiece.model""" , local_dir=_lowerCAmelCase )
class __UpperCamelCase (tf.keras.layers.Layer ):
def __init__( self ) -> int:
'''simple docstring'''
super().__init__()
lowercase = text.SentencepieceTokenizer(
model=tf.io.gfile.GFile(os.path.join(_lowerCAmelCase , """spiece.model""" ) , """rb""" ).read() )
lowercase = TFAutoModelForSeqaSeqLM.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
def _a ( self , _lowerCAmelCase , *_lowerCAmelCase , **_lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
lowercase = self.tokenizer.tokenize(_lowerCAmelCase )
lowercase , lowercase = text.pad_model_inputs(
_lowerCAmelCase , max_seq_length=64 , pad_value=self.model.config.pad_token_id )
lowercase = self.model.generate(input_ids=_lowerCAmelCase , attention_mask=_lowerCAmelCase )
return self.tokenizer.detokenize(_lowerCAmelCase )
lowercase = CompleteSentenceTransformer()
lowercase = tf.keras.layers.Input(shape=(1,) , dtype=tf.string , name="""inputs""" )
lowercase = complete_model(_lowerCAmelCase )
lowercase = tf.keras.Model(_lowerCAmelCase , _lowerCAmelCase )
keras_model.save(_lowerCAmelCase )
def _a ( self ) -> List[str]:
'''simple docstring'''
lowercase = {
"""do_sample""": True,
"""num_beams""": 1,
"""top_p""": 0.7,
"""top_k""": 10,
"""temperature""": 0.7,
}
lowercase = 14
lowercase = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
lowercase = """Hello, my dog is cute and"""
lowercase = tokenizer(_lowerCAmelCase , return_tensors="""tf""" )
lowercase = TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
lowercase = 638
# forces the generation to happen on CPU, to avoid GPU-related quirks
with tf.device(""":/CPU:0""" ):
tf.random.set_seed(0 )
lowercase = model.generate(**_lowerCAmelCase , eos_token_id=_lowerCAmelCase , **_lowerCAmelCase )
self.assertTrue(expectation == len(generated_tokens[0] ) )
lowercase = [638, 198]
with tf.device(""":/CPU:0""" ):
tf.random.set_seed(0 )
lowercase = model.generate(**_lowerCAmelCase , eos_token_id=_lowerCAmelCase , **_lowerCAmelCase )
self.assertTrue(expectation == len(generated_tokens[0] ) )
def _a ( self ) -> Optional[int]:
'''simple docstring'''
lowercase = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bart""" )
lowercase = """Hugging Face is a technology company based in New York and Paris."""
lowercase = bart_tokenizer(_lowerCAmelCase , return_tensors="""tf""" ).input_ids
lowercase = TFBartForConditionalGeneration.from_pretrained("""hf-internal-testing/tiny-random-bart""" )
lowercase = bart_model.generate(_lowerCAmelCase ).numpy()
class __UpperCamelCase (_UpperCAmelCase ):
def _a ( self , _lowerCAmelCase , _lowerCAmelCase=None , **_lowerCAmelCase ) -> Any:
'''simple docstring'''
return super().call(_lowerCAmelCase , **_lowerCAmelCase )
lowercase = FakeBart.from_pretrained("""hf-internal-testing/tiny-random-bart""" )
lowercase = bart_model.generate(_lowerCAmelCase , foo="""bar""" ).numpy()
self.assertTrue(np.array_equal(_lowerCAmelCase , _lowerCAmelCase ) )
class __UpperCamelCase (bart_model.model.encoder.__class__ ):
def _a ( self , _lowerCAmelCase , **_lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
return super().call(_lowerCAmelCase , **_lowerCAmelCase )
lowercase = FakeEncoder(bart_model.config , bart_model.model.shared )
lowercase = fake_encoder
# Normal generation still works (the output will be different because the encoder weights are different)
lowercase = bart_model.generate(_lowerCAmelCase ).numpy()
with self.assertRaises(_lowerCAmelCase ):
# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
bart_model.generate(_lowerCAmelCase , foo="""bar""" )
| 653 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
@skip_mps
class __UpperCamelCase (_UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
__A = StableDiffusionPanoramaPipeline
__A = TEXT_TO_IMAGE_PARAMS
__A = TEXT_TO_IMAGE_BATCH_PARAMS
__A = TEXT_TO_IMAGE_IMAGE_PARAMS
__A = TEXT_TO_IMAGE_IMAGE_PARAMS
def _a ( self ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
lowercase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=1 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
lowercase = DDIMScheduler()
torch.manual_seed(0 )
lowercase = 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 , )
torch.manual_seed(0 )
lowercase = 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=1000 , )
lowercase = CLIPTextModel(_lowerCAmelCase )
lowercase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowercase = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def _a ( self , _lowerCAmelCase , _lowerCAmelCase=0 ) -> Optional[int]:
'''simple docstring'''
lowercase = torch.manual_seed(_lowerCAmelCase )
lowercase = {
"""prompt""": """a photo of the dolomites""",
"""generator""": generator,
# Setting height and width to None to prevent OOMs on CPU.
"""height""": None,
"""width""": None,
"""num_inference_steps""": 1,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def _a ( self ) -> int:
'''simple docstring'''
lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase = self.get_dummy_components()
lowercase = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase = self.get_dummy_inputs(_lowerCAmelCase )
lowercase = sd_pipe(**_lowerCAmelCase ).images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase = np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
super().test_inference_batch_consistent(batch_sizes=[1, 2] )
def _a ( self ) -> str:
'''simple docstring'''
super().test_inference_batch_single_identical(batch_size=2 , expected_max_diff=3.25E-3 )
def _a ( self ) -> List[Any]:
'''simple docstring'''
lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase = self.get_dummy_components()
lowercase = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase = self.get_dummy_inputs(_lowerCAmelCase )
lowercase = """french fries"""
lowercase = sd_pipe(**_lowerCAmelCase , negative_prompt=_lowerCAmelCase )
lowercase = output.images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase = np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> Tuple:
'''simple docstring'''
lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase = self.get_dummy_components()
lowercase = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase = self.get_dummy_inputs(_lowerCAmelCase )
lowercase = sd_pipe(**_lowerCAmelCase , view_batch_size=2 )
lowercase = output.images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase = np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> Any:
'''simple docstring'''
lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase = self.get_dummy_components()
lowercase = EulerAncestralDiscreteScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" )
lowercase = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase = self.get_dummy_inputs(_lowerCAmelCase )
lowercase = sd_pipe(**_lowerCAmelCase ).images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase = np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> Dict:
'''simple docstring'''
lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase = self.get_dummy_components()
lowercase = PNDMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , skip_prk_steps=_lowerCAmelCase )
lowercase = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase = self.get_dummy_inputs(_lowerCAmelCase )
lowercase = sd_pipe(**_lowerCAmelCase ).images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase = np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class __UpperCamelCase (unittest.TestCase ):
def _a ( self ) -> List[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self , _lowerCAmelCase=0 ) -> Optional[int]:
'''simple docstring'''
lowercase = torch.manual_seed(_lowerCAmelCase )
lowercase = {
"""prompt""": """a photo of the dolomites""",
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase = """stabilityai/stable-diffusion-2-base"""
lowercase = DDIMScheduler.from_pretrained(_lowerCAmelCase , subfolder="""scheduler""" )
lowercase = StableDiffusionPanoramaPipeline.from_pretrained(_lowerCAmelCase , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing()
lowercase = self.get_inputs()
lowercase = pipe(**_lowerCAmelCase ).images
lowercase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2048, 3)
lowercase = np.array(
[
0.3696_8392,
0.2702_5372,
0.3244_6766,
0.2837_9387,
0.3636_3274,
0.3073_3347,
0.2710_0027,
0.2705_4125,
0.2553_6096,
] )
assert np.abs(expected_slice - image_slice ).max() < 1E-2
def _a ( self ) -> str:
'''simple docstring'''
lowercase = StableDiffusionPanoramaPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2-base""" , safety_checker=_lowerCAmelCase )
lowercase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing()
lowercase = self.get_inputs()
lowercase = pipe(**_lowerCAmelCase ).images
lowercase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2048, 3)
lowercase = np.array(
[
[
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
] )
assert np.abs(expected_slice - image_slice ).max() < 1E-3
def _a ( self ) -> Any:
'''simple docstring'''
lowercase = 0
def callback_fn(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> None:
lowercase = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
lowercase = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
lowercase = latents[0, -3:, -3:, -1]
lowercase = np.array(
[
0.1868_1869,
0.3390_7816,
0.536_1276,
0.1443_2865,
-0.0285_6611,
-0.7394_1123,
0.2339_7987,
0.4732_2682,
-0.3782_3164,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
elif step == 2:
lowercase = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
lowercase = latents[0, -3:, -3:, -1]
lowercase = np.array(
[
0.1853_9645,
0.3398_7248,
0.537_8559,
0.1443_7142,
-0.0245_5261,
-0.733_8317,
0.2399_0755,
0.4735_6272,
-0.378_6505,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
lowercase = False
lowercase = """stabilityai/stable-diffusion-2-base"""
lowercase = DDIMScheduler.from_pretrained(_lowerCAmelCase , subfolder="""scheduler""" )
lowercase = StableDiffusionPanoramaPipeline.from_pretrained(_lowerCAmelCase , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase )
lowercase = pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing()
lowercase = self.get_inputs()
pipe(**_lowerCAmelCase , callback=_lowerCAmelCase , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def _a ( self ) -> int:
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
lowercase = """stabilityai/stable-diffusion-2-base"""
lowercase = DDIMScheduler.from_pretrained(_lowerCAmelCase , subfolder="""scheduler""" )
lowercase = StableDiffusionPanoramaPipeline.from_pretrained(_lowerCAmelCase , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase )
lowercase = pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
lowercase = self.get_inputs()
lowercase = pipe(**_lowerCAmelCase )
lowercase = torch.cuda.max_memory_allocated()
# make sure that less than 5.2 GB is allocated
assert mem_bytes < 5.5 * 10**9
| 653 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ : Dict = logging.get_logger(__name__)
lowercase_ : Any = {
'''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}
class __UpperCamelCase (_UpperCAmelCase ):
__A = '''efficientnet'''
def __init__( self , _lowerCAmelCase = 3 , _lowerCAmelCase = 600 , _lowerCAmelCase = 2.0 , _lowerCAmelCase = 3.1 , _lowerCAmelCase = 8 , _lowerCAmelCase = [3, 3, 5, 3, 5, 5, 3] , _lowerCAmelCase = [32, 16, 24, 40, 80, 112, 192] , _lowerCAmelCase = [16, 24, 40, 80, 112, 192, 320] , _lowerCAmelCase = [] , _lowerCAmelCase = [1, 2, 2, 2, 1, 2, 1] , _lowerCAmelCase = [1, 2, 2, 3, 3, 4, 1] , _lowerCAmelCase = [1, 6, 6, 6, 6, 6, 6] , _lowerCAmelCase = 0.25 , _lowerCAmelCase = "swish" , _lowerCAmelCase = 2560 , _lowerCAmelCase = "mean" , _lowerCAmelCase = 0.02 , _lowerCAmelCase = 0.001 , _lowerCAmelCase = 0.99 , _lowerCAmelCase = 0.5 , _lowerCAmelCase = 0.2 , **_lowerCAmelCase , ) -> List[Any]:
'''simple docstring'''
super().__init__(**_lowerCAmelCase )
lowercase = num_channels
lowercase = image_size
lowercase = width_coefficient
lowercase = depth_coefficient
lowercase = depth_divisor
lowercase = kernel_sizes
lowercase = in_channels
lowercase = out_channels
lowercase = depthwise_padding
lowercase = strides
lowercase = num_block_repeats
lowercase = expand_ratios
lowercase = squeeze_expansion_ratio
lowercase = hidden_act
lowercase = hidden_dim
lowercase = pooling_type
lowercase = initializer_range
lowercase = batch_norm_eps
lowercase = batch_norm_momentum
lowercase = dropout_rate
lowercase = drop_connect_rate
lowercase = sum(_lowerCAmelCase ) * 4
class __UpperCamelCase (_UpperCAmelCase ):
__A = version.parse('''1.11''' )
@property
def _a ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def _a ( self ) -> float:
'''simple docstring'''
return 1E-5
| 653 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
lowercase_ : Tuple = logging.getLogger(__name__)
@dataclass
class __UpperCamelCase :
__A = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''Whether tp freeze the encoder.'''} )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''Whether to freeze the embeddings.'''} )
@dataclass
class __UpperCamelCase :
__A = field(
metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''} )
__A = field(
default='''summarization''' , metadata={'''help''': '''Task name, summarization (or summarization_{dataset} for pegasus) or translation'''} , )
__A = field(
default=1024 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
__A = field(
default=128 , metadata={
'''help''': (
'''The maximum total sequence length for target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
__A = field(
default=142 , metadata={
'''help''': (
'''The maximum total sequence length for validation target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded. '''
'''This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '''
'''during ``evaluate`` and ``predict``.'''
)
} , )
__A = field(
default=142 , metadata={
'''help''': (
'''The maximum total sequence length for test target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
__A = field(default=-1 , metadata={'''help''': '''# training examples. -1 means use all.'''} )
__A = field(default=-1 , metadata={'''help''': '''# validation examples. -1 means use all.'''} )
__A = field(default=-1 , metadata={'''help''': '''# test examples. -1 means use all.'''} )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''Source language id for translation.'''} )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''Target language id for translation.'''} )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''# num_beams to use for evaluation.'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''} , )
def SCREAMING_SNAKE_CASE ( lowercase_ : List[Any] , lowercase_ : int , lowercase_ : List[Any] ):
logger.info(F"""***** {split} metrics *****""" )
for key in sorted(metrics.keys() ):
logger.info(F""" {key} = {metrics[key]}""" )
save_json(lowercase_ , os.path.join(lowercase_ , F"""{split}_results.json""" ) )
def SCREAMING_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.
lowercase = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
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.
lowercase , lowercase , lowercase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase , lowercase , lowercase = parser.parse_args_into_dataclasses()
check_output_dir(lowercase_ )
# 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.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# 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""" , lowercase_ )
# Set seed
set_seed(training_args.seed )
# 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 , cache_dir=model_args.cache_dir , )
lowercase = ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
for p in extra_model_params:
if getattr(lowercase_ , lowercase_ , lowercase_ ):
assert hasattr(lowercase_ , lowercase_ ), F"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
setattr(lowercase_ , lowercase_ , getattr(lowercase_ , lowercase_ ) )
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 = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf=""".ckpt""" in model_args.model_name_or_path , config=lowercase_ , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(lowercase_ , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
lowercase = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(lowercase_ , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(lowercase_ , lowercase_ ):
lowercase = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
lowercase = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(lowercase_ )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
lowercase = SeqaSeqDataset
# Get datasets
lowercase = (
dataset_class(
lowercase_ , type_path="""train""" , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_train
else None
)
lowercase = (
dataset_class(
lowercase_ , type_path="""val""" , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
lowercase = (
dataset_class(
lowercase_ , type_path="""test""" , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_predict
else None
)
# Initialize our Trainer
lowercase = (
build_compute_metrics_fn(data_args.task , lowercase_ ) if training_args.predict_with_generate else None
)
lowercase = SeqaSeqTrainer(
model=lowercase_ , args=lowercase_ , data_args=lowercase_ , train_dataset=lowercase_ , eval_dataset=lowercase_ , data_collator=SeqaSeqDataCollator(
lowercase_ , lowercase_ , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=lowercase_ , tokenizer=lowercase_ , )
lowercase = {}
# Training
if training_args.do_train:
logger.info("""*** Train ***""" )
lowercase = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
lowercase = train_result.metrics
lowercase = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics("""train""" , lowercase_ , training_args.output_dir )
all_metrics.update(lowercase_ )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , """trainer_state.json""" ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
lowercase = trainer.evaluate(metric_key_prefix="""val""" )
lowercase = data_args.n_val
lowercase = round(metrics["""val_loss"""] , 4 )
if trainer.is_world_process_zero():
handle_metrics("""val""" , lowercase_ , training_args.output_dir )
all_metrics.update(lowercase_ )
if training_args.do_predict:
logger.info("""*** Predict ***""" )
lowercase = trainer.predict(test_dataset=lowercase_ , metric_key_prefix="""test""" )
lowercase = test_output.metrics
lowercase = data_args.n_test
if trainer.is_world_process_zero():
lowercase = round(metrics["""test_loss"""] , 4 )
handle_metrics("""test""" , lowercase_ , training_args.output_dir )
all_metrics.update(lowercase_ )
if training_args.predict_with_generate:
lowercase = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=lowercase_ , clean_up_tokenization_spaces=lowercase_ )
lowercase = lmap(str.strip , lowercase_ )
write_txt_file(lowercase_ , os.path.join(training_args.output_dir , """test_generations.txt""" ) )
if trainer.is_world_process_zero():
save_json(lowercase_ , os.path.join(training_args.output_dir , """all_results.json""" ) )
return all_metrics
def SCREAMING_SNAKE_CASE ( lowercase_ : Dict ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 653 | 1 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
lowercase_ : Optional[int] = TypeVar('''KEY''')
lowercase_ : List[Any] = TypeVar('''VAL''')
@dataclass(frozen=_UpperCAmelCase , slots=_UpperCAmelCase )
class __UpperCamelCase (Generic[KEY, VAL] ):
__A = 42
__A = 42
class __UpperCamelCase (_Item ):
def __init__( self ) -> None:
'''simple docstring'''
super().__init__(_lowerCAmelCase , _lowerCAmelCase )
def __bool__( self ) -> bool:
'''simple docstring'''
return False
lowercase_ : List[str] = _DeletedItem()
class __UpperCamelCase (MutableMapping[KEY, VAL] ):
def __init__( self , _lowerCAmelCase = 8 , _lowerCAmelCase = 0.75 ) -> None:
'''simple docstring'''
lowercase = initial_block_size
lowercase = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
lowercase = capacity_factor
lowercase = 0
def _a ( self , _lowerCAmelCase ) -> int:
'''simple docstring'''
return hash(_lowerCAmelCase ) % len(self._buckets )
def _a ( self , _lowerCAmelCase ) -> int:
'''simple docstring'''
return (ind + 1) % len(self._buckets )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> bool:
'''simple docstring'''
lowercase = self._buckets[ind]
if not stored:
lowercase = _Item(_lowerCAmelCase , _lowerCAmelCase )
self._len += 1
return True
elif stored.key == key:
lowercase = _Item(_lowerCAmelCase , _lowerCAmelCase )
return True
else:
return False
def _a ( self ) -> bool:
'''simple docstring'''
lowercase = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(_lowerCAmelCase )
def _a ( self ) -> bool:
'''simple docstring'''
if len(self._buckets ) <= self._initial_block_size:
return False
lowercase = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def _a ( self , _lowerCAmelCase ) -> None:
'''simple docstring'''
lowercase = self._buckets
lowercase = [None] * new_size
lowercase = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val )
def _a ( self ) -> None:
'''simple docstring'''
self._resize(len(self._buckets ) * 2 )
def _a ( self ) -> None:
'''simple docstring'''
self._resize(len(self._buckets ) // 2 )
def _a ( self , _lowerCAmelCase ) -> Iterator[int]:
'''simple docstring'''
lowercase = self._get_bucket_index(_lowerCAmelCase )
for _ in range(len(self._buckets ) ):
yield ind
lowercase = self._get_next_ind(_lowerCAmelCase )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase ) -> None:
'''simple docstring'''
for ind in self._iterate_buckets(_lowerCAmelCase ):
if self._try_set(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
break
def __setitem__( self , _lowerCAmelCase , _lowerCAmelCase ) -> None:
'''simple docstring'''
if self._is_full():
self._size_up()
self._add_item(_lowerCAmelCase , _lowerCAmelCase )
def __delitem__( self , _lowerCAmelCase ) -> None:
'''simple docstring'''
for ind in self._iterate_buckets(_lowerCAmelCase ):
lowercase = self._buckets[ind]
if item is None:
raise KeyError(_lowerCAmelCase )
if item is _deleted:
continue
if item.key == key:
lowercase = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self , _lowerCAmelCase ) -> VAL:
'''simple docstring'''
for ind in self._iterate_buckets(_lowerCAmelCase ):
lowercase = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(_lowerCAmelCase )
def __len__( self ) -> int:
'''simple docstring'''
return self._len
def __iter__( self ) -> Iterator[KEY]:
'''simple docstring'''
yield from (item.key for item in self._buckets if item)
def __repr__( self ) -> str:
'''simple docstring'''
lowercase = """ ,""".join(
F"""{item.key}: {item.val}""" for item in self._buckets if item )
return F"""HashMap({val_string})"""
| 653 |
'''simple docstring'''
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_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
lowercase_ : Union[str, Any] = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase )
class __UpperCamelCase (_UpperCAmelCase ):
def __init__( self , *_lowerCAmelCase , **_lowerCAmelCase ) -> Dict:
'''simple docstring'''
super().__init__(*_lowerCAmelCase , **_lowerCAmelCase )
requires_backends(self , """vision""" )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING )
def _a ( self , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase=None ) -> str:
'''simple docstring'''
lowercase = {}
lowercase = {}
if prompt is not None:
lowercase = prompt
if generate_kwargs is not None:
lowercase = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
lowercase = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
"""'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
""" please use only one""" )
lowercase = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self , _lowerCAmelCase , **_lowerCAmelCase ) -> Any:
'''simple docstring'''
return super().__call__(_lowerCAmelCase , **_lowerCAmelCase )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase=None ) -> List[str]:
'''simple docstring'''
lowercase = load_image(_lowerCAmelCase )
if prompt is not None:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise ValueError(
F"""Received an invalid text input, got - {type(_lowerCAmelCase )} - but expected a single string. """
"""Note also that one single text can be provided for conditional image to text generation.""" )
lowercase = self.model.config.model_type
if model_type == "git":
lowercase = self.image_processor(images=_lowerCAmelCase , return_tensors=self.framework )
lowercase = self.tokenizer(text=_lowerCAmelCase , add_special_tokens=_lowerCAmelCase ).input_ids
lowercase = [self.tokenizer.cls_token_id] + input_ids
lowercase = torch.tensor(_lowerCAmelCase ).unsqueeze(0 )
model_inputs.update({"""input_ids""": input_ids} )
elif model_type == "pix2struct":
lowercase = self.image_processor(images=_lowerCAmelCase , header_text=_lowerCAmelCase , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
lowercase = self.image_processor(images=_lowerCAmelCase , return_tensors=self.framework )
lowercase = self.tokenizer(_lowerCAmelCase , return_tensors=self.framework )
model_inputs.update(_lowerCAmelCase )
else:
raise ValueError(F"""Model type {model_type} does not support conditional text generation""" )
else:
lowercase = self.image_processor(images=_lowerCAmelCase , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
lowercase = None
return model_inputs
def _a ( self , _lowerCAmelCase , _lowerCAmelCase=None ) -> Union[str, Any]:
'''simple docstring'''
if (
"input_ids" in model_inputs
and isinstance(model_inputs["""input_ids"""] , _lowerCAmelCase )
and all(x is None for x in model_inputs["""input_ids"""] )
):
lowercase = None
if generate_kwargs is None:
lowercase = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
lowercase = model_inputs.pop(self.model.main_input_name )
lowercase = self.model.generate(_lowerCAmelCase , **_lowerCAmelCase , **_lowerCAmelCase )
return model_outputs
def _a ( self , _lowerCAmelCase ) -> List[str]:
'''simple docstring'''
lowercase = []
for output_ids in model_outputs:
lowercase = {
"""generated_text""": self.tokenizer.decode(
_lowerCAmelCase , skip_special_tokens=_lowerCAmelCase , )
}
records.append(_lowerCAmelCase )
return records
| 653 | 1 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
lowercase_ : Tuple = 16
lowercase_ : Dict = 32
def SCREAMING_SNAKE_CASE ( lowercase_ : Accelerator , lowercase_ : int = 16 ):
lowercase = AutoTokenizer.from_pretrained("""bert-base-cased""" )
lowercase = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(lowercase_ : List[Any] ):
# max_length=None => use the model max length (it's actually the default)
lowercase = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowercase_ , max_length=lowercase_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
lowercase = datasets.map(
lowercase_ , batched=lowercase_ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowercase = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowercase_ : Tuple ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowercase = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
lowercase = 16
elif accelerator.mixed_precision != "no":
lowercase = 8
else:
lowercase = None
return tokenizer.pad(
lowercase_ , padding="""longest""" , max_length=lowercase_ , pad_to_multiple_of=lowercase_ , return_tensors="""pt""" , )
# Instantiate dataloaders.
lowercase = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowercase_ , collate_fn=lowercase_ , batch_size=lowercase_ )
lowercase = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowercase_ , collate_fn=lowercase_ , batch_size=lowercase_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
lowercase_ : List[Any] = mocked_dataloaders # noqa: F811
def SCREAMING_SNAKE_CASE ( lowercase_ : Union[str, Any] , lowercase_ : Dict ):
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , lowercase_ ) == "1":
lowercase = 2
# New Code #
lowercase = int(args.gradient_accumulation_steps )
lowercase = int(args.local_sgd_steps )
# Initialize accelerator
lowercase = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=lowercase_ )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)""" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase = config["""lr"""]
lowercase = int(config["""num_epochs"""] )
lowercase = int(config["""seed"""] )
lowercase = int(config["""batch_size"""] )
lowercase = evaluate.load("""glue""" , """mrpc""" )
set_seed(lowercase_ )
lowercase , lowercase = get_dataloaders(lowercase_ , lowercase_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=lowercase_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
lowercase = model.to(accelerator.device )
# Instantiate optimizer
lowercase = AdamW(params=model.parameters() , lr=lowercase_ )
# Instantiate scheduler
lowercase = get_linear_schedule_with_warmup(
optimizer=lowercase_ , num_warmup_steps=100 , num_training_steps=(len(lowercase_ ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowercase , lowercase , lowercase , lowercase , lowercase = accelerator.prepare(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
# Now we train the model
for epoch in range(lowercase_ ):
model.train()
with LocalSGD(
accelerator=lowercase_ , model=lowercase_ , local_sgd_steps=lowercase_ , enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(lowercase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(lowercase_ ):
lowercase = model(**lowercase_ )
lowercase = output.loss
accelerator.backward(lowercase_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(lowercase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowercase = model(**lowercase_ )
lowercase = outputs.logits.argmax(dim=-1 )
lowercase , lowercase = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=lowercase_ , references=lowercase_ , )
lowercase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""" , lowercase_ )
def SCREAMING_SNAKE_CASE ( ):
lowercase = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=lowercase_ , default=lowercase_ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
# New Code #
parser.add_argument(
"""--gradient_accumulation_steps""" , type=lowercase_ , default=1 , help="""The number of minibatches to be ran before gradients are accumulated.""" , )
parser.add_argument(
"""--local_sgd_steps""" , type=lowercase_ , default=8 , help="""Number of local SGD steps or None to disable local SGD""" )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
lowercase = parser.parse_args()
lowercase = {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(lowercase_ , lowercase_ )
if __name__ == "__main__":
main()
| 653 |
'''simple docstring'''
from ... import PretrainedConfig
lowercase_ : int = {
'''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}
class __UpperCamelCase (_UpperCAmelCase ):
__A = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
__A = '''nezha'''
def __init__( self , _lowerCAmelCase=2_1128 , _lowerCAmelCase=768 , _lowerCAmelCase=12 , _lowerCAmelCase=12 , _lowerCAmelCase=3072 , _lowerCAmelCase="gelu" , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=512 , _lowerCAmelCase=64 , _lowerCAmelCase=2 , _lowerCAmelCase=0.02 , _lowerCAmelCase=1E-12 , _lowerCAmelCase=0.1 , _lowerCAmelCase=0 , _lowerCAmelCase=2 , _lowerCAmelCase=3 , _lowerCAmelCase=True , **_lowerCAmelCase , ) -> int:
'''simple docstring'''
super().__init__(pad_token_id=_lowerCAmelCase , bos_token_id=_lowerCAmelCase , eos_token_id=_lowerCAmelCase , **_lowerCAmelCase )
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = hidden_act
lowercase = intermediate_size
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = max_relative_position
lowercase = type_vocab_size
lowercase = initializer_range
lowercase = layer_norm_eps
lowercase = classifier_dropout
lowercase = use_cache
| 653 | 1 |
'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
lowercase_ : int = logging.get_logger(__name__)
lowercase_ : Tuple = {'''vocab_file''': '''spiece.model'''}
lowercase_ : Any = {
'''vocab_file''': {
'''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
'''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
'''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
'''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
'''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
}
}
# TODO(PVP) - this should be removed in Transformers v5
lowercase_ : Optional[Any] = {
'''t5-small''': 512,
'''t5-base''': 512,
'''t5-large''': 512,
'''t5-3b''': 512,
'''t5-11b''': 512,
}
lowercase_ : int = '''▁'''
class __UpperCamelCase (_UpperCAmelCase ):
__A = VOCAB_FILES_NAMES
__A = PRETRAINED_VOCAB_FILES_MAP
__A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A = ['''input_ids''', '''attention_mask''']
def __init__( self , _lowerCAmelCase , _lowerCAmelCase="</s>" , _lowerCAmelCase="<unk>" , _lowerCAmelCase="<pad>" , _lowerCAmelCase=100 , _lowerCAmelCase=None , _lowerCAmelCase = None , _lowerCAmelCase=True , **_lowerCAmelCase , ) -> None:
'''simple docstring'''
if extra_ids > 0 and additional_special_tokens is None:
lowercase = [F"""<extra_id_{i}>""" for i in range(_lowerCAmelCase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
lowercase = len(set(filter(lambda _lowerCAmelCase : bool("""extra_id""" in str(_lowerCAmelCase ) ) , _lowerCAmelCase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
""" provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
""" tokens""" )
if legacy:
logger.warning_once(
F"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
""" read the related pull request available at https://github.com/huggingface/transformers/pull/24565""" )
lowercase = legacy
lowercase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_lowerCAmelCase , unk_token=_lowerCAmelCase , pad_token=_lowerCAmelCase , extra_ids=_lowerCAmelCase , additional_special_tokens=_lowerCAmelCase , sp_model_kwargs=self.sp_model_kwargs , legacy=_lowerCAmelCase , **_lowerCAmelCase , )
lowercase = vocab_file
lowercase = extra_ids
lowercase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowerCAmelCase )
@staticmethod
def _a ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Dict:
'''simple docstring'''
if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
lowercase = TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
"""This tokenizer was incorrectly instantiated with a model max length of"""
F""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
""" behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
""" `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
F""" {pretrained_model_name_or_path} automatically truncating your input to"""
F""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
F""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
""" `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
""" instantiate this tokenizer with `model_max_length` set to your preferred value.""" , _lowerCAmelCase , )
return max_model_length
@property
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
return self.sp_model.get_piece_size() + self._extra_ids
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase = {self.convert_ids_to_tokens(_lowerCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _a ( self , _lowerCAmelCase , _lowerCAmelCase = None , _lowerCAmelCase = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCAmelCase , token_ids_a=_lowerCAmelCase , already_has_special_tokens=_lowerCAmelCase )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(_lowerCAmelCase )) + [1]
return ([0] * len(_lowerCAmelCase )) + [1] + ([0] * len(_lowerCAmelCase )) + [1]
def _a ( self ) -> List[Any]:
'''simple docstring'''
return list(
set(filter(lambda _lowerCAmelCase : bool(re.search(r"""<extra_id_\d+>""" , _lowerCAmelCase ) ) is not None , self.additional_special_tokens ) ) )
def _a ( self ) -> List[str]:
'''simple docstring'''
return [self._convert_token_to_id(_lowerCAmelCase ) for token in self.get_sentinel_tokens()]
def _a ( self , _lowerCAmelCase ) -> List[int]:
'''simple docstring'''
if len(_lowerCAmelCase ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
""" eos tokens being added.""" )
return token_ids
else:
return token_ids + [self.eos_token_id]
def _a ( self , _lowerCAmelCase , _lowerCAmelCase = None ) -> List[int]:
'''simple docstring'''
lowercase = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def _a ( self , _lowerCAmelCase , _lowerCAmelCase = None ) -> List[int]:
'''simple docstring'''
lowercase = self._add_eos_if_not_present(_lowerCAmelCase )
if token_ids_a is None:
return token_ids_a
else:
lowercase = self._add_eos_if_not_present(_lowerCAmelCase )
return token_ids_a + token_ids_a
def __getstate__( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase = self.__dict__.copy()
lowercase = None
return state
def __setstate__( self , _lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
lowercase = {}
lowercase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _a ( self , _lowerCAmelCase , **_lowerCAmelCase ) -> List[str]:
'''simple docstring'''
if not self.legacy:
lowercase = SPIECE_UNDERLINE + text.replace(_lowerCAmelCase , """ """ )
return super().tokenize(_lowerCAmelCase , **_lowerCAmelCase )
def _a ( self , _lowerCAmelCase , **_lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
if not self.legacy:
lowercase = text.startswith(_lowerCAmelCase )
if is_first:
lowercase = text[1:]
lowercase = self.sp_model.encode(_lowerCAmelCase , out_type=_lowerCAmelCase )
if not self.legacy and not is_first and not text.startswith(""" """ ) and tokens[0].startswith(_lowerCAmelCase ):
lowercase = ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def _a ( self , _lowerCAmelCase ) -> int:
'''simple docstring'''
if token.startswith("""<extra_id_""" ):
lowercase = re.match(r"""<extra_id_(\d+)>""" , _lowerCAmelCase )
lowercase = int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(_lowerCAmelCase )
def _a ( self , _lowerCAmelCase ) -> Optional[int]:
'''simple docstring'''
if index < self.sp_model.get_piece_size():
lowercase = self.sp_model.IdToPiece(_lowerCAmelCase )
else:
lowercase = F"""<extra_id_{self.vocab_size - 1 - index}>"""
return token
def _a ( self , _lowerCAmelCase ) -> List[str]:
'''simple docstring'''
lowercase = []
lowercase = """"""
lowercase = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_lowerCAmelCase ) + token
lowercase = True
lowercase = []
else:
current_sub_tokens.append(_lowerCAmelCase )
lowercase = False
out_string += self.sp_model.decode(_lowerCAmelCase )
return out_string.strip()
def _a ( self , _lowerCAmelCase , _lowerCAmelCase = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(_lowerCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowercase = os.path.join(
_lowerCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowerCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCAmelCase , """wb""" ) as fi:
lowercase = self.sp_model.serialized_model_proto()
fi.write(_lowerCAmelCase )
return (out_vocab_file,)
| 653 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
lowercase_ : Tuple = logging.getLogger(__name__)
def SCREAMING_SNAKE_CASE ( lowercase_ : str ):
lowercase = git.Repo(search_parent_directories=lowercase_ )
lowercase = {
"""repo_id""": str(lowercase_ ),
"""repo_sha""": str(repo.head.object.hexsha ),
"""repo_branch""": str(repo.active_branch ),
}
with open(os.path.join(lowercase_ , """git_log.json""" ) , """w""" ) as f:
json.dump(lowercase_ , lowercase_ , indent=4 )
def SCREAMING_SNAKE_CASE ( lowercase_ : str ):
if params.n_gpu <= 0:
lowercase = 0
lowercase = -1
lowercase = True
lowercase = False
return
assert torch.cuda.is_available()
logger.info("""Initializing GPUs""" )
if params.n_gpu > 1:
assert params.local_rank != -1
lowercase = int(os.environ["""WORLD_SIZE"""] )
lowercase = int(os.environ["""N_GPU_NODE"""] )
lowercase = int(os.environ["""RANK"""] )
# number of nodes / node ID
lowercase = params.world_size // params.n_gpu_per_node
lowercase = params.global_rank // params.n_gpu_per_node
lowercase = True
assert params.n_nodes == int(os.environ["""N_NODES"""] )
assert params.node_id == int(os.environ["""NODE_RANK"""] )
# local job (single GPU)
else:
assert params.local_rank == -1
lowercase = 1
lowercase = 0
lowercase = 0
lowercase = 0
lowercase = 1
lowercase = 1
lowercase = False
# sanity checks
assert params.n_nodes >= 1
assert 0 <= params.node_id < params.n_nodes
assert 0 <= params.local_rank <= params.global_rank < params.world_size
assert params.world_size == params.n_nodes * params.n_gpu_per_node
# define whether this is the master process / if we are in multi-node distributed mode
lowercase = params.node_id == 0 and params.local_rank == 0
lowercase = params.n_nodes > 1
# summary
lowercase = F"""--- Global rank: {params.global_rank} - """
logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes )
logger.info(PREFIX + """Node ID : %i""" % params.node_id )
logger.info(PREFIX + """Local rank : %i""" % params.local_rank )
logger.info(PREFIX + """World size : %i""" % params.world_size )
logger.info(PREFIX + """GPUs per node : %i""" % params.n_gpu_per_node )
logger.info(PREFIX + """Master : %s""" % str(params.is_master ) )
logger.info(PREFIX + """Multi-node : %s""" % str(params.multi_node ) )
logger.info(PREFIX + """Multi-GPU : %s""" % str(params.multi_gpu ) )
logger.info(PREFIX + """Hostname : %s""" % socket.gethostname() )
# set GPU device
torch.cuda.set_device(params.local_rank )
# initialize multi-GPU
if params.multi_gpu:
logger.info("""Initializing PyTorch distributed""" )
torch.distributed.init_process_group(
init_method="""env://""" , backend="""nccl""" , )
def SCREAMING_SNAKE_CASE ( lowercase_ : Optional[Any] ):
np.random.seed(args.seed )
torch.manual_seed(args.seed )
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed )
| 653 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ : Tuple = {
'''configuration_longformer''': [
'''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''LongformerConfig''',
'''LongformerOnnxConfig''',
],
'''tokenization_longformer''': ['''LongformerTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : str = ['''LongformerTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : Optional[int] = [
'''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LongformerForMaskedLM''',
'''LongformerForMultipleChoice''',
'''LongformerForQuestionAnswering''',
'''LongformerForSequenceClassification''',
'''LongformerForTokenClassification''',
'''LongformerModel''',
'''LongformerPreTrainedModel''',
'''LongformerSelfAttention''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : Optional[int] = [
'''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFLongformerForMaskedLM''',
'''TFLongformerForMultipleChoice''',
'''TFLongformerForQuestionAnswering''',
'''TFLongformerForSequenceClassification''',
'''TFLongformerForTokenClassification''',
'''TFLongformerModel''',
'''TFLongformerPreTrainedModel''',
'''TFLongformerSelfAttention''',
]
if TYPE_CHECKING:
from .configuration_longformer import (
LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
LongformerConfig,
LongformerOnnxConfig,
)
from .tokenization_longformer import LongformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_longformer_fast import LongformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longformer import (
LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
LongformerForMaskedLM,
LongformerForMultipleChoice,
LongformerForQuestionAnswering,
LongformerForSequenceClassification,
LongformerForTokenClassification,
LongformerModel,
LongformerPreTrainedModel,
LongformerSelfAttention,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_longformer import (
TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLongformerForMaskedLM,
TFLongformerForMultipleChoice,
TFLongformerForQuestionAnswering,
TFLongformerForSequenceClassification,
TFLongformerForTokenClassification,
TFLongformerModel,
TFLongformerPreTrainedModel,
TFLongformerSelfAttention,
)
else:
import sys
lowercase_ : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 653 |
'''simple docstring'''
from __future__ import annotations
import os
from typing import Any
import requests
lowercase_ : List[str] = '''https://api.github.com'''
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
lowercase_ : Any = BASE_URL + '''/user'''
# https://github.com/settings/tokens
lowercase_ : Union[str, Any] = os.environ.get('''USER_TOKEN''', '''''')
def SCREAMING_SNAKE_CASE ( lowercase_ : str ):
lowercase = {
"""Authorization""": F"""token {auth_token}""",
"""Accept""": """application/vnd.github.v3+json""",
}
return requests.get(lowercase_ , headers=lowercase_ ).json()
if __name__ == "__main__": # pragma: no cover
if USER_TOKEN:
for key, value in fetch_github_info(USER_TOKEN).items():
print(f'''{key}: {value}''')
else:
raise ValueError('''\'USER_TOKEN\' field cannot be empty.''')
| 653 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __UpperCamelCase (unittest.TestCase ):
def _a ( self ) -> List[str]:
'''simple docstring'''
lowercase = 10
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase = [1, 2, 3, 4]
lowercase = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
self.assertEqual(truncate_or_pad(_lowerCAmelCase , self.block_size , 0 ) , _lowerCAmelCase )
def _a ( self ) -> List[Any]:
'''simple docstring'''
lowercase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
lowercase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(_lowerCAmelCase , self.block_size , 0 ) , _lowerCAmelCase )
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
lowercase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(_lowerCAmelCase , self.block_size , 0 ) , _lowerCAmelCase )
def _a ( self ) -> str:
'''simple docstring'''
lowercase = """It was the year of Our Lord one thousand seven hundred and
seventy-five.\n\nSpiritual revelations were conceded to England at that
favoured period, as at this."""
lowercase , lowercase = process_story(_lowerCAmelCase )
self.assertEqual(_lowerCAmelCase , [] )
def _a ( self ) -> int:
'''simple docstring'''
lowercase = """"""
lowercase , lowercase = process_story(_lowerCAmelCase )
self.assertEqual(_lowerCAmelCase , [] )
self.assertEqual(_lowerCAmelCase , [] )
def _a ( self ) -> Optional[int]:
'''simple docstring'''
lowercase = (
"""It was the year of Our Lord one thousand seven hundred and """
"""seventy-five\n\nSpiritual revelations were conceded to England """
"""at that favoured period, as at this.\n@highlight\n\nIt was the best of times"""
)
lowercase , lowercase = process_story(_lowerCAmelCase )
lowercase = [
"""It was the year of Our Lord one thousand seven hundred and seventy-five.""",
"""Spiritual revelations were conceded to England at that favoured period, as at this.""",
]
self.assertEqual(_lowerCAmelCase , _lowerCAmelCase )
lowercase = ["""It was the best of times."""]
self.assertEqual(_lowerCAmelCase , _lowerCAmelCase )
def _a ( self ) -> Optional[int]:
'''simple docstring'''
lowercase = torch.tensor([1, 2, 3, 4] )
lowercase = torch.tensor([1, 1, 1, 1] )
np.testing.assert_array_equal(build_mask(_lowerCAmelCase , 0 ).numpy() , expected.numpy() )
def _a ( self ) -> int:
'''simple docstring'''
lowercase = torch.tensor([1, 2, 3, 4, 23, 23, 23] )
lowercase = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(_lowerCAmelCase , 23 ).numpy() , expected.numpy() )
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase = torch.tensor([8, 2, 3, 4, 1, 1, 1] )
lowercase = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(_lowerCAmelCase , 1 ).numpy() , expected.numpy() )
def _a ( self ) -> List[Any]:
'''simple docstring'''
lowercase = 101
lowercase = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]] )
lowercase = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )
lowercase = compute_token_type_ids(_lowerCAmelCase , _lowerCAmelCase )
np.testing.assert_array_equal(_lowerCAmelCase , _lowerCAmelCase )
| 653 |
'''simple docstring'''
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowercase_ : Union[str, Any] = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
require_version('''datasets>=1.14.0''', '''To fix: pip install -r examples/pytorch/audio-classification/requirements.txt''')
def SCREAMING_SNAKE_CASE ( lowercase_ : np.ndarray , lowercase_ : float , lowercase_ : int = 1_6000 ):
lowercase = int(round(sample_rate * max_length ) )
if len(lowercase_ ) <= sample_length:
return wav
lowercase = randint(0 , len(lowercase_ ) - sample_length - 1 )
return wav[random_offset : random_offset + sample_length]
@dataclass
class __UpperCamelCase :
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''Name of a dataset from the datasets package'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''A file containing the training audio paths and labels.'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''A file containing the validation audio paths and labels.'''} )
__A = field(
default='''train''' , metadata={
'''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
} , )
__A = field(
default='''validation''' , metadata={
'''help''': (
'''The name of the training data set split to use (via the datasets library). Defaults to \'validation\''''
)
} , )
__A = field(
default='''audio''' , metadata={'''help''': '''The name of the dataset column containing the audio data. Defaults to \'audio\''''} , )
__A = field(
default='''label''' , metadata={'''help''': '''The name of the dataset column containing the labels. Defaults to \'label\''''} )
__A = field(
default=_UpperCAmelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
__A = field(
default=_UpperCAmelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
__A = field(
default=20 , metadata={'''help''': '''Audio clips will be randomly cut to this length during training if the value is set.'''} , )
@dataclass
class __UpperCamelCase :
__A = field(
default='''facebook/wav2vec2-base''' , metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} , )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from the Hub'''} )
__A = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Name or path of preprocessor config.'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Whether to freeze the feature encoder layers of the model.'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Whether to generate an attention mask in the feature extractor.'''} )
__A = field(
default=_UpperCAmelCase , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''} , )
def _a ( self ) -> List[Any]:
'''simple docstring'''
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
"""The argument `--freeze_feature_extractor` is deprecated and """
"""will be removed in a future version. Use `--freeze_feature_encoder`"""
"""instead. Setting `freeze_feature_encoder==True`.""" , _lowerCAmelCase , )
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
"""The argument `--freeze_feature_extractor` is deprecated and """
"""should not be used in combination with `--freeze_feature_encoder`."""
"""Only make use of `--freeze_feature_encoder`.""" )
def SCREAMING_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.
lowercase = 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.
lowercase , lowercase , lowercase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase , lowercase , lowercase = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_audio_classification""" , lowercase_ , lowercase_ )
# 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 )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
lowercase = training_args.get_process_log_level()
logger.setLevel(lowercase_ )
transformers.utils.logging.set_verbosity(lowercase_ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# 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}""" )
logger.info(F"""Training/evaluation parameters {training_args}""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Detecting last checkpoint.
lowercase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowercase = 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 train from scratch.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is 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.""" )
# Initialize our dataset and prepare it for the audio classification task.
lowercase = DatasetDict()
lowercase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.train_split_name , use_auth_token=True if model_args.use_auth_token else None , )
lowercase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.eval_split_name , use_auth_token=True if model_args.use_auth_token else None , )
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
F"""--audio_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. """
"""Make sure to set `--audio_column_name` to the correct audio column - one of """
F"""{', '.join(raw_datasets['train'].column_names )}.""" )
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
F"""--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. """
"""Make sure to set `--label_column_name` to the correct text column - one of """
F"""{', '.join(raw_datasets['train'].column_names )}.""" )
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
lowercase = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path , return_attention_mask=model_args.attention_mask , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
lowercase = raw_datasets.cast_column(
data_args.audio_column_name , datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )
lowercase = feature_extractor.model_input_names[0]
def train_transforms(lowercase_ : int ):
lowercase = []
for audio in batch[data_args.audio_column_name]:
lowercase = random_subsample(
audio["""array"""] , max_length=data_args.max_length_seconds , sample_rate=feature_extractor.sampling_rate )
subsampled_wavs.append(lowercase_ )
lowercase = feature_extractor(lowercase_ , sampling_rate=feature_extractor.sampling_rate )
lowercase = {model_input_name: inputs.get(lowercase_ )}
lowercase = list(batch[data_args.label_column_name] )
return output_batch
def val_transforms(lowercase_ : Dict ):
lowercase = [audio["""array"""] for audio in batch[data_args.audio_column_name]]
lowercase = feature_extractor(lowercase_ , sampling_rate=feature_extractor.sampling_rate )
lowercase = {model_input_name: inputs.get(lowercase_ )}
lowercase = list(batch[data_args.label_column_name] )
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
lowercase = raw_datasets["""train"""].features[data_args.label_column_name].names
lowercase , lowercase = {}, {}
for i, label in enumerate(lowercase_ ):
lowercase = str(lowercase_ )
lowercase = label
# Load the accuracy metric from the datasets package
lowercase = evaluate.load("""accuracy""" )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(lowercase_ : Tuple ):
lowercase = np.argmax(eval_pred.predictions , axis=1 )
return metric.compute(predictions=lowercase_ , references=eval_pred.label_ids )
lowercase = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(lowercase_ ) , labelaid=lowercase_ , idalabel=lowercase_ , finetuning_task="""audio-classification""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
lowercase = AutoModelForAudioClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=lowercase_ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
lowercase = (
raw_datasets["""train"""].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
raw_datasets["train"].set_transform(lowercase_ , output_all_columns=lowercase_ )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
lowercase = (
raw_datasets["""eval"""].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
raw_datasets["eval"].set_transform(lowercase_ , output_all_columns=lowercase_ )
# Initialize our trainer
lowercase = Trainer(
model=lowercase_ , args=lowercase_ , train_dataset=raw_datasets["""train"""] if training_args.do_train else None , eval_dataset=raw_datasets["""eval"""] if training_args.do_eval else None , compute_metrics=lowercase_ , tokenizer=lowercase_ , )
# Training
if training_args.do_train:
lowercase = None
if training_args.resume_from_checkpoint is not None:
lowercase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowercase = last_checkpoint
lowercase = trainer.train(resume_from_checkpoint=lowercase_ )
trainer.save_model()
trainer.log_metrics("""train""" , train_result.metrics )
trainer.save_metrics("""train""" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
lowercase = trainer.evaluate()
trainer.log_metrics("""eval""" , lowercase_ )
trainer.save_metrics("""eval""" , lowercase_ )
# Write model card and (optionally) push to hub
lowercase = {
"""finetuned_from""": model_args.model_name_or_path,
"""tasks""": """audio-classification""",
"""dataset""": data_args.dataset_name,
"""tags""": ["""audio-classification"""],
}
if training_args.push_to_hub:
trainer.push_to_hub(**lowercase_ )
else:
trainer.create_model_card(**lowercase_ )
if __name__ == "__main__":
main()
| 653 | 1 |
'''simple docstring'''
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.
# this script dumps information about the environment
import os
import sys
import transformers
lowercase_ : str = '''3'''
print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)
try:
import torch
print('''Torch version:''', torch.__version__)
print('''Cuda available:''', torch.cuda.is_available())
print('''Cuda version:''', torch.version.cuda)
print('''CuDNN version:''', torch.backends.cudnn.version())
print('''Number of GPUs available:''', torch.cuda.device_count())
print('''NCCL version:''', torch.cuda.nccl.version())
except ImportError:
print('''Torch version:''', None)
try:
import deepspeed
print('''DeepSpeed version:''', deepspeed.__version__)
except ImportError:
print('''DeepSpeed version:''', None)
try:
import tensorflow as tf
print('''TensorFlow version:''', tf.__version__)
print('''TF GPUs available:''', bool(tf.config.list_physical_devices('''GPU''')))
print('''Number of TF GPUs available:''', len(tf.config.list_physical_devices('''GPU''')))
except ImportError:
print('''TensorFlow version:''', None)
| 653 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
lowercase_ : Union[str, Any] = logging.get_logger(__name__)
@dataclass
class __UpperCamelCase (_UpperCAmelCase ):
__A = [
'''no_inference''',
'''no_cuda''',
'''no_tpu''',
'''no_speed''',
'''no_memory''',
'''no_env_print''',
'''no_multi_process''',
]
def __init__( self , **_lowerCAmelCase ) -> Optional[int]:
'''simple docstring'''
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
lowercase = deprecated_arg[3:]
lowercase = not kwargs.pop(_lowerCAmelCase )
logger.warning(
F"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
F""" {positive_arg}={kwargs[positive_arg]}""" )
lowercase = kwargs.pop("""tpu_name""" , self.tpu_name )
lowercase = kwargs.pop("""device_idx""" , self.device_idx )
lowercase = kwargs.pop("""eager_mode""" , self.eager_mode )
lowercase = kwargs.pop("""use_xla""" , self.use_xla )
super().__init__(**_lowerCAmelCase )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Name of TPU'''} , )
__A = field(
default=0 , metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''} , )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''Benchmark models in eager model.'''} )
__A = field(
default=_UpperCAmelCase , metadata={
'''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
} , )
@cached_property
def _a ( self ) -> Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
'''simple docstring'''
requires_backends(self , ["""tf"""] )
lowercase = None
if self.tpu:
try:
if self.tpu_name:
lowercase = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
else:
lowercase = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
lowercase = None
return tpu
@cached_property
def _a ( self ) -> Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
'''simple docstring'''
requires_backends(self , ["""tf"""] )
if self.is_tpu:
tf.config.experimental_connect_to_cluster(self._setup_tpu )
tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )
lowercase = tf.distribute.TPUStrategy(self._setup_tpu )
else:
# currently no multi gpu is allowed
if self.is_gpu:
# TODO: Currently only single GPU is supported
tf.config.set_visible_devices(self.gpu_list[self.device_idx] , """GPU""" )
lowercase = tf.distribute.OneDeviceStrategy(device=F"""/gpu:{self.device_idx}""" )
else:
tf.config.set_visible_devices([] , """GPU""" ) # disable GPU
lowercase = tf.distribute.OneDeviceStrategy(device=F"""/cpu:{self.device_idx}""" )
return strategy
@property
def _a ( self ) -> bool:
'''simple docstring'''
requires_backends(self , ["""tf"""] )
return self._setup_tpu is not None
@property
def _a ( self ) -> "tf.distribute.Strategy":
'''simple docstring'''
requires_backends(self , ["""tf"""] )
return self._setup_strategy
@property
def _a ( self ) -> Tuple:
'''simple docstring'''
requires_backends(self , ["""tf"""] )
return tf.config.list_physical_devices("""GPU""" )
@property
def _a ( self ) -> int:
'''simple docstring'''
requires_backends(self , ["""tf"""] )
if self.cuda:
return len(self.gpu_list )
return 0
@property
def _a ( self ) -> bool:
'''simple docstring'''
return self.n_gpu > 0
| 653 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : int ):
if not isinstance(lowercase_ , lowercase_ ):
raise ValueError("""iterations must be defined as integers""" )
if not isinstance(lowercase_ , lowercase_ ) or not number >= 1:
raise ValueError(
"""starting number must be
and integer and be more than 0""" )
if not iterations >= 1:
raise ValueError("""Iterations must be done more than 0 times to play FizzBuzz""" )
lowercase = """"""
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(lowercase_ )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 653 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : Any = logging.get_logger(__name__)
lowercase_ : str = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class __UpperCamelCase (_UpperCAmelCase ):
__A = '''vit_msn'''
def __init__( self , _lowerCAmelCase=768 , _lowerCAmelCase=12 , _lowerCAmelCase=12 , _lowerCAmelCase=3072 , _lowerCAmelCase="gelu" , _lowerCAmelCase=0.0 , _lowerCAmelCase=0.0 , _lowerCAmelCase=0.02 , _lowerCAmelCase=1E-06 , _lowerCAmelCase=224 , _lowerCAmelCase=16 , _lowerCAmelCase=3 , _lowerCAmelCase=True , **_lowerCAmelCase , ) -> List[Any]:
'''simple docstring'''
super().__init__(**_lowerCAmelCase )
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = intermediate_size
lowercase = hidden_act
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = initializer_range
lowercase = layer_norm_eps
lowercase = image_size
lowercase = patch_size
lowercase = num_channels
lowercase = qkv_bias
| 653 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase_ : Any = {
'''configuration_rag''': ['''RagConfig'''],
'''retrieval_rag''': ['''RagRetriever'''],
'''tokenization_rag''': ['''RagTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : List[Any] = [
'''RagModel''',
'''RagPreTrainedModel''',
'''RagSequenceForGeneration''',
'''RagTokenForGeneration''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : Optional[int] = [
'''TFRagModel''',
'''TFRagPreTrainedModel''',
'''TFRagSequenceForGeneration''',
'''TFRagTokenForGeneration''',
]
if TYPE_CHECKING:
from .configuration_rag import RagConfig
from .retrieval_rag import RagRetriever
from .tokenization_rag import RagTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rag import (
TFRagModel,
TFRagPreTrainedModel,
TFRagSequenceForGeneration,
TFRagTokenForGeneration,
)
else:
import sys
lowercase_ : int = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 653 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowercase_ : Union[str, Any] , lowercase_ : str ):
lowercase = """"""
for i in table:
res += inp[i - 1]
return res
def SCREAMING_SNAKE_CASE ( lowercase_ : List[Any] ):
return data[1:] + data[0]
def SCREAMING_SNAKE_CASE ( lowercase_ : Tuple , lowercase_ : Dict ):
lowercase = """"""
for i in range(len(lowercase_ ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def SCREAMING_SNAKE_CASE ( lowercase_ : str , lowercase_ : str ):
lowercase = int("""0b""" + data[0] + data[-1] , 2 )
lowercase = int("""0b""" + data[1:3] , 2 )
return bin(s[row][col] )[2:]
def SCREAMING_SNAKE_CASE ( lowercase_ : Dict , lowercase_ : Tuple , lowercase_ : Tuple , lowercase_ : str , lowercase_ : Any ):
lowercase = message[:4]
lowercase = message[4:]
lowercase = apply_table(lowercase_ , lowercase_ )
lowercase = xor(lowercase_ , lowercase_ )
lowercase = apply_sbox(lowercase_ , temp[:4] ) # noqa: E741
lowercase = apply_sbox(lowercase_ , temp[4:] )
lowercase = """0""" * (2 - len(lowercase_ )) + l # noqa: E741
lowercase = """0""" * (2 - len(lowercase_ )) + r
lowercase = apply_table(l + r , lowercase_ )
lowercase = xor(lowercase_ , lowercase_ )
return temp + right
if __name__ == "__main__":
lowercase_ : Tuple = input('''Enter 10 bit key: ''')
lowercase_ : Any = input('''Enter 8 bit message: ''')
lowercase_ : Dict = [6, 3, 7, 4, 8, 5, 10, 9]
lowercase_ : str = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
lowercase_ : List[Any] = [2, 4, 3, 1]
lowercase_ : List[str] = [2, 6, 3, 1, 4, 8, 5, 7]
lowercase_ : Tuple = [4, 1, 3, 5, 7, 2, 8, 6]
lowercase_ : Optional[Any] = [4, 1, 2, 3, 2, 3, 4, 1]
lowercase_ : List[str] = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
lowercase_ : List[Any] = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
lowercase_ : Union[str, Any] = apply_table(key, paa_table)
lowercase_ : Optional[Any] = temp[:5]
lowercase_ : int = temp[5:]
lowercase_ : List[str] = left_shift(left)
lowercase_ : int = left_shift(right)
lowercase_ : Tuple = apply_table(left + right, pa_table)
lowercase_ : List[str] = left_shift(left)
lowercase_ : Optional[Any] = left_shift(right)
lowercase_ : Union[str, Any] = left_shift(left)
lowercase_ : Union[str, Any] = left_shift(right)
lowercase_ : Optional[int] = apply_table(left + right, pa_table)
# encryption
lowercase_ : int = apply_table(message, IP)
lowercase_ : Dict = function(expansion, sa, sa, keya, temp)
lowercase_ : Any = temp[4:] + temp[:4]
lowercase_ : List[Any] = function(expansion, sa, sa, keya, temp)
lowercase_ : Tuple = apply_table(temp, IP_inv)
print('''Cipher text is:''', CT)
# decryption
lowercase_ : List[str] = apply_table(CT, IP)
lowercase_ : Optional[int] = function(expansion, sa, sa, keya, temp)
lowercase_ : Optional[Any] = temp[4:] + temp[:4]
lowercase_ : Optional[int] = function(expansion, sa, sa, keya, temp)
lowercase_ : Optional[Any] = apply_table(temp, IP_inv)
print('''Plain text after decypting is:''', PT)
| 653 | 1 |
'''simple docstring'''
import requests
def SCREAMING_SNAKE_CASE ( lowercase_ : str , lowercase_ : str ):
lowercase = {"""Content-Type""": """application/json"""}
lowercase = requests.post(lowercase_ , json={"""text""": message_body} , headers=lowercase_ )
if response.status_code != 200:
lowercase = (
"""Request to slack returned an error """
F"""{response.status_code}, the response is:\n{response.text}"""
)
raise ValueError(lowercase_ )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message('''<YOUR MESSAGE BODY>''', '''<SLACK CHANNEL URL>''')
| 653 |
'''simple docstring'''
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
lowercase_ : int = 50_0000
lowercase_ , lowercase_ : Union[str, Any] = os.path.split(__file__)
lowercase_ : Optional[Any] = os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))
@get_duration
def SCREAMING_SNAKE_CASE ( lowercase_ : datasets.Dataset , **lowercase_ : Dict ):
lowercase = dataset.map(**lowercase_ )
@get_duration
def SCREAMING_SNAKE_CASE ( lowercase_ : datasets.Dataset , **lowercase_ : Optional[int] ):
lowercase = dataset.filter(**lowercase_ )
def SCREAMING_SNAKE_CASE ( ):
lowercase = {"""num examples""": SPEED_TEST_N_EXAMPLES}
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase = datasets.Features({"""text""": datasets.Value("""string""" ), """numbers""": datasets.Value("""float32""" )} )
lowercase = generate_example_dataset(
os.path.join(lowercase_ , """dataset.arrow""" ) , lowercase_ , num_examples=lowercase_ )
lowercase = transformers.AutoTokenizer.from_pretrained("""bert-base-cased""" , use_fast=lowercase_ )
def tokenize(lowercase_ : Dict ):
return tokenizer(examples["""text"""] )
lowercase = map(lowercase_ )
lowercase = map(lowercase_ , batched=lowercase_ )
lowercase = map(lowercase_ , function=lambda lowercase_ : None , batched=lowercase_ )
with dataset.formatted_as(type="""numpy""" ):
lowercase = map(lowercase_ , function=lambda lowercase_ : None , batched=lowercase_ )
with dataset.formatted_as(type="""pandas""" ):
lowercase = map(lowercase_ , function=lambda lowercase_ : None , batched=lowercase_ )
with dataset.formatted_as(type="""torch""" , columns="""numbers""" ):
lowercase = map(lowercase_ , function=lambda lowercase_ : None , batched=lowercase_ )
with dataset.formatted_as(type="""tensorflow""" , columns="""numbers""" ):
lowercase = map(lowercase_ , function=lambda lowercase_ : None , batched=lowercase_ )
lowercase = map(lowercase_ , function=lowercase_ , batched=lowercase_ )
lowercase = filter(lowercase_ )
# Activate later when tokenizer support batched inputs
# with dataset.formatted_as(type='numpy'):
# times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)
with open(lowercase_ , """wb""" ) as f:
f.write(json.dumps(lowercase_ ).encode("""utf-8""" ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_map_filter()
| 653 | 1 |
'''simple docstring'''
import math
lowercase_ : Any = 10
lowercase_ : Any = 7
lowercase_ : str = BALLS_PER_COLOUR * NUM_COLOURS
def SCREAMING_SNAKE_CASE ( lowercase_ : int = 20 ):
lowercase = math.comb(lowercase_ , lowercase_ )
lowercase = math.comb(NUM_BALLS - BALLS_PER_COLOUR , lowercase_ )
lowercase = NUM_COLOURS * (1 - missing_colour / total)
return F"""{result:.9f}"""
if __name__ == "__main__":
print(solution(20))
| 653 |
'''simple docstring'''
from random import shuffle
import tensorflow as tf
from numpy import array
def SCREAMING_SNAKE_CASE ( lowercase_ : List[str] , lowercase_ : Optional[int] ):
lowercase = int(lowercase_ )
assert noofclusters < len(lowercase_ )
# Find out the dimensionality
lowercase = len(vectors[0] )
# Will help select random centroids from among the available vectors
lowercase = list(range(len(lowercase_ ) ) )
shuffle(lowercase_ )
# GRAPH OF COMPUTATION
# We initialize a new graph and set it as the default during each run
# of this algorithm. This ensures that as this function is called
# multiple times, the default graph doesn't keep getting crowded with
# unused ops and Variables from previous function calls.
lowercase = tf.Graph()
with graph.as_default():
# SESSION OF COMPUTATION
lowercase = tf.Session()
##CONSTRUCTING THE ELEMENTS OF COMPUTATION
##First lets ensure we have a Variable vector for each centroid,
##initialized to one of the vectors from the available data points
lowercase = [
tf.Variable(vectors[vector_indices[i]] ) for i in range(lowercase_ )
]
##These nodes will assign the centroid Variables the appropriate
##values
lowercase = tf.placeholder("""float64""" , [dim] )
lowercase = []
for centroid in centroids:
cent_assigns.append(tf.assign(lowercase_ , lowercase_ ) )
##Variables for cluster assignments of individual vectors(initialized
##to 0 at first)
lowercase = [tf.Variable(0 ) for i in range(len(lowercase_ ) )]
##These nodes will assign an assignment Variable the appropriate
##value
lowercase = tf.placeholder("""int32""" )
lowercase = []
for assignment in assignments:
cluster_assigns.append(tf.assign(lowercase_ , lowercase_ ) )
##Now lets construct the node that will compute the mean
# The placeholder for the input
lowercase = tf.placeholder("""float""" , [None, dim] )
# The Node/op takes the input and computes a mean along the 0th
# dimension, i.e. the list of input vectors
lowercase = tf.reduce_mean(lowercase_ , 0 )
##Node for computing Euclidean distances
# Placeholders for input
lowercase = tf.placeholder("""float""" , [dim] )
lowercase = tf.placeholder("""float""" , [dim] )
lowercase = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(lowercase_ , lowercase_ ) , 2 ) ) )
##This node will figure out which cluster to assign a vector to,
##based on Euclidean distances of the vector from the centroids.
# Placeholder for input
lowercase = tf.placeholder("""float""" , [noofclusters] )
lowercase = tf.argmin(lowercase_ , 0 )
##INITIALIZING STATE VARIABLES
##This will help initialization of all Variables defined with respect
##to the graph. The Variable-initializer should be defined after
##all the Variables have been constructed, so that each of them
##will be included in the initialization.
lowercase = tf.initialize_all_variables()
# Initialize all variables
sess.run(lowercase_ )
##CLUSTERING ITERATIONS
# Now perform the Expectation-Maximization steps of K-Means clustering
# iterations. To keep things simple, we will only do a set number of
# iterations, instead of using a Stopping Criterion.
lowercase = 100
for _ in range(lowercase_ ):
##EXPECTATION STEP
##Based on the centroid locations till last iteration, compute
##the _expected_ centroid assignments.
# Iterate over each vector
for vector_n in range(len(lowercase_ ) ):
lowercase = vectors[vector_n]
# Compute Euclidean distance between this vector and each
# centroid. Remember that this list cannot be named
#'centroid_distances', since that is the input to the
# cluster assignment node.
lowercase = [
sess.run(lowercase_ , feed_dict={va: vect, va: sess.run(lowercase_ )} )
for centroid in centroids
]
# Now use the cluster assignment node, with the distances
# as the input
lowercase = sess.run(
lowercase_ , feed_dict={centroid_distances: distances} )
# Now assign the value to the appropriate state variable
sess.run(
cluster_assigns[vector_n] , feed_dict={assignment_value: assignment} )
##MAXIMIZATION STEP
# Based on the expected state computed from the Expectation Step,
# compute the locations of the centroids so as to maximize the
# overall objective of minimizing within-cluster Sum-of-Squares
for cluster_n in range(lowercase_ ):
# Collect all the vectors assigned to this cluster
lowercase = [
vectors[i]
for i in range(len(lowercase_ ) )
if sess.run(assignments[i] ) == cluster_n
]
# Compute new centroid location
lowercase = sess.run(
lowercase_ , feed_dict={mean_input: array(lowercase_ )} )
# Assign value to appropriate variable
sess.run(
cent_assigns[cluster_n] , feed_dict={centroid_value: new_location} )
# Return centroids and assignments
lowercase = sess.run(lowercase_ )
lowercase = sess.run(lowercase_ )
return centroids, assignments
| 653 | 1 |
'''simple docstring'''
from collections import defaultdict
from math import ceil, sqrt
def SCREAMING_SNAKE_CASE ( lowercase_ : int = 100_0000 , lowercase_ : int = 10 ):
lowercase = defaultdict(lowercase_ )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if outer_width * outer_width > t_limit:
lowercase = max(
ceil(sqrt(outer_width * outer_width - t_limit ) ) , 1 )
else:
lowercase = 1
hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2
for hole_width in range(lowercase_ , outer_width - 1 , 2 ):
count[outer_width * outer_width - hole_width * hole_width] += 1
return sum(1 for n in count.values() if 1 <= n <= 10 )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 653 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : int , lowercase_ : list[list[int]] ):
def update_area_of_max_square(lowercase_ : int , lowercase_ : int ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
lowercase = update_area_of_max_square(lowercase_ , col + 1 )
lowercase = update_area_of_max_square(row + 1 , col + 1 )
lowercase = update_area_of_max_square(row + 1 , lowercase_ )
if mat[row][col]:
lowercase = 1 + min([right, diagonal, down] )
lowercase = max(largest_square_area[0] , lowercase_ )
return sub_problem_sol
else:
return 0
lowercase = [0]
update_area_of_max_square(0 , 0 )
return largest_square_area[0]
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : int , lowercase_ : list[list[int]] ):
def update_area_of_max_square_using_dp_array(
lowercase_ : int , lowercase_ : int , lowercase_ : list[list[int]] ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
lowercase = update_area_of_max_square_using_dp_array(lowercase_ , col + 1 , lowercase_ )
lowercase = update_area_of_max_square_using_dp_array(row + 1 , col + 1 , lowercase_ )
lowercase = update_area_of_max_square_using_dp_array(row + 1 , lowercase_ , lowercase_ )
if mat[row][col]:
lowercase = 1 + min([right, diagonal, down] )
lowercase = max(largest_square_area[0] , lowercase_ )
lowercase = sub_problem_sol
return sub_problem_sol
else:
return 0
lowercase = [0]
lowercase = [[-1] * cols for _ in range(lowercase_ )]
update_area_of_max_square_using_dp_array(0 , 0 , lowercase_ )
return largest_square_area[0]
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : int , lowercase_ : list[list[int]] ):
lowercase = [[0] * (cols + 1) for _ in range(rows + 1 )]
lowercase = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
lowercase = dp_array[row][col + 1]
lowercase = dp_array[row + 1][col + 1]
lowercase = dp_array[row + 1][col]
if mat[row][col] == 1:
lowercase = 1 + min(lowercase_ , lowercase_ , lowercase_ )
lowercase = max(dp_array[row][col] , lowercase_ )
else:
lowercase = 0
return largest_square_area
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : int , lowercase_ : list[list[int]] ):
lowercase = [0] * (cols + 1)
lowercase = [0] * (cols + 1)
lowercase = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
lowercase = current_row[col + 1]
lowercase = next_row[col + 1]
lowercase = next_row[col]
if mat[row][col] == 1:
lowercase = 1 + min(lowercase_ , lowercase_ , lowercase_ )
lowercase = max(current_row[col] , lowercase_ )
else:
lowercase = 0
lowercase = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 653 | 1 |
'''simple docstring'''
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import AutoTokenizer, HfArgumentParser
lowercase_ : Optional[int] = re.compile(r'''\s+''')
def SCREAMING_SNAKE_CASE ( lowercase_ : str ):
return {"hash": hashlib.mda(re.sub(lowercase_ , """""" , example["""content"""] ).encode("""utf-8""" ) ).hexdigest()}
def SCREAMING_SNAKE_CASE ( lowercase_ : Any ):
lowercase = [len(lowercase_ ) for line in example["""content"""].splitlines()]
return {"line_mean": np.mean(lowercase_ ), "line_max": max(lowercase_ )}
def SCREAMING_SNAKE_CASE ( lowercase_ : List[Any] ):
lowercase = np.mean([c.isalnum() for c in example["""content"""]] )
return {"alpha_frac": alpha_frac}
def SCREAMING_SNAKE_CASE ( lowercase_ : List[str] , lowercase_ : Optional[Any] ):
if example["hash"] in uniques:
uniques.remove(example["""hash"""] )
return True
else:
return False
def SCREAMING_SNAKE_CASE ( lowercase_ : List[str] , lowercase_ : Optional[Any]=5 ):
lowercase = ["""auto-generated""", """autogenerated""", """automatically generated"""]
lowercase = example["""content"""].splitlines()
for _, line in zip(range(lowercase_ ) , lowercase_ ):
for keyword in keywords:
if keyword in line.lower():
return {"autogenerated": True}
else:
return {"autogenerated": False}
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : int=5 , lowercase_ : Optional[Any]=0.05 ):
lowercase = ["""unit tests""", """test file""", """configuration file"""]
lowercase = example["""content"""].splitlines()
lowercase = 0
lowercase = 0
# first test
for _, line in zip(range(lowercase_ ) , lowercase_ ):
for keyword in keywords:
if keyword in line.lower():
return {"config_or_test": True}
# second test
lowercase = example["""content"""].count("""\n""" )
lowercase = int(coeff * nlines )
for line in lines:
count_config += line.lower().count("""config""" )
count_test += line.lower().count("""test""" )
if count_config > threshold or count_test > threshold:
return {"config_or_test": True}
return {"config_or_test": False}
def SCREAMING_SNAKE_CASE ( lowercase_ : List[str] ):
lowercase = ["""def """, """class """, """for """, """while """]
lowercase = example["""content"""].splitlines()
for line in lines:
for keyword in keywords:
if keyword in line.lower():
return {"has_no_keywords": False}
return {"has_no_keywords": True}
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : Tuple=4 ):
lowercase = example["""content"""].splitlines()
lowercase = 0
for line in lines:
counter += line.lower().count("""=""" )
if counter > minimum:
return {"has_few_assignments": False}
return {"has_few_assignments": True}
def SCREAMING_SNAKE_CASE ( lowercase_ : List[str] ):
lowercase = tokenizer(example["""content"""] , truncation=lowercase_ )["""input_ids"""]
lowercase = len(example["""content"""] ) / len(lowercase_ )
return {"ratio": ratio}
def SCREAMING_SNAKE_CASE ( lowercase_ : List[Any] ):
lowercase = {}
results.update(get_hash(lowercase_ ) )
results.update(line_stats(lowercase_ ) )
results.update(alpha_stats(lowercase_ ) )
results.update(char_token_ratio(lowercase_ ) )
results.update(is_autogenerated(lowercase_ ) )
results.update(is_config_or_test(lowercase_ ) )
results.update(has_no_keywords(lowercase_ ) )
results.update(has_few_assignments(lowercase_ ) )
return results
def SCREAMING_SNAKE_CASE ( lowercase_ : Tuple , lowercase_ : str , lowercase_ : Optional[int] ):
if not check_uniques(lowercase_ , lowercase_ ):
return False
elif example["autogenerated"]:
return False
elif example["line_max"] > args.line_max:
return False
elif example["line_mean"] > args.line_mean:
return False
elif example["alpha_frac"] < args.alpha_frac:
return False
elif example["ratio"] < args.min_token_ratio:
return False
elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_few_assignments"]:
return False
else:
return True
def SCREAMING_SNAKE_CASE ( lowercase_ : Any ):
with open(lowercase_ , """rb""" ) as f_in:
with gzip.open(str(lowercase_ ) + """.gz""" , """wb""" , compresslevel=6 ) as f_out:
shutil.copyfileobj(lowercase_ , lowercase_ )
os.unlink(lowercase_ )
# Settings
lowercase_ : Optional[int] = HfArgumentParser(PreprocessingArguments)
lowercase_ : Union[str, Any] = parser.parse_args()
if args.num_workers is None:
lowercase_ : Union[str, Any] = multiprocessing.cpu_count()
lowercase_ : Optional[Any] = AutoTokenizer.from_pretrained(args.tokenizer_dir)
# Load dataset
lowercase_ : Union[str, Any] = time.time()
lowercase_ : Optional[int] = load_dataset(args.dataset_name, split='''train''')
print(f'''Time to load dataset: {time.time()-t_start:.2f}''')
# Run preprocessing
lowercase_ : Optional[Any] = time.time()
lowercase_ : Dict = ds.map(preprocess, num_proc=args.num_workers)
print(f'''Time to preprocess dataset: {time.time()-t_start:.2f}''')
# Deduplicate hashes
lowercase_ : Any = set(ds.unique('''hash'''))
lowercase_ : str = len(uniques) / len(ds)
print(f'''Fraction of duplicates: {1-frac:.2%}''')
# Deduplicate data and apply heuristics
lowercase_ : int = time.time()
lowercase_ : List[Any] = ds.filter(filter, fn_kwargs={'''uniques''': uniques, '''args''': args})
print(f'''Time to filter dataset: {time.time()-t_start:.2f}''')
print(f'''Size of filtered dataset: {len(ds_filter)}''')
# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
lowercase_ : List[str] = time.time()
lowercase_ , lowercase_ : int = deduplicate_dataset(ds_filter, args.jaccard_threshold)
print(f'''Time to deduplicate dataset: {time.time()-t_start:.2f}''')
print(f'''Size of deduplicate dataset: {len(ds_filter)}''')
# Save data in batches of samples_per_file
lowercase_ : int = Path(args.output_dir)
output_dir.mkdir(exist_ok=True)
# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
with open(output_dir / '''duplicate_clusters.json''', '''w''') as f:
json.dump(duplicate_clusters, f)
lowercase_ : List[str] = output_dir / '''data'''
data_dir.mkdir(exist_ok=True)
lowercase_ : List[Any] = time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
lowercase_ : Optional[int] = str(data_dir / f'''file-{file_number+1:012}.json''')
lowercase_ : Any = min(len(ds_filter), index + args.samples_per_file)
ds_filter.select(list(range(index, end_index))).to_json(file_path)
compress_file(file_path)
print(f'''Time to save dataset: {time.time()-t_start:.2f}''')
| 653 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : Optional[Any] = logging.get_logger(__name__)
lowercase_ : int = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}
class __UpperCamelCase (_UpperCAmelCase ):
__A = '''gpt_bigcode'''
__A = ['''past_key_values''']
__A = {
'''hidden_size''': '''n_embd''',
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self , _lowerCAmelCase=5_0257 , _lowerCAmelCase=1024 , _lowerCAmelCase=768 , _lowerCAmelCase=12 , _lowerCAmelCase=12 , _lowerCAmelCase=None , _lowerCAmelCase="gelu_pytorch_tanh" , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=1E-5 , _lowerCAmelCase=0.02 , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=5_0256 , _lowerCAmelCase=5_0256 , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=True , **_lowerCAmelCase , ) -> Optional[int]:
'''simple docstring'''
lowercase = vocab_size
lowercase = n_positions
lowercase = n_embd
lowercase = n_layer
lowercase = n_head
lowercase = n_inner
lowercase = activation_function
lowercase = resid_pdrop
lowercase = embd_pdrop
lowercase = attn_pdrop
lowercase = layer_norm_epsilon
lowercase = initializer_range
lowercase = scale_attn_weights
lowercase = use_cache
lowercase = attention_softmax_in_fpaa
lowercase = scale_attention_softmax_in_fpaa
lowercase = multi_query
lowercase = bos_token_id
lowercase = eos_token_id
super().__init__(bos_token_id=_lowerCAmelCase , eos_token_id=_lowerCAmelCase , **_lowerCAmelCase )
| 653 | 1 |
'''simple docstring'''
from jiwer import compute_measures
import datasets
lowercase_ : List[str] = '''\
@inproceedings{inproceedings,
author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
year = {2004},
month = {01},
pages = {},
title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
'''
lowercase_ : List[str] = '''\
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.
The general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.
This problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.
Word error rate can then be computed as:
WER = (S + D + I) / N = (S + D + I) / (S + D + C)
where
S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct words,
N is the number of words in the reference (N=S+D+C).
This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
'''
lowercase_ : Any = '''
Compute WER score of transcribed segments against references.
Args:
references: List of references for each speech input.
predictions: List of transcriptions to score.
concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.
Returns:
(float): the word error rate
Examples:
>>> predictions = ["this is the prediction", "there is an other sample"]
>>> references = ["this is the reference", "there is another one"]
>>> wer = datasets.load_metric("wer")
>>> wer_score = wer.compute(predictions=predictions, references=references)
>>> print(wer_score)
0.5
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __UpperCamelCase (datasets.Metric ):
def _a ( self ) -> Tuple:
'''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/jitsi/jiwer/"""] , reference_urls=[
"""https://en.wikipedia.org/wiki/Word_error_rate""",
] , )
def _a ( self , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase=False ) -> Union[str, Any]:
'''simple docstring'''
if concatenate_texts:
return compute_measures(_lowerCAmelCase , _lowerCAmelCase )["wer"]
else:
lowercase = 0
lowercase = 0
for prediction, reference in zip(_lowerCAmelCase , _lowerCAmelCase ):
lowercase = compute_measures(_lowerCAmelCase , _lowerCAmelCase )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 653 |
'''simple docstring'''
import requests
def SCREAMING_SNAKE_CASE ( lowercase_ : str , lowercase_ : str ):
lowercase = {"""Content-Type""": """application/json"""}
lowercase = requests.post(lowercase_ , json={"""text""": message_body} , headers=lowercase_ )
if response.status_code != 200:
lowercase = (
"""Request to slack returned an error """
F"""{response.status_code}, the response is:\n{response.text}"""
)
raise ValueError(lowercase_ )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message('''<YOUR MESSAGE BODY>''', '''<SLACK CHANNEL URL>''')
| 653 | 1 |
'''simple docstring'''
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
Benchmark,
Memory,
MemorySummary,
measure_peak_memory_cpu,
start_memory_tracing,
stop_memory_tracing,
)
if is_tf_available():
import tensorflow as tf
from tensorflow.python.framework.errors_impl import ResourceExhaustedError
from .benchmark_args_tf import TensorFlowBenchmarkArguments
if is_pyanvml_available():
import pyanvml.pyanvml as nvml
lowercase_ : Dict = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE ( lowercase_ : bool , lowercase_ : bool ):
def run_func(lowercase_ : Union[str, Any] ):
@wraps(lowercase_ )
def run_in_eager_mode(*lowercase_ : List[Any] , **lowercase_ : Optional[int] ):
return func(*lowercase_ , **lowercase_ )
@wraps(lowercase_ )
@tf.function(experimental_compile=lowercase_ )
def run_in_graph_mode(*lowercase_ : str , **lowercase_ : Union[str, Any] ):
return func(*lowercase_ , **lowercase_ )
if do_eager_mode is True:
if use_xla is not False:
raise ValueError(
"""Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.""" )
return run_in_eager_mode
else:
return run_in_graph_mode
return run_func
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : int , lowercase_ : int ):
lowercase = random.Random()
lowercase = [rng.randint(0 , vocab_size - 1 ) for i in range(batch_size * sequence_length )]
return tf.constant(lowercase_ , shape=(batch_size, sequence_length) , dtype=tf.intaa )
class __UpperCamelCase (_UpperCAmelCase ):
__A = 42
__A = 42
__A = "TensorFlow"
@property
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
return tf.__version__
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> float:
'''simple docstring'''
lowercase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
lowercase = self._prepare_inference_func(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return self._measure_speed(_inference )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> float:
'''simple docstring'''
lowercase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
lowercase = self._prepare_train_func(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return self._measure_speed(_train )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> [Memory, Optional[MemorySummary]]:
'''simple docstring'''
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , _lowerCAmelCase )
lowercase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
lowercase = self._prepare_inference_func(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return self._measure_memory(_inference )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> [Memory, Optional[MemorySummary]]:
'''simple docstring'''
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , _lowerCAmelCase )
lowercase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
lowercase = self._prepare_train_func(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return self._measure_memory(_train )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Callable[[], None]:
'''simple docstring'''
lowercase = self.config_dict[model_name]
if self.args.fpaa:
raise NotImplementedError("""Mixed precision is currently not supported.""" )
lowercase = (
hasattr(_lowerCAmelCase , """architectures""" )
and isinstance(config.architectures , _lowerCAmelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
lowercase = """TF""" + config.architectures[0] # prepend 'TF' for tensorflow model
lowercase = __import__("""transformers""" , fromlist=[model_class] )
lowercase = getattr(_lowerCAmelCase , _lowerCAmelCase )
lowercase = model_cls(_lowerCAmelCase )
except ImportError:
raise ImportError(
F"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
""" set `--only_pretrain_model` or `args.only_pretrain_model=True`.""" )
else:
lowercase = TF_MODEL_MAPPING[config.__class__](_lowerCAmelCase )
# encoder-decoder has vocab size saved differently
lowercase = config.vocab_size if hasattr(_lowerCAmelCase , """vocab_size""" ) else config.encoder.vocab_size
lowercase = random_input_ids(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_forward():
return model(_lowerCAmelCase , decoder_input_ids=_lowerCAmelCase , training=_lowerCAmelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_forward():
return model(_lowerCAmelCase , training=_lowerCAmelCase )
lowercase = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
return _inference
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Callable[[], None]:
'''simple docstring'''
lowercase = self.config_dict[model_name]
if self.args.eager_mode is not False:
raise ValueError("""Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.""" )
if self.args.fpaa:
raise NotImplementedError("""Mixed precision is currently not supported.""" )
lowercase = (
hasattr(_lowerCAmelCase , """architectures""" )
and isinstance(config.architectures , _lowerCAmelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
lowercase = """TF""" + config.architectures[0] # prepend 'TF' for tensorflow model
lowercase = __import__("""transformers""" , fromlist=[model_class] )
lowercase = getattr(_lowerCAmelCase , _lowerCAmelCase )
lowercase = model_cls(_lowerCAmelCase )
except ImportError:
raise ImportError(
F"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
""" set `--only_pretrain_model` or `args.only_pretrain_model=True`.""" )
else:
lowercase = TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](_lowerCAmelCase )
# encoder-decoder has vocab size saved differently
lowercase = config.vocab_size if hasattr(_lowerCAmelCase , """vocab_size""" ) else config.encoder.vocab_size
lowercase = random_input_ids(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_train():
lowercase = model(_lowerCAmelCase , decoder_input_ids=_lowerCAmelCase , labels=_lowerCAmelCase , training=_lowerCAmelCase )[0]
lowercase = tf.gradients(_lowerCAmelCase , model.trainable_variables )
return gradients
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_train():
lowercase = model(_lowerCAmelCase , labels=_lowerCAmelCase , training=_lowerCAmelCase )[0]
lowercase = tf.gradients(_lowerCAmelCase , model.trainable_variables )
return gradients
lowercase = encoder_decoder_train if config.is_encoder_decoder else encoder_train
return _train
def _a ( self , _lowerCAmelCase ) -> float:
'''simple docstring'''
with self.args.strategy.scope():
try:
if self.args.is_tpu or self.args.use_xla:
# run additional 10 times to stabilize compilation for tpu
logger.info("""Do inference on TPU. Running model 5 times to stabilize compilation""" )
timeit.repeat(_lowerCAmelCase , repeat=1 , number=5 )
# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
lowercase = timeit.repeat(
_lowerCAmelCase , repeat=self.args.repeat , number=10 , )
return min(_lowerCAmelCase ) / 10.0
except ResourceExhaustedError as e:
self.print_fn(F"""Doesn't fit on GPU. {e}""" )
def _a ( self , _lowerCAmelCase ) -> [Memory, MemorySummary]:
'''simple docstring'''
logger.info(
"""Note that TensorFlow allocates more memory than """
"""it might need to speed up computation. """
"""The memory reported here corresponds to the memory """
"""reported by `nvidia-smi`, which can vary depending """
"""on total available memory on the GPU that is used.""" )
with self.args.strategy.scope():
try:
if self.args.trace_memory_line_by_line:
if not self.args.eager_mode:
raise ValueError(
"""`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"""
""" consumption line by line.""" )
lowercase = start_memory_tracing("""transformers""" )
if self.args.is_tpu:
# tpu
raise NotImplementedError(
"""Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"""
""" with `args.memory=False`""" )
elif self.args.is_gpu:
# gpu
if not is_pyanvml_available():
logger.warning(
"""py3nvml not installed, we won't log GPU memory usage. """
"""Install py3nvml (pip install py3nvml) to log information about GPU.""" )
lowercase = """N/A"""
else:
logger.info(
"""Measuring total GPU usage on GPU device. Make sure to not have additional processes"""
""" running on the same GPU.""" )
# init nvml
nvml.nvmlInit()
func()
lowercase = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
lowercase = nvml.nvmlDeviceGetMemoryInfo(_lowerCAmelCase )
lowercase = meminfo.used
lowercase = Memory(_lowerCAmelCase )
# shutdown nvml
nvml.nvmlShutdown()
else:
# cpu
if self.args.trace_memory_line_by_line:
logger.info(
"""When enabling line by line tracing, the max peak memory for CPU is inaccurate in"""
""" TensorFlow.""" )
lowercase = None
else:
lowercase = measure_peak_memory_cpu(_lowerCAmelCase )
lowercase = Memory(_lowerCAmelCase ) if isinstance(_lowerCAmelCase , _lowerCAmelCase ) else memory_bytes
if self.args.trace_memory_line_by_line:
lowercase = stop_memory_tracing(_lowerCAmelCase )
if memory is None:
lowercase = summary.total
else:
lowercase = None
return memory, summary
except ResourceExhaustedError as e:
self.print_fn(F"""Doesn't fit on GPU. {e}""" )
return "N/A", None
| 653 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
lowercase_ : List[str] = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE ( lowercase_ : int ):
lowercase = MobileViTConfig()
# size of the architecture
if "mobilevit_s" in mobilevit_name:
lowercase = [144, 192, 240]
lowercase = [16, 32, 64, 96, 128, 160, 640]
elif "mobilevit_xs" in mobilevit_name:
lowercase = [96, 120, 144]
lowercase = [16, 32, 48, 64, 80, 96, 384]
elif "mobilevit_xxs" in mobilevit_name:
lowercase = [64, 80, 96]
lowercase = [16, 16, 24, 48, 64, 80, 320]
lowercase = 0.05
lowercase = 2.0
if mobilevit_name.startswith("""deeplabv3_""" ):
lowercase = 512
lowercase = 16
lowercase = 21
lowercase = """pascal-voc-id2label.json"""
else:
lowercase = 1000
lowercase = """imagenet-1k-id2label.json"""
lowercase = """huggingface/label-files"""
lowercase = json.load(open(hf_hub_download(lowercase_ , lowercase_ , repo_type="""dataset""" ) , """r""" ) )
lowercase = {int(lowercase_ ): v for k, v in idalabel.items()}
lowercase = idalabel
lowercase = {v: k for k, v in idalabel.items()}
return config
def SCREAMING_SNAKE_CASE ( lowercase_ : Any , lowercase_ : Any=False ):
for i in range(1 , 6 ):
if F"""layer_{i}.""" in name:
lowercase = name.replace(F"""layer_{i}.""" , F"""encoder.layer.{i - 1}.""" )
if "conv_1." in name:
lowercase = name.replace("""conv_1.""" , """conv_stem.""" )
if ".block." in name:
lowercase = name.replace(""".block.""" , """.""" )
if "exp_1x1" in name:
lowercase = name.replace("""exp_1x1""" , """expand_1x1""" )
if "red_1x1" in name:
lowercase = name.replace("""red_1x1""" , """reduce_1x1""" )
if ".local_rep.conv_3x3." in name:
lowercase = name.replace(""".local_rep.conv_3x3.""" , """.conv_kxk.""" )
if ".local_rep.conv_1x1." in name:
lowercase = name.replace(""".local_rep.conv_1x1.""" , """.conv_1x1.""" )
if ".norm." in name:
lowercase = name.replace(""".norm.""" , """.normalization.""" )
if ".conv." in name:
lowercase = name.replace(""".conv.""" , """.convolution.""" )
if ".conv_proj." in name:
lowercase = name.replace(""".conv_proj.""" , """.conv_projection.""" )
for i in range(0 , 2 ):
for j in range(0 , 4 ):
if F""".{i}.{j}.""" in name:
lowercase = name.replace(F""".{i}.{j}.""" , F""".{i}.layer.{j}.""" )
for i in range(2 , 6 ):
for j in range(0 , 4 ):
if F""".{i}.{j}.""" in name:
lowercase = name.replace(F""".{i}.{j}.""" , F""".{i}.""" )
if "expand_1x1" in name:
lowercase = name.replace("""expand_1x1""" , """downsampling_layer.expand_1x1""" )
if "conv_3x3" in name:
lowercase = name.replace("""conv_3x3""" , """downsampling_layer.conv_3x3""" )
if "reduce_1x1" in name:
lowercase = name.replace("""reduce_1x1""" , """downsampling_layer.reduce_1x1""" )
for i in range(2 , 5 ):
if F""".global_rep.{i}.weight""" in name:
lowercase = name.replace(F""".global_rep.{i}.weight""" , """.layernorm.weight""" )
if F""".global_rep.{i}.bias""" in name:
lowercase = name.replace(F""".global_rep.{i}.bias""" , """.layernorm.bias""" )
if ".global_rep." in name:
lowercase = name.replace(""".global_rep.""" , """.transformer.""" )
if ".pre_norm_mha.0." in name:
lowercase = name.replace(""".pre_norm_mha.0.""" , """.layernorm_before.""" )
if ".pre_norm_mha.1.out_proj." in name:
lowercase = name.replace(""".pre_norm_mha.1.out_proj.""" , """.attention.output.dense.""" )
if ".pre_norm_ffn.0." in name:
lowercase = name.replace(""".pre_norm_ffn.0.""" , """.layernorm_after.""" )
if ".pre_norm_ffn.1." in name:
lowercase = name.replace(""".pre_norm_ffn.1.""" , """.intermediate.dense.""" )
if ".pre_norm_ffn.4." in name:
lowercase = name.replace(""".pre_norm_ffn.4.""" , """.output.dense.""" )
if ".transformer." in name:
lowercase = name.replace(""".transformer.""" , """.transformer.layer.""" )
if ".aspp_layer." in name:
lowercase = name.replace(""".aspp_layer.""" , """.""" )
if ".aspp_pool." in name:
lowercase = name.replace(""".aspp_pool.""" , """.""" )
if "seg_head." in name:
lowercase = name.replace("""seg_head.""" , """segmentation_head.""" )
if "segmentation_head.classifier.classifier." in name:
lowercase = name.replace("""segmentation_head.classifier.classifier.""" , """segmentation_head.classifier.""" )
if "classifier.fc." in name:
lowercase = name.replace("""classifier.fc.""" , """classifier.""" )
elif (not base_model) and ("segmentation_head." not in name):
lowercase = """mobilevit.""" + name
return name
def SCREAMING_SNAKE_CASE ( lowercase_ : Optional[Any] , lowercase_ : List[Any] , lowercase_ : str=False ):
if base_model:
lowercase = """"""
else:
lowercase = """mobilevit."""
for key in orig_state_dict.copy().keys():
lowercase = orig_state_dict.pop(lowercase_ )
if key[:8] == "encoder.":
lowercase = key[8:]
if "qkv" in key:
lowercase = key.split(""".""" )
lowercase = int(key_split[0][6:] ) - 1
lowercase = int(key_split[3] )
lowercase = model.get_submodule(F"""{model_prefix}encoder.layer.{layer_num}""" )
lowercase = layer.transformer.layer[transformer_num].attention.attention.all_head_size
lowercase = (
F"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
)
if "weight" in key:
lowercase = val[:dim, :]
lowercase = val[dim : dim * 2, :]
lowercase = val[-dim:, :]
else:
lowercase = val[:dim]
lowercase = val[dim : dim * 2]
lowercase = val[-dim:]
else:
lowercase = val
return orig_state_dict
def SCREAMING_SNAKE_CASE ( ):
lowercase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowercase = Image.open(requests.get(lowercase_ , stream=lowercase_ ).raw )
return im
@torch.no_grad()
def SCREAMING_SNAKE_CASE ( lowercase_ : Dict , lowercase_ : List[Any] , lowercase_ : Any , lowercase_ : List[str]=False ):
lowercase = get_mobilevit_config(lowercase_ )
# load original state_dict
lowercase = torch.load(lowercase_ , map_location="""cpu""" )
# load 🤗 model
if mobilevit_name.startswith("""deeplabv3_""" ):
lowercase = MobileViTForSemanticSegmentation(lowercase_ ).eval()
else:
lowercase = MobileViTForImageClassification(lowercase_ ).eval()
lowercase = convert_state_dict(lowercase_ , lowercase_ )
model.load_state_dict(lowercase_ )
# Check outputs on an image, prepared by MobileViTImageProcessor
lowercase = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
lowercase = image_processor(images=prepare_img() , return_tensors="""pt""" )
lowercase = model(**lowercase_ )
lowercase = outputs.logits
if mobilevit_name.startswith("""deeplabv3_""" ):
assert logits.shape == (1, 21, 32, 32)
if mobilevit_name == "deeplabv3_mobilevit_s":
lowercase = torch.tensor(
[
[[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
[[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
[[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xs":
lowercase = torch.tensor(
[
[[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
[[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
[[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xxs":
lowercase = torch.tensor(
[
[[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
[[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
[[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
] )
else:
raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )
assert torch.allclose(logits[0, :3, :3, :3] , lowercase_ , atol=1E-4 )
else:
assert logits.shape == (1, 1000)
if mobilevit_name == "mobilevit_s":
lowercase = torch.tensor([-0.9_866, 0.2_392, -1.1_241] )
elif mobilevit_name == "mobilevit_xs":
lowercase = torch.tensor([-2.4_761, -0.9_399, -1.9_587] )
elif mobilevit_name == "mobilevit_xxs":
lowercase = torch.tensor([-1.9_364, -1.2_327, -0.4_653] )
else:
raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )
assert torch.allclose(logits[0, :3] , lowercase_ , atol=1E-4 )
Path(lowercase_ ).mkdir(exist_ok=lowercase_ )
print(F"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(lowercase_ )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(lowercase_ )
if push_to_hub:
lowercase = {
"""mobilevit_s""": """mobilevit-small""",
"""mobilevit_xs""": """mobilevit-x-small""",
"""mobilevit_xxs""": """mobilevit-xx-small""",
"""deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
"""deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
"""deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
}
print("""Pushing to the hub...""" )
lowercase = model_mapping[mobilevit_name]
image_processor.push_to_hub(lowercase_ , organization="""apple""" )
model.push_to_hub(lowercase_ , organization="""apple""" )
if __name__ == "__main__":
lowercase_ : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--mobilevit_name''',
default='''mobilevit_s''',
type=str,
help=(
'''Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','''
''' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'''
),
)
parser.add_argument(
'''--checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
lowercase_ : List[str] = parser.parse_args()
convert_movilevit_checkpoint(
args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 653 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowercase_ : int = 1000 ):
lowercase , lowercase = 1, 1
lowercase = []
for i in range(1 , n + 1 ):
lowercase = prev_numerator + 2 * prev_denominator
lowercase = prev_numerator + prev_denominator
if len(str(lowercase_ ) ) > len(str(lowercase_ ) ):
result.append(lowercase_ )
lowercase = numerator
lowercase = denominator
return len(lowercase_ )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 653 |
'''simple docstring'''
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
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 SwiftFormerForImageClassification, SwiftFormerModel
from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __UpperCamelCase :
def __init__( self , _lowerCAmelCase , _lowerCAmelCase=13 , _lowerCAmelCase=3 , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=224 , _lowerCAmelCase=1000 , _lowerCAmelCase=[3, 3, 6, 4] , _lowerCAmelCase=[48, 56, 112, 220] , ) -> List[str]:
'''simple docstring'''
lowercase = parent
lowercase = batch_size
lowercase = num_channels
lowercase = is_training
lowercase = use_labels
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = num_labels
lowercase = image_size
lowercase = layer_depths
lowercase = embed_dims
def _a ( self ) -> Tuple:
'''simple docstring'''
lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase = None
if self.use_labels:
lowercase = ids_tensor([self.batch_size] , self.num_labels )
lowercase = self.get_config()
return config, pixel_values, labels
def _a ( self ) -> int:
'''simple docstring'''
return SwiftFormerConfig(
depths=self.layer_depths , embed_dims=self.embed_dims , mlp_ratio=4 , downsamples=[True, True, True, True] , hidden_act="""gelu""" , num_labels=self.num_labels , down_patch_size=3 , down_stride=2 , down_pad=1 , drop_rate=0.0 , drop_path_rate=0.0 , use_layer_scale=_lowerCAmelCase , layer_scale_init_value=1E-5 , )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowercase = SwiftFormerModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
lowercase = model(_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dims[-1], 7, 7) )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowercase = self.num_labels
lowercase = SwiftFormerForImageClassification(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
lowercase = model(_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
lowercase = SwiftFormerForImageClassification(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase = model(_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
((lowercase) , (lowercase) , (lowercase)) = self.prepare_config_and_inputs()
lowercase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __UpperCamelCase (_UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
__A = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
__A = (
{'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
if is_torch_available()
else {}
)
__A = False
__A = False
__A = False
__A = False
__A = False
def _a ( self ) -> Dict:
'''simple docstring'''
lowercase = SwiftFormerModelTester(self )
lowercase = ConfigTester(
self , config_class=_lowerCAmelCase , has_text_modality=_lowerCAmelCase , hidden_size=37 , num_attention_heads=12 , num_hidden_layers=12 , )
def _a ( self ) -> List[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="""SwiftFormer does not use inputs_embeds""" )
def _a ( self ) -> List[str]:
'''simple docstring'''
pass
def _a ( self ) -> Dict:
'''simple docstring'''
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase = model_class(_lowerCAmelCase )
lowercase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowerCAmelCase , nn.Linear ) )
def _a ( self ) -> int:
'''simple docstring'''
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase = model_class(_lowerCAmelCase )
lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase = [*signature.parameters.keys()]
lowercase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
def _a ( self ) -> List[str]:
'''simple docstring'''
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase )
@slow
def _a ( self ) -> Any:
'''simple docstring'''
for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase = SwiftFormerModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
@unittest.skip(reason="""SwiftFormer does not output attentions""" )
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
pass
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
def check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
with torch.no_grad():
lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
lowercase = outputs.hidden_states
lowercase = 8
self.assertEqual(len(_lowerCAmelCase ) , _lowerCAmelCase ) # TODO
# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
# with the width and height being successively divided by 2, after every 2 blocks
for i in range(len(_lowerCAmelCase ) ):
self.assertEqual(
hidden_states[i].shape , torch.Size(
[
self.model_tester.batch_size,
self.model_tester.embed_dims[i // 2],
(self.model_tester.image_size // 4) // 2 ** (i // 2),
(self.model_tester.image_size // 4) // 2 ** (i // 2),
] ) , )
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def _a ( self ) -> Dict:
'''simple docstring'''
def _config_zero_init(_lowerCAmelCase ):
lowercase = copy.deepcopy(_lowerCAmelCase )
for key in configs_no_init.__dict__.keys():
if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
setattr(_lowerCAmelCase , _lowerCAmelCase , 1E-10 )
if isinstance(getattr(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) , _lowerCAmelCase ):
lowercase = _config_zero_init(getattr(_lowerCAmelCase , _lowerCAmelCase ) )
setattr(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return configs_no_init
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = _config_zero_init(_lowerCAmelCase )
for model_class in self.all_model_classes:
lowercase = model_class(config=_lowerCAmelCase )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9) / 1E9).round().item() , [0.0, 1.0] , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def _a ( self ) -> Any:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE ( ):
lowercase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class __UpperCamelCase (unittest.TestCase ):
@cached_property
def _a ( self ) -> List[str]:
'''simple docstring'''
return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs""" ) if is_vision_available() else None
@slow
def _a ( self ) -> List[Any]:
'''simple docstring'''
lowercase = SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs""" ).to(_lowerCAmelCase )
lowercase = self.default_image_processor
lowercase = prepare_img()
lowercase = image_processor(images=_lowerCAmelCase , return_tensors="""pt""" ).to(_lowerCAmelCase )
# forward pass
with torch.no_grad():
lowercase = model(**_lowerCAmelCase )
# verify the logits
lowercase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
lowercase = torch.tensor([[-2.17_03E00, 2.11_07E00, -2.08_11E00]] ).to(_lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowerCAmelCase , atol=1E-4 ) )
| 653 | 1 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class __UpperCamelCase (tf.keras.layers.Layer ):
def __init__( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase=1 , _lowerCAmelCase=False , **_lowerCAmelCase ) -> Tuple:
'''simple docstring'''
super().__init__(**_lowerCAmelCase )
lowercase = vocab_size
lowercase = d_embed
lowercase = d_proj
lowercase = cutoffs + [vocab_size]
lowercase = [0] + self.cutoffs
lowercase = div_val
lowercase = self.cutoffs[0]
lowercase = len(self.cutoffs ) - 1
lowercase = self.shortlist_size + self.n_clusters
lowercase = keep_order
lowercase = []
lowercase = []
def _a ( self , _lowerCAmelCase ) -> str:
'''simple docstring'''
if self.n_clusters > 0:
lowercase = self.add_weight(
shape=(self.n_clusters, self.d_embed) , initializer="""zeros""" , trainable=_lowerCAmelCase , name="""cluster_weight""" )
lowercase = self.add_weight(
shape=(self.n_clusters,) , initializer="""zeros""" , trainable=_lowerCAmelCase , name="""cluster_bias""" )
if self.div_val == 1:
for i in range(len(self.cutoffs ) ):
if self.d_proj != self.d_embed:
lowercase = self.add_weight(
shape=(self.d_embed, self.d_proj) , initializer="""zeros""" , trainable=_lowerCAmelCase , name=F"""out_projs_._{i}""" , )
self.out_projs.append(_lowerCAmelCase )
else:
self.out_projs.append(_lowerCAmelCase )
lowercase = self.add_weight(
shape=(self.vocab_size, self.d_embed) , initializer="""zeros""" , trainable=_lowerCAmelCase , name=F"""out_layers_._{i}_._weight""" , )
lowercase = self.add_weight(
shape=(self.vocab_size,) , initializer="""zeros""" , trainable=_lowerCAmelCase , name=F"""out_layers_._{i}_._bias""" , )
self.out_layers.append((weight, bias) )
else:
for i in range(len(self.cutoffs ) ):
lowercase , lowercase = self.cutoff_ends[i], self.cutoff_ends[i + 1]
lowercase = self.d_embed // (self.div_val**i)
lowercase = self.add_weight(
shape=(d_emb_i, self.d_proj) , initializer="""zeros""" , trainable=_lowerCAmelCase , name=F"""out_projs_._{i}""" )
self.out_projs.append(_lowerCAmelCase )
lowercase = self.add_weight(
shape=(r_idx - l_idx, d_emb_i) , initializer="""zeros""" , trainable=_lowerCAmelCase , name=F"""out_layers_._{i}_._weight""" , )
lowercase = self.add_weight(
shape=(r_idx - l_idx,) , initializer="""zeros""" , trainable=_lowerCAmelCase , name=F"""out_layers_._{i}_._bias""" , )
self.out_layers.append((weight, bias) )
super().build(_lowerCAmelCase )
@staticmethod
def _a ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase=None ) -> Optional[int]:
'''simple docstring'''
lowercase = x
if proj is not None:
lowercase = tf.einsum("""ibd,ed->ibe""" , _lowerCAmelCase , _lowerCAmelCase )
return tf.einsum("""ibd,nd->ibn""" , _lowerCAmelCase , _lowerCAmelCase ) + b
@staticmethod
def _a ( _lowerCAmelCase , _lowerCAmelCase ) -> Any:
'''simple docstring'''
lowercase = shape_list(_lowerCAmelCase )
lowercase = tf.range(lp_size[0] , dtype=target.dtype )
lowercase = tf.stack([r, target] , 1 )
return tf.gather_nd(_lowerCAmelCase , _lowerCAmelCase )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase=True , _lowerCAmelCase=False ) -> Dict:
'''simple docstring'''
lowercase = 0
if self.n_clusters == 0:
lowercase = self._logit(_lowerCAmelCase , self.out_layers[0][0] , self.out_layers[0][1] , self.out_projs[0] )
if target is not None:
lowercase = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=_lowerCAmelCase , logits=_lowerCAmelCase )
lowercase = tf.nn.log_softmax(_lowerCAmelCase , axis=-1 )
else:
lowercase = shape_list(_lowerCAmelCase )
lowercase = []
lowercase = tf.zeros(hidden_sizes[:2] )
for i in range(len(self.cutoffs ) ):
lowercase , lowercase = self.cutoff_ends[i], self.cutoff_ends[i + 1]
if target is not None:
lowercase = (target >= l_idx) & (target < r_idx)
lowercase = tf.where(_lowerCAmelCase )
lowercase = tf.boolean_mask(_lowerCAmelCase , _lowerCAmelCase ) - l_idx
if self.div_val == 1:
lowercase = self.out_layers[0][0][l_idx:r_idx]
lowercase = self.out_layers[0][1][l_idx:r_idx]
else:
lowercase = self.out_layers[i][0]
lowercase = self.out_layers[i][1]
if i == 0:
lowercase = tf.concat([cur_W, self.cluster_weight] , 0 )
lowercase = tf.concat([cur_b, self.cluster_bias] , 0 )
lowercase = self._logit(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , self.out_projs[0] )
lowercase = tf.nn.log_softmax(_lowerCAmelCase )
out.append(head_logprob[..., : self.cutoffs[0]] )
if target is not None:
lowercase = tf.boolean_mask(_lowerCAmelCase , _lowerCAmelCase )
lowercase = self._gather_logprob(_lowerCAmelCase , _lowerCAmelCase )
else:
lowercase = self._logit(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , self.out_projs[i] )
lowercase = tf.nn.log_softmax(_lowerCAmelCase )
lowercase = self.cutoffs[0] + i - 1 # No probability for the head cluster
lowercase = head_logprob[..., cluster_prob_idx, None] + tail_logprob
out.append(_lowerCAmelCase )
if target is not None:
lowercase = tf.boolean_mask(_lowerCAmelCase , _lowerCAmelCase )
lowercase = tf.boolean_mask(_lowerCAmelCase , _lowerCAmelCase )
lowercase = self._gather_logprob(_lowerCAmelCase , _lowerCAmelCase )
cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
if target is not None:
loss += tf.scatter_nd(_lowerCAmelCase , -cur_logprob , shape_list(_lowerCAmelCase ) )
lowercase = tf.concat(_lowerCAmelCase , axis=-1 )
if target is not None:
if return_mean:
lowercase = tf.reduce_mean(_lowerCAmelCase )
# Add the training-time loss value to the layer using `self.add_loss()`.
self.add_loss(_lowerCAmelCase )
# Log the loss as a metric (we could log arbitrary metrics,
# including different metrics for training and inference.
self.add_metric(_lowerCAmelCase , name=self.name , aggregation="""mean""" if return_mean else """""" )
return out
| 653 |
'''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def SCREAMING_SNAKE_CASE ( ):
lowercase = HfArgumentParser(lowercase_ )
lowercase = parser.parse_args_into_dataclasses()[0]
lowercase = TensorFlowBenchmark(args=lowercase_ )
try:
lowercase = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
lowercase = """Arg --no_{0} is no longer used, please use --no-{0} instead."""
lowercase = """ """.join(str(lowercase_ ).split(""" """ )[:-1] )
lowercase = """"""
lowercase = eval(str(lowercase_ ).split(""" """ )[-1] )
lowercase = []
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(lowercase_ )
if len(lowercase_ ) > 0:
lowercase = full_error_msg + begin_error_msg + str(lowercase_ )
raise ValueError(lowercase_ )
benchmark.run()
if __name__ == "__main__":
main()
| 653 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
@skip_mps
class __UpperCamelCase (_UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
__A = StableDiffusionPanoramaPipeline
__A = TEXT_TO_IMAGE_PARAMS
__A = TEXT_TO_IMAGE_BATCH_PARAMS
__A = TEXT_TO_IMAGE_IMAGE_PARAMS
__A = TEXT_TO_IMAGE_IMAGE_PARAMS
def _a ( self ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
lowercase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=1 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
lowercase = DDIMScheduler()
torch.manual_seed(0 )
lowercase = 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 , )
torch.manual_seed(0 )
lowercase = 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=1000 , )
lowercase = CLIPTextModel(_lowerCAmelCase )
lowercase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowercase = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def _a ( self , _lowerCAmelCase , _lowerCAmelCase=0 ) -> Optional[int]:
'''simple docstring'''
lowercase = torch.manual_seed(_lowerCAmelCase )
lowercase = {
"""prompt""": """a photo of the dolomites""",
"""generator""": generator,
# Setting height and width to None to prevent OOMs on CPU.
"""height""": None,
"""width""": None,
"""num_inference_steps""": 1,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def _a ( self ) -> int:
'''simple docstring'''
lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase = self.get_dummy_components()
lowercase = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase = self.get_dummy_inputs(_lowerCAmelCase )
lowercase = sd_pipe(**_lowerCAmelCase ).images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase = np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
super().test_inference_batch_consistent(batch_sizes=[1, 2] )
def _a ( self ) -> str:
'''simple docstring'''
super().test_inference_batch_single_identical(batch_size=2 , expected_max_diff=3.25E-3 )
def _a ( self ) -> List[Any]:
'''simple docstring'''
lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase = self.get_dummy_components()
lowercase = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase = self.get_dummy_inputs(_lowerCAmelCase )
lowercase = """french fries"""
lowercase = sd_pipe(**_lowerCAmelCase , negative_prompt=_lowerCAmelCase )
lowercase = output.images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase = np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> Tuple:
'''simple docstring'''
lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase = self.get_dummy_components()
lowercase = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase = self.get_dummy_inputs(_lowerCAmelCase )
lowercase = sd_pipe(**_lowerCAmelCase , view_batch_size=2 )
lowercase = output.images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase = np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> Any:
'''simple docstring'''
lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase = self.get_dummy_components()
lowercase = EulerAncestralDiscreteScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" )
lowercase = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase = self.get_dummy_inputs(_lowerCAmelCase )
lowercase = sd_pipe(**_lowerCAmelCase ).images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase = np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> Dict:
'''simple docstring'''
lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase = self.get_dummy_components()
lowercase = PNDMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , skip_prk_steps=_lowerCAmelCase )
lowercase = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase = self.get_dummy_inputs(_lowerCAmelCase )
lowercase = sd_pipe(**_lowerCAmelCase ).images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase = np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class __UpperCamelCase (unittest.TestCase ):
def _a ( self ) -> List[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self , _lowerCAmelCase=0 ) -> Optional[int]:
'''simple docstring'''
lowercase = torch.manual_seed(_lowerCAmelCase )
lowercase = {
"""prompt""": """a photo of the dolomites""",
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase = """stabilityai/stable-diffusion-2-base"""
lowercase = DDIMScheduler.from_pretrained(_lowerCAmelCase , subfolder="""scheduler""" )
lowercase = StableDiffusionPanoramaPipeline.from_pretrained(_lowerCAmelCase , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing()
lowercase = self.get_inputs()
lowercase = pipe(**_lowerCAmelCase ).images
lowercase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2048, 3)
lowercase = np.array(
[
0.3696_8392,
0.2702_5372,
0.3244_6766,
0.2837_9387,
0.3636_3274,
0.3073_3347,
0.2710_0027,
0.2705_4125,
0.2553_6096,
] )
assert np.abs(expected_slice - image_slice ).max() < 1E-2
def _a ( self ) -> str:
'''simple docstring'''
lowercase = StableDiffusionPanoramaPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2-base""" , safety_checker=_lowerCAmelCase )
lowercase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing()
lowercase = self.get_inputs()
lowercase = pipe(**_lowerCAmelCase ).images
lowercase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2048, 3)
lowercase = np.array(
[
[
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
] )
assert np.abs(expected_slice - image_slice ).max() < 1E-3
def _a ( self ) -> Any:
'''simple docstring'''
lowercase = 0
def callback_fn(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> None:
lowercase = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
lowercase = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
lowercase = latents[0, -3:, -3:, -1]
lowercase = np.array(
[
0.1868_1869,
0.3390_7816,
0.536_1276,
0.1443_2865,
-0.0285_6611,
-0.7394_1123,
0.2339_7987,
0.4732_2682,
-0.3782_3164,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
elif step == 2:
lowercase = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
lowercase = latents[0, -3:, -3:, -1]
lowercase = np.array(
[
0.1853_9645,
0.3398_7248,
0.537_8559,
0.1443_7142,
-0.0245_5261,
-0.733_8317,
0.2399_0755,
0.4735_6272,
-0.378_6505,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
lowercase = False
lowercase = """stabilityai/stable-diffusion-2-base"""
lowercase = DDIMScheduler.from_pretrained(_lowerCAmelCase , subfolder="""scheduler""" )
lowercase = StableDiffusionPanoramaPipeline.from_pretrained(_lowerCAmelCase , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase )
lowercase = pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing()
lowercase = self.get_inputs()
pipe(**_lowerCAmelCase , callback=_lowerCAmelCase , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def _a ( self ) -> int:
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
lowercase = """stabilityai/stable-diffusion-2-base"""
lowercase = DDIMScheduler.from_pretrained(_lowerCAmelCase , subfolder="""scheduler""" )
lowercase = StableDiffusionPanoramaPipeline.from_pretrained(_lowerCAmelCase , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase )
lowercase = pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
lowercase = self.get_inputs()
lowercase = pipe(**_lowerCAmelCase )
lowercase = torch.cuda.max_memory_allocated()
# make sure that less than 5.2 GB is allocated
assert mem_bytes < 5.5 * 10**9
| 653 |
'''simple docstring'''
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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.
# this script dumps information about the environment
import os
import platform
import sys
lowercase_ : List[str] = '''3'''
print('''Python version:''', sys.version)
print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())
try:
import torch
print('''Torch version:''', torch.__version__)
print('''Cuda available:''', torch.cuda.is_available())
print('''Cuda version:''', torch.version.cuda)
print('''CuDNN version:''', torch.backends.cudnn.version())
print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
print('''Torch version:''', None)
try:
import transformers
print('''transformers version:''', transformers.__version__)
except ImportError:
print('''transformers version:''', None)
| 653 | 1 |
'''simple docstring'''
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
lowercase_ : Union[str, Any] = subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
lowercase_ : Tuple = (
subprocess.check_output(f'''git diff --diff-filter=d --name-only {fork_point_sha}'''.split()).decode('''utf-8''').split()
)
lowercase_ : Optional[int] = '''|'''.join(sys.argv[1:])
lowercase_ : str = re.compile(rf'''^({joined_dirs}).*?\.py$''')
lowercase_ : str = [x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')
| 653 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase_ : Optional[Any] = logging.get_logger(__name__)
lowercase_ : int = {'''vocab_file''': '''spm_char.model'''}
lowercase_ : int = {
'''vocab_file''': {
'''microsoft/speecht5_asr''': '''https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model''',
'''microsoft/speecht5_tts''': '''https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model''',
'''microsoft/speecht5_vc''': '''https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model''',
}
}
lowercase_ : Optional[Any] = {
'''microsoft/speecht5_asr''': 1024,
'''microsoft/speecht5_tts''': 1024,
'''microsoft/speecht5_vc''': 1024,
}
class __UpperCamelCase (_UpperCAmelCase ):
__A = VOCAB_FILES_NAMES
__A = PRETRAINED_VOCAB_FILES_MAP
__A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A = ['''input_ids''', '''attention_mask''']
def __init__( self , _lowerCAmelCase , _lowerCAmelCase="<s>" , _lowerCAmelCase="</s>" , _lowerCAmelCase="<unk>" , _lowerCAmelCase="<pad>" , _lowerCAmelCase = None , **_lowerCAmelCase , ) -> None:
'''simple docstring'''
lowercase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_lowerCAmelCase , eos_token=_lowerCAmelCase , unk_token=_lowerCAmelCase , pad_token=_lowerCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **_lowerCAmelCase , )
lowercase = vocab_file
lowercase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowerCAmelCase )
@property
def _a ( self ) -> List[Any]:
'''simple docstring'''
return self.sp_model.get_piece_size()
def _a ( self ) -> str:
'''simple docstring'''
lowercase = {self.convert_ids_to_tokens(_lowerCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase = self.__dict__.copy()
lowercase = None
return state
def __setstate__( self , _lowerCAmelCase ) -> Optional[int]:
'''simple docstring'''
lowercase = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
lowercase = {}
lowercase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _a ( self , _lowerCAmelCase ) -> List[str]:
'''simple docstring'''
return self.sp_model.encode(_lowerCAmelCase , out_type=_lowerCAmelCase )
def _a ( self , _lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
return self.sp_model.piece_to_id(_lowerCAmelCase )
def _a ( self , _lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase = self.sp_model.IdToPiece(_lowerCAmelCase )
return token
def _a ( self , _lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowercase = []
lowercase = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(_lowerCAmelCase ) + token
lowercase = []
else:
current_sub_tokens.append(_lowerCAmelCase )
out_string += self.sp_model.decode(_lowerCAmelCase )
return out_string.strip()
def _a ( self , _lowerCAmelCase , _lowerCAmelCase=None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def _a ( self , _lowerCAmelCase , _lowerCAmelCase = None , _lowerCAmelCase = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCAmelCase , token_ids_a=_lowerCAmelCase , already_has_special_tokens=_lowerCAmelCase )
lowercase = [1]
if token_ids_a is None:
return ([0] * len(_lowerCAmelCase )) + suffix_ones
return ([0] * len(_lowerCAmelCase )) + ([0] * len(_lowerCAmelCase )) + suffix_ones
def _a ( self , _lowerCAmelCase , _lowerCAmelCase = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(_lowerCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowercase = os.path.join(
_lowerCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowerCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCAmelCase , """wb""" ) as fi:
lowercase = self.sp_model.serialized_model_proto()
fi.write(_lowerCAmelCase )
return (out_vocab_file,)
| 653 | 1 |
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
lowercase_ : List[str] = 16
lowercase_ : Optional[Any] = 32
def SCREAMING_SNAKE_CASE ( lowercase_ : Accelerator , lowercase_ : DatasetDict , lowercase_ : List[int] , lowercase_ : List[int] , lowercase_ : int = 16 ):
lowercase = AutoTokenizer.from_pretrained("""bert-base-cased""" )
lowercase = DatasetDict(
{
"""train""": dataset["""train"""].select(lowercase_ ),
"""validation""": dataset["""train"""].select(lowercase_ ),
"""test""": dataset["""validation"""],
} )
def tokenize_function(lowercase_ : Dict ):
# max_length=None => use the model max length (it's actually the default)
lowercase = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowercase_ , max_length=lowercase_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
lowercase = datasets.map(
lowercase_ , batched=lowercase_ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowercase = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowercase_ : Tuple ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowercase = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
lowercase = 16
elif accelerator.mixed_precision != "no":
lowercase = 8
else:
lowercase = None
return tokenizer.pad(
lowercase_ , padding="""longest""" , max_length=lowercase_ , pad_to_multiple_of=lowercase_ , return_tensors="""pt""" , )
# Instantiate dataloaders.
lowercase = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowercase_ , collate_fn=lowercase_ , batch_size=lowercase_ )
lowercase = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowercase_ , collate_fn=lowercase_ , batch_size=lowercase_ )
lowercase = DataLoader(
tokenized_datasets["""test"""] , shuffle=lowercase_ , collate_fn=lowercase_ , batch_size=lowercase_ )
return train_dataloader, eval_dataloader, test_dataloader
def SCREAMING_SNAKE_CASE ( lowercase_ : List[str] , lowercase_ : Any ):
# New Code #
lowercase = []
# Download the dataset
lowercase = load_dataset("""glue""" , """mrpc""" )
# Create our splits
lowercase = StratifiedKFold(n_splits=int(args.num_folds ) )
# Initialize accelerator
lowercase = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase = config["""lr"""]
lowercase = int(config["""num_epochs"""] )
lowercase = int(config["""seed"""] )
lowercase = int(config["""batch_size"""] )
lowercase = evaluate.load("""glue""" , """mrpc""" )
# If the batch size is too big we use gradient accumulation
lowercase = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
lowercase = batch_size // MAX_GPU_BATCH_SIZE
lowercase = MAX_GPU_BATCH_SIZE
set_seed(lowercase_ )
# New Code #
# Create our folds:
lowercase = kfold.split(np.zeros(datasets["""train"""].num_rows ) , datasets["""train"""]["""label"""] )
lowercase = []
# Iterate over them
for i, (train_idxs, valid_idxs) in enumerate(lowercase_ ):
lowercase , lowercase , lowercase = get_fold_dataloaders(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=lowercase_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
lowercase = model.to(accelerator.device )
# Instantiate optimizer
lowercase = AdamW(params=model.parameters() , lr=lowercase_ )
# Instantiate scheduler
lowercase = get_linear_schedule_with_warmup(
optimizer=lowercase_ , num_warmup_steps=100 , num_training_steps=(len(lowercase_ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowercase , lowercase , lowercase , lowercase , lowercase = accelerator.prepare(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
# Now we train the model
for epoch in range(lowercase_ ):
model.train()
for step, batch in enumerate(lowercase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
lowercase = model(**lowercase_ )
lowercase = outputs.loss
lowercase = loss / gradient_accumulation_steps
accelerator.backward(lowercase_ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowercase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowercase = model(**lowercase_ )
lowercase = outputs.logits.argmax(dim=-1 )
lowercase , lowercase = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=lowercase_ , references=lowercase_ , )
lowercase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""" , lowercase_ )
# New Code #
# We also run predictions on the test set at the very end
lowercase = []
for step, batch in enumerate(lowercase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowercase = model(**lowercase_ )
lowercase = outputs.logits
lowercase , lowercase = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
fold_predictions.append(predictions.cpu() )
if i == 0:
# We need all of the test predictions
test_references.append(references.cpu() )
# Use accelerator.print to print only on the main process.
test_predictions.append(torch.cat(lowercase_ , dim=0 ) )
# We now need to release all our memory and get rid of the current model, optimizer, etc
accelerator.free_memory()
# New Code #
# Finally we check the accuracy of our folded results:
lowercase = torch.cat(lowercase_ , dim=0 )
lowercase = torch.stack(lowercase_ , dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
lowercase = metric.compute(predictions=lowercase_ , references=lowercase_ )
accelerator.print("""Average test metrics from all folds:""" , lowercase_ )
def SCREAMING_SNAKE_CASE ( ):
lowercase = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=lowercase_ , default=lowercase_ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
# New Code #
parser.add_argument("""--num_folds""" , type=lowercase_ , default=3 , help="""The number of splits to perform across the dataset""" )
lowercase = parser.parse_args()
lowercase = {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(lowercase_ , lowercase_ )
if __name__ == "__main__":
main()
| 653 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( ):
lowercase = []
lowercase = 1
while len(lowercase_ ) < 1E6:
constant.append(str(lowercase_ ) )
i += 1
lowercase = """""".join(lowercase_ )
return (
int(constant[0] )
* int(constant[9] )
* int(constant[99] )
* int(constant[999] )
* int(constant[9999] )
* int(constant[9_9999] )
* int(constant[99_9999] )
)
if __name__ == "__main__":
print(solution())
| 653 | 1 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __UpperCamelCase (_UpperCAmelCase ):
__A = ['''image_processor''', '''tokenizer''']
__A = '''ChineseCLIPImageProcessor'''
__A = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__( self , _lowerCAmelCase=None , _lowerCAmelCase=None , **_lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowercase = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , _lowerCAmelCase , )
lowercase = kwargs.pop("""feature_extractor""" )
lowercase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(_lowerCAmelCase , _lowerCAmelCase )
lowercase = self.image_processor
def __call__( self , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase=None , **_lowerCAmelCase ) -> Tuple:
'''simple docstring'''
if text is None and images is None:
raise ValueError("""You have to specify either text or images. Both cannot be none.""" )
if text is not None:
lowercase = self.tokenizer(_lowerCAmelCase , return_tensors=_lowerCAmelCase , **_lowerCAmelCase )
if images is not None:
lowercase = self.image_processor(_lowerCAmelCase , return_tensors=_lowerCAmelCase , **_lowerCAmelCase )
if text is not None and images is not None:
lowercase = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**_lowerCAmelCase ) , tensor_type=_lowerCAmelCase )
def _a ( self , *_lowerCAmelCase , **_lowerCAmelCase ) -> str:
'''simple docstring'''
return self.tokenizer.batch_decode(*_lowerCAmelCase , **_lowerCAmelCase )
def _a ( self , *_lowerCAmelCase , **_lowerCAmelCase ) -> str:
'''simple docstring'''
return self.tokenizer.decode(*_lowerCAmelCase , **_lowerCAmelCase )
@property
def _a ( self ) -> Tuple:
'''simple docstring'''
lowercase = self.tokenizer.model_input_names
lowercase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def _a ( self ) -> str:
'''simple docstring'''
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , _lowerCAmelCase , )
return self.image_processor_class
| 653 |
'''simple docstring'''
import os
def SCREAMING_SNAKE_CASE ( ):
lowercase = os.path.join(os.path.dirname(lowercase_ ) , """num.txt""" )
with open(lowercase_ ) as file_hand:
return str(sum(int(lowercase_ ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 653 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowercase_ : list[list[float]] ):
lowercase = []
for data in source_data:
for i, el in enumerate(lowercase_ ):
if len(lowercase_ ) < i + 1:
data_lists.append([] )
data_lists[i].append(float(lowercase_ ) )
return data_lists
def SCREAMING_SNAKE_CASE ( lowercase_ : list[list[float]] , lowercase_ : list[int] ):
lowercase = []
for dlist, weight in zip(lowercase_ , lowercase_ ):
lowercase = min(lowercase_ )
lowercase = max(lowercase_ )
lowercase = []
# for weight 0 score is 1 - actual score
if weight == 0:
for item in dlist:
try:
score.append(1 - ((item - mind) / (maxd - mind)) )
except ZeroDivisionError:
score.append(1 )
elif weight == 1:
for item in dlist:
try:
score.append((item - mind) / (maxd - mind) )
except ZeroDivisionError:
score.append(0 )
# weight not 0 or 1
else:
lowercase = F"""Invalid weight of {weight:f} provided"""
raise ValueError(lowercase_ )
score_lists.append(lowercase_ )
return score_lists
def SCREAMING_SNAKE_CASE ( lowercase_ : list[list[float]] ):
lowercase = [0 for i in range(len(score_lists[0] ) )]
for slist in score_lists:
for j, ele in enumerate(lowercase_ ):
lowercase = final_scores[j] + ele
return final_scores
def SCREAMING_SNAKE_CASE ( lowercase_ : list[list[float]] , lowercase_ : list[int] ):
lowercase = get_data(lowercase_ )
lowercase = calculate_each_score(lowercase_ , lowercase_ )
lowercase = generate_final_scores(lowercase_ )
# append scores to source data
for i, ele in enumerate(lowercase_ ):
source_data[i].append(lowercase_ )
return source_data
| 653 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
@skip_mps
class __UpperCamelCase (_UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
__A = StableDiffusionPanoramaPipeline
__A = TEXT_TO_IMAGE_PARAMS
__A = TEXT_TO_IMAGE_BATCH_PARAMS
__A = TEXT_TO_IMAGE_IMAGE_PARAMS
__A = TEXT_TO_IMAGE_IMAGE_PARAMS
def _a ( self ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
lowercase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=1 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
lowercase = DDIMScheduler()
torch.manual_seed(0 )
lowercase = 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 , )
torch.manual_seed(0 )
lowercase = 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=1000 , )
lowercase = CLIPTextModel(_lowerCAmelCase )
lowercase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowercase = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def _a ( self , _lowerCAmelCase , _lowerCAmelCase=0 ) -> Optional[int]:
'''simple docstring'''
lowercase = torch.manual_seed(_lowerCAmelCase )
lowercase = {
"""prompt""": """a photo of the dolomites""",
"""generator""": generator,
# Setting height and width to None to prevent OOMs on CPU.
"""height""": None,
"""width""": None,
"""num_inference_steps""": 1,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def _a ( self ) -> int:
'''simple docstring'''
lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase = self.get_dummy_components()
lowercase = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase = self.get_dummy_inputs(_lowerCAmelCase )
lowercase = sd_pipe(**_lowerCAmelCase ).images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase = np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
super().test_inference_batch_consistent(batch_sizes=[1, 2] )
def _a ( self ) -> str:
'''simple docstring'''
super().test_inference_batch_single_identical(batch_size=2 , expected_max_diff=3.25E-3 )
def _a ( self ) -> List[Any]:
'''simple docstring'''
lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase = self.get_dummy_components()
lowercase = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase = self.get_dummy_inputs(_lowerCAmelCase )
lowercase = """french fries"""
lowercase = sd_pipe(**_lowerCAmelCase , negative_prompt=_lowerCAmelCase )
lowercase = output.images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase = np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> Tuple:
'''simple docstring'''
lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase = self.get_dummy_components()
lowercase = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase = self.get_dummy_inputs(_lowerCAmelCase )
lowercase = sd_pipe(**_lowerCAmelCase , view_batch_size=2 )
lowercase = output.images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase = np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> Any:
'''simple docstring'''
lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase = self.get_dummy_components()
lowercase = EulerAncestralDiscreteScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" )
lowercase = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase = self.get_dummy_inputs(_lowerCAmelCase )
lowercase = sd_pipe(**_lowerCAmelCase ).images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase = np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> Dict:
'''simple docstring'''
lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase = self.get_dummy_components()
lowercase = PNDMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , skip_prk_steps=_lowerCAmelCase )
lowercase = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase = self.get_dummy_inputs(_lowerCAmelCase )
lowercase = sd_pipe(**_lowerCAmelCase ).images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase = np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class __UpperCamelCase (unittest.TestCase ):
def _a ( self ) -> List[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self , _lowerCAmelCase=0 ) -> Optional[int]:
'''simple docstring'''
lowercase = torch.manual_seed(_lowerCAmelCase )
lowercase = {
"""prompt""": """a photo of the dolomites""",
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase = """stabilityai/stable-diffusion-2-base"""
lowercase = DDIMScheduler.from_pretrained(_lowerCAmelCase , subfolder="""scheduler""" )
lowercase = StableDiffusionPanoramaPipeline.from_pretrained(_lowerCAmelCase , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing()
lowercase = self.get_inputs()
lowercase = pipe(**_lowerCAmelCase ).images
lowercase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2048, 3)
lowercase = np.array(
[
0.3696_8392,
0.2702_5372,
0.3244_6766,
0.2837_9387,
0.3636_3274,
0.3073_3347,
0.2710_0027,
0.2705_4125,
0.2553_6096,
] )
assert np.abs(expected_slice - image_slice ).max() < 1E-2
def _a ( self ) -> str:
'''simple docstring'''
lowercase = StableDiffusionPanoramaPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2-base""" , safety_checker=_lowerCAmelCase )
lowercase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing()
lowercase = self.get_inputs()
lowercase = pipe(**_lowerCAmelCase ).images
lowercase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2048, 3)
lowercase = np.array(
[
[
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
] )
assert np.abs(expected_slice - image_slice ).max() < 1E-3
def _a ( self ) -> Any:
'''simple docstring'''
lowercase = 0
def callback_fn(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> None:
lowercase = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
lowercase = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
lowercase = latents[0, -3:, -3:, -1]
lowercase = np.array(
[
0.1868_1869,
0.3390_7816,
0.536_1276,
0.1443_2865,
-0.0285_6611,
-0.7394_1123,
0.2339_7987,
0.4732_2682,
-0.3782_3164,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
elif step == 2:
lowercase = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
lowercase = latents[0, -3:, -3:, -1]
lowercase = np.array(
[
0.1853_9645,
0.3398_7248,
0.537_8559,
0.1443_7142,
-0.0245_5261,
-0.733_8317,
0.2399_0755,
0.4735_6272,
-0.378_6505,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
lowercase = False
lowercase = """stabilityai/stable-diffusion-2-base"""
lowercase = DDIMScheduler.from_pretrained(_lowerCAmelCase , subfolder="""scheduler""" )
lowercase = StableDiffusionPanoramaPipeline.from_pretrained(_lowerCAmelCase , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase )
lowercase = pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing()
lowercase = self.get_inputs()
pipe(**_lowerCAmelCase , callback=_lowerCAmelCase , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def _a ( self ) -> int:
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
lowercase = """stabilityai/stable-diffusion-2-base"""
lowercase = DDIMScheduler.from_pretrained(_lowerCAmelCase , subfolder="""scheduler""" )
lowercase = StableDiffusionPanoramaPipeline.from_pretrained(_lowerCAmelCase , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase )
lowercase = pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
lowercase = self.get_inputs()
lowercase = pipe(**_lowerCAmelCase )
lowercase = torch.cuda.max_memory_allocated()
# make sure that less than 5.2 GB is allocated
assert mem_bytes < 5.5 * 10**9
| 653 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
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_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
lowercase_ : Any = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE ( lowercase_ : Union[str, Any] ):
if isinstance(lowercase_ , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(lowercase_ , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(lowercase_ ):
return [[videos]]
raise ValueError(F"""Could not make batched video from {videos}""" )
class __UpperCamelCase (_UpperCAmelCase ):
__A = ['''pixel_values''']
def __init__( self , _lowerCAmelCase = True , _lowerCAmelCase = None , _lowerCAmelCase = PILImageResampling.BILINEAR , _lowerCAmelCase = True , _lowerCAmelCase = None , _lowerCAmelCase = True , _lowerCAmelCase = 1 / 255 , _lowerCAmelCase = True , _lowerCAmelCase = True , _lowerCAmelCase = None , _lowerCAmelCase = None , **_lowerCAmelCase , ) -> None:
'''simple docstring'''
super().__init__(**_lowerCAmelCase )
lowercase = size if size is not None else {"""shortest_edge""": 256}
lowercase = get_size_dict(_lowerCAmelCase , default_to_square=_lowerCAmelCase )
lowercase = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
lowercase = get_size_dict(_lowerCAmelCase , param_name="""crop_size""" )
lowercase = do_resize
lowercase = size
lowercase = do_center_crop
lowercase = crop_size
lowercase = resample
lowercase = do_rescale
lowercase = rescale_factor
lowercase = offset
lowercase = do_normalize
lowercase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowercase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = PILImageResampling.BILINEAR , _lowerCAmelCase = None , **_lowerCAmelCase , ) -> np.ndarray:
'''simple docstring'''
lowercase = get_size_dict(_lowerCAmelCase , default_to_square=_lowerCAmelCase )
if "shortest_edge" in size:
lowercase = get_resize_output_image_size(_lowerCAmelCase , size["""shortest_edge"""] , default_to_square=_lowerCAmelCase )
elif "height" in size and "width" in size:
lowercase = (size["""height"""], size["""width"""])
else:
raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}""" )
return resize(_lowerCAmelCase , size=_lowerCAmelCase , resample=_lowerCAmelCase , data_format=_lowerCAmelCase , **_lowerCAmelCase )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = None , **_lowerCAmelCase , ) -> np.ndarray:
'''simple docstring'''
lowercase = get_size_dict(_lowerCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}""" )
return center_crop(_lowerCAmelCase , size=(size["""height"""], size["""width"""]) , data_format=_lowerCAmelCase , **_lowerCAmelCase )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = True , _lowerCAmelCase = None , **_lowerCAmelCase , ) -> Optional[int]:
'''simple docstring'''
lowercase = image.astype(np.floataa )
if offset:
lowercase = image - (scale / 2)
return rescale(_lowerCAmelCase , scale=_lowerCAmelCase , data_format=_lowerCAmelCase , **_lowerCAmelCase )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = None , **_lowerCAmelCase , ) -> np.ndarray:
'''simple docstring'''
return normalize(_lowerCAmelCase , mean=_lowerCAmelCase , std=_lowerCAmelCase , data_format=_lowerCAmelCase , **_lowerCAmelCase )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = ChannelDimension.FIRST , ) -> np.ndarray:
'''simple docstring'''
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_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.""" )
if offset and not do_rescale:
raise ValueError("""For offset, do_rescale must also be set to True.""" )
# All transformations expect numpy arrays.
lowercase = to_numpy_array(_lowerCAmelCase )
if do_resize:
lowercase = self.resize(image=_lowerCAmelCase , size=_lowerCAmelCase , resample=_lowerCAmelCase )
if do_center_crop:
lowercase = self.center_crop(_lowerCAmelCase , size=_lowerCAmelCase )
if do_rescale:
lowercase = self.rescale(image=_lowerCAmelCase , scale=_lowerCAmelCase , offset=_lowerCAmelCase )
if do_normalize:
lowercase = self.normalize(image=_lowerCAmelCase , mean=_lowerCAmelCase , std=_lowerCAmelCase )
lowercase = to_channel_dimension_format(_lowerCAmelCase , _lowerCAmelCase )
return image
def _a ( self , _lowerCAmelCase , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = ChannelDimension.FIRST , **_lowerCAmelCase , ) -> PIL.Image.Image:
'''simple docstring'''
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 = offset if offset is not None else self.offset
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(_lowerCAmelCase , default_to_square=_lowerCAmelCase )
lowercase = crop_size if crop_size is not None else self.crop_size
lowercase = get_size_dict(_lowerCAmelCase , param_name="""crop_size""" )
if not valid_images(_lowerCAmelCase ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
lowercase = make_batched(_lowerCAmelCase )
lowercase = [
[
self._preprocess_image(
image=_lowerCAmelCase , do_resize=_lowerCAmelCase , size=_lowerCAmelCase , resample=_lowerCAmelCase , do_center_crop=_lowerCAmelCase , crop_size=_lowerCAmelCase , do_rescale=_lowerCAmelCase , rescale_factor=_lowerCAmelCase , offset=_lowerCAmelCase , do_normalize=_lowerCAmelCase , image_mean=_lowerCAmelCase , image_std=_lowerCAmelCase , data_format=_lowerCAmelCase , )
for img in video
]
for video in videos
]
lowercase = {"""pixel_values""": videos}
return BatchFeature(data=_lowerCAmelCase , tensor_type=_lowerCAmelCase )
| 653 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
lowercase_ : Tuple = logging.getLogger(__name__)
@dataclass
class __UpperCamelCase :
__A = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''Whether tp freeze the encoder.'''} )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''Whether to freeze the embeddings.'''} )
@dataclass
class __UpperCamelCase :
__A = field(
metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''} )
__A = field(
default='''summarization''' , metadata={'''help''': '''Task name, summarization (or summarization_{dataset} for pegasus) or translation'''} , )
__A = field(
default=1024 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
__A = field(
default=128 , metadata={
'''help''': (
'''The maximum total sequence length for target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
__A = field(
default=142 , metadata={
'''help''': (
'''The maximum total sequence length for validation target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded. '''
'''This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '''
'''during ``evaluate`` and ``predict``.'''
)
} , )
__A = field(
default=142 , metadata={
'''help''': (
'''The maximum total sequence length for test target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
__A = field(default=-1 , metadata={'''help''': '''# training examples. -1 means use all.'''} )
__A = field(default=-1 , metadata={'''help''': '''# validation examples. -1 means use all.'''} )
__A = field(default=-1 , metadata={'''help''': '''# test examples. -1 means use all.'''} )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''Source language id for translation.'''} )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''Target language id for translation.'''} )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''# num_beams to use for evaluation.'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''} , )
def SCREAMING_SNAKE_CASE ( lowercase_ : List[Any] , lowercase_ : int , lowercase_ : List[Any] ):
logger.info(F"""***** {split} metrics *****""" )
for key in sorted(metrics.keys() ):
logger.info(F""" {key} = {metrics[key]}""" )
save_json(lowercase_ , os.path.join(lowercase_ , F"""{split}_results.json""" ) )
def SCREAMING_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.
lowercase = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
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.
lowercase , lowercase , lowercase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase , lowercase , lowercase = parser.parse_args_into_dataclasses()
check_output_dir(lowercase_ )
# 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.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# 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""" , lowercase_ )
# Set seed
set_seed(training_args.seed )
# 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 , cache_dir=model_args.cache_dir , )
lowercase = ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
for p in extra_model_params:
if getattr(lowercase_ , lowercase_ , lowercase_ ):
assert hasattr(lowercase_ , lowercase_ ), F"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
setattr(lowercase_ , lowercase_ , getattr(lowercase_ , lowercase_ ) )
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 = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf=""".ckpt""" in model_args.model_name_or_path , config=lowercase_ , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(lowercase_ , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
lowercase = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(lowercase_ , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(lowercase_ , lowercase_ ):
lowercase = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
lowercase = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(lowercase_ )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
lowercase = SeqaSeqDataset
# Get datasets
lowercase = (
dataset_class(
lowercase_ , type_path="""train""" , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_train
else None
)
lowercase = (
dataset_class(
lowercase_ , type_path="""val""" , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
lowercase = (
dataset_class(
lowercase_ , type_path="""test""" , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_predict
else None
)
# Initialize our Trainer
lowercase = (
build_compute_metrics_fn(data_args.task , lowercase_ ) if training_args.predict_with_generate else None
)
lowercase = SeqaSeqTrainer(
model=lowercase_ , args=lowercase_ , data_args=lowercase_ , train_dataset=lowercase_ , eval_dataset=lowercase_ , data_collator=SeqaSeqDataCollator(
lowercase_ , lowercase_ , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=lowercase_ , tokenizer=lowercase_ , )
lowercase = {}
# Training
if training_args.do_train:
logger.info("""*** Train ***""" )
lowercase = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
lowercase = train_result.metrics
lowercase = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics("""train""" , lowercase_ , training_args.output_dir )
all_metrics.update(lowercase_ )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , """trainer_state.json""" ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
lowercase = trainer.evaluate(metric_key_prefix="""val""" )
lowercase = data_args.n_val
lowercase = round(metrics["""val_loss"""] , 4 )
if trainer.is_world_process_zero():
handle_metrics("""val""" , lowercase_ , training_args.output_dir )
all_metrics.update(lowercase_ )
if training_args.do_predict:
logger.info("""*** Predict ***""" )
lowercase = trainer.predict(test_dataset=lowercase_ , metric_key_prefix="""test""" )
lowercase = test_output.metrics
lowercase = data_args.n_test
if trainer.is_world_process_zero():
lowercase = round(metrics["""test_loss"""] , 4 )
handle_metrics("""test""" , lowercase_ , training_args.output_dir )
all_metrics.update(lowercase_ )
if training_args.predict_with_generate:
lowercase = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=lowercase_ , clean_up_tokenization_spaces=lowercase_ )
lowercase = lmap(str.strip , lowercase_ )
write_txt_file(lowercase_ , os.path.join(training_args.output_dir , """test_generations.txt""" ) )
if trainer.is_world_process_zero():
save_json(lowercase_ , os.path.join(training_args.output_dir , """all_results.json""" ) )
return all_metrics
def SCREAMING_SNAKE_CASE ( lowercase_ : Dict ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 653 | 1 |
'''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.
from pathlib import Path
import torch
from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter
lowercase_ : List[Any] = '''Create a default config file for Accelerate with only a few flags set.'''
def SCREAMING_SNAKE_CASE ( lowercase_ : Optional[int]="no" , lowercase_ : str = default_json_config_file , lowercase_ : bool = False ):
lowercase = Path(lowercase_ )
path.parent.mkdir(parents=lowercase_ , exist_ok=lowercase_ )
if path.exists():
print(
F"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.""" )
return False
lowercase = mixed_precision.lower()
if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
raise ValueError(
F"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}""" )
lowercase = {
"""compute_environment""": """LOCAL_MACHINE""",
"""mixed_precision""": mixed_precision,
}
if torch.cuda.is_available():
lowercase = torch.cuda.device_count()
lowercase = num_gpus
lowercase = False
if num_gpus > 1:
lowercase = """MULTI_GPU"""
else:
lowercase = """NO"""
elif is_xpu_available() and use_xpu:
lowercase = torch.xpu.device_count()
lowercase = num_xpus
lowercase = False
if num_xpus > 1:
lowercase = """MULTI_XPU"""
else:
lowercase = """NO"""
elif is_npu_available():
lowercase = torch.npu.device_count()
lowercase = num_npus
lowercase = False
if num_npus > 1:
lowercase = """MULTI_NPU"""
else:
lowercase = """NO"""
else:
lowercase = 0
lowercase = True
lowercase = 1
lowercase = """NO"""
lowercase = ClusterConfig(**lowercase_ )
config.to_json_file(lowercase_ )
return path
def SCREAMING_SNAKE_CASE ( lowercase_ : str , lowercase_ : Optional[Any] ):
lowercase = parser.add_parser("""default""" , parents=lowercase_ , help=lowercase_ , formatter_class=lowercase_ )
parser.add_argument(
"""--config_file""" , default=lowercase_ , help=(
"""The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
"""location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
"""such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
"""with 'huggingface'."""
) , dest="""save_location""" , )
parser.add_argument(
"""--mixed_precision""" , choices=["""no""", """fp16""", """bf16"""] , type=lowercase_ , help="""Whether or not to use mixed precision training. """
"""Choose between FP16 and BF16 (bfloat16) training. """
"""BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.""" , default="""no""" , )
parser.set_defaults(func=lowercase_ )
return parser
def SCREAMING_SNAKE_CASE ( lowercase_ : int ):
lowercase = write_basic_config(args.mixed_precision , args.save_location )
if config_file:
print(F"""accelerate configuration saved at {config_file}""" )
| 653 |
'''simple docstring'''
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_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
lowercase_ : Union[str, Any] = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase )
class __UpperCamelCase (_UpperCAmelCase ):
def __init__( self , *_lowerCAmelCase , **_lowerCAmelCase ) -> Dict:
'''simple docstring'''
super().__init__(*_lowerCAmelCase , **_lowerCAmelCase )
requires_backends(self , """vision""" )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING )
def _a ( self , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase=None ) -> str:
'''simple docstring'''
lowercase = {}
lowercase = {}
if prompt is not None:
lowercase = prompt
if generate_kwargs is not None:
lowercase = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
lowercase = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
"""'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
""" please use only one""" )
lowercase = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self , _lowerCAmelCase , **_lowerCAmelCase ) -> Any:
'''simple docstring'''
return super().__call__(_lowerCAmelCase , **_lowerCAmelCase )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase=None ) -> List[str]:
'''simple docstring'''
lowercase = load_image(_lowerCAmelCase )
if prompt is not None:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise ValueError(
F"""Received an invalid text input, got - {type(_lowerCAmelCase )} - but expected a single string. """
"""Note also that one single text can be provided for conditional image to text generation.""" )
lowercase = self.model.config.model_type
if model_type == "git":
lowercase = self.image_processor(images=_lowerCAmelCase , return_tensors=self.framework )
lowercase = self.tokenizer(text=_lowerCAmelCase , add_special_tokens=_lowerCAmelCase ).input_ids
lowercase = [self.tokenizer.cls_token_id] + input_ids
lowercase = torch.tensor(_lowerCAmelCase ).unsqueeze(0 )
model_inputs.update({"""input_ids""": input_ids} )
elif model_type == "pix2struct":
lowercase = self.image_processor(images=_lowerCAmelCase , header_text=_lowerCAmelCase , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
lowercase = self.image_processor(images=_lowerCAmelCase , return_tensors=self.framework )
lowercase = self.tokenizer(_lowerCAmelCase , return_tensors=self.framework )
model_inputs.update(_lowerCAmelCase )
else:
raise ValueError(F"""Model type {model_type} does not support conditional text generation""" )
else:
lowercase = self.image_processor(images=_lowerCAmelCase , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
lowercase = None
return model_inputs
def _a ( self , _lowerCAmelCase , _lowerCAmelCase=None ) -> Union[str, Any]:
'''simple docstring'''
if (
"input_ids" in model_inputs
and isinstance(model_inputs["""input_ids"""] , _lowerCAmelCase )
and all(x is None for x in model_inputs["""input_ids"""] )
):
lowercase = None
if generate_kwargs is None:
lowercase = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
lowercase = model_inputs.pop(self.model.main_input_name )
lowercase = self.model.generate(_lowerCAmelCase , **_lowerCAmelCase , **_lowerCAmelCase )
return model_outputs
def _a ( self , _lowerCAmelCase ) -> List[str]:
'''simple docstring'''
lowercase = []
for output_ids in model_outputs:
lowercase = {
"""generated_text""": self.tokenizer.decode(
_lowerCAmelCase , skip_special_tokens=_lowerCAmelCase , )
}
records.append(_lowerCAmelCase )
return records
| 653 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.roberta.modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
)
class __UpperCamelCase (unittest.TestCase ):
def __init__( self , _lowerCAmelCase , _lowerCAmelCase=13 , _lowerCAmelCase=7 , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=99 , _lowerCAmelCase=32 , _lowerCAmelCase=5 , _lowerCAmelCase=4 , _lowerCAmelCase=37 , _lowerCAmelCase="gelu" , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=512 , _lowerCAmelCase=16 , _lowerCAmelCase=2 , _lowerCAmelCase=0.02 , _lowerCAmelCase=4 , ) -> List[Any]:
'''simple docstring'''
lowercase = parent
lowercase = batch_size
lowercase = seq_length
lowercase = is_training
lowercase = use_attention_mask
lowercase = use_token_type_ids
lowercase = use_labels
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = intermediate_size
lowercase = hidden_act
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = type_vocab_size
lowercase = type_sequence_label_size
lowercase = initializer_range
lowercase = num_choices
def _a ( self ) -> Any:
'''simple docstring'''
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase = None
if self.use_attention_mask:
lowercase = random_attention_mask([self.batch_size, self.seq_length] )
lowercase = None
if self.use_token_type_ids:
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase = RobertaConfig(
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 , )
return config, input_ids, token_type_ids, attention_mask
def _a ( self ) -> Any:
'''simple docstring'''
lowercase = self.prepare_config_and_inputs()
lowercase , lowercase , lowercase , lowercase = config_and_inputs
lowercase = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def _a ( self ) -> List[str]:
'''simple docstring'''
lowercase = self.prepare_config_and_inputs()
lowercase , lowercase , lowercase , lowercase = config_and_inputs
lowercase = True
lowercase = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
lowercase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class __UpperCamelCase (_UpperCAmelCase , unittest.TestCase ):
__A = True
__A = (
(
FlaxRobertaModel,
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _a ( self ) -> Dict:
'''simple docstring'''
lowercase = FlaxRobertaModelTester(self )
@slow
def _a ( self ) -> int:
'''simple docstring'''
for model_class_name in self.all_model_classes:
lowercase = model_class_name.from_pretrained("""roberta-base""" , from_pt=_lowerCAmelCase )
lowercase = model(np.ones((1, 1) ) )
self.assertIsNotNone(_lowerCAmelCase )
| 653 |
'''simple docstring'''
from ... import PretrainedConfig
lowercase_ : int = {
'''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}
class __UpperCamelCase (_UpperCAmelCase ):
__A = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
__A = '''nezha'''
def __init__( self , _lowerCAmelCase=2_1128 , _lowerCAmelCase=768 , _lowerCAmelCase=12 , _lowerCAmelCase=12 , _lowerCAmelCase=3072 , _lowerCAmelCase="gelu" , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=512 , _lowerCAmelCase=64 , _lowerCAmelCase=2 , _lowerCAmelCase=0.02 , _lowerCAmelCase=1E-12 , _lowerCAmelCase=0.1 , _lowerCAmelCase=0 , _lowerCAmelCase=2 , _lowerCAmelCase=3 , _lowerCAmelCase=True , **_lowerCAmelCase , ) -> int:
'''simple docstring'''
super().__init__(pad_token_id=_lowerCAmelCase , bos_token_id=_lowerCAmelCase , eos_token_id=_lowerCAmelCase , **_lowerCAmelCase )
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = hidden_act
lowercase = intermediate_size
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = max_relative_position
lowercase = type_vocab_size
lowercase = initializer_range
lowercase = layer_norm_eps
lowercase = classifier_dropout
lowercase = use_cache
| 653 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
lowercase_ : Any = logging.get_logger(__name__)
class __UpperCamelCase (_UpperCAmelCase ):
def __init__( self , *_lowerCAmelCase , **_lowerCAmelCase ) -> None:
'''simple docstring'''
warnings.warn(
"""The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use PoolFormerImageProcessor instead.""" , _lowerCAmelCase , )
super().__init__(*_lowerCAmelCase , **_lowerCAmelCase )
| 653 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
lowercase_ : Tuple = logging.getLogger(__name__)
def SCREAMING_SNAKE_CASE ( lowercase_ : str ):
lowercase = git.Repo(search_parent_directories=lowercase_ )
lowercase = {
"""repo_id""": str(lowercase_ ),
"""repo_sha""": str(repo.head.object.hexsha ),
"""repo_branch""": str(repo.active_branch ),
}
with open(os.path.join(lowercase_ , """git_log.json""" ) , """w""" ) as f:
json.dump(lowercase_ , lowercase_ , indent=4 )
def SCREAMING_SNAKE_CASE ( lowercase_ : str ):
if params.n_gpu <= 0:
lowercase = 0
lowercase = -1
lowercase = True
lowercase = False
return
assert torch.cuda.is_available()
logger.info("""Initializing GPUs""" )
if params.n_gpu > 1:
assert params.local_rank != -1
lowercase = int(os.environ["""WORLD_SIZE"""] )
lowercase = int(os.environ["""N_GPU_NODE"""] )
lowercase = int(os.environ["""RANK"""] )
# number of nodes / node ID
lowercase = params.world_size // params.n_gpu_per_node
lowercase = params.global_rank // params.n_gpu_per_node
lowercase = True
assert params.n_nodes == int(os.environ["""N_NODES"""] )
assert params.node_id == int(os.environ["""NODE_RANK"""] )
# local job (single GPU)
else:
assert params.local_rank == -1
lowercase = 1
lowercase = 0
lowercase = 0
lowercase = 0
lowercase = 1
lowercase = 1
lowercase = False
# sanity checks
assert params.n_nodes >= 1
assert 0 <= params.node_id < params.n_nodes
assert 0 <= params.local_rank <= params.global_rank < params.world_size
assert params.world_size == params.n_nodes * params.n_gpu_per_node
# define whether this is the master process / if we are in multi-node distributed mode
lowercase = params.node_id == 0 and params.local_rank == 0
lowercase = params.n_nodes > 1
# summary
lowercase = F"""--- Global rank: {params.global_rank} - """
logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes )
logger.info(PREFIX + """Node ID : %i""" % params.node_id )
logger.info(PREFIX + """Local rank : %i""" % params.local_rank )
logger.info(PREFIX + """World size : %i""" % params.world_size )
logger.info(PREFIX + """GPUs per node : %i""" % params.n_gpu_per_node )
logger.info(PREFIX + """Master : %s""" % str(params.is_master ) )
logger.info(PREFIX + """Multi-node : %s""" % str(params.multi_node ) )
logger.info(PREFIX + """Multi-GPU : %s""" % str(params.multi_gpu ) )
logger.info(PREFIX + """Hostname : %s""" % socket.gethostname() )
# set GPU device
torch.cuda.set_device(params.local_rank )
# initialize multi-GPU
if params.multi_gpu:
logger.info("""Initializing PyTorch distributed""" )
torch.distributed.init_process_group(
init_method="""env://""" , backend="""nccl""" , )
def SCREAMING_SNAKE_CASE ( lowercase_ : Optional[Any] ):
np.random.seed(args.seed )
torch.manual_seed(args.seed )
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed )
| 653 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __UpperCamelCase (_UpperCAmelCase ):
__A = ['''image_processor''', '''tokenizer''']
__A = '''Pix2StructImageProcessor'''
__A = ('''T5Tokenizer''', '''T5TokenizerFast''')
def __init__( self , _lowerCAmelCase , _lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
lowercase = False
super().__init__(_lowerCAmelCase , _lowerCAmelCase )
def __call__( self , _lowerCAmelCase=None , _lowerCAmelCase = None , _lowerCAmelCase = True , _lowerCAmelCase = False , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = 2048 , _lowerCAmelCase = 0 , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = False , _lowerCAmelCase = False , _lowerCAmelCase = False , _lowerCAmelCase = False , _lowerCAmelCase = False , _lowerCAmelCase = True , _lowerCAmelCase = None , **_lowerCAmelCase , ) -> BatchEncoding:
'''simple docstring'''
if images is None and text is None:
raise ValueError("""You have to specify either images or text.""" )
# Get only text
if images is None and not self.image_processor.is_vqa:
lowercase = self.tokenizer
lowercase = self.tokenizer(
text=_lowerCAmelCase , add_special_tokens=_lowerCAmelCase , padding=_lowerCAmelCase , truncation=_lowerCAmelCase , max_length=_lowerCAmelCase , stride=_lowerCAmelCase , pad_to_multiple_of=_lowerCAmelCase , return_attention_mask=_lowerCAmelCase , return_overflowing_tokens=_lowerCAmelCase , return_special_tokens_mask=_lowerCAmelCase , return_offsets_mapping=_lowerCAmelCase , return_token_type_ids=_lowerCAmelCase , return_length=_lowerCAmelCase , verbose=_lowerCAmelCase , return_tensors=_lowerCAmelCase , **_lowerCAmelCase , )
return text_encoding
if not self.image_processor.is_vqa:
# add pixel_values
lowercase = self.image_processor(
_lowerCAmelCase , return_tensors=_lowerCAmelCase , max_patches=_lowerCAmelCase , **_lowerCAmelCase )
else:
# add pixel_values and bbox
lowercase = self.image_processor(
_lowerCAmelCase , return_tensors=_lowerCAmelCase , max_patches=_lowerCAmelCase , header_text=_lowerCAmelCase , **_lowerCAmelCase )
if text is not None and not self.image_processor.is_vqa:
lowercase = self.tokenizer(
text=_lowerCAmelCase , add_special_tokens=_lowerCAmelCase , padding=_lowerCAmelCase , truncation=_lowerCAmelCase , max_length=_lowerCAmelCase , stride=_lowerCAmelCase , pad_to_multiple_of=_lowerCAmelCase , return_attention_mask=_lowerCAmelCase , return_overflowing_tokens=_lowerCAmelCase , return_special_tokens_mask=_lowerCAmelCase , return_offsets_mapping=_lowerCAmelCase , return_token_type_ids=_lowerCAmelCase , return_length=_lowerCAmelCase , verbose=_lowerCAmelCase , return_tensors=_lowerCAmelCase , **_lowerCAmelCase , )
if "attention_mask" in text_encoding:
lowercase = text_encoding.pop("""attention_mask""" )
if "input_ids" in text_encoding:
lowercase = text_encoding.pop("""input_ids""" )
else:
lowercase = None
if text_encoding is not None:
encoding_image_processor.update(_lowerCAmelCase )
return encoding_image_processor
def _a ( self , *_lowerCAmelCase , **_lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
return self.tokenizer.batch_decode(*_lowerCAmelCase , **_lowerCAmelCase )
def _a ( self , *_lowerCAmelCase , **_lowerCAmelCase ) -> int:
'''simple docstring'''
return self.tokenizer.decode(*_lowerCAmelCase , **_lowerCAmelCase )
@property
def _a ( self ) -> Any:
'''simple docstring'''
lowercase = self.tokenizer.model_input_names
lowercase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 653 |
'''simple docstring'''
from __future__ import annotations
import os
from typing import Any
import requests
lowercase_ : List[str] = '''https://api.github.com'''
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
lowercase_ : Any = BASE_URL + '''/user'''
# https://github.com/settings/tokens
lowercase_ : Union[str, Any] = os.environ.get('''USER_TOKEN''', '''''')
def SCREAMING_SNAKE_CASE ( lowercase_ : str ):
lowercase = {
"""Authorization""": F"""token {auth_token}""",
"""Accept""": """application/vnd.github.v3+json""",
}
return requests.get(lowercase_ , headers=lowercase_ ).json()
if __name__ == "__main__": # pragma: no cover
if USER_TOKEN:
for key, value in fetch_github_info(USER_TOKEN).items():
print(f'''{key}: {value}''')
else:
raise ValueError('''\'USER_TOKEN\' field cannot be empty.''')
| 653 | 1 |
'''simple docstring'''
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 653 |
'''simple docstring'''
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowercase_ : Union[str, Any] = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
require_version('''datasets>=1.14.0''', '''To fix: pip install -r examples/pytorch/audio-classification/requirements.txt''')
def SCREAMING_SNAKE_CASE ( lowercase_ : np.ndarray , lowercase_ : float , lowercase_ : int = 1_6000 ):
lowercase = int(round(sample_rate * max_length ) )
if len(lowercase_ ) <= sample_length:
return wav
lowercase = randint(0 , len(lowercase_ ) - sample_length - 1 )
return wav[random_offset : random_offset + sample_length]
@dataclass
class __UpperCamelCase :
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''Name of a dataset from the datasets package'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''A file containing the training audio paths and labels.'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''A file containing the validation audio paths and labels.'''} )
__A = field(
default='''train''' , metadata={
'''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
} , )
__A = field(
default='''validation''' , metadata={
'''help''': (
'''The name of the training data set split to use (via the datasets library). Defaults to \'validation\''''
)
} , )
__A = field(
default='''audio''' , metadata={'''help''': '''The name of the dataset column containing the audio data. Defaults to \'audio\''''} , )
__A = field(
default='''label''' , metadata={'''help''': '''The name of the dataset column containing the labels. Defaults to \'label\''''} )
__A = field(
default=_UpperCAmelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
__A = field(
default=_UpperCAmelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
__A = field(
default=20 , metadata={'''help''': '''Audio clips will be randomly cut to this length during training if the value is set.'''} , )
@dataclass
class __UpperCamelCase :
__A = field(
default='''facebook/wav2vec2-base''' , metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} , )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from the Hub'''} )
__A = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Name or path of preprocessor config.'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Whether to freeze the feature encoder layers of the model.'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Whether to generate an attention mask in the feature extractor.'''} )
__A = field(
default=_UpperCAmelCase , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''} , )
def _a ( self ) -> List[Any]:
'''simple docstring'''
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
"""The argument `--freeze_feature_extractor` is deprecated and """
"""will be removed in a future version. Use `--freeze_feature_encoder`"""
"""instead. Setting `freeze_feature_encoder==True`.""" , _lowerCAmelCase , )
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
"""The argument `--freeze_feature_extractor` is deprecated and """
"""should not be used in combination with `--freeze_feature_encoder`."""
"""Only make use of `--freeze_feature_encoder`.""" )
def SCREAMING_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.
lowercase = 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.
lowercase , lowercase , lowercase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase , lowercase , lowercase = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_audio_classification""" , lowercase_ , lowercase_ )
# 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 )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
lowercase = training_args.get_process_log_level()
logger.setLevel(lowercase_ )
transformers.utils.logging.set_verbosity(lowercase_ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# 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}""" )
logger.info(F"""Training/evaluation parameters {training_args}""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Detecting last checkpoint.
lowercase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowercase = 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 train from scratch.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is 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.""" )
# Initialize our dataset and prepare it for the audio classification task.
lowercase = DatasetDict()
lowercase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.train_split_name , use_auth_token=True if model_args.use_auth_token else None , )
lowercase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.eval_split_name , use_auth_token=True if model_args.use_auth_token else None , )
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
F"""--audio_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. """
"""Make sure to set `--audio_column_name` to the correct audio column - one of """
F"""{', '.join(raw_datasets['train'].column_names )}.""" )
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
F"""--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. """
"""Make sure to set `--label_column_name` to the correct text column - one of """
F"""{', '.join(raw_datasets['train'].column_names )}.""" )
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
lowercase = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path , return_attention_mask=model_args.attention_mask , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
lowercase = raw_datasets.cast_column(
data_args.audio_column_name , datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )
lowercase = feature_extractor.model_input_names[0]
def train_transforms(lowercase_ : int ):
lowercase = []
for audio in batch[data_args.audio_column_name]:
lowercase = random_subsample(
audio["""array"""] , max_length=data_args.max_length_seconds , sample_rate=feature_extractor.sampling_rate )
subsampled_wavs.append(lowercase_ )
lowercase = feature_extractor(lowercase_ , sampling_rate=feature_extractor.sampling_rate )
lowercase = {model_input_name: inputs.get(lowercase_ )}
lowercase = list(batch[data_args.label_column_name] )
return output_batch
def val_transforms(lowercase_ : Dict ):
lowercase = [audio["""array"""] for audio in batch[data_args.audio_column_name]]
lowercase = feature_extractor(lowercase_ , sampling_rate=feature_extractor.sampling_rate )
lowercase = {model_input_name: inputs.get(lowercase_ )}
lowercase = list(batch[data_args.label_column_name] )
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
lowercase = raw_datasets["""train"""].features[data_args.label_column_name].names
lowercase , lowercase = {}, {}
for i, label in enumerate(lowercase_ ):
lowercase = str(lowercase_ )
lowercase = label
# Load the accuracy metric from the datasets package
lowercase = evaluate.load("""accuracy""" )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(lowercase_ : Tuple ):
lowercase = np.argmax(eval_pred.predictions , axis=1 )
return metric.compute(predictions=lowercase_ , references=eval_pred.label_ids )
lowercase = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(lowercase_ ) , labelaid=lowercase_ , idalabel=lowercase_ , finetuning_task="""audio-classification""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
lowercase = AutoModelForAudioClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=lowercase_ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
lowercase = (
raw_datasets["""train"""].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
raw_datasets["train"].set_transform(lowercase_ , output_all_columns=lowercase_ )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
lowercase = (
raw_datasets["""eval"""].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
raw_datasets["eval"].set_transform(lowercase_ , output_all_columns=lowercase_ )
# Initialize our trainer
lowercase = Trainer(
model=lowercase_ , args=lowercase_ , train_dataset=raw_datasets["""train"""] if training_args.do_train else None , eval_dataset=raw_datasets["""eval"""] if training_args.do_eval else None , compute_metrics=lowercase_ , tokenizer=lowercase_ , )
# Training
if training_args.do_train:
lowercase = None
if training_args.resume_from_checkpoint is not None:
lowercase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowercase = last_checkpoint
lowercase = trainer.train(resume_from_checkpoint=lowercase_ )
trainer.save_model()
trainer.log_metrics("""train""" , train_result.metrics )
trainer.save_metrics("""train""" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
lowercase = trainer.evaluate()
trainer.log_metrics("""eval""" , lowercase_ )
trainer.save_metrics("""eval""" , lowercase_ )
# Write model card and (optionally) push to hub
lowercase = {
"""finetuned_from""": model_args.model_name_or_path,
"""tasks""": """audio-classification""",
"""dataset""": data_args.dataset_name,
"""tags""": ["""audio-classification"""],
}
if training_args.push_to_hub:
trainer.push_to_hub(**lowercase_ )
else:
trainer.create_model_card(**lowercase_ )
if __name__ == "__main__":
main()
| 653 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
lowercase_ : Tuple = logging.get_logger(__name__)
lowercase_ : List[Any] = {
'''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config.json''',
# See all DPT models at https://huggingface.co/models?filter=dpt
}
class __UpperCamelCase (_UpperCAmelCase ):
__A = '''dpt'''
def __init__( self , _lowerCAmelCase=768 , _lowerCAmelCase=12 , _lowerCAmelCase=12 , _lowerCAmelCase=3072 , _lowerCAmelCase="gelu" , _lowerCAmelCase=0.0 , _lowerCAmelCase=0.0 , _lowerCAmelCase=0.02 , _lowerCAmelCase=1E-12 , _lowerCAmelCase=384 , _lowerCAmelCase=16 , _lowerCAmelCase=3 , _lowerCAmelCase=False , _lowerCAmelCase=True , _lowerCAmelCase=[2, 5, 8, 11] , _lowerCAmelCase="project" , _lowerCAmelCase=[4, 2, 1, 0.5] , _lowerCAmelCase=[96, 192, 384, 768] , _lowerCAmelCase=256 , _lowerCAmelCase=-1 , _lowerCAmelCase=False , _lowerCAmelCase=True , _lowerCAmelCase=0.4 , _lowerCAmelCase=255 , _lowerCAmelCase=0.1 , _lowerCAmelCase=[1, 1024, 24, 24] , _lowerCAmelCase=[0, 1] , _lowerCAmelCase=None , **_lowerCAmelCase , ) -> Optional[int]:
'''simple docstring'''
super().__init__(**_lowerCAmelCase )
lowercase = hidden_size
lowercase = is_hybrid
if self.is_hybrid:
if backbone_config is None:
logger.info("""Initializing the config with a `BiT` backbone.""" )
lowercase = {
"""global_padding""": """same""",
"""layer_type""": """bottleneck""",
"""depths""": [3, 4, 9],
"""out_features""": ["""stage1""", """stage2""", """stage3"""],
"""embedding_dynamic_padding""": True,
}
lowercase = BitConfig(**_lowerCAmelCase )
elif isinstance(_lowerCAmelCase , _lowerCAmelCase ):
logger.info("""Initializing the config with a `BiT` backbone.""" )
lowercase = BitConfig(**_lowerCAmelCase )
elif isinstance(_lowerCAmelCase , _lowerCAmelCase ):
lowercase = backbone_config
else:
raise ValueError(
F"""backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.""" )
lowercase = backbone_featmap_shape
lowercase = neck_ignore_stages
if readout_type != "project":
raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode.""" )
else:
lowercase = None
lowercase = None
lowercase = []
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = intermediate_size
lowercase = hidden_act
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = initializer_range
lowercase = layer_norm_eps
lowercase = image_size
lowercase = patch_size
lowercase = num_channels
lowercase = qkv_bias
lowercase = backbone_out_indices
if readout_type not in ["ignore", "add", "project"]:
raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']""" )
lowercase = readout_type
lowercase = reassemble_factors
lowercase = neck_hidden_sizes
lowercase = fusion_hidden_size
lowercase = head_in_index
lowercase = use_batch_norm_in_fusion_residual
# auxiliary head attributes (semantic segmentation)
lowercase = use_auxiliary_head
lowercase = auxiliary_loss_weight
lowercase = semantic_loss_ignore_index
lowercase = semantic_classifier_dropout
def _a ( self ) -> Dict:
'''simple docstring'''
lowercase = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
lowercase = self.backbone_config.to_dict()
lowercase = self.__class__.model_type
return output
| 653 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
lowercase_ : Union[str, Any] = logging.get_logger(__name__)
@dataclass
class __UpperCamelCase (_UpperCAmelCase ):
__A = [
'''no_inference''',
'''no_cuda''',
'''no_tpu''',
'''no_speed''',
'''no_memory''',
'''no_env_print''',
'''no_multi_process''',
]
def __init__( self , **_lowerCAmelCase ) -> Optional[int]:
'''simple docstring'''
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
lowercase = deprecated_arg[3:]
lowercase = not kwargs.pop(_lowerCAmelCase )
logger.warning(
F"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
F""" {positive_arg}={kwargs[positive_arg]}""" )
lowercase = kwargs.pop("""tpu_name""" , self.tpu_name )
lowercase = kwargs.pop("""device_idx""" , self.device_idx )
lowercase = kwargs.pop("""eager_mode""" , self.eager_mode )
lowercase = kwargs.pop("""use_xla""" , self.use_xla )
super().__init__(**_lowerCAmelCase )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Name of TPU'''} , )
__A = field(
default=0 , metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''} , )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''Benchmark models in eager model.'''} )
__A = field(
default=_UpperCAmelCase , metadata={
'''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
} , )
@cached_property
def _a ( self ) -> Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
'''simple docstring'''
requires_backends(self , ["""tf"""] )
lowercase = None
if self.tpu:
try:
if self.tpu_name:
lowercase = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
else:
lowercase = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
lowercase = None
return tpu
@cached_property
def _a ( self ) -> Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
'''simple docstring'''
requires_backends(self , ["""tf"""] )
if self.is_tpu:
tf.config.experimental_connect_to_cluster(self._setup_tpu )
tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )
lowercase = tf.distribute.TPUStrategy(self._setup_tpu )
else:
# currently no multi gpu is allowed
if self.is_gpu:
# TODO: Currently only single GPU is supported
tf.config.set_visible_devices(self.gpu_list[self.device_idx] , """GPU""" )
lowercase = tf.distribute.OneDeviceStrategy(device=F"""/gpu:{self.device_idx}""" )
else:
tf.config.set_visible_devices([] , """GPU""" ) # disable GPU
lowercase = tf.distribute.OneDeviceStrategy(device=F"""/cpu:{self.device_idx}""" )
return strategy
@property
def _a ( self ) -> bool:
'''simple docstring'''
requires_backends(self , ["""tf"""] )
return self._setup_tpu is not None
@property
def _a ( self ) -> "tf.distribute.Strategy":
'''simple docstring'''
requires_backends(self , ["""tf"""] )
return self._setup_strategy
@property
def _a ( self ) -> Tuple:
'''simple docstring'''
requires_backends(self , ["""tf"""] )
return tf.config.list_physical_devices("""GPU""" )
@property
def _a ( self ) -> int:
'''simple docstring'''
requires_backends(self , ["""tf"""] )
if self.cuda:
return len(self.gpu_list )
return 0
@property
def _a ( self ) -> bool:
'''simple docstring'''
return self.n_gpu > 0
| 653 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class __UpperCamelCase (_UpperCAmelCase ):
def __init__( self , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = False , _lowerCAmelCase = False , _lowerCAmelCase = None , **_lowerCAmelCase , ) -> int:
'''simple docstring'''
lowercase = path_or_paths
lowercase = split if split or isinstance(_lowerCAmelCase , _lowerCAmelCase ) else """train"""
lowercase = features
lowercase = cache_dir
lowercase = keep_in_memory
lowercase = streaming
lowercase = num_proc
lowercase = kwargs
@abstractmethod
def _a ( self ) -> Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]:
'''simple docstring'''
pass
class __UpperCamelCase (_UpperCAmelCase ):
def __init__( self , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = False , _lowerCAmelCase = False , _lowerCAmelCase = None , **_lowerCAmelCase , ) -> List[str]:
'''simple docstring'''
lowercase = features
lowercase = cache_dir
lowercase = keep_in_memory
lowercase = streaming
lowercase = num_proc
lowercase = kwargs
@abstractmethod
def _a ( self ) -> Union[Dataset, IterableDataset]:
'''simple docstring'''
pass
| 653 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : Any = logging.get_logger(__name__)
lowercase_ : str = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class __UpperCamelCase (_UpperCAmelCase ):
__A = '''vit_msn'''
def __init__( self , _lowerCAmelCase=768 , _lowerCAmelCase=12 , _lowerCAmelCase=12 , _lowerCAmelCase=3072 , _lowerCAmelCase="gelu" , _lowerCAmelCase=0.0 , _lowerCAmelCase=0.0 , _lowerCAmelCase=0.02 , _lowerCAmelCase=1E-06 , _lowerCAmelCase=224 , _lowerCAmelCase=16 , _lowerCAmelCase=3 , _lowerCAmelCase=True , **_lowerCAmelCase , ) -> List[Any]:
'''simple docstring'''
super().__init__(**_lowerCAmelCase )
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = intermediate_size
lowercase = hidden_act
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = initializer_range
lowercase = layer_norm_eps
lowercase = image_size
lowercase = patch_size
lowercase = num_channels
lowercase = qkv_bias
| 653 | 1 |
'''simple docstring'''
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_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
lowercase_ : Union[str, Any] = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase )
class __UpperCamelCase (_UpperCAmelCase ):
def __init__( self , *_lowerCAmelCase , **_lowerCAmelCase ) -> Dict:
'''simple docstring'''
super().__init__(*_lowerCAmelCase , **_lowerCAmelCase )
requires_backends(self , """vision""" )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING )
def _a ( self , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase=None ) -> str:
'''simple docstring'''
lowercase = {}
lowercase = {}
if prompt is not None:
lowercase = prompt
if generate_kwargs is not None:
lowercase = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
lowercase = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
"""'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
""" please use only one""" )
lowercase = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self , _lowerCAmelCase , **_lowerCAmelCase ) -> Any:
'''simple docstring'''
return super().__call__(_lowerCAmelCase , **_lowerCAmelCase )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase=None ) -> List[str]:
'''simple docstring'''
lowercase = load_image(_lowerCAmelCase )
if prompt is not None:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise ValueError(
F"""Received an invalid text input, got - {type(_lowerCAmelCase )} - but expected a single string. """
"""Note also that one single text can be provided for conditional image to text generation.""" )
lowercase = self.model.config.model_type
if model_type == "git":
lowercase = self.image_processor(images=_lowerCAmelCase , return_tensors=self.framework )
lowercase = self.tokenizer(text=_lowerCAmelCase , add_special_tokens=_lowerCAmelCase ).input_ids
lowercase = [self.tokenizer.cls_token_id] + input_ids
lowercase = torch.tensor(_lowerCAmelCase ).unsqueeze(0 )
model_inputs.update({"""input_ids""": input_ids} )
elif model_type == "pix2struct":
lowercase = self.image_processor(images=_lowerCAmelCase , header_text=_lowerCAmelCase , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
lowercase = self.image_processor(images=_lowerCAmelCase , return_tensors=self.framework )
lowercase = self.tokenizer(_lowerCAmelCase , return_tensors=self.framework )
model_inputs.update(_lowerCAmelCase )
else:
raise ValueError(F"""Model type {model_type} does not support conditional text generation""" )
else:
lowercase = self.image_processor(images=_lowerCAmelCase , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
lowercase = None
return model_inputs
def _a ( self , _lowerCAmelCase , _lowerCAmelCase=None ) -> Union[str, Any]:
'''simple docstring'''
if (
"input_ids" in model_inputs
and isinstance(model_inputs["""input_ids"""] , _lowerCAmelCase )
and all(x is None for x in model_inputs["""input_ids"""] )
):
lowercase = None
if generate_kwargs is None:
lowercase = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
lowercase = model_inputs.pop(self.model.main_input_name )
lowercase = self.model.generate(_lowerCAmelCase , **_lowerCAmelCase , **_lowerCAmelCase )
return model_outputs
def _a ( self , _lowerCAmelCase ) -> List[str]:
'''simple docstring'''
lowercase = []
for output_ids in model_outputs:
lowercase = {
"""generated_text""": self.tokenizer.decode(
_lowerCAmelCase , skip_special_tokens=_lowerCAmelCase , )
}
records.append(_lowerCAmelCase )
return records
| 653 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowercase_ : Union[str, Any] , lowercase_ : str ):
lowercase = """"""
for i in table:
res += inp[i - 1]
return res
def SCREAMING_SNAKE_CASE ( lowercase_ : List[Any] ):
return data[1:] + data[0]
def SCREAMING_SNAKE_CASE ( lowercase_ : Tuple , lowercase_ : Dict ):
lowercase = """"""
for i in range(len(lowercase_ ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def SCREAMING_SNAKE_CASE ( lowercase_ : str , lowercase_ : str ):
lowercase = int("""0b""" + data[0] + data[-1] , 2 )
lowercase = int("""0b""" + data[1:3] , 2 )
return bin(s[row][col] )[2:]
def SCREAMING_SNAKE_CASE ( lowercase_ : Dict , lowercase_ : Tuple , lowercase_ : Tuple , lowercase_ : str , lowercase_ : Any ):
lowercase = message[:4]
lowercase = message[4:]
lowercase = apply_table(lowercase_ , lowercase_ )
lowercase = xor(lowercase_ , lowercase_ )
lowercase = apply_sbox(lowercase_ , temp[:4] ) # noqa: E741
lowercase = apply_sbox(lowercase_ , temp[4:] )
lowercase = """0""" * (2 - len(lowercase_ )) + l # noqa: E741
lowercase = """0""" * (2 - len(lowercase_ )) + r
lowercase = apply_table(l + r , lowercase_ )
lowercase = xor(lowercase_ , lowercase_ )
return temp + right
if __name__ == "__main__":
lowercase_ : Tuple = input('''Enter 10 bit key: ''')
lowercase_ : Any = input('''Enter 8 bit message: ''')
lowercase_ : Dict = [6, 3, 7, 4, 8, 5, 10, 9]
lowercase_ : str = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
lowercase_ : List[Any] = [2, 4, 3, 1]
lowercase_ : List[str] = [2, 6, 3, 1, 4, 8, 5, 7]
lowercase_ : Tuple = [4, 1, 3, 5, 7, 2, 8, 6]
lowercase_ : Optional[Any] = [4, 1, 2, 3, 2, 3, 4, 1]
lowercase_ : List[str] = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
lowercase_ : List[Any] = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
lowercase_ : Union[str, Any] = apply_table(key, paa_table)
lowercase_ : Optional[Any] = temp[:5]
lowercase_ : int = temp[5:]
lowercase_ : List[str] = left_shift(left)
lowercase_ : int = left_shift(right)
lowercase_ : Tuple = apply_table(left + right, pa_table)
lowercase_ : List[str] = left_shift(left)
lowercase_ : Optional[Any] = left_shift(right)
lowercase_ : Union[str, Any] = left_shift(left)
lowercase_ : Union[str, Any] = left_shift(right)
lowercase_ : Optional[int] = apply_table(left + right, pa_table)
# encryption
lowercase_ : int = apply_table(message, IP)
lowercase_ : Dict = function(expansion, sa, sa, keya, temp)
lowercase_ : Any = temp[4:] + temp[:4]
lowercase_ : List[Any] = function(expansion, sa, sa, keya, temp)
lowercase_ : Tuple = apply_table(temp, IP_inv)
print('''Cipher text is:''', CT)
# decryption
lowercase_ : List[str] = apply_table(CT, IP)
lowercase_ : Optional[int] = function(expansion, sa, sa, keya, temp)
lowercase_ : Optional[Any] = temp[4:] + temp[:4]
lowercase_ : Optional[int] = function(expansion, sa, sa, keya, temp)
lowercase_ : Optional[Any] = apply_table(temp, IP_inv)
print('''Plain text after decypting is:''', PT)
| 653 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowercase_ : list[int] , lowercase_ : list[int] ):
# Check if the input is valid
if not len(lowercase_ ) == len(lowercase_ ) == 3:
raise ValueError("""Please enter a valid equation.""" )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise ValueError("""Both a & b of two equations can't be zero.""" )
# Extract the coefficients
lowercase , lowercase , lowercase = equationa
lowercase , lowercase , lowercase = equationa
# Calculate the determinants of the matrices
lowercase = aa * ba - aa * ba
lowercase = ca * ba - ca * ba
lowercase = aa * ca - aa * ca
# Check if the system of linear equations has a solution (using Cramer's rule)
if determinant == 0:
if determinant_x == determinant_y == 0:
raise ValueError("""Infinite solutions. (Consistent system)""" )
else:
raise ValueError("""No solution. (Inconsistent system)""" )
else:
if determinant_x == determinant_y == 0:
# Trivial solution (Inconsistent system)
return (0.0, 0.0)
else:
lowercase = determinant_x / determinant
lowercase = determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
| 653 |
'''simple docstring'''
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
lowercase_ : int = 50_0000
lowercase_ , lowercase_ : Union[str, Any] = os.path.split(__file__)
lowercase_ : Optional[Any] = os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))
@get_duration
def SCREAMING_SNAKE_CASE ( lowercase_ : datasets.Dataset , **lowercase_ : Dict ):
lowercase = dataset.map(**lowercase_ )
@get_duration
def SCREAMING_SNAKE_CASE ( lowercase_ : datasets.Dataset , **lowercase_ : Optional[int] ):
lowercase = dataset.filter(**lowercase_ )
def SCREAMING_SNAKE_CASE ( ):
lowercase = {"""num examples""": SPEED_TEST_N_EXAMPLES}
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase = datasets.Features({"""text""": datasets.Value("""string""" ), """numbers""": datasets.Value("""float32""" )} )
lowercase = generate_example_dataset(
os.path.join(lowercase_ , """dataset.arrow""" ) , lowercase_ , num_examples=lowercase_ )
lowercase = transformers.AutoTokenizer.from_pretrained("""bert-base-cased""" , use_fast=lowercase_ )
def tokenize(lowercase_ : Dict ):
return tokenizer(examples["""text"""] )
lowercase = map(lowercase_ )
lowercase = map(lowercase_ , batched=lowercase_ )
lowercase = map(lowercase_ , function=lambda lowercase_ : None , batched=lowercase_ )
with dataset.formatted_as(type="""numpy""" ):
lowercase = map(lowercase_ , function=lambda lowercase_ : None , batched=lowercase_ )
with dataset.formatted_as(type="""pandas""" ):
lowercase = map(lowercase_ , function=lambda lowercase_ : None , batched=lowercase_ )
with dataset.formatted_as(type="""torch""" , columns="""numbers""" ):
lowercase = map(lowercase_ , function=lambda lowercase_ : None , batched=lowercase_ )
with dataset.formatted_as(type="""tensorflow""" , columns="""numbers""" ):
lowercase = map(lowercase_ , function=lambda lowercase_ : None , batched=lowercase_ )
lowercase = map(lowercase_ , function=lowercase_ , batched=lowercase_ )
lowercase = filter(lowercase_ )
# Activate later when tokenizer support batched inputs
# with dataset.formatted_as(type='numpy'):
# times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)
with open(lowercase_ , """wb""" ) as f:
f.write(json.dumps(lowercase_ ).encode("""utf-8""" ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_map_filter()
| 653 | 1 |
'''simple docstring'''
from typing import Dict, 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_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
lowercase_ : Union[str, Any] = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE ( lowercase_ : Tuple ):
if isinstance(lowercase_ , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(lowercase_ , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(lowercase_ ):
return [[videos]]
raise ValueError(F"""Could not make batched video from {videos}""" )
class __UpperCamelCase (_UpperCAmelCase ):
__A = ['''pixel_values''']
def __init__( self , _lowerCAmelCase = True , _lowerCAmelCase = None , _lowerCAmelCase = PILImageResampling.BILINEAR , _lowerCAmelCase = True , _lowerCAmelCase = None , _lowerCAmelCase = True , _lowerCAmelCase = 1 / 255 , _lowerCAmelCase = True , _lowerCAmelCase = None , _lowerCAmelCase = None , **_lowerCAmelCase , ) -> None:
'''simple docstring'''
super().__init__(**_lowerCAmelCase )
lowercase = size if size is not None else {"""shortest_edge""": 224}
lowercase = get_size_dict(_lowerCAmelCase , default_to_square=_lowerCAmelCase )
lowercase = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
lowercase = get_size_dict(_lowerCAmelCase , param_name="""crop_size""" )
lowercase = do_resize
lowercase = size
lowercase = do_center_crop
lowercase = crop_size
lowercase = resample
lowercase = do_rescale
lowercase = rescale_factor
lowercase = do_normalize
lowercase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowercase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = PILImageResampling.BILINEAR , _lowerCAmelCase = None , **_lowerCAmelCase , ) -> np.ndarray:
'''simple docstring'''
lowercase = get_size_dict(_lowerCAmelCase , default_to_square=_lowerCAmelCase )
if "shortest_edge" in size:
lowercase = get_resize_output_image_size(_lowerCAmelCase , size["""shortest_edge"""] , default_to_square=_lowerCAmelCase )
elif "height" in size and "width" in size:
lowercase = (size["""height"""], size["""width"""])
else:
raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}""" )
return resize(_lowerCAmelCase , size=_lowerCAmelCase , resample=_lowerCAmelCase , data_format=_lowerCAmelCase , **_lowerCAmelCase )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = None , **_lowerCAmelCase , ) -> np.ndarray:
'''simple docstring'''
lowercase = get_size_dict(_lowerCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}""" )
return center_crop(_lowerCAmelCase , size=(size["""height"""], size["""width"""]) , data_format=_lowerCAmelCase , **_lowerCAmelCase )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = None , **_lowerCAmelCase , ) -> List[str]:
'''simple docstring'''
return rescale(_lowerCAmelCase , scale=_lowerCAmelCase , data_format=_lowerCAmelCase , **_lowerCAmelCase )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = None , **_lowerCAmelCase , ) -> np.ndarray:
'''simple docstring'''
return normalize(_lowerCAmelCase , mean=_lowerCAmelCase , std=_lowerCAmelCase , data_format=_lowerCAmelCase , **_lowerCAmelCase )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = ChannelDimension.FIRST , ) -> np.ndarray:
'''simple docstring'''
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_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(_lowerCAmelCase )
if do_resize:
lowercase = self.resize(image=_lowerCAmelCase , size=_lowerCAmelCase , resample=_lowerCAmelCase )
if do_center_crop:
lowercase = self.center_crop(_lowerCAmelCase , size=_lowerCAmelCase )
if do_rescale:
lowercase = self.rescale(image=_lowerCAmelCase , scale=_lowerCAmelCase )
if do_normalize:
lowercase = self.normalize(image=_lowerCAmelCase , mean=_lowerCAmelCase , std=_lowerCAmelCase )
lowercase = to_channel_dimension_format(_lowerCAmelCase , _lowerCAmelCase )
return image
def _a ( self , _lowerCAmelCase , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = ChannelDimension.FIRST , **_lowerCAmelCase , ) -> PIL.Image.Image:
'''simple docstring'''
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(_lowerCAmelCase , default_to_square=_lowerCAmelCase )
lowercase = crop_size if crop_size is not None else self.crop_size
lowercase = get_size_dict(_lowerCAmelCase , param_name="""crop_size""" )
if not valid_images(_lowerCAmelCase ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
lowercase = make_batched(_lowerCAmelCase )
lowercase = [
[
self._preprocess_image(
image=_lowerCAmelCase , do_resize=_lowerCAmelCase , size=_lowerCAmelCase , resample=_lowerCAmelCase , do_center_crop=_lowerCAmelCase , crop_size=_lowerCAmelCase , do_rescale=_lowerCAmelCase , rescale_factor=_lowerCAmelCase , do_normalize=_lowerCAmelCase , image_mean=_lowerCAmelCase , image_std=_lowerCAmelCase , data_format=_lowerCAmelCase , )
for img in video
]
for video in videos
]
lowercase = {"""pixel_values""": videos}
return BatchFeature(data=_lowerCAmelCase , tensor_type=_lowerCAmelCase )
| 653 |
'''simple docstring'''
from random import shuffle
import tensorflow as tf
from numpy import array
def SCREAMING_SNAKE_CASE ( lowercase_ : List[str] , lowercase_ : Optional[int] ):
lowercase = int(lowercase_ )
assert noofclusters < len(lowercase_ )
# Find out the dimensionality
lowercase = len(vectors[0] )
# Will help select random centroids from among the available vectors
lowercase = list(range(len(lowercase_ ) ) )
shuffle(lowercase_ )
# GRAPH OF COMPUTATION
# We initialize a new graph and set it as the default during each run
# of this algorithm. This ensures that as this function is called
# multiple times, the default graph doesn't keep getting crowded with
# unused ops and Variables from previous function calls.
lowercase = tf.Graph()
with graph.as_default():
# SESSION OF COMPUTATION
lowercase = tf.Session()
##CONSTRUCTING THE ELEMENTS OF COMPUTATION
##First lets ensure we have a Variable vector for each centroid,
##initialized to one of the vectors from the available data points
lowercase = [
tf.Variable(vectors[vector_indices[i]] ) for i in range(lowercase_ )
]
##These nodes will assign the centroid Variables the appropriate
##values
lowercase = tf.placeholder("""float64""" , [dim] )
lowercase = []
for centroid in centroids:
cent_assigns.append(tf.assign(lowercase_ , lowercase_ ) )
##Variables for cluster assignments of individual vectors(initialized
##to 0 at first)
lowercase = [tf.Variable(0 ) for i in range(len(lowercase_ ) )]
##These nodes will assign an assignment Variable the appropriate
##value
lowercase = tf.placeholder("""int32""" )
lowercase = []
for assignment in assignments:
cluster_assigns.append(tf.assign(lowercase_ , lowercase_ ) )
##Now lets construct the node that will compute the mean
# The placeholder for the input
lowercase = tf.placeholder("""float""" , [None, dim] )
# The Node/op takes the input and computes a mean along the 0th
# dimension, i.e. the list of input vectors
lowercase = tf.reduce_mean(lowercase_ , 0 )
##Node for computing Euclidean distances
# Placeholders for input
lowercase = tf.placeholder("""float""" , [dim] )
lowercase = tf.placeholder("""float""" , [dim] )
lowercase = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(lowercase_ , lowercase_ ) , 2 ) ) )
##This node will figure out which cluster to assign a vector to,
##based on Euclidean distances of the vector from the centroids.
# Placeholder for input
lowercase = tf.placeholder("""float""" , [noofclusters] )
lowercase = tf.argmin(lowercase_ , 0 )
##INITIALIZING STATE VARIABLES
##This will help initialization of all Variables defined with respect
##to the graph. The Variable-initializer should be defined after
##all the Variables have been constructed, so that each of them
##will be included in the initialization.
lowercase = tf.initialize_all_variables()
# Initialize all variables
sess.run(lowercase_ )
##CLUSTERING ITERATIONS
# Now perform the Expectation-Maximization steps of K-Means clustering
# iterations. To keep things simple, we will only do a set number of
# iterations, instead of using a Stopping Criterion.
lowercase = 100
for _ in range(lowercase_ ):
##EXPECTATION STEP
##Based on the centroid locations till last iteration, compute
##the _expected_ centroid assignments.
# Iterate over each vector
for vector_n in range(len(lowercase_ ) ):
lowercase = vectors[vector_n]
# Compute Euclidean distance between this vector and each
# centroid. Remember that this list cannot be named
#'centroid_distances', since that is the input to the
# cluster assignment node.
lowercase = [
sess.run(lowercase_ , feed_dict={va: vect, va: sess.run(lowercase_ )} )
for centroid in centroids
]
# Now use the cluster assignment node, with the distances
# as the input
lowercase = sess.run(
lowercase_ , feed_dict={centroid_distances: distances} )
# Now assign the value to the appropriate state variable
sess.run(
cluster_assigns[vector_n] , feed_dict={assignment_value: assignment} )
##MAXIMIZATION STEP
# Based on the expected state computed from the Expectation Step,
# compute the locations of the centroids so as to maximize the
# overall objective of minimizing within-cluster Sum-of-Squares
for cluster_n in range(lowercase_ ):
# Collect all the vectors assigned to this cluster
lowercase = [
vectors[i]
for i in range(len(lowercase_ ) )
if sess.run(assignments[i] ) == cluster_n
]
# Compute new centroid location
lowercase = sess.run(
lowercase_ , feed_dict={mean_input: array(lowercase_ )} )
# Assign value to appropriate variable
sess.run(
cent_assigns[cluster_n] , feed_dict={centroid_value: new_location} )
# Return centroids and assignments
lowercase = sess.run(lowercase_ )
lowercase = sess.run(lowercase_ )
return centroids, assignments
| 653 | 1 |
'''simple docstring'''
import sys
lowercase_ : int = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648950445244523161731856403098711121722383113'''
'''62229893423380308135336276614282806444486645238749'''
'''30358907296290491560440772390713810515859307960866'''
'''70172427121883998797908792274921901699720888093776'''
'''65727333001053367881220235421809751254540594752243'''
'''52584907711670556013604839586446706324415722155397'''
'''53697817977846174064955149290862569321978468622482'''
'''83972241375657056057490261407972968652414535100474'''
'''82166370484403199890008895243450658541227588666881'''
'''16427171479924442928230863465674813919123162824586'''
'''17866458359124566529476545682848912883142607690042'''
'''24219022671055626321111109370544217506941658960408'''
'''07198403850962455444362981230987879927244284909188'''
'''84580156166097919133875499200524063689912560717606'''
'''05886116467109405077541002256983155200055935729725'''
'''71636269561882670428252483600823257530420752963450'''
)
def SCREAMING_SNAKE_CASE ( lowercase_ : str = N ):
lowercase = -sys.maxsize - 1
for i in range(len(lowercase_ ) - 12 ):
lowercase = 1
for j in range(13 ):
product *= int(n[i + j] )
if product > largest_product:
lowercase = product
return largest_product
if __name__ == "__main__":
print(f'''{solution() = }''')
| 653 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : int , lowercase_ : list[list[int]] ):
def update_area_of_max_square(lowercase_ : int , lowercase_ : int ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
lowercase = update_area_of_max_square(lowercase_ , col + 1 )
lowercase = update_area_of_max_square(row + 1 , col + 1 )
lowercase = update_area_of_max_square(row + 1 , lowercase_ )
if mat[row][col]:
lowercase = 1 + min([right, diagonal, down] )
lowercase = max(largest_square_area[0] , lowercase_ )
return sub_problem_sol
else:
return 0
lowercase = [0]
update_area_of_max_square(0 , 0 )
return largest_square_area[0]
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : int , lowercase_ : list[list[int]] ):
def update_area_of_max_square_using_dp_array(
lowercase_ : int , lowercase_ : int , lowercase_ : list[list[int]] ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
lowercase = update_area_of_max_square_using_dp_array(lowercase_ , col + 1 , lowercase_ )
lowercase = update_area_of_max_square_using_dp_array(row + 1 , col + 1 , lowercase_ )
lowercase = update_area_of_max_square_using_dp_array(row + 1 , lowercase_ , lowercase_ )
if mat[row][col]:
lowercase = 1 + min([right, diagonal, down] )
lowercase = max(largest_square_area[0] , lowercase_ )
lowercase = sub_problem_sol
return sub_problem_sol
else:
return 0
lowercase = [0]
lowercase = [[-1] * cols for _ in range(lowercase_ )]
update_area_of_max_square_using_dp_array(0 , 0 , lowercase_ )
return largest_square_area[0]
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : int , lowercase_ : list[list[int]] ):
lowercase = [[0] * (cols + 1) for _ in range(rows + 1 )]
lowercase = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
lowercase = dp_array[row][col + 1]
lowercase = dp_array[row + 1][col + 1]
lowercase = dp_array[row + 1][col]
if mat[row][col] == 1:
lowercase = 1 + min(lowercase_ , lowercase_ , lowercase_ )
lowercase = max(dp_array[row][col] , lowercase_ )
else:
lowercase = 0
return largest_square_area
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : int , lowercase_ : list[list[int]] ):
lowercase = [0] * (cols + 1)
lowercase = [0] * (cols + 1)
lowercase = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
lowercase = current_row[col + 1]
lowercase = next_row[col + 1]
lowercase = next_row[col]
if mat[row][col] == 1:
lowercase = 1 + min(lowercase_ , lowercase_ , lowercase_ )
lowercase = max(current_row[col] , lowercase_ )
else:
lowercase = 0
lowercase = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 653 | 1 |
'''simple docstring'''
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertModel,
)
class __UpperCamelCase :
def __init__( self , _lowerCAmelCase , _lowerCAmelCase=13 , _lowerCAmelCase=7 , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=99 , _lowerCAmelCase=32 , _lowerCAmelCase=2 , _lowerCAmelCase=4 , _lowerCAmelCase=37 , _lowerCAmelCase="gelu" , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=512 , _lowerCAmelCase=16 , _lowerCAmelCase=2 , _lowerCAmelCase=0.02 , _lowerCAmelCase=3 , _lowerCAmelCase=4 , _lowerCAmelCase=None , ) -> int:
'''simple docstring'''
lowercase = parent
lowercase = 13
lowercase = 7
lowercase = True
lowercase = True
lowercase = True
lowercase = True
lowercase = 99
lowercase = 384
lowercase = 2
lowercase = 4
lowercase = 37
lowercase = """gelu"""
lowercase = 0.1
lowercase = 0.1
lowercase = 512
lowercase = 16
lowercase = 2
lowercase = 0.02
lowercase = 3
lowercase = 4
lowercase = 128
lowercase = 2
lowercase = 9
lowercase = 1
lowercase = None
def _a ( self ) -> Any:
'''simple docstring'''
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase = None
if self.use_input_mask:
lowercase = random_attention_mask([self.batch_size, self.seq_length] )
lowercase = None
if self.use_token_type_ids:
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase = None
lowercase = None
lowercase = None
if self.use_labels:
lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase = ids_tensor([self.batch_size] , self.num_choices )
lowercase = ConvBertConfig(
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 , initializer_range=self.initializer_range , return_dict=_lowerCAmelCase , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowercase = TFConvBertModel(config=_lowerCAmelCase )
lowercase = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
lowercase = [input_ids, input_mask]
lowercase = model(_lowerCAmelCase )
lowercase = model(_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Dict:
'''simple docstring'''
lowercase = TFConvBertForMaskedLM(config=_lowerCAmelCase )
lowercase = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
lowercase = model(_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Dict:
'''simple docstring'''
lowercase = self.num_labels
lowercase = TFConvBertForSequenceClassification(config=_lowerCAmelCase )
lowercase = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
lowercase = model(_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> int:
'''simple docstring'''
lowercase = self.num_choices
lowercase = TFConvBertForMultipleChoice(config=_lowerCAmelCase )
lowercase = tf.tile(tf.expand_dims(_lowerCAmelCase , 1 ) , (1, self.num_choices, 1) )
lowercase = tf.tile(tf.expand_dims(_lowerCAmelCase , 1 ) , (1, self.num_choices, 1) )
lowercase = tf.tile(tf.expand_dims(_lowerCAmelCase , 1 ) , (1, self.num_choices, 1) )
lowercase = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
"""token_type_ids""": multiple_choice_token_type_ids,
}
lowercase = model(_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Dict:
'''simple docstring'''
lowercase = self.num_labels
lowercase = TFConvBertForTokenClassification(config=_lowerCAmelCase )
lowercase = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
lowercase = model(_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> List[str]:
'''simple docstring'''
lowercase = TFConvBertForQuestionAnswering(config=_lowerCAmelCase )
lowercase = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
lowercase = model(_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 _a ( self ) -> Tuple:
'''simple docstring'''
lowercase = self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) = config_and_inputs
lowercase = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class __UpperCamelCase (_UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
__A = (
(
TFConvBertModel,
TFConvBertForMaskedLM,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertForMultipleChoice,
)
if is_tf_available()
else ()
)
__A = (
{
'''feature-extraction''': TFConvBertModel,
'''fill-mask''': TFConvBertForMaskedLM,
'''question-answering''': TFConvBertForQuestionAnswering,
'''text-classification''': TFConvBertForSequenceClassification,
'''token-classification''': TFConvBertForTokenClassification,
'''zero-shot''': TFConvBertForSequenceClassification,
}
if is_tf_available()
else {}
)
__A = False
__A = False
__A = False
def _a ( self ) -> int:
'''simple docstring'''
lowercase = TFConvBertModelTester(self )
lowercase = ConfigTester(self , config_class=_lowerCAmelCase , hidden_size=37 )
def _a ( self ) -> int:
'''simple docstring'''
self.config_tester.run_common_tests()
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
def _a ( self ) -> Any:
'''simple docstring'''
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_lowerCAmelCase )
def _a ( self ) -> List[Any]:
'''simple docstring'''
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*_lowerCAmelCase )
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_lowerCAmelCase )
def _a ( self ) -> int:
'''simple docstring'''
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_lowerCAmelCase )
def _a ( self ) -> Dict:
'''simple docstring'''
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_lowerCAmelCase )
@slow
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = True
lowercase = True
if hasattr(_lowerCAmelCase , """use_cache""" ):
lowercase = True
lowercase = getattr(self.model_tester , """encoder_seq_length""" , self.model_tester.seq_length )
lowercase = getattr(self.model_tester , """key_length""" , _lowerCAmelCase )
for model_class in self.all_model_classes:
lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
lowercase = model_class(_lowerCAmelCase )
lowercase = len(model(_lowerCAmelCase ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_lowerCAmelCase , saved_model=_lowerCAmelCase )
lowercase = os.path.join(_lowerCAmelCase , """saved_model""" , """1""" )
lowercase = tf.keras.models.load_model(_lowerCAmelCase )
lowercase = model(_lowerCAmelCase )
if self.is_encoder_decoder:
lowercase = outputs["""encoder_hidden_states"""]
lowercase = outputs["""encoder_attentions"""]
else:
lowercase = outputs["""hidden_states"""]
lowercase = outputs["""attentions"""]
self.assertEqual(len(_lowerCAmelCase ) , _lowerCAmelCase )
lowercase = getattr(
self.model_tester , """expected_num_hidden_layers""" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(_lowerCAmelCase ) , _lowerCAmelCase )
self.assertListEqual(
list(output_hidden_states[0].shape[-2:] ) , [self.model_tester.seq_length, self.model_tester.hidden_size] , )
self.assertEqual(len(_lowerCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(output_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
@slow
def _a ( self ) -> Dict:
'''simple docstring'''
lowercase = TFConvBertModel.from_pretrained("""YituTech/conv-bert-base""" )
self.assertIsNotNone(_lowerCAmelCase )
def _a ( self ) -> Optional[int]:
'''simple docstring'''
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = True
lowercase = getattr(self.model_tester , """decoder_seq_length""" , self.model_tester.seq_length )
lowercase = getattr(self.model_tester , """encoder_seq_length""" , self.model_tester.seq_length )
lowercase = getattr(self.model_tester , """key_length""" , _lowerCAmelCase )
lowercase = getattr(self.model_tester , """key_length""" , _lowerCAmelCase )
def check_decoder_attentions_output(_lowerCAmelCase ):
lowercase = len(_lowerCAmelCase )
self.assertEqual(out_len % 2 , 0 )
lowercase = outputs.decoder_attentions
self.assertEqual(len(_lowerCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length] , )
def check_encoder_attentions_output(_lowerCAmelCase ):
lowercase = [
t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
]
self.assertEqual(len(_lowerCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
for model_class in self.all_model_classes:
lowercase = True
lowercase = False
lowercase = model_class(_lowerCAmelCase )
lowercase = model(self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
lowercase = len(_lowerCAmelCase )
self.assertEqual(config.output_hidden_states , _lowerCAmelCase )
check_encoder_attentions_output(_lowerCAmelCase )
if self.is_encoder_decoder:
lowercase = model_class(_lowerCAmelCase )
lowercase = model(self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
self.assertEqual(config.output_hidden_states , _lowerCAmelCase )
check_decoder_attentions_output(_lowerCAmelCase )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
lowercase = True
lowercase = model_class(_lowerCAmelCase )
lowercase = model(self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
self.assertEqual(config.output_hidden_states , _lowerCAmelCase )
check_encoder_attentions_output(_lowerCAmelCase )
# Check attention is always last and order is fine
lowercase = True
lowercase = True
lowercase = model_class(_lowerCAmelCase )
lowercase = model(self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(_lowerCAmelCase ) )
self.assertEqual(model.config.output_hidden_states , _lowerCAmelCase )
check_encoder_attentions_output(_lowerCAmelCase )
@require_tf
class __UpperCamelCase (unittest.TestCase ):
@slow
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase = TFConvBertModel.from_pretrained("""YituTech/conv-bert-base""" )
lowercase = tf.constant([[0, 1, 2, 3, 4, 5]] )
lowercase = model(_lowerCAmelCase )[0]
lowercase = [1, 6, 768]
self.assertEqual(output.shape , _lowerCAmelCase )
lowercase = tf.constant(
[
[
[-0.0347_5493, -0.468_6034, -0.3063_8832],
[0.2263_7248, -0.2698_8646, -0.742_3424],
[0.1032_4868, -0.4501_3508, -0.5828_0784],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , _lowerCAmelCase , atol=1E-4 )
| 653 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : Optional[Any] = logging.get_logger(__name__)
lowercase_ : int = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}
class __UpperCamelCase (_UpperCAmelCase ):
__A = '''gpt_bigcode'''
__A = ['''past_key_values''']
__A = {
'''hidden_size''': '''n_embd''',
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self , _lowerCAmelCase=5_0257 , _lowerCAmelCase=1024 , _lowerCAmelCase=768 , _lowerCAmelCase=12 , _lowerCAmelCase=12 , _lowerCAmelCase=None , _lowerCAmelCase="gelu_pytorch_tanh" , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=1E-5 , _lowerCAmelCase=0.02 , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=5_0256 , _lowerCAmelCase=5_0256 , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=True , **_lowerCAmelCase , ) -> Optional[int]:
'''simple docstring'''
lowercase = vocab_size
lowercase = n_positions
lowercase = n_embd
lowercase = n_layer
lowercase = n_head
lowercase = n_inner
lowercase = activation_function
lowercase = resid_pdrop
lowercase = embd_pdrop
lowercase = attn_pdrop
lowercase = layer_norm_epsilon
lowercase = initializer_range
lowercase = scale_attn_weights
lowercase = use_cache
lowercase = attention_softmax_in_fpaa
lowercase = scale_attention_softmax_in_fpaa
lowercase = multi_query
lowercase = bos_token_id
lowercase = eos_token_id
super().__init__(bos_token_id=_lowerCAmelCase , eos_token_id=_lowerCAmelCase , **_lowerCAmelCase )
| 653 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : Any = logging.get_logger(__name__)
lowercase_ : str = {
'''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json''',
}
class __UpperCamelCase (_UpperCAmelCase ):
__A = '''lxmert'''
__A = {}
def __init__( self , _lowerCAmelCase=3_0522 , _lowerCAmelCase=768 , _lowerCAmelCase=12 , _lowerCAmelCase=9500 , _lowerCAmelCase=1600 , _lowerCAmelCase=400 , _lowerCAmelCase=3072 , _lowerCAmelCase="gelu" , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=512 , _lowerCAmelCase=2 , _lowerCAmelCase=0.02 , _lowerCAmelCase=1E-12 , _lowerCAmelCase=9 , _lowerCAmelCase=5 , _lowerCAmelCase=5 , _lowerCAmelCase=2048 , _lowerCAmelCase=4 , _lowerCAmelCase=6.67 , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=True , **_lowerCAmelCase , ) -> Any:
'''simple docstring'''
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_attention_heads
lowercase = hidden_act
lowercase = intermediate_size
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = type_vocab_size
lowercase = initializer_range
lowercase = layer_norm_eps
lowercase = num_qa_labels
lowercase = num_object_labels
lowercase = num_attr_labels
lowercase = l_layers
lowercase = x_layers
lowercase = r_layers
lowercase = visual_feat_dim
lowercase = visual_pos_dim
lowercase = visual_loss_normalizer
lowercase = task_matched
lowercase = task_mask_lm
lowercase = task_obj_predict
lowercase = task_qa
lowercase = visual_obj_loss
lowercase = visual_attr_loss
lowercase = visual_feat_loss
lowercase = {"""vision""": r_layers, """cross_encoder""": x_layers, """language""": l_layers}
super().__init__(**_lowerCAmelCase )
| 653 |
'''simple docstring'''
import requests
def SCREAMING_SNAKE_CASE ( lowercase_ : str , lowercase_ : str ):
lowercase = {"""Content-Type""": """application/json"""}
lowercase = requests.post(lowercase_ , json={"""text""": message_body} , headers=lowercase_ )
if response.status_code != 200:
lowercase = (
"""Request to slack returned an error """
F"""{response.status_code}, the response is:\n{response.text}"""
)
raise ValueError(lowercase_ )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message('''<YOUR MESSAGE BODY>''', '''<SLACK CHANNEL URL>''')
| 653 | 1 |
'''simple docstring'''
import collections
import importlib.util
import os
import re
from pathlib import Path
lowercase_ : Union[str, Any] = '''src/transformers'''
# Matches is_xxx_available()
lowercase_ : Tuple = re.compile(r'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
lowercase_ : str = re.compile(r'''^_import_structure\s+=\s+\{([^\}]+)\}''')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
lowercase_ : List[Any] = re.compile(r'''\s+"\S*":\s+\[([^\]]*)\]''')
# Catches a line if not is_foo_available
lowercase_ : Dict = re.compile(r'''^\s*if\s+not\s+is\_[a-z_]*\_available\(\)''')
# Catches a line _import_struct["bla"].append("foo")
lowercase_ : Dict = re.compile(r'''^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)''')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
lowercase_ : str = re.compile(r'''^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]''')
# Catches a line with an object between quotes and a comma: "MyModel",
lowercase_ : List[Any] = re.compile('''^\s+"([^"]+)",''')
# Catches a line with objects between brackets only: ["foo", "bar"],
lowercase_ : Tuple = re.compile('''^\s+\[([^\]]+)\]''')
# Catches a line with from foo import bar, bla, boo
lowercase_ : Tuple = re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
# Catches a line with try:
lowercase_ : str = re.compile(r'''^\s*try:''')
# Catches a line with else:
lowercase_ : str = re.compile(r'''^\s*else:''')
def SCREAMING_SNAKE_CASE ( lowercase_ : str ):
if _re_test_backend.search(lowercase_ ) is None:
return None
lowercase = [b[0] for b in _re_backend.findall(lowercase_ )]
backends.sort()
return "_and_".join(lowercase_ )
def SCREAMING_SNAKE_CASE ( lowercase_ : int ):
with open(lowercase_ , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowercase = f.readlines()
lowercase = 0
while line_index < len(lowercase_ ) and not lines[line_index].startswith("""_import_structure = {""" ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(lowercase_ ):
return None
# First grab the objects without a specific backend in _import_structure
lowercase = []
while not lines[line_index].startswith("""if TYPE_CHECKING""" ) and find_backend(lines[line_index] ) is None:
lowercase = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(lowercase_ ):
lowercase = _re_one_line_import_struct.search(lowercase_ ).groups()[0]
lowercase = re.findall("""\[([^\]]+)\]""" , lowercase_ )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(""", """ )] )
line_index += 1
continue
lowercase = _re_import_struct_key_value.search(lowercase_ )
if single_line_import_search is not None:
lowercase = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(""", """ ) if len(lowercase_ ) > 0]
objects.extend(lowercase_ )
elif line.startswith(""" """ * 8 + """\"""" ):
objects.append(line[9:-3] )
line_index += 1
lowercase = {"""none""": objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith("""if TYPE_CHECKING""" ):
# If the line is an if not is_backend_available, we grab all objects associated.
lowercase = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
lowercase = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
lowercase = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(""" """ * 4 ):
lowercase = lines[line_index]
if _re_import_struct_add_one.search(lowercase_ ) is not None:
objects.append(_re_import_struct_add_one.search(lowercase_ ).groups()[0] )
elif _re_import_struct_add_many.search(lowercase_ ) is not None:
lowercase = _re_import_struct_add_many.search(lowercase_ ).groups()[0].split(""", """ )
lowercase = [obj[1:-1] for obj in imports if len(lowercase_ ) > 0]
objects.extend(lowercase_ )
elif _re_between_brackets.search(lowercase_ ) is not None:
lowercase = _re_between_brackets.search(lowercase_ ).groups()[0].split(""", """ )
lowercase = [obj[1:-1] for obj in imports if len(lowercase_ ) > 0]
objects.extend(lowercase_ )
elif _re_quote_object.search(lowercase_ ) is not None:
objects.append(_re_quote_object.search(lowercase_ ).groups()[0] )
elif line.startswith(""" """ * 8 + """\"""" ):
objects.append(line[9:-3] )
elif line.startswith(""" """ * 12 + """\"""" ):
objects.append(line[13:-3] )
line_index += 1
lowercase = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
lowercase = []
while (
line_index < len(lowercase_ )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith("""else""" )
):
lowercase = lines[line_index]
lowercase = _re_import.search(lowercase_ )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(""", """ ) )
elif line.startswith(""" """ * 8 ):
objects.append(line[8:-2] )
line_index += 1
lowercase = {"""none""": objects}
# Let's continue with backend-specific objects
while line_index < len(lowercase_ ):
# If the line is an if is_backend_available, we grab all objects associated.
lowercase = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
lowercase = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
lowercase = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(""" """ * 8 ):
lowercase = lines[line_index]
lowercase = _re_import.search(lowercase_ )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(""", """ ) )
elif line.startswith(""" """ * 12 ):
objects.append(line[12:-2] )
line_index += 1
lowercase = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def SCREAMING_SNAKE_CASE ( lowercase_ : List[Any] , lowercase_ : str ):
def find_duplicates(lowercase_ : List[str] ):
return [k for k, v in collections.Counter(lowercase_ ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
lowercase = []
for key in import_dict_objects.keys():
lowercase = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F"""Duplicate _import_structure definitions for: {duplicate_imports}""" )
lowercase = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(F"""Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}""" )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
lowercase = """base imports""" if key == """none""" else F"""{key} backend"""
errors.append(F"""Differences for {name}:""" )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(F""" {a} in TYPE_HINT but not in _import_structure.""" )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(F""" {a} in _import_structure but not in TYPE_HINT.""" )
return errors
def SCREAMING_SNAKE_CASE ( ):
lowercase = []
for root, _, files in os.walk(lowercase_ ):
if "__init__.py" in files:
lowercase = os.path.join(lowercase_ , """__init__.py""" )
lowercase = parse_init(lowercase_ )
if objects is not None:
lowercase = analyze_results(*lowercase_ )
if len(lowercase_ ) > 0:
lowercase = F"""Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"""
failures.append("""\n""".join(lowercase_ ) )
if len(lowercase_ ) > 0:
raise ValueError("""\n\n""".join(lowercase_ ) )
def SCREAMING_SNAKE_CASE ( ):
lowercase = []
for path, directories, files in os.walk(lowercase_ ):
for folder in directories:
# Ignore private modules
if folder.startswith("""_""" ):
directories.remove(lowercase_ )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(lowercase_ ) / folder).glob("""*.py""" ) ) ) == 0:
continue
lowercase = str((Path(lowercase_ ) / folder).relative_to(lowercase_ ) )
lowercase = short_path.replace(os.path.sep , """.""" )
submodules.append(lowercase_ )
for fname in files:
if fname == "__init__.py":
continue
lowercase = str((Path(lowercase_ ) / fname).relative_to(lowercase_ ) )
lowercase = short_path.replace(""".py""" , """""" ).replace(os.path.sep , """.""" )
if len(submodule.split(""".""" ) ) == 1:
submodules.append(lowercase_ )
return submodules
lowercase_ : Tuple = [
'''convert_pytorch_checkpoint_to_tf2''',
'''modeling_flax_pytorch_utils''',
]
def SCREAMING_SNAKE_CASE ( ):
# This is to make sure the transformers module imported is the one in the repo.
lowercase = importlib.util.spec_from_file_location(
"""transformers""" , os.path.join(lowercase_ , """__init__.py""" ) , submodule_search_locations=[PATH_TO_TRANSFORMERS] , )
lowercase = spec.loader.load_module()
lowercase = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
]
if len(lowercase_ ) > 0:
lowercase = """\n""".join(F"""- {module}""" for module in module_not_registered )
raise ValueError(
"""The following submodules are not properly registered in the main init of Transformers:\n"""
F"""{list_of_modules}\n"""
"""Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.""" )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 653 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
lowercase_ : List[str] = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE ( lowercase_ : int ):
lowercase = MobileViTConfig()
# size of the architecture
if "mobilevit_s" in mobilevit_name:
lowercase = [144, 192, 240]
lowercase = [16, 32, 64, 96, 128, 160, 640]
elif "mobilevit_xs" in mobilevit_name:
lowercase = [96, 120, 144]
lowercase = [16, 32, 48, 64, 80, 96, 384]
elif "mobilevit_xxs" in mobilevit_name:
lowercase = [64, 80, 96]
lowercase = [16, 16, 24, 48, 64, 80, 320]
lowercase = 0.05
lowercase = 2.0
if mobilevit_name.startswith("""deeplabv3_""" ):
lowercase = 512
lowercase = 16
lowercase = 21
lowercase = """pascal-voc-id2label.json"""
else:
lowercase = 1000
lowercase = """imagenet-1k-id2label.json"""
lowercase = """huggingface/label-files"""
lowercase = json.load(open(hf_hub_download(lowercase_ , lowercase_ , repo_type="""dataset""" ) , """r""" ) )
lowercase = {int(lowercase_ ): v for k, v in idalabel.items()}
lowercase = idalabel
lowercase = {v: k for k, v in idalabel.items()}
return config
def SCREAMING_SNAKE_CASE ( lowercase_ : Any , lowercase_ : Any=False ):
for i in range(1 , 6 ):
if F"""layer_{i}.""" in name:
lowercase = name.replace(F"""layer_{i}.""" , F"""encoder.layer.{i - 1}.""" )
if "conv_1." in name:
lowercase = name.replace("""conv_1.""" , """conv_stem.""" )
if ".block." in name:
lowercase = name.replace(""".block.""" , """.""" )
if "exp_1x1" in name:
lowercase = name.replace("""exp_1x1""" , """expand_1x1""" )
if "red_1x1" in name:
lowercase = name.replace("""red_1x1""" , """reduce_1x1""" )
if ".local_rep.conv_3x3." in name:
lowercase = name.replace(""".local_rep.conv_3x3.""" , """.conv_kxk.""" )
if ".local_rep.conv_1x1." in name:
lowercase = name.replace(""".local_rep.conv_1x1.""" , """.conv_1x1.""" )
if ".norm." in name:
lowercase = name.replace(""".norm.""" , """.normalization.""" )
if ".conv." in name:
lowercase = name.replace(""".conv.""" , """.convolution.""" )
if ".conv_proj." in name:
lowercase = name.replace(""".conv_proj.""" , """.conv_projection.""" )
for i in range(0 , 2 ):
for j in range(0 , 4 ):
if F""".{i}.{j}.""" in name:
lowercase = name.replace(F""".{i}.{j}.""" , F""".{i}.layer.{j}.""" )
for i in range(2 , 6 ):
for j in range(0 , 4 ):
if F""".{i}.{j}.""" in name:
lowercase = name.replace(F""".{i}.{j}.""" , F""".{i}.""" )
if "expand_1x1" in name:
lowercase = name.replace("""expand_1x1""" , """downsampling_layer.expand_1x1""" )
if "conv_3x3" in name:
lowercase = name.replace("""conv_3x3""" , """downsampling_layer.conv_3x3""" )
if "reduce_1x1" in name:
lowercase = name.replace("""reduce_1x1""" , """downsampling_layer.reduce_1x1""" )
for i in range(2 , 5 ):
if F""".global_rep.{i}.weight""" in name:
lowercase = name.replace(F""".global_rep.{i}.weight""" , """.layernorm.weight""" )
if F""".global_rep.{i}.bias""" in name:
lowercase = name.replace(F""".global_rep.{i}.bias""" , """.layernorm.bias""" )
if ".global_rep." in name:
lowercase = name.replace(""".global_rep.""" , """.transformer.""" )
if ".pre_norm_mha.0." in name:
lowercase = name.replace(""".pre_norm_mha.0.""" , """.layernorm_before.""" )
if ".pre_norm_mha.1.out_proj." in name:
lowercase = name.replace(""".pre_norm_mha.1.out_proj.""" , """.attention.output.dense.""" )
if ".pre_norm_ffn.0." in name:
lowercase = name.replace(""".pre_norm_ffn.0.""" , """.layernorm_after.""" )
if ".pre_norm_ffn.1." in name:
lowercase = name.replace(""".pre_norm_ffn.1.""" , """.intermediate.dense.""" )
if ".pre_norm_ffn.4." in name:
lowercase = name.replace(""".pre_norm_ffn.4.""" , """.output.dense.""" )
if ".transformer." in name:
lowercase = name.replace(""".transformer.""" , """.transformer.layer.""" )
if ".aspp_layer." in name:
lowercase = name.replace(""".aspp_layer.""" , """.""" )
if ".aspp_pool." in name:
lowercase = name.replace(""".aspp_pool.""" , """.""" )
if "seg_head." in name:
lowercase = name.replace("""seg_head.""" , """segmentation_head.""" )
if "segmentation_head.classifier.classifier." in name:
lowercase = name.replace("""segmentation_head.classifier.classifier.""" , """segmentation_head.classifier.""" )
if "classifier.fc." in name:
lowercase = name.replace("""classifier.fc.""" , """classifier.""" )
elif (not base_model) and ("segmentation_head." not in name):
lowercase = """mobilevit.""" + name
return name
def SCREAMING_SNAKE_CASE ( lowercase_ : Optional[Any] , lowercase_ : List[Any] , lowercase_ : str=False ):
if base_model:
lowercase = """"""
else:
lowercase = """mobilevit."""
for key in orig_state_dict.copy().keys():
lowercase = orig_state_dict.pop(lowercase_ )
if key[:8] == "encoder.":
lowercase = key[8:]
if "qkv" in key:
lowercase = key.split(""".""" )
lowercase = int(key_split[0][6:] ) - 1
lowercase = int(key_split[3] )
lowercase = model.get_submodule(F"""{model_prefix}encoder.layer.{layer_num}""" )
lowercase = layer.transformer.layer[transformer_num].attention.attention.all_head_size
lowercase = (
F"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
)
if "weight" in key:
lowercase = val[:dim, :]
lowercase = val[dim : dim * 2, :]
lowercase = val[-dim:, :]
else:
lowercase = val[:dim]
lowercase = val[dim : dim * 2]
lowercase = val[-dim:]
else:
lowercase = val
return orig_state_dict
def SCREAMING_SNAKE_CASE ( ):
lowercase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowercase = Image.open(requests.get(lowercase_ , stream=lowercase_ ).raw )
return im
@torch.no_grad()
def SCREAMING_SNAKE_CASE ( lowercase_ : Dict , lowercase_ : List[Any] , lowercase_ : Any , lowercase_ : List[str]=False ):
lowercase = get_mobilevit_config(lowercase_ )
# load original state_dict
lowercase = torch.load(lowercase_ , map_location="""cpu""" )
# load 🤗 model
if mobilevit_name.startswith("""deeplabv3_""" ):
lowercase = MobileViTForSemanticSegmentation(lowercase_ ).eval()
else:
lowercase = MobileViTForImageClassification(lowercase_ ).eval()
lowercase = convert_state_dict(lowercase_ , lowercase_ )
model.load_state_dict(lowercase_ )
# Check outputs on an image, prepared by MobileViTImageProcessor
lowercase = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
lowercase = image_processor(images=prepare_img() , return_tensors="""pt""" )
lowercase = model(**lowercase_ )
lowercase = outputs.logits
if mobilevit_name.startswith("""deeplabv3_""" ):
assert logits.shape == (1, 21, 32, 32)
if mobilevit_name == "deeplabv3_mobilevit_s":
lowercase = torch.tensor(
[
[[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
[[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
[[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xs":
lowercase = torch.tensor(
[
[[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
[[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
[[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xxs":
lowercase = torch.tensor(
[
[[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
[[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
[[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
] )
else:
raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )
assert torch.allclose(logits[0, :3, :3, :3] , lowercase_ , atol=1E-4 )
else:
assert logits.shape == (1, 1000)
if mobilevit_name == "mobilevit_s":
lowercase = torch.tensor([-0.9_866, 0.2_392, -1.1_241] )
elif mobilevit_name == "mobilevit_xs":
lowercase = torch.tensor([-2.4_761, -0.9_399, -1.9_587] )
elif mobilevit_name == "mobilevit_xxs":
lowercase = torch.tensor([-1.9_364, -1.2_327, -0.4_653] )
else:
raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )
assert torch.allclose(logits[0, :3] , lowercase_ , atol=1E-4 )
Path(lowercase_ ).mkdir(exist_ok=lowercase_ )
print(F"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(lowercase_ )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(lowercase_ )
if push_to_hub:
lowercase = {
"""mobilevit_s""": """mobilevit-small""",
"""mobilevit_xs""": """mobilevit-x-small""",
"""mobilevit_xxs""": """mobilevit-xx-small""",
"""deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
"""deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
"""deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
}
print("""Pushing to the hub...""" )
lowercase = model_mapping[mobilevit_name]
image_processor.push_to_hub(lowercase_ , organization="""apple""" )
model.push_to_hub(lowercase_ , organization="""apple""" )
if __name__ == "__main__":
lowercase_ : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--mobilevit_name''',
default='''mobilevit_s''',
type=str,
help=(
'''Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','''
''' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'''
),
)
parser.add_argument(
'''--checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
lowercase_ : List[str] = parser.parse_args()
convert_movilevit_checkpoint(
args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 653 | 1 |
'''simple docstring'''
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
lowercase_ : List[str] = logging.getLogger(__name__)
lowercase_ : Optional[Any] = tf.data.AUTOTUNE
def SCREAMING_SNAKE_CASE ( ):
lowercase = argparse.ArgumentParser(description="""Train a masked language model on TPU.""" )
parser.add_argument(
"""--pretrained_model_config""" , type=lowercase_ , default="""roberta-base""" , help="""The model config to use. Note that we don't copy the model's weights, only the config!""" , )
parser.add_argument(
"""--tokenizer""" , type=lowercase_ , default="""unigram-tokenizer-wikitext""" , help="""The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model's vocab size.""" , )
parser.add_argument(
"""--per_replica_batch_size""" , type=lowercase_ , default=8 , help="""Batch size per TPU core.""" , )
parser.add_argument(
"""--no_tpu""" , action="""store_true""" , help="""If set, run on CPU and don't try to initialize a TPU. Useful for debugging on non-TPU instances.""" , )
parser.add_argument(
"""--tpu_name""" , type=lowercase_ , help="""Name of TPU resource to initialize. Should be blank on Colab, and 'local' on TPU VMs.""" , default="""local""" , )
parser.add_argument(
"""--tpu_zone""" , type=lowercase_ , help="""Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.""" , )
parser.add_argument(
"""--gcp_project""" , type=lowercase_ , help="""Google cloud project name. Only used for non-Colab TPU nodes.""" )
parser.add_argument(
"""--bfloat16""" , action="""store_true""" , help="""Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.""" , )
parser.add_argument(
"""--train_dataset""" , type=lowercase_ , help="""Path to training dataset to load. If the path begins with `gs://`"""
""" then the dataset will be loaded from a Google Cloud Storage bucket.""" , )
parser.add_argument(
"""--shuffle_buffer_size""" , type=lowercase_ , default=2**18 , help="""Size of the shuffle buffer (in samples)""" , )
parser.add_argument(
"""--eval_dataset""" , type=lowercase_ , help="""Path to evaluation dataset to load. If the path begins with `gs://`"""
""" then the dataset will be loaded from a Google Cloud Storage bucket.""" , )
parser.add_argument(
"""--num_epochs""" , type=lowercase_ , default=1 , help="""Number of epochs to train for.""" , )
parser.add_argument(
"""--learning_rate""" , type=lowercase_ , default=1E-4 , help="""Learning rate to use for training.""" , )
parser.add_argument(
"""--weight_decay_rate""" , type=lowercase_ , default=1E-3 , help="""Weight decay rate to use for training.""" , )
parser.add_argument(
"""--max_length""" , type=lowercase_ , default=512 , help="""Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py""" , )
parser.add_argument(
"""--mlm_probability""" , type=lowercase_ , default=0.15 , help="""Fraction of tokens to mask during training.""" , )
parser.add_argument("""--output_dir""" , type=lowercase_ , required=lowercase_ , help="""Path to save model checkpoints to.""" )
parser.add_argument("""--hub_model_id""" , type=lowercase_ , help="""Model ID to upload to on the Hugging Face Hub.""" )
lowercase = parser.parse_args()
return args
def SCREAMING_SNAKE_CASE ( lowercase_ : Dict ):
try:
if args.tpu_name:
lowercase = tf.distribute.cluster_resolver.TPUClusterResolver(
args.tpu_name , zone=args.tpu_zone , project=args.gcp_project )
else:
lowercase = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
raise RuntimeError(
"""Couldn't connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or """
"""--gcp_project. When running on a TPU VM, use --tpu_name local.""" )
tf.config.experimental_connect_to_cluster(lowercase_ )
tf.tpu.experimental.initialize_tpu_system(lowercase_ )
return tpu
def SCREAMING_SNAKE_CASE ( lowercase_ : Optional[Any] ):
lowercase = 0
for file in file_list:
lowercase = file.split("""/""" )[-1]
lowercase = re.search(R"""-\d+-(\d+)\.tfrecord""" , lowercase_ ).group(1 )
lowercase = int(lowercase_ )
num_samples += sample_count
return num_samples
def SCREAMING_SNAKE_CASE ( lowercase_ : Union[str, Any] , lowercase_ : Dict , lowercase_ : Optional[Any] , lowercase_ : Any , lowercase_ : Union[str, Any] , lowercase_ : Dict=None ):
lowercase = count_samples(lowercase_ )
lowercase = tf.data.Dataset.from_tensor_slices(lowercase_ )
if shuffle:
lowercase = dataset.shuffle(len(lowercase_ ) )
lowercase = tf.data.TFRecordDataset(lowercase_ , num_parallel_reads=lowercase_ )
# TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
lowercase = dataset.apply(tf.data.experimental.assert_cardinality(lowercase_ ) )
lowercase = dataset.map(lowercase_ , num_parallel_calls=lowercase_ )
if shuffle:
assert shuffle_buffer_size is not None
lowercase = dataset.shuffle(args.shuffle_buffer_size )
lowercase = dataset.batch(lowercase_ , drop_remainder=lowercase_ )
lowercase = dataset.map(lowercase_ , num_parallel_calls=lowercase_ )
lowercase = dataset.prefetch(lowercase_ )
return dataset
def SCREAMING_SNAKE_CASE ( lowercase_ : Optional[int] ):
if not args.no_tpu:
lowercase = initialize_tpu(lowercase_ )
lowercase = tf.distribute.TPUStrategy(lowercase_ )
else:
lowercase = tf.distribute.OneDeviceStrategy(device="""/gpu:0""" )
if args.bfloataa:
tf.keras.mixed_precision.set_global_policy("""mixed_bfloat16""" )
lowercase = AutoTokenizer.from_pretrained(args.tokenizer )
lowercase = AutoConfig.from_pretrained(args.pretrained_model_config )
lowercase = tokenizer.vocab_size
lowercase = tf.io.gfile.glob(os.path.join(args.train_dataset , """*.tfrecord""" ) )
if not training_records:
raise ValueError(F"""No .tfrecord files found in {args.train_dataset}.""" )
lowercase = tf.io.gfile.glob(os.path.join(args.eval_dataset , """*.tfrecord""" ) )
if not eval_records:
raise ValueError(F"""No .tfrecord files found in {args.eval_dataset}.""" )
lowercase = count_samples(lowercase_ )
lowercase = num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
lowercase = steps_per_epoch * args.num_epochs
with strategy.scope():
lowercase = TFAutoModelForMaskedLM.from_config(lowercase_ )
model(model.dummy_inputs ) # Pass some dummy inputs through the model to ensure all the weights are built
lowercase , lowercase = create_optimizer(
num_train_steps=lowercase_ , num_warmup_steps=total_train_steps // 20 , init_lr=args.learning_rate , weight_decay_rate=args.weight_decay_rate , )
# Transformers models compute the right loss for their task by default when labels are passed, and will
# use this for training unless you specify your own loss function in compile().
model.compile(optimizer=lowercase_ , metrics=["""accuracy"""] )
def decode_fn(lowercase_ : List[Any] ):
lowercase = {
"""input_ids""": tf.io.FixedLenFeature(dtype=tf.intaa , shape=(args.max_length,) ),
"""attention_mask""": tf.io.FixedLenFeature(dtype=tf.intaa , shape=(args.max_length,) ),
}
return tf.io.parse_single_example(lowercase_ , lowercase_ )
# Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
# use their methods in our data pipeline.
lowercase = DataCollatorForLanguageModeling(
tokenizer=lowercase_ , mlm_probability=args.mlm_probability , mlm=lowercase_ , return_tensors="""tf""" )
def mask_with_collator(lowercase_ : List[Any] ):
# TF really needs an isin() function
lowercase = (
~tf.cast(batch["""attention_mask"""] , tf.bool )
| (batch["""input_ids"""] == tokenizer.cls_token_id)
| (batch["""input_ids"""] == tokenizer.sep_token_id)
)
lowercase , lowercase = data_collator.tf_mask_tokens(
batch["""input_ids"""] , vocab_size=len(lowercase_ ) , mask_token_id=tokenizer.mask_token_id , special_tokens_mask=lowercase_ , )
return batch
lowercase = args.per_replica_batch_size * strategy.num_replicas_in_sync
lowercase = prepare_dataset(
lowercase_ , decode_fn=lowercase_ , mask_fn=lowercase_ , batch_size=lowercase_ , shuffle=lowercase_ , shuffle_buffer_size=args.shuffle_buffer_size , )
lowercase = prepare_dataset(
lowercase_ , decode_fn=lowercase_ , mask_fn=lowercase_ , batch_size=lowercase_ , shuffle=lowercase_ , )
lowercase = []
if args.hub_model_id:
callbacks.append(
PushToHubCallback(output_dir=args.output_dir , hub_model_id=args.hub_model_id , tokenizer=lowercase_ ) )
model.fit(
lowercase_ , validation_data=lowercase_ , epochs=args.num_epochs , callbacks=lowercase_ , )
model.save_pretrained(args.output_dir )
if __name__ == "__main__":
lowercase_ : List[str] = parse_args()
main(args)
| 653 |
'''simple docstring'''
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
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 SwiftFormerForImageClassification, SwiftFormerModel
from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __UpperCamelCase :
def __init__( self , _lowerCAmelCase , _lowerCAmelCase=13 , _lowerCAmelCase=3 , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=224 , _lowerCAmelCase=1000 , _lowerCAmelCase=[3, 3, 6, 4] , _lowerCAmelCase=[48, 56, 112, 220] , ) -> List[str]:
'''simple docstring'''
lowercase = parent
lowercase = batch_size
lowercase = num_channels
lowercase = is_training
lowercase = use_labels
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = num_labels
lowercase = image_size
lowercase = layer_depths
lowercase = embed_dims
def _a ( self ) -> Tuple:
'''simple docstring'''
lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase = None
if self.use_labels:
lowercase = ids_tensor([self.batch_size] , self.num_labels )
lowercase = self.get_config()
return config, pixel_values, labels
def _a ( self ) -> int:
'''simple docstring'''
return SwiftFormerConfig(
depths=self.layer_depths , embed_dims=self.embed_dims , mlp_ratio=4 , downsamples=[True, True, True, True] , hidden_act="""gelu""" , num_labels=self.num_labels , down_patch_size=3 , down_stride=2 , down_pad=1 , drop_rate=0.0 , drop_path_rate=0.0 , use_layer_scale=_lowerCAmelCase , layer_scale_init_value=1E-5 , )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowercase = SwiftFormerModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
lowercase = model(_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dims[-1], 7, 7) )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowercase = self.num_labels
lowercase = SwiftFormerForImageClassification(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
lowercase = model(_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
lowercase = SwiftFormerForImageClassification(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase = model(_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
((lowercase) , (lowercase) , (lowercase)) = self.prepare_config_and_inputs()
lowercase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __UpperCamelCase (_UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
__A = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
__A = (
{'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
if is_torch_available()
else {}
)
__A = False
__A = False
__A = False
__A = False
__A = False
def _a ( self ) -> Dict:
'''simple docstring'''
lowercase = SwiftFormerModelTester(self )
lowercase = ConfigTester(
self , config_class=_lowerCAmelCase , has_text_modality=_lowerCAmelCase , hidden_size=37 , num_attention_heads=12 , num_hidden_layers=12 , )
def _a ( self ) -> List[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="""SwiftFormer does not use inputs_embeds""" )
def _a ( self ) -> List[str]:
'''simple docstring'''
pass
def _a ( self ) -> Dict:
'''simple docstring'''
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase = model_class(_lowerCAmelCase )
lowercase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowerCAmelCase , nn.Linear ) )
def _a ( self ) -> int:
'''simple docstring'''
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase = model_class(_lowerCAmelCase )
lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase = [*signature.parameters.keys()]
lowercase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
def _a ( self ) -> List[str]:
'''simple docstring'''
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase )
@slow
def _a ( self ) -> Any:
'''simple docstring'''
for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase = SwiftFormerModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
@unittest.skip(reason="""SwiftFormer does not output attentions""" )
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
pass
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
def check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
with torch.no_grad():
lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
lowercase = outputs.hidden_states
lowercase = 8
self.assertEqual(len(_lowerCAmelCase ) , _lowerCAmelCase ) # TODO
# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
# with the width and height being successively divided by 2, after every 2 blocks
for i in range(len(_lowerCAmelCase ) ):
self.assertEqual(
hidden_states[i].shape , torch.Size(
[
self.model_tester.batch_size,
self.model_tester.embed_dims[i // 2],
(self.model_tester.image_size // 4) // 2 ** (i // 2),
(self.model_tester.image_size // 4) // 2 ** (i // 2),
] ) , )
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def _a ( self ) -> Dict:
'''simple docstring'''
def _config_zero_init(_lowerCAmelCase ):
lowercase = copy.deepcopy(_lowerCAmelCase )
for key in configs_no_init.__dict__.keys():
if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
setattr(_lowerCAmelCase , _lowerCAmelCase , 1E-10 )
if isinstance(getattr(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) , _lowerCAmelCase ):
lowercase = _config_zero_init(getattr(_lowerCAmelCase , _lowerCAmelCase ) )
setattr(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return configs_no_init
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = _config_zero_init(_lowerCAmelCase )
for model_class in self.all_model_classes:
lowercase = model_class(config=_lowerCAmelCase )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9) / 1E9).round().item() , [0.0, 1.0] , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def _a ( self ) -> Any:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE ( ):
lowercase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class __UpperCamelCase (unittest.TestCase ):
@cached_property
def _a ( self ) -> List[str]:
'''simple docstring'''
return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs""" ) if is_vision_available() else None
@slow
def _a ( self ) -> List[Any]:
'''simple docstring'''
lowercase = SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs""" ).to(_lowerCAmelCase )
lowercase = self.default_image_processor
lowercase = prepare_img()
lowercase = image_processor(images=_lowerCAmelCase , return_tensors="""pt""" ).to(_lowerCAmelCase )
# forward pass
with torch.no_grad():
lowercase = model(**_lowerCAmelCase )
# verify the logits
lowercase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
lowercase = torch.tensor([[-2.17_03E00, 2.11_07E00, -2.08_11E00]] ).to(_lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowerCAmelCase , atol=1E-4 ) )
| 653 | 1 |
'''simple docstring'''
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig
lowercase_ : List[str] = logging.get_logger(__name__)
# General docstring
lowercase_ : str = '''PoolFormerConfig'''
# Base docstring
lowercase_ : Optional[int] = '''sail/poolformer_s12'''
lowercase_ : str = [1, 512, 7, 7]
# Image classification docstring
lowercase_ : int = '''sail/poolformer_s12'''
lowercase_ : Dict = '''tabby, tabby cat'''
lowercase_ : int = [
'''sail/poolformer_s12''',
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]
def SCREAMING_SNAKE_CASE ( lowercase_ : Union[str, Any] , lowercase_ : float = 0.0 , lowercase_ : bool = False ):
if drop_prob == 0.0 or not training:
return input
lowercase = 1 - drop_prob
lowercase = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
lowercase = keep_prob + torch.rand(lowercase_ , dtype=input.dtype , device=input.device )
random_tensor.floor_() # binarize
lowercase = input.div(lowercase_ ) * random_tensor
return output
class __UpperCamelCase (nn.Module ):
def __init__( self , _lowerCAmelCase = None ) -> None:
'''simple docstring'''
super().__init__()
lowercase = drop_prob
def _a ( self , _lowerCAmelCase ) -> torch.Tensor:
'''simple docstring'''
return drop_path(_lowerCAmelCase , self.drop_prob , self.training )
def _a ( self ) -> str:
'''simple docstring'''
return "p={}".format(self.drop_prob )
class __UpperCamelCase (nn.Module ):
def __init__( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase=None ) -> List[str]:
'''simple docstring'''
super().__init__()
lowercase = patch_size if isinstance(_lowerCAmelCase , collections.abc.Iterable ) else (patch_size, patch_size)
lowercase = stride if isinstance(_lowerCAmelCase , collections.abc.Iterable ) else (stride, stride)
lowercase = padding if isinstance(_lowerCAmelCase , collections.abc.Iterable ) else (padding, padding)
lowercase = nn.Convad(_lowerCAmelCase , _lowerCAmelCase , kernel_size=_lowerCAmelCase , stride=_lowerCAmelCase , padding=_lowerCAmelCase )
lowercase = norm_layer(_lowerCAmelCase ) if norm_layer else nn.Identity()
def _a ( self , _lowerCAmelCase ) -> int:
'''simple docstring'''
lowercase = self.projection(_lowerCAmelCase )
lowercase = self.norm(_lowerCAmelCase )
return embeddings
class __UpperCamelCase (nn.GroupNorm ):
def __init__( self , _lowerCAmelCase , **_lowerCAmelCase ) -> Tuple:
'''simple docstring'''
super().__init__(1 , _lowerCAmelCase , **_lowerCAmelCase )
class __UpperCamelCase (nn.Module ):
def __init__( self , _lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
super().__init__()
lowercase = nn.AvgPoolad(_lowerCAmelCase , stride=1 , padding=pool_size // 2 , count_include_pad=_lowerCAmelCase )
def _a ( self , _lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
return self.pool(_lowerCAmelCase ) - hidden_states
class __UpperCamelCase (nn.Module ):
def __init__( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> str:
'''simple docstring'''
super().__init__()
lowercase = nn.Convad(_lowerCAmelCase , _lowerCAmelCase , 1 )
lowercase = nn.Convad(_lowerCAmelCase , _lowerCAmelCase , 1 )
lowercase = PoolFormerDropPath(_lowerCAmelCase )
if isinstance(config.hidden_act , _lowerCAmelCase ):
lowercase = ACTaFN[config.hidden_act]
else:
lowercase = config.hidden_act
def _a ( self , _lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
lowercase = self.conva(_lowerCAmelCase )
lowercase = self.act_fn(_lowerCAmelCase )
lowercase = self.drop(_lowerCAmelCase )
lowercase = self.conva(_lowerCAmelCase )
lowercase = self.drop(_lowerCAmelCase )
return hidden_states
class __UpperCamelCase (nn.Module ):
def __init__( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
super().__init__()
lowercase = PoolFormerPooling(_lowerCAmelCase )
lowercase = PoolFormerOutput(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
lowercase = PoolFormerGroupNorm(_lowerCAmelCase )
lowercase = PoolFormerGroupNorm(_lowerCAmelCase )
# Useful for training neural nets
lowercase = PoolFormerDropPath(_lowerCAmelCase ) if drop_path > 0.0 else nn.Identity()
lowercase = config.use_layer_scale
if config.use_layer_scale:
lowercase = nn.Parameter(
config.layer_scale_init_value * torch.ones((_lowerCAmelCase) ) , requires_grad=_lowerCAmelCase )
lowercase = nn.Parameter(
config.layer_scale_init_value * torch.ones((_lowerCAmelCase) ) , requires_grad=_lowerCAmelCase )
def _a ( self , _lowerCAmelCase ) -> List[str]:
'''simple docstring'''
if self.use_layer_scale:
lowercase = self.pooling(self.before_norm(_lowerCAmelCase ) )
lowercase = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * pooling_output
# First residual connection
lowercase = hidden_states + self.drop_path(_lowerCAmelCase )
lowercase = ()
lowercase = self.output(self.after_norm(_lowerCAmelCase ) )
lowercase = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * layer_output
# Second residual connection
lowercase = hidden_states + self.drop_path(_lowerCAmelCase )
lowercase = (output,) + outputs
return outputs
else:
lowercase = self.drop_path(self.pooling(self.before_norm(_lowerCAmelCase ) ) )
# First residual connection
lowercase = pooling_output + hidden_states
lowercase = ()
# Second residual connection inside the PoolFormerOutput block
lowercase = self.drop_path(self.output(self.after_norm(_lowerCAmelCase ) ) )
lowercase = hidden_states + layer_output
lowercase = (output,) + outputs
return outputs
class __UpperCamelCase (nn.Module ):
def __init__( self , _lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
super().__init__()
lowercase = config
# stochastic depth decay rule
lowercase = [x.item() for x in torch.linspace(0 , config.drop_path_rate , sum(config.depths ) )]
# patch embeddings
lowercase = []
for i in range(config.num_encoder_blocks ):
embeddings.append(
PoolFormerEmbeddings(
patch_size=config.patch_sizes[i] , stride=config.strides[i] , padding=config.padding[i] , num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1] , hidden_size=config.hidden_sizes[i] , ) )
lowercase = nn.ModuleList(_lowerCAmelCase )
# Transformer blocks
lowercase = []
lowercase = 0
for i in range(config.num_encoder_blocks ):
# each block consists of layers
lowercase = []
if i != 0:
cur += config.depths[i - 1]
for j in range(config.depths[i] ):
layers.append(
PoolFormerLayer(
_lowerCAmelCase , num_channels=config.hidden_sizes[i] , pool_size=config.pool_size , hidden_size=config.hidden_sizes[i] , intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio ) , drop_path=dpr[cur + j] , ) )
blocks.append(nn.ModuleList(_lowerCAmelCase ) )
lowercase = nn.ModuleList(_lowerCAmelCase )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase=False , _lowerCAmelCase=True ) -> Tuple:
'''simple docstring'''
lowercase = () if output_hidden_states else None
lowercase = pixel_values
for idx, layers in enumerate(zip(self.patch_embeddings , self.block ) ):
lowercase , lowercase = layers
# Get patch embeddings from hidden_states
lowercase = embedding_layer(_lowerCAmelCase )
# Send the embeddings through the blocks
for _, blk in enumerate(_lowerCAmelCase ):
lowercase = blk(_lowerCAmelCase )
lowercase = layer_outputs[0]
if output_hidden_states:
lowercase = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=_lowerCAmelCase , hidden_states=_lowerCAmelCase )
class __UpperCamelCase (_UpperCAmelCase ):
__A = PoolFormerConfig
__A = '''poolformer'''
__A = '''pixel_values'''
__A = True
def _a ( self , _lowerCAmelCase ) -> List[str]:
'''simple docstring'''
if isinstance(_lowerCAmelCase , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(_lowerCAmelCase , nn.LayerNorm ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase=False ) -> int:
'''simple docstring'''
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
lowercase = value
lowercase_ : Any = r'''
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.
Parameters:
config ([`PoolFormerConfig`]): 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.
'''
lowercase_ : List[str] = r'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`PoolFormerImageProcessor.__call__`] for details.
'''
@add_start_docstrings(
'''The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top.''' , _UpperCAmelCase , )
class __UpperCamelCase (_UpperCAmelCase ):
def __init__( self , _lowerCAmelCase ) -> str:
'''simple docstring'''
super().__init__(_lowerCAmelCase )
lowercase = config
lowercase = PoolFormerEncoder(_lowerCAmelCase )
# Initialize weights and apply final processing
self.post_init()
def _a ( self ) -> Tuple:
'''simple docstring'''
return self.embeddings.patch_embeddings
@add_start_docstrings_to_model_forward(_lowerCAmelCase )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=_lowerCAmelCase , config_class=_CONFIG_FOR_DOC , modality="""vision""" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def _a ( self , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , ) -> Union[Tuple, BaseModelOutputWithNoAttention]:
'''simple docstring'''
lowercase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("""You have to specify pixel_values""" )
lowercase = self.encoder(
_lowerCAmelCase , output_hidden_states=_lowerCAmelCase , return_dict=_lowerCAmelCase , )
lowercase = encoder_outputs[0]
if not return_dict:
return (sequence_output, None) + encoder_outputs[1:]
return BaseModelOutputWithNoAttention(
last_hidden_state=_lowerCAmelCase , hidden_states=encoder_outputs.hidden_states , )
class __UpperCamelCase (nn.Module ):
def __init__( self , _lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
super().__init__()
lowercase = nn.Linear(config.hidden_size , config.hidden_size )
def _a ( self , _lowerCAmelCase ) -> int:
'''simple docstring'''
lowercase = self.dense(_lowerCAmelCase )
return output
@add_start_docstrings(
'''
PoolFormer Model transformer with an image classification head on top
''' , _UpperCAmelCase , )
class __UpperCamelCase (_UpperCAmelCase ):
def __init__( self , _lowerCAmelCase ) -> str:
'''simple docstring'''
super().__init__(_lowerCAmelCase )
lowercase = config.num_labels
lowercase = PoolFormerModel(_lowerCAmelCase )
# Final norm
lowercase = PoolFormerGroupNorm(config.hidden_sizes[-1] )
# Classifier head
lowercase = (
nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity()
)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_lowerCAmelCase )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=_lowerCAmelCase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def _a ( self , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , ) -> Union[Tuple, ImageClassifierOutputWithNoAttention]:
'''simple docstring'''
lowercase = return_dict if return_dict is not None else self.config.use_return_dict
lowercase = self.poolformer(
_lowerCAmelCase , output_hidden_states=_lowerCAmelCase , return_dict=_lowerCAmelCase , )
lowercase = outputs[0]
lowercase = self.classifier(self.norm(_lowerCAmelCase ).mean([-2, -1] ) )
lowercase = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
lowercase = """regression"""
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
lowercase = """single_label_classification"""
else:
lowercase = """multi_label_classification"""
if self.config.problem_type == "regression":
lowercase = MSELoss()
if self.num_labels == 1:
lowercase = loss_fct(logits.squeeze() , labels.squeeze() )
else:
lowercase = loss_fct(_lowerCAmelCase , _lowerCAmelCase )
elif self.config.problem_type == "single_label_classification":
lowercase = CrossEntropyLoss()
lowercase = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
lowercase = BCEWithLogitsLoss()
lowercase = loss_fct(_lowerCAmelCase , _lowerCAmelCase )
if not return_dict:
lowercase = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=_lowerCAmelCase , logits=_lowerCAmelCase , hidden_states=outputs.hidden_states )
| 653 |
'''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def SCREAMING_SNAKE_CASE ( ):
lowercase = HfArgumentParser(lowercase_ )
lowercase = parser.parse_args_into_dataclasses()[0]
lowercase = TensorFlowBenchmark(args=lowercase_ )
try:
lowercase = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
lowercase = """Arg --no_{0} is no longer used, please use --no-{0} instead."""
lowercase = """ """.join(str(lowercase_ ).split(""" """ )[:-1] )
lowercase = """"""
lowercase = eval(str(lowercase_ ).split(""" """ )[-1] )
lowercase = []
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(lowercase_ )
if len(lowercase_ ) > 0:
lowercase = full_error_msg + begin_error_msg + str(lowercase_ )
raise ValueError(lowercase_ )
benchmark.run()
if __name__ == "__main__":
main()
| 653 | 1 |
'''simple docstring'''
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
lowercase_ : Tuple = '''src/transformers'''
# This is to make sure the transformers module imported is the one in the repo.
lowercase_ : str = direct_transformers_import(TRANSFORMERS_PATH)
# Regexes that match TF/Flax/PT model names.
lowercase_ : str = re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
lowercase_ : List[Any] = re.compile(r'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowercase_ : Tuple = re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowercase_ : Union[str, Any] = [
('''pretraining''', '''MODEL_FOR_PRETRAINING_MAPPING_NAMES''', '''AutoModelForPreTraining'''),
('''feature-extraction''', '''MODEL_MAPPING_NAMES''', '''AutoModel'''),
('''audio-classification''', '''MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForAudioClassification'''),
('''text-generation''', '''MODEL_FOR_CAUSAL_LM_MAPPING_NAMES''', '''AutoModelForCausalLM'''),
('''automatic-speech-recognition''', '''MODEL_FOR_CTC_MAPPING_NAMES''', '''AutoModelForCTC'''),
('''image-classification''', '''MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForImageClassification'''),
('''image-segmentation''', '''MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES''', '''AutoModelForImageSegmentation'''),
('''fill-mask''', '''MODEL_FOR_MASKED_LM_MAPPING_NAMES''', '''AutoModelForMaskedLM'''),
('''object-detection''', '''MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES''', '''AutoModelForObjectDetection'''),
(
'''zero-shot-object-detection''',
'''MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES''',
'''AutoModelForZeroShotObjectDetection''',
),
('''question-answering''', '''MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES''', '''AutoModelForQuestionAnswering'''),
('''text2text-generation''', '''MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES''', '''AutoModelForSeq2SeqLM'''),
('''text-classification''', '''MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForSequenceClassification'''),
('''automatic-speech-recognition''', '''MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES''', '''AutoModelForSpeechSeq2Seq'''),
(
'''table-question-answering''',
'''MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES''',
'''AutoModelForTableQuestionAnswering''',
),
('''token-classification''', '''MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForTokenClassification'''),
('''multiple-choice''', '''MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES''', '''AutoModelForMultipleChoice'''),
(
'''next-sentence-prediction''',
'''MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES''',
'''AutoModelForNextSentencePrediction''',
),
(
'''audio-frame-classification''',
'''MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES''',
'''AutoModelForAudioFrameClassification''',
),
('''audio-xvector''', '''MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES''', '''AutoModelForAudioXVector'''),
(
'''document-question-answering''',
'''MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES''',
'''AutoModelForDocumentQuestionAnswering''',
),
(
'''visual-question-answering''',
'''MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES''',
'''AutoModelForVisualQuestionAnswering''',
),
('''image-to-text''', '''MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES''', '''AutoModelForVision2Seq'''),
(
'''zero-shot-image-classification''',
'''MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES''',
'''AutoModelForZeroShotImageClassification''',
),
('''depth-estimation''', '''MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES''', '''AutoModelForDepthEstimation'''),
('''video-classification''', '''MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForVideoClassification'''),
('''mask-generation''', '''MODEL_FOR_MASK_GENERATION_MAPPING_NAMES''', '''AutoModelForMaskGeneration'''),
]
def SCREAMING_SNAKE_CASE ( lowercase_ : List[str] ):
lowercase = re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" , lowercase_ )
return [m.group(0 ) for m in matches]
def SCREAMING_SNAKE_CASE ( ):
lowercase = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
lowercase = {
config.replace("""Config""" , """""" ): model_type for model_type, config in config_maping_names.items()
}
# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
lowercase = collections.defaultdict(lowercase_ )
lowercase = collections.defaultdict(lowercase_ )
lowercase = collections.defaultdict(lowercase_ )
# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
for attr_name in dir(lowercase_ ):
lowercase = None
if _re_tf_models.match(lowercase_ ) is not None:
lowercase = tf_models
lowercase = _re_tf_models.match(lowercase_ ).groups()[0]
elif _re_flax_models.match(lowercase_ ) is not None:
lowercase = flax_models
lowercase = _re_flax_models.match(lowercase_ ).groups()[0]
elif _re_pt_models.match(lowercase_ ) is not None:
lowercase = pt_models
lowercase = _re_pt_models.match(lowercase_ ).groups()[0]
if lookup_dict is not None:
while len(lowercase_ ) > 0:
if attr_name in model_prefix_to_model_type:
lowercase = True
break
# Try again after removing the last word in the name
lowercase = """""".join(camel_case_split(lowercase_ )[:-1] )
lowercase = set(list(pt_models.keys() ) + list(tf_models.keys() ) + list(flax_models.keys() ) )
lowercase = list(lowercase_ )
all_models.sort()
lowercase = {"""model_type""": all_models}
lowercase = [pt_models[t] for t in all_models]
lowercase = [tf_models[t] for t in all_models]
lowercase = [flax_models[t] for t in all_models]
# Now let's use the auto-mapping names to make sure
lowercase = {}
for t in all_models:
if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
lowercase = """AutoProcessor"""
elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
lowercase = """AutoTokenizer"""
elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
lowercase = """AutoFeatureExtractor"""
else:
# Default to AutoTokenizer if a model has nothing, for backward compatibility.
lowercase = """AutoTokenizer"""
lowercase = [processors[t] for t in all_models]
return pd.DataFrame(lowercase_ )
def SCREAMING_SNAKE_CASE ( lowercase_ : Optional[int] ):
lowercase = [
transformers_module.models.auto.modeling_auto,
transformers_module.models.auto.modeling_tf_auto,
transformers_module.models.auto.modeling_flax_auto,
]
for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
lowercase = [model_mapping, F"""TF_{model_mapping}""", F"""FLAX_{model_mapping}"""]
lowercase = [auto_class, F"""TF_{auto_class}""", F"""Flax_{auto_class}"""]
# Loop through all three frameworks
for module, cls, mapping in zip(lowercase_ , lowercase_ , lowercase_ ):
# The type of pipeline may not exist in this framework
if not hasattr(lowercase_ , lowercase_ ):
continue
# First extract all model_names
lowercase = []
for name in getattr(lowercase_ , lowercase_ ).values():
if isinstance(lowercase_ , lowercase_ ):
model_names.append(lowercase_ )
else:
model_names.extend(list(lowercase_ ) )
# Add pipeline tag and auto model class for those models
table.update({model_name: (pipeline_tag, cls) for model_name in model_names} )
return table
def SCREAMING_SNAKE_CASE ( lowercase_ : Dict , lowercase_ : Any ):
lowercase = get_frameworks_table()
lowercase = Dataset.from_pandas(lowercase_ )
lowercase = hf_hub_download(
"""huggingface/transformers-metadata""" , """pipeline_tags.json""" , repo_type="""dataset""" , token=lowercase_ )
lowercase = Dataset.from_json(lowercase_ )
lowercase = {
tags_dataset[i]["""model_class"""]: (tags_dataset[i]["""pipeline_tag"""], tags_dataset[i]["""auto_class"""])
for i in range(len(lowercase_ ) )
}
lowercase = update_pipeline_and_auto_class_table(lowercase_ )
# Sort the model classes to avoid some nondeterministic updates to create false update commits.
lowercase = sorted(table.keys() )
lowercase = pd.DataFrame(
{
"""model_class""": model_classes,
"""pipeline_tag""": [table[m][0] for m in model_classes],
"""auto_class""": [table[m][1] for m in model_classes],
} )
lowercase = Dataset.from_pandas(lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
frameworks_dataset.to_json(os.path.join(lowercase_ , """frameworks.json""" ) )
tags_dataset.to_json(os.path.join(lowercase_ , """pipeline_tags.json""" ) )
if commit_sha is not None:
lowercase = (
F"""Update with commit {commit_sha}\n\nSee: """
F"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
)
else:
lowercase = """Update"""
upload_folder(
repo_id="""huggingface/transformers-metadata""" , folder_path=lowercase_ , repo_type="""dataset""" , token=lowercase_ , commit_message=lowercase_ , )
def SCREAMING_SNAKE_CASE ( ):
lowercase = {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
lowercase = transformers_module.pipelines.SUPPORTED_TASKS
lowercase = []
for key in pipeline_tasks:
if key not in in_table:
lowercase = pipeline_tasks[key]["""pt"""]
if isinstance(lowercase_ , (list, tuple) ):
lowercase = model[0]
lowercase = model.__name__
if model not in in_table.values():
missing.append(lowercase_ )
if len(lowercase_ ) > 0:
lowercase = """, """.join(lowercase_ )
raise ValueError(
"""The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside """
F"""`utils/update_metadata.py`: {msg}. Please add them!""" )
if __name__ == "__main__":
lowercase_ : Any = argparse.ArgumentParser()
parser.add_argument('''--token''', type=str, help='''The token to use to push to the transformers-metadata dataset.''')
parser.add_argument('''--commit_sha''', type=str, help='''The sha of the commit going with this update.''')
parser.add_argument('''--check-only''', action='''store_true''', help='''Activate to just check all pipelines are present.''')
lowercase_ : Optional[Any] = parser.parse_args()
if args.check_only:
check_pipeline_tags()
else:
update_metadata(args.token, args.commit_sha)
| 653 |
'''simple docstring'''
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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.
# this script dumps information about the environment
import os
import platform
import sys
lowercase_ : List[str] = '''3'''
print('''Python version:''', sys.version)
print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())
try:
import torch
print('''Torch version:''', torch.__version__)
print('''Cuda available:''', torch.cuda.is_available())
print('''Cuda version:''', torch.version.cuda)
print('''CuDNN version:''', torch.backends.cudnn.version())
print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
print('''Torch version:''', None)
try:
import transformers
print('''transformers version:''', transformers.__version__)
except ImportError:
print('''transformers version:''', None)
| 653 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __UpperCamelCase (unittest.TestCase ):
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase = [[1, 2, 4], [1, 2, 3, 4]]
lowercase = DisjunctiveConstraint(_lowerCAmelCase )
self.assertTrue(isinstance(dc.token_ids , _lowerCAmelCase ) )
with self.assertRaises(_lowerCAmelCase ):
DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )
with self.assertRaises(_lowerCAmelCase ):
DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )
def _a ( self ) -> Any:
'''simple docstring'''
lowercase = [[1, 2], [1, 2, 3, 4]]
with self.assertRaises(_lowerCAmelCase ):
DisjunctiveConstraint(_lowerCAmelCase ) # fails here
def _a ( self ) -> Tuple:
'''simple docstring'''
lowercase = [[1, 2, 3], [1, 2, 4]]
lowercase = DisjunctiveConstraint(_lowerCAmelCase )
lowercase , lowercase , lowercase = dc.update(1 )
lowercase = stepped is True and completed is False and reset is False
self.assertTrue(_lowerCAmelCase )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
lowercase , lowercase , lowercase = dc.update(2 )
lowercase = stepped is True and completed is False and reset is False
self.assertTrue(_lowerCAmelCase )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
lowercase , lowercase , lowercase = dc.update(3 )
lowercase = stepped is True and completed is True and reset is False
self.assertTrue(_lowerCAmelCase )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 3] )
def _a ( self ) -> str:
'''simple docstring'''
lowercase = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]
lowercase = DisjunctiveConstraint(_lowerCAmelCase )
lowercase , lowercase , lowercase = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
lowercase , lowercase , lowercase = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
lowercase , lowercase , lowercase = dc.update(4 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2, 4] )
lowercase , lowercase , lowercase = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 4, 5] )
dc.reset()
lowercase , lowercase , lowercase = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 3 )
self.assertTrue(dc.current_seq == [1] )
lowercase , lowercase , lowercase = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 2 )
self.assertTrue(dc.current_seq == [1, 2] )
lowercase , lowercase , lowercase = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.remaining() == 0 )
self.assertTrue(dc.current_seq == [1, 2, 5] )
| 653 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase_ : Optional[Any] = logging.get_logger(__name__)
lowercase_ : int = {'''vocab_file''': '''spm_char.model'''}
lowercase_ : int = {
'''vocab_file''': {
'''microsoft/speecht5_asr''': '''https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model''',
'''microsoft/speecht5_tts''': '''https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model''',
'''microsoft/speecht5_vc''': '''https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model''',
}
}
lowercase_ : Optional[Any] = {
'''microsoft/speecht5_asr''': 1024,
'''microsoft/speecht5_tts''': 1024,
'''microsoft/speecht5_vc''': 1024,
}
class __UpperCamelCase (_UpperCAmelCase ):
__A = VOCAB_FILES_NAMES
__A = PRETRAINED_VOCAB_FILES_MAP
__A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A = ['''input_ids''', '''attention_mask''']
def __init__( self , _lowerCAmelCase , _lowerCAmelCase="<s>" , _lowerCAmelCase="</s>" , _lowerCAmelCase="<unk>" , _lowerCAmelCase="<pad>" , _lowerCAmelCase = None , **_lowerCAmelCase , ) -> None:
'''simple docstring'''
lowercase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_lowerCAmelCase , eos_token=_lowerCAmelCase , unk_token=_lowerCAmelCase , pad_token=_lowerCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **_lowerCAmelCase , )
lowercase = vocab_file
lowercase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowerCAmelCase )
@property
def _a ( self ) -> List[Any]:
'''simple docstring'''
return self.sp_model.get_piece_size()
def _a ( self ) -> str:
'''simple docstring'''
lowercase = {self.convert_ids_to_tokens(_lowerCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase = self.__dict__.copy()
lowercase = None
return state
def __setstate__( self , _lowerCAmelCase ) -> Optional[int]:
'''simple docstring'''
lowercase = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
lowercase = {}
lowercase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _a ( self , _lowerCAmelCase ) -> List[str]:
'''simple docstring'''
return self.sp_model.encode(_lowerCAmelCase , out_type=_lowerCAmelCase )
def _a ( self , _lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
return self.sp_model.piece_to_id(_lowerCAmelCase )
def _a ( self , _lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase = self.sp_model.IdToPiece(_lowerCAmelCase )
return token
def _a ( self , _lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowercase = []
lowercase = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(_lowerCAmelCase ) + token
lowercase = []
else:
current_sub_tokens.append(_lowerCAmelCase )
out_string += self.sp_model.decode(_lowerCAmelCase )
return out_string.strip()
def _a ( self , _lowerCAmelCase , _lowerCAmelCase=None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def _a ( self , _lowerCAmelCase , _lowerCAmelCase = None , _lowerCAmelCase = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCAmelCase , token_ids_a=_lowerCAmelCase , already_has_special_tokens=_lowerCAmelCase )
lowercase = [1]
if token_ids_a is None:
return ([0] * len(_lowerCAmelCase )) + suffix_ones
return ([0] * len(_lowerCAmelCase )) + ([0] * len(_lowerCAmelCase )) + suffix_ones
def _a ( self , _lowerCAmelCase , _lowerCAmelCase = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(_lowerCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowercase = os.path.join(
_lowerCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowerCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCAmelCase , """wb""" ) as fi:
lowercase = self.sp_model.serialized_model_proto()
fi.write(_lowerCAmelCase )
return (out_vocab_file,)
| 653 | 1 |
'''simple docstring'''
from random import randint
from tempfile import TemporaryFile
import numpy as np
def SCREAMING_SNAKE_CASE ( lowercase_ : List[str] , lowercase_ : Optional[int] , lowercase_ : Tuple ):
lowercase = 0
if start < end:
lowercase = randint(lowercase_ , lowercase_ )
lowercase = a[end]
lowercase = a[pivot]
lowercase = temp
lowercase , lowercase = _in_place_partition(lowercase_ , lowercase_ , lowercase_ )
count += _in_place_quick_sort(lowercase_ , lowercase_ , p - 1 )
count += _in_place_quick_sort(lowercase_ , p + 1 , lowercase_ )
return count
def SCREAMING_SNAKE_CASE ( lowercase_ : List[Any] , lowercase_ : Union[str, Any] , lowercase_ : int ):
lowercase = 0
lowercase = randint(lowercase_ , lowercase_ )
lowercase = a[end]
lowercase = a[pivot]
lowercase = temp
lowercase = start - 1
for index in range(lowercase_ , lowercase_ ):
count += 1
if a[index] < a[end]: # check if current val is less than pivot value
lowercase = new_pivot_index + 1
lowercase = a[new_pivot_index]
lowercase = a[index]
lowercase = temp
lowercase = a[new_pivot_index + 1]
lowercase = a[end]
lowercase = temp
return new_pivot_index + 1, count
lowercase_ : int = TemporaryFile()
lowercase_ : Optional[Any] = 100 # 1000 elements are to be sorted
lowercase_ , lowercase_ : Optional[int] = 0, 1 # mean and standard deviation
lowercase_ : Optional[Any] = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)
outfile.seek(0) # using the same array
lowercase_ : Any = np.load(outfile)
lowercase_ : List[str] = len(M) - 1
lowercase_ : List[Any] = _in_place_quick_sort(M, 0, r)
print(
'''No of Comparisons for 100 elements selected from a standard normal distribution'''
'''is :'''
)
print(z)
| 653 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( ):
lowercase = []
lowercase = 1
while len(lowercase_ ) < 1E6:
constant.append(str(lowercase_ ) )
i += 1
lowercase = """""".join(lowercase_ )
return (
int(constant[0] )
* int(constant[9] )
* int(constant[99] )
* int(constant[999] )
* int(constant[9999] )
* int(constant[9_9999] )
* int(constant[99_9999] )
)
if __name__ == "__main__":
print(solution())
| 653 | 1 |
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
lowercase_ : Any = get_logger(__name__)
class __UpperCamelCase :
def __init__( self , _lowerCAmelCase , _lowerCAmelCase=None ) -> Union[str, Any]:
'''simple docstring'''
lowercase = attrs or []
if module is not None:
for key in module.__dict__:
if key in attrs or not key.startswith("""__""" ):
setattr(self , _lowerCAmelCase , getattr(_lowerCAmelCase , _lowerCAmelCase ) )
lowercase = module._original_module if isinstance(_lowerCAmelCase , _PatchedModuleObj ) else module
class __UpperCamelCase :
__A = []
def __init__( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase=None ) -> List[Any]:
'''simple docstring'''
lowercase = obj
lowercase = target
lowercase = new
lowercase = target.split(""".""" )[0]
lowercase = {}
lowercase = attrs or []
def __enter__( self ) -> Dict:
'''simple docstring'''
*lowercase , lowercase = self.target.split(""".""" )
# Patch modules:
# it's used to patch attributes of submodules like "os.path.join";
# in this case we need to patch "os" and "os.path"
for i in range(len(_lowerCAmelCase ) ):
try:
lowercase = import_module(""".""".join(submodules[: i + 1] ) )
except ModuleNotFoundError:
continue
# We iterate over all the globals in self.obj in case we find "os" or "os.path"
for attr in self.obj.__dir__():
lowercase = getattr(self.obj , _lowerCAmelCase )
# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
# This allows to patch renamed modules like "from os import path as ospath".
if obj_attr is submodule or (
(isinstance(_lowerCAmelCase , _PatchedModuleObj ) and obj_attr._original_module is submodule)
):
lowercase = obj_attr
# patch at top level
setattr(self.obj , _lowerCAmelCase , _PatchedModuleObj(_lowerCAmelCase , attrs=self.attrs ) )
lowercase = getattr(self.obj , _lowerCAmelCase )
# construct lower levels patches
for key in submodules[i + 1 :]:
setattr(_lowerCAmelCase , _lowerCAmelCase , _PatchedModuleObj(getattr(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) , attrs=self.attrs ) )
lowercase = getattr(_lowerCAmelCase , _lowerCAmelCase )
# finally set the target attribute
setattr(_lowerCAmelCase , _lowerCAmelCase , self.new )
# Patch attribute itself:
# it's used for builtins like "open",
# and also to patch "os.path.join" we may also need to patch "join"
# itself if it was imported as "from os.path import join".
if submodules: # if it's an attribute of a submodule like "os.path.join"
try:
lowercase = getattr(import_module(""".""".join(_lowerCAmelCase ) ) , _lowerCAmelCase )
except (AttributeError, ModuleNotFoundError):
return
# We iterate over all the globals in self.obj in case we find "os.path.join"
for attr in self.obj.__dir__():
# We don't check for the name of the global, but rather if its value *is* "os.path.join".
# This allows to patch renamed attributes like "from os.path import join as pjoin".
if getattr(self.obj , _lowerCAmelCase ) is attr_value:
lowercase = getattr(self.obj , _lowerCAmelCase )
setattr(self.obj , _lowerCAmelCase , self.new )
elif target_attr in globals()["__builtins__"]: # if it'a s builtin like "open"
lowercase = globals()["""__builtins__"""][target_attr]
setattr(self.obj , _lowerCAmelCase , self.new )
else:
raise RuntimeError(F"""Tried to patch attribute {target_attr} instead of a submodule.""" )
def __exit__( self , *_lowerCAmelCase ) -> Tuple:
'''simple docstring'''
for attr in list(self.original ):
setattr(self.obj , _lowerCAmelCase , self.original.pop(_lowerCAmelCase ) )
def _a ( self ) -> List[str]:
'''simple docstring'''
self.__enter__()
self._active_patches.append(self )
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
try:
self._active_patches.remove(self )
except ValueError:
# If the patch hasn't been started this will fail
return None
return self.__exit__()
| 653 |
'''simple docstring'''
import os
def SCREAMING_SNAKE_CASE ( ):
lowercase = os.path.join(os.path.dirname(lowercase_ ) , """num.txt""" )
with open(lowercase_ ) as file_hand:
return str(sum(int(lowercase_ ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 653 | 1 |
'''simple docstring'''
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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.
# this script dumps information about the environment
import os
import platform
import sys
lowercase_ : List[str] = '''3'''
print('''Python version:''', sys.version)
print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())
try:
import torch
print('''Torch version:''', torch.__version__)
print('''Cuda available:''', torch.cuda.is_available())
print('''Cuda version:''', torch.version.cuda)
print('''CuDNN version:''', torch.backends.cudnn.version())
print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
print('''Torch version:''', None)
try:
import transformers
print('''transformers version:''', transformers.__version__)
except ImportError:
print('''transformers version:''', None)
| 653 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
@skip_mps
class __UpperCamelCase (_UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
__A = StableDiffusionPanoramaPipeline
__A = TEXT_TO_IMAGE_PARAMS
__A = TEXT_TO_IMAGE_BATCH_PARAMS
__A = TEXT_TO_IMAGE_IMAGE_PARAMS
__A = TEXT_TO_IMAGE_IMAGE_PARAMS
def _a ( self ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
lowercase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=1 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
lowercase = DDIMScheduler()
torch.manual_seed(0 )
lowercase = 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 , )
torch.manual_seed(0 )
lowercase = 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=1000 , )
lowercase = CLIPTextModel(_lowerCAmelCase )
lowercase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowercase = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def _a ( self , _lowerCAmelCase , _lowerCAmelCase=0 ) -> Optional[int]:
'''simple docstring'''
lowercase = torch.manual_seed(_lowerCAmelCase )
lowercase = {
"""prompt""": """a photo of the dolomites""",
"""generator""": generator,
# Setting height and width to None to prevent OOMs on CPU.
"""height""": None,
"""width""": None,
"""num_inference_steps""": 1,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def _a ( self ) -> int:
'''simple docstring'''
lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase = self.get_dummy_components()
lowercase = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase = self.get_dummy_inputs(_lowerCAmelCase )
lowercase = sd_pipe(**_lowerCAmelCase ).images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase = np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
super().test_inference_batch_consistent(batch_sizes=[1, 2] )
def _a ( self ) -> str:
'''simple docstring'''
super().test_inference_batch_single_identical(batch_size=2 , expected_max_diff=3.25E-3 )
def _a ( self ) -> List[Any]:
'''simple docstring'''
lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase = self.get_dummy_components()
lowercase = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase = self.get_dummy_inputs(_lowerCAmelCase )
lowercase = """french fries"""
lowercase = sd_pipe(**_lowerCAmelCase , negative_prompt=_lowerCAmelCase )
lowercase = output.images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase = np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> Tuple:
'''simple docstring'''
lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase = self.get_dummy_components()
lowercase = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase = self.get_dummy_inputs(_lowerCAmelCase )
lowercase = sd_pipe(**_lowerCAmelCase , view_batch_size=2 )
lowercase = output.images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase = np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> Any:
'''simple docstring'''
lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase = self.get_dummy_components()
lowercase = EulerAncestralDiscreteScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" )
lowercase = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase = self.get_dummy_inputs(_lowerCAmelCase )
lowercase = sd_pipe(**_lowerCAmelCase ).images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase = np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> Dict:
'''simple docstring'''
lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase = self.get_dummy_components()
lowercase = PNDMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , skip_prk_steps=_lowerCAmelCase )
lowercase = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase = self.get_dummy_inputs(_lowerCAmelCase )
lowercase = sd_pipe(**_lowerCAmelCase ).images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase = np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class __UpperCamelCase (unittest.TestCase ):
def _a ( self ) -> List[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self , _lowerCAmelCase=0 ) -> Optional[int]:
'''simple docstring'''
lowercase = torch.manual_seed(_lowerCAmelCase )
lowercase = {
"""prompt""": """a photo of the dolomites""",
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase = """stabilityai/stable-diffusion-2-base"""
lowercase = DDIMScheduler.from_pretrained(_lowerCAmelCase , subfolder="""scheduler""" )
lowercase = StableDiffusionPanoramaPipeline.from_pretrained(_lowerCAmelCase , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing()
lowercase = self.get_inputs()
lowercase = pipe(**_lowerCAmelCase ).images
lowercase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2048, 3)
lowercase = np.array(
[
0.3696_8392,
0.2702_5372,
0.3244_6766,
0.2837_9387,
0.3636_3274,
0.3073_3347,
0.2710_0027,
0.2705_4125,
0.2553_6096,
] )
assert np.abs(expected_slice - image_slice ).max() < 1E-2
def _a ( self ) -> str:
'''simple docstring'''
lowercase = StableDiffusionPanoramaPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2-base""" , safety_checker=_lowerCAmelCase )
lowercase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing()
lowercase = self.get_inputs()
lowercase = pipe(**_lowerCAmelCase ).images
lowercase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2048, 3)
lowercase = np.array(
[
[
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
] )
assert np.abs(expected_slice - image_slice ).max() < 1E-3
def _a ( self ) -> Any:
'''simple docstring'''
lowercase = 0
def callback_fn(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> None:
lowercase = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
lowercase = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
lowercase = latents[0, -3:, -3:, -1]
lowercase = np.array(
[
0.1868_1869,
0.3390_7816,
0.536_1276,
0.1443_2865,
-0.0285_6611,
-0.7394_1123,
0.2339_7987,
0.4732_2682,
-0.3782_3164,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
elif step == 2:
lowercase = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
lowercase = latents[0, -3:, -3:, -1]
lowercase = np.array(
[
0.1853_9645,
0.3398_7248,
0.537_8559,
0.1443_7142,
-0.0245_5261,
-0.733_8317,
0.2399_0755,
0.4735_6272,
-0.378_6505,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
lowercase = False
lowercase = """stabilityai/stable-diffusion-2-base"""
lowercase = DDIMScheduler.from_pretrained(_lowerCAmelCase , subfolder="""scheduler""" )
lowercase = StableDiffusionPanoramaPipeline.from_pretrained(_lowerCAmelCase , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase )
lowercase = pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing()
lowercase = self.get_inputs()
pipe(**_lowerCAmelCase , callback=_lowerCAmelCase , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def _a ( self ) -> int:
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
lowercase = """stabilityai/stable-diffusion-2-base"""
lowercase = DDIMScheduler.from_pretrained(_lowerCAmelCase , subfolder="""scheduler""" )
lowercase = StableDiffusionPanoramaPipeline.from_pretrained(_lowerCAmelCase , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase )
lowercase = pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
lowercase = self.get_inputs()
lowercase = pipe(**_lowerCAmelCase )
lowercase = torch.cuda.max_memory_allocated()
# make sure that less than 5.2 GB is allocated
assert mem_bytes < 5.5 * 10**9
| 653 | 1 |
'''simple docstring'''
from __future__ import annotations
import os
from typing import Any
import requests
lowercase_ : List[str] = '''https://api.github.com'''
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
lowercase_ : Any = BASE_URL + '''/user'''
# https://github.com/settings/tokens
lowercase_ : Union[str, Any] = os.environ.get('''USER_TOKEN''', '''''')
def SCREAMING_SNAKE_CASE ( lowercase_ : str ):
lowercase = {
"""Authorization""": F"""token {auth_token}""",
"""Accept""": """application/vnd.github.v3+json""",
}
return requests.get(lowercase_ , headers=lowercase_ ).json()
if __name__ == "__main__": # pragma: no cover
if USER_TOKEN:
for key, value in fetch_github_info(USER_TOKEN).items():
print(f'''{key}: {value}''')
else:
raise ValueError('''\'USER_TOKEN\' field cannot be empty.''')
| 653 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
lowercase_ : Tuple = logging.getLogger(__name__)
@dataclass
class __UpperCamelCase :
__A = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''Whether tp freeze the encoder.'''} )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''Whether to freeze the embeddings.'''} )
@dataclass
class __UpperCamelCase :
__A = field(
metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''} )
__A = field(
default='''summarization''' , metadata={'''help''': '''Task name, summarization (or summarization_{dataset} for pegasus) or translation'''} , )
__A = field(
default=1024 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
__A = field(
default=128 , metadata={
'''help''': (
'''The maximum total sequence length for target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
__A = field(
default=142 , metadata={
'''help''': (
'''The maximum total sequence length for validation target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded. '''
'''This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '''
'''during ``evaluate`` and ``predict``.'''
)
} , )
__A = field(
default=142 , metadata={
'''help''': (
'''The maximum total sequence length for test target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
__A = field(default=-1 , metadata={'''help''': '''# training examples. -1 means use all.'''} )
__A = field(default=-1 , metadata={'''help''': '''# validation examples. -1 means use all.'''} )
__A = field(default=-1 , metadata={'''help''': '''# test examples. -1 means use all.'''} )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''Source language id for translation.'''} )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''Target language id for translation.'''} )
__A = field(default=_UpperCAmelCase , metadata={'''help''': '''# num_beams to use for evaluation.'''} )
__A = field(
default=_UpperCAmelCase , metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''} , )
def SCREAMING_SNAKE_CASE ( lowercase_ : List[Any] , lowercase_ : int , lowercase_ : List[Any] ):
logger.info(F"""***** {split} metrics *****""" )
for key in sorted(metrics.keys() ):
logger.info(F""" {key} = {metrics[key]}""" )
save_json(lowercase_ , os.path.join(lowercase_ , F"""{split}_results.json""" ) )
def SCREAMING_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.
lowercase = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
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.
lowercase , lowercase , lowercase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase , lowercase , lowercase = parser.parse_args_into_dataclasses()
check_output_dir(lowercase_ )
# 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.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# 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""" , lowercase_ )
# Set seed
set_seed(training_args.seed )
# 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 , cache_dir=model_args.cache_dir , )
lowercase = ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
for p in extra_model_params:
if getattr(lowercase_ , lowercase_ , lowercase_ ):
assert hasattr(lowercase_ , lowercase_ ), F"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
setattr(lowercase_ , lowercase_ , getattr(lowercase_ , lowercase_ ) )
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 = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf=""".ckpt""" in model_args.model_name_or_path , config=lowercase_ , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(lowercase_ , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
lowercase = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(lowercase_ , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(lowercase_ , lowercase_ ):
lowercase = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
lowercase = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(lowercase_ )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
lowercase = SeqaSeqDataset
# Get datasets
lowercase = (
dataset_class(
lowercase_ , type_path="""train""" , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_train
else None
)
lowercase = (
dataset_class(
lowercase_ , type_path="""val""" , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
lowercase = (
dataset_class(
lowercase_ , type_path="""test""" , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_predict
else None
)
# Initialize our Trainer
lowercase = (
build_compute_metrics_fn(data_args.task , lowercase_ ) if training_args.predict_with_generate else None
)
lowercase = SeqaSeqTrainer(
model=lowercase_ , args=lowercase_ , data_args=lowercase_ , train_dataset=lowercase_ , eval_dataset=lowercase_ , data_collator=SeqaSeqDataCollator(
lowercase_ , lowercase_ , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=lowercase_ , tokenizer=lowercase_ , )
lowercase = {}
# Training
if training_args.do_train:
logger.info("""*** Train ***""" )
lowercase = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
lowercase = train_result.metrics
lowercase = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics("""train""" , lowercase_ , training_args.output_dir )
all_metrics.update(lowercase_ )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , """trainer_state.json""" ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
lowercase = trainer.evaluate(metric_key_prefix="""val""" )
lowercase = data_args.n_val
lowercase = round(metrics["""val_loss"""] , 4 )
if trainer.is_world_process_zero():
handle_metrics("""val""" , lowercase_ , training_args.output_dir )
all_metrics.update(lowercase_ )
if training_args.do_predict:
logger.info("""*** Predict ***""" )
lowercase = trainer.predict(test_dataset=lowercase_ , metric_key_prefix="""test""" )
lowercase = test_output.metrics
lowercase = data_args.n_test
if trainer.is_world_process_zero():
lowercase = round(metrics["""test_loss"""] , 4 )
handle_metrics("""test""" , lowercase_ , training_args.output_dir )
all_metrics.update(lowercase_ )
if training_args.predict_with_generate:
lowercase = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=lowercase_ , clean_up_tokenization_spaces=lowercase_ )
lowercase = lmap(str.strip , lowercase_ )
write_txt_file(lowercase_ , os.path.join(training_args.output_dir , """test_generations.txt""" ) )
if trainer.is_world_process_zero():
save_json(lowercase_ , os.path.join(training_args.output_dir , """all_results.json""" ) )
return all_metrics
def SCREAMING_SNAKE_CASE ( lowercase_ : Dict ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 653 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase_ : Any = {
'''configuration_conditional_detr''': [
'''CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''ConditionalDetrConfig''',
'''ConditionalDetrOnnxConfig''',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : Union[str, Any] = ['''ConditionalDetrFeatureExtractor''']
lowercase_ : Union[str, Any] = ['''ConditionalDetrImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : int = [
'''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
lowercase_ : Union[str, Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 653 |
'''simple docstring'''
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_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
lowercase_ : Union[str, Any] = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase )
class __UpperCamelCase (_UpperCAmelCase ):
def __init__( self , *_lowerCAmelCase , **_lowerCAmelCase ) -> Dict:
'''simple docstring'''
super().__init__(*_lowerCAmelCase , **_lowerCAmelCase )
requires_backends(self , """vision""" )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING )
def _a ( self , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase=None ) -> str:
'''simple docstring'''
lowercase = {}
lowercase = {}
if prompt is not None:
lowercase = prompt
if generate_kwargs is not None:
lowercase = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
lowercase = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
"""'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
""" please use only one""" )
lowercase = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self , _lowerCAmelCase , **_lowerCAmelCase ) -> Any:
'''simple docstring'''
return super().__call__(_lowerCAmelCase , **_lowerCAmelCase )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase=None ) -> List[str]:
'''simple docstring'''
lowercase = load_image(_lowerCAmelCase )
if prompt is not None:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise ValueError(
F"""Received an invalid text input, got - {type(_lowerCAmelCase )} - but expected a single string. """
"""Note also that one single text can be provided for conditional image to text generation.""" )
lowercase = self.model.config.model_type
if model_type == "git":
lowercase = self.image_processor(images=_lowerCAmelCase , return_tensors=self.framework )
lowercase = self.tokenizer(text=_lowerCAmelCase , add_special_tokens=_lowerCAmelCase ).input_ids
lowercase = [self.tokenizer.cls_token_id] + input_ids
lowercase = torch.tensor(_lowerCAmelCase ).unsqueeze(0 )
model_inputs.update({"""input_ids""": input_ids} )
elif model_type == "pix2struct":
lowercase = self.image_processor(images=_lowerCAmelCase , header_text=_lowerCAmelCase , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
lowercase = self.image_processor(images=_lowerCAmelCase , return_tensors=self.framework )
lowercase = self.tokenizer(_lowerCAmelCase , return_tensors=self.framework )
model_inputs.update(_lowerCAmelCase )
else:
raise ValueError(F"""Model type {model_type} does not support conditional text generation""" )
else:
lowercase = self.image_processor(images=_lowerCAmelCase , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
lowercase = None
return model_inputs
def _a ( self , _lowerCAmelCase , _lowerCAmelCase=None ) -> Union[str, Any]:
'''simple docstring'''
if (
"input_ids" in model_inputs
and isinstance(model_inputs["""input_ids"""] , _lowerCAmelCase )
and all(x is None for x in model_inputs["""input_ids"""] )
):
lowercase = None
if generate_kwargs is None:
lowercase = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
lowercase = model_inputs.pop(self.model.main_input_name )
lowercase = self.model.generate(_lowerCAmelCase , **_lowerCAmelCase , **_lowerCAmelCase )
return model_outputs
def _a ( self , _lowerCAmelCase ) -> List[str]:
'''simple docstring'''
lowercase = []
for output_ids in model_outputs:
lowercase = {
"""generated_text""": self.tokenizer.decode(
_lowerCAmelCase , skip_special_tokens=_lowerCAmelCase , )
}
records.append(_lowerCAmelCase )
return records
| 653 | 1 |
'''simple docstring'''
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
lowercase_ : int = 50_0000
lowercase_ , lowercase_ : Union[str, Any] = os.path.split(__file__)
lowercase_ : Optional[Any] = os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))
@get_duration
def SCREAMING_SNAKE_CASE ( lowercase_ : datasets.Dataset , **lowercase_ : Dict ):
lowercase = dataset.map(**lowercase_ )
@get_duration
def SCREAMING_SNAKE_CASE ( lowercase_ : datasets.Dataset , **lowercase_ : Optional[int] ):
lowercase = dataset.filter(**lowercase_ )
def SCREAMING_SNAKE_CASE ( ):
lowercase = {"""num examples""": SPEED_TEST_N_EXAMPLES}
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase = datasets.Features({"""text""": datasets.Value("""string""" ), """numbers""": datasets.Value("""float32""" )} )
lowercase = generate_example_dataset(
os.path.join(lowercase_ , """dataset.arrow""" ) , lowercase_ , num_examples=lowercase_ )
lowercase = transformers.AutoTokenizer.from_pretrained("""bert-base-cased""" , use_fast=lowercase_ )
def tokenize(lowercase_ : Dict ):
return tokenizer(examples["""text"""] )
lowercase = map(lowercase_ )
lowercase = map(lowercase_ , batched=lowercase_ )
lowercase = map(lowercase_ , function=lambda lowercase_ : None , batched=lowercase_ )
with dataset.formatted_as(type="""numpy""" ):
lowercase = map(lowercase_ , function=lambda lowercase_ : None , batched=lowercase_ )
with dataset.formatted_as(type="""pandas""" ):
lowercase = map(lowercase_ , function=lambda lowercase_ : None , batched=lowercase_ )
with dataset.formatted_as(type="""torch""" , columns="""numbers""" ):
lowercase = map(lowercase_ , function=lambda lowercase_ : None , batched=lowercase_ )
with dataset.formatted_as(type="""tensorflow""" , columns="""numbers""" ):
lowercase = map(lowercase_ , function=lambda lowercase_ : None , batched=lowercase_ )
lowercase = map(lowercase_ , function=lowercase_ , batched=lowercase_ )
lowercase = filter(lowercase_ )
# Activate later when tokenizer support batched inputs
# with dataset.formatted_as(type='numpy'):
# times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)
with open(lowercase_ , """wb""" ) as f:
f.write(json.dumps(lowercase_ ).encode("""utf-8""" ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_map_filter()
| 653 |
'''simple docstring'''
from ... import PretrainedConfig
lowercase_ : int = {
'''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}
class __UpperCamelCase (_UpperCAmelCase ):
__A = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
__A = '''nezha'''
def __init__( self , _lowerCAmelCase=2_1128 , _lowerCAmelCase=768 , _lowerCAmelCase=12 , _lowerCAmelCase=12 , _lowerCAmelCase=3072 , _lowerCAmelCase="gelu" , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=512 , _lowerCAmelCase=64 , _lowerCAmelCase=2 , _lowerCAmelCase=0.02 , _lowerCAmelCase=1E-12 , _lowerCAmelCase=0.1 , _lowerCAmelCase=0 , _lowerCAmelCase=2 , _lowerCAmelCase=3 , _lowerCAmelCase=True , **_lowerCAmelCase , ) -> int:
'''simple docstring'''
super().__init__(pad_token_id=_lowerCAmelCase , bos_token_id=_lowerCAmelCase , eos_token_id=_lowerCAmelCase , **_lowerCAmelCase )
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = hidden_act
lowercase = intermediate_size
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = max_relative_position
lowercase = type_vocab_size
lowercase = initializer_range
lowercase = layer_norm_eps
lowercase = classifier_dropout
lowercase = use_cache
| 653 | 1 |
'''simple docstring'''
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
AlbertConfig,
BartConfig,
BertConfig,
CamembertConfig,
CTRLConfig,
DistilBertConfig,
DPRConfig,
ElectraConfig,
FlaubertConfig,
GPTaConfig,
LayoutLMConfig,
LxmertConfig,
OpenAIGPTConfig,
RobertaConfig,
TaConfig,
TFAlbertForPreTraining,
TFBartForConditionalGeneration,
TFBartForSequenceClassification,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFCamembertForMaskedLM,
TFCTRLLMHeadModel,
TFDistilBertForMaskedLM,
TFDistilBertForQuestionAnswering,
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
TFElectraForPreTraining,
TFFlaubertWithLMHeadModel,
TFGPTaLMHeadModel,
TFLayoutLMForMaskedLM,
TFLxmertForPreTraining,
TFLxmertVisualFeatureEncoder,
TFOpenAIGPTLMHeadModel,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForSequenceClassification,
TFTaForConditionalGeneration,
TFTransfoXLLMHeadModel,
TFWavaVecaModel,
TFXLMRobertaForMaskedLM,
TFXLMWithLMHeadModel,
TFXLNetLMHeadModel,
TransfoXLConfig,
WavaVecaConfig,
WavaVecaModel,
XLMConfig,
XLMRobertaConfig,
XLNetConfig,
is_torch_available,
load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging
if is_torch_available():
import numpy as np
import torch
from . import (
AlbertForPreTraining,
BartForConditionalGeneration,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
CamembertForMaskedLM,
CTRLLMHeadModel,
DistilBertForMaskedLM,
DistilBertForQuestionAnswering,
DPRContextEncoder,
DPRQuestionEncoder,
DPRReader,
ElectraForPreTraining,
FlaubertWithLMHeadModel,
GPTaLMHeadModel,
LayoutLMForMaskedLM,
LxmertForPreTraining,
LxmertVisualFeatureEncoder,
OpenAIGPTLMHeadModel,
RobertaForMaskedLM,
RobertaForSequenceClassification,
TaForConditionalGeneration,
TransfoXLLMHeadModel,
XLMRobertaForMaskedLM,
XLMWithLMHeadModel,
XLNetLMHeadModel,
)
logging.set_verbosity_info()
lowercase_ : Optional[int] = {
'''bart''': (
BartConfig,
TFBartForConditionalGeneration,
TFBartForSequenceClassification,
BartForConditionalGeneration,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
),
'''bert''': (
BertConfig,
TFBertForPreTraining,
BertForPreTraining,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''bert-large-uncased-whole-word-masking-finetuned-squad''': (
BertConfig,
TFBertForQuestionAnswering,
BertForQuestionAnswering,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''bert-large-cased-whole-word-masking-finetuned-squad''': (
BertConfig,
TFBertForQuestionAnswering,
BertForQuestionAnswering,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''bert-base-cased-finetuned-mrpc''': (
BertConfig,
TFBertForSequenceClassification,
BertForSequenceClassification,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''dpr''': (
DPRConfig,
TFDPRQuestionEncoder,
TFDPRContextEncoder,
TFDPRReader,
DPRQuestionEncoder,
DPRContextEncoder,
DPRReader,
DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
),
'''gpt2''': (
GPTaConfig,
TFGPTaLMHeadModel,
GPTaLMHeadModel,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''xlnet''': (
XLNetConfig,
TFXLNetLMHeadModel,
XLNetLMHeadModel,
XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''xlm''': (
XLMConfig,
TFXLMWithLMHeadModel,
XLMWithLMHeadModel,
XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''xlm-roberta''': (
XLMRobertaConfig,
TFXLMRobertaForMaskedLM,
XLMRobertaForMaskedLM,
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''transfo-xl''': (
TransfoXLConfig,
TFTransfoXLLMHeadModel,
TransfoXLLMHeadModel,
TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''openai-gpt''': (
OpenAIGPTConfig,
TFOpenAIGPTLMHeadModel,
OpenAIGPTLMHeadModel,
OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''roberta''': (
RobertaConfig,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
RobertaForMaskedLM,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''layoutlm''': (
LayoutLMConfig,
TFLayoutLMForMaskedLM,
LayoutLMForMaskedLM,
LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
),
'''roberta-large-mnli''': (
RobertaConfig,
TFRobertaForSequenceClassification,
RobertaForSequenceClassification,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''camembert''': (
CamembertConfig,
TFCamembertForMaskedLM,
CamembertForMaskedLM,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''flaubert''': (
FlaubertConfig,
TFFlaubertWithLMHeadModel,
FlaubertWithLMHeadModel,
FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''distilbert''': (
DistilBertConfig,
TFDistilBertForMaskedLM,
DistilBertForMaskedLM,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''distilbert-base-distilled-squad''': (
DistilBertConfig,
TFDistilBertForQuestionAnswering,
DistilBertForQuestionAnswering,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''lxmert''': (
LxmertConfig,
TFLxmertForPreTraining,
LxmertForPreTraining,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''lxmert-visual-feature-encoder''': (
LxmertConfig,
TFLxmertVisualFeatureEncoder,
LxmertVisualFeatureEncoder,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''ctrl''': (
CTRLConfig,
TFCTRLLMHeadModel,
CTRLLMHeadModel,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''albert''': (
AlbertConfig,
TFAlbertForPreTraining,
AlbertForPreTraining,
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''t5''': (
TaConfig,
TFTaForConditionalGeneration,
TaForConditionalGeneration,
T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''electra''': (
ElectraConfig,
TFElectraForPreTraining,
ElectraForPreTraining,
ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''wav2vec2''': (
WavaVecaConfig,
TFWavaVecaModel,
WavaVecaModel,
WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
}
def SCREAMING_SNAKE_CASE ( lowercase_ : Tuple , lowercase_ : Dict , lowercase_ : Union[str, Any] , lowercase_ : str , lowercase_ : List[str]=False , lowercase_ : str=True ):
if model_type not in MODEL_CLASSES:
raise ValueError(F"""Unrecognized model type, should be one of {list(MODEL_CLASSES.keys() )}.""" )
lowercase , lowercase , lowercase , lowercase = MODEL_CLASSES[model_type]
# Initialise TF model
if config_file in aws_config_map:
lowercase = cached_file(lowercase_ , lowercase_ , force_download=not use_cached_models )
lowercase = config_class.from_json_file(lowercase_ )
lowercase = True
lowercase = True
print(F"""Building TensorFlow model from configuration: {config}""" )
lowercase = model_class(lowercase_ )
# Load weights from tf checkpoint
if pytorch_checkpoint_path in aws_config_map.keys():
lowercase = cached_file(
lowercase_ , lowercase_ , force_download=not use_cached_models )
# Load PyTorch checkpoint in tf2 model:
lowercase = load_pytorch_checkpoint_in_tfa_model(lowercase_ , lowercase_ )
if compare_with_pt_model:
lowercase = tf_model(tf_model.dummy_inputs , training=lowercase_ ) # build the network
lowercase = torch.load(lowercase_ , map_location="""cpu""" )
lowercase = pt_model_class.from_pretrained(
pretrained_model_name_or_path=lowercase_ , config=lowercase_ , state_dict=lowercase_ )
with torch.no_grad():
lowercase = pt_model(**pt_model.dummy_inputs )
lowercase = pto[0].numpy()
lowercase = tfo[0].numpy()
lowercase = np.amax(np.abs(np_pt - np_tf ) )
print(F"""Max absolute difference between models outputs {diff}""" )
assert diff <= 2E-2, F"""Error, model absolute difference is >2e-2: {diff}"""
# Save pytorch-model
print(F"""Save TensorFlow model to {tf_dump_path}""" )
tf_model.save_weights(lowercase_ , save_format="""h5""" )
def SCREAMING_SNAKE_CASE ( lowercase_ : Tuple , lowercase_ : int , lowercase_ : Tuple=None , lowercase_ : Optional[int]=None , lowercase_ : int=False , lowercase_ : List[str]=False , lowercase_ : Dict=False , lowercase_ : List[Any]=False , ):
if args_model_type is None:
lowercase = list(MODEL_CLASSES.keys() )
else:
lowercase = [args_model_type]
for j, model_type in enumerate(lowercase_ , start=1 ):
print("""=""" * 100 )
print(F""" Converting model type {j}/{len(lowercase_ )}: {model_type}""" )
print("""=""" * 100 )
if model_type not in MODEL_CLASSES:
raise ValueError(F"""Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys() )}.""" )
lowercase , lowercase , lowercase , lowercase , lowercase = MODEL_CLASSES[model_type]
if model_shortcut_names_or_path is None:
lowercase = list(aws_model_maps.keys() )
if config_shortcut_names_or_path is None:
lowercase = model_shortcut_names_or_path
for i, (model_shortcut_name, config_shortcut_name) in enumerate(
zip(lowercase_ , lowercase_ ) , start=1 ):
print("""-""" * 100 )
if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
if not only_convert_finetuned_models:
print(F""" Skipping finetuned checkpoint {model_shortcut_name}""" )
continue
lowercase = model_shortcut_name
elif only_convert_finetuned_models:
print(F""" Skipping not finetuned checkpoint {model_shortcut_name}""" )
continue
print(
F""" Converting checkpoint {i}/{len(lowercase_ )}: {model_shortcut_name} - model_type {model_type}""" )
print("""-""" * 100 )
if config_shortcut_name in aws_config_map:
lowercase = cached_file(lowercase_ , lowercase_ , force_download=not use_cached_models )
else:
lowercase = config_shortcut_name
if model_shortcut_name in aws_model_maps:
lowercase = cached_file(lowercase_ , lowercase_ , force_download=not use_cached_models )
else:
lowercase = model_shortcut_name
if os.path.isfile(lowercase_ ):
lowercase = """converted_model"""
convert_pt_checkpoint_to_tf(
model_type=lowercase_ , pytorch_checkpoint_path=lowercase_ , config_file=lowercase_ , tf_dump_path=os.path.join(lowercase_ , model_shortcut_name + """-tf_model.h5""" ) , compare_with_pt_model=lowercase_ , )
if remove_cached_files:
os.remove(lowercase_ )
os.remove(lowercase_ )
if __name__ == "__main__":
lowercase_ : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_dump_path''', default=None, type=str, required=True, help='''Path to the output Tensorflow dump file.'''
)
parser.add_argument(
'''--model_type''',
default=None,
type=str,
help=(
f'''Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '''
'''convert all the models from AWS.'''
),
)
parser.add_argument(
'''--pytorch_checkpoint_path''',
default=None,
type=str,
help=(
'''Path to the PyTorch checkpoint path or shortcut name to download from AWS. '''
'''If not given, will download and convert all the checkpoints from AWS.'''
),
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
help=(
'''The config json file corresponding to the pre-trained model. \n'''
'''This specifies the model architecture. If not given and '''
'''--pytorch_checkpoint_path is not given or is a shortcut name '''
'''use the configuration associated to the shortcut name on the AWS'''
),
)
parser.add_argument(
'''--compare_with_pt_model''', action='''store_true''', help='''Compare Tensorflow and PyTorch model predictions.'''
)
parser.add_argument(
'''--use_cached_models''',
action='''store_true''',
help='''Use cached models if possible instead of updating to latest checkpoint versions.''',
)
parser.add_argument(
'''--remove_cached_files''',
action='''store_true''',
help='''Remove pytorch models after conversion (save memory when converting in batches).''',
)
parser.add_argument('''--only_convert_finetuned_models''', action='''store_true''', help='''Only convert finetuned models.''')
lowercase_ : Union[str, Any] = parser.parse_args()
# if args.pytorch_checkpoint_path is not None:
# convert_pt_checkpoint_to_tf(args.model_type.lower(),
# args.pytorch_checkpoint_path,
# args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
# args.tf_dump_path,
# compare_with_pt_model=args.compare_with_pt_model,
# use_cached_models=args.use_cached_models)
# else:
convert_all_pt_checkpoints_to_tf(
args.model_type.lower() if args.model_type is not None else None,
args.tf_dump_path,
model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
if args.pytorch_checkpoint_path is not None
else None,
config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
compare_with_pt_model=args.compare_with_pt_model,
use_cached_models=args.use_cached_models,
remove_cached_files=args.remove_cached_files,
only_convert_finetuned_models=args.only_convert_finetuned_models,
)
| 653 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
lowercase_ : Tuple = logging.getLogger(__name__)
def SCREAMING_SNAKE_CASE ( lowercase_ : str ):
lowercase = git.Repo(search_parent_directories=lowercase_ )
lowercase = {
"""repo_id""": str(lowercase_ ),
"""repo_sha""": str(repo.head.object.hexsha ),
"""repo_branch""": str(repo.active_branch ),
}
with open(os.path.join(lowercase_ , """git_log.json""" ) , """w""" ) as f:
json.dump(lowercase_ , lowercase_ , indent=4 )
def SCREAMING_SNAKE_CASE ( lowercase_ : str ):
if params.n_gpu <= 0:
lowercase = 0
lowercase = -1
lowercase = True
lowercase = False
return
assert torch.cuda.is_available()
logger.info("""Initializing GPUs""" )
if params.n_gpu > 1:
assert params.local_rank != -1
lowercase = int(os.environ["""WORLD_SIZE"""] )
lowercase = int(os.environ["""N_GPU_NODE"""] )
lowercase = int(os.environ["""RANK"""] )
# number of nodes / node ID
lowercase = params.world_size // params.n_gpu_per_node
lowercase = params.global_rank // params.n_gpu_per_node
lowercase = True
assert params.n_nodes == int(os.environ["""N_NODES"""] )
assert params.node_id == int(os.environ["""NODE_RANK"""] )
# local job (single GPU)
else:
assert params.local_rank == -1
lowercase = 1
lowercase = 0
lowercase = 0
lowercase = 0
lowercase = 1
lowercase = 1
lowercase = False
# sanity checks
assert params.n_nodes >= 1
assert 0 <= params.node_id < params.n_nodes
assert 0 <= params.local_rank <= params.global_rank < params.world_size
assert params.world_size == params.n_nodes * params.n_gpu_per_node
# define whether this is the master process / if we are in multi-node distributed mode
lowercase = params.node_id == 0 and params.local_rank == 0
lowercase = params.n_nodes > 1
# summary
lowercase = F"""--- Global rank: {params.global_rank} - """
logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes )
logger.info(PREFIX + """Node ID : %i""" % params.node_id )
logger.info(PREFIX + """Local rank : %i""" % params.local_rank )
logger.info(PREFIX + """World size : %i""" % params.world_size )
logger.info(PREFIX + """GPUs per node : %i""" % params.n_gpu_per_node )
logger.info(PREFIX + """Master : %s""" % str(params.is_master ) )
logger.info(PREFIX + """Multi-node : %s""" % str(params.multi_node ) )
logger.info(PREFIX + """Multi-GPU : %s""" % str(params.multi_gpu ) )
logger.info(PREFIX + """Hostname : %s""" % socket.gethostname() )
# set GPU device
torch.cuda.set_device(params.local_rank )
# initialize multi-GPU
if params.multi_gpu:
logger.info("""Initializing PyTorch distributed""" )
torch.distributed.init_process_group(
init_method="""env://""" , backend="""nccl""" , )
def SCREAMING_SNAKE_CASE ( lowercase_ : Optional[Any] ):
np.random.seed(args.seed )
torch.manual_seed(args.seed )
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed )
| 653 | 1 |
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