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"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}
lowercase__ = {
'vocab_file': {
'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
'allenai/longformer-large-4096': (
'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
),
'allenai/longformer-large-4096-finetuned-triviaqa': (
'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
),
'allenai/longformer-base-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
),
'allenai/longformer-large-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
),
},
'merges_file': {
'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
'allenai/longformer-large-4096': (
'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
),
'allenai/longformer-large-4096-finetuned-triviaqa': (
'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
),
'allenai/longformer-base-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
),
'allenai/longformer-large-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
),
},
}
lowercase__ = {
'allenai/longformer-base-4096': 4096,
'allenai/longformer-large-4096': 4096,
'allenai/longformer-large-4096-finetuned-triviaqa': 4096,
'allenai/longformer-base-4096-extra.pos.embd.only': 4096,
'allenai/longformer-large-4096-extra.pos.embd.only': 4096,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def _snake_case ( ):
_lowerCamelCase : Any = (
list(range(ord('!' ) , ord('~' ) + 1 ) ) + list(range(ord('¡' ) , ord('¬' ) + 1 ) ) + list(range(ord('®' ) , ord('ÿ' ) + 1 ) )
)
_lowerCamelCase : int = bs[:]
_lowerCamelCase : str = 0
for b in range(2**8 ):
if b not in bs:
bs.append(UpperCAmelCase_ )
cs.append(2**8 + n )
n += 1
_lowerCamelCase : Tuple = [chr(UpperCAmelCase_ ) for n in cs]
return dict(zip(UpperCAmelCase_ , UpperCAmelCase_ ) )
def _snake_case ( lowercase__ ):
_lowerCamelCase : List[str] = set()
_lowerCamelCase : List[Any] = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_lowerCamelCase : str = char
return pairs
class lowerCAmelCase__ ( _lowerCamelCase ):
'''simple docstring'''
lowerCamelCase__ = VOCAB_FILES_NAMES
lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ = ["""input_ids""", """attention_mask"""]
def __init__( self , lowercase , lowercase , lowercase="replace" , lowercase="<s>" , lowercase="</s>" , lowercase="</s>" , lowercase="<s>" , lowercase="<unk>" , lowercase="<pad>" , lowercase="<mask>" , lowercase=False , **lowercase , ):
_lowerCamelCase : List[str] = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else bos_token
_lowerCamelCase : Optional[int] = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else eos_token
_lowerCamelCase : Tuple = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else sep_token
_lowerCamelCase : Tuple = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else cls_token
_lowerCamelCase : List[Any] = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else unk_token
_lowerCamelCase : int = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
_lowerCamelCase : Tuple = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else mask_token
super().__init__(
errors=lowercase , bos_token=lowercase , eos_token=lowercase , unk_token=lowercase , sep_token=lowercase , cls_token=lowercase , pad_token=lowercase , mask_token=lowercase , add_prefix_space=lowercase , **lowercase , )
with open(lowercase , encoding='utf-8' ) as vocab_handle:
_lowerCamelCase : Union[str, Any] = json.load(lowercase )
_lowerCamelCase : Union[str, Any] = {v: k for k, v in self.encoder.items()}
_lowerCamelCase : Optional[Any] = errors # how to handle errors in decoding
_lowerCamelCase : List[Any] = bytes_to_unicode()
_lowerCamelCase : Optional[Any] = {v: k for k, v in self.byte_encoder.items()}
with open(lowercase , encoding='utf-8' ) as merges_handle:
_lowerCamelCase : List[str] = merges_handle.read().split('\n' )[1:-1]
_lowerCamelCase : List[Any] = [tuple(merge.split() ) for merge in bpe_merges]
_lowerCamelCase : int = dict(zip(lowercase , range(len(lowercase ) ) ) )
_lowerCamelCase : Union[str, Any] = {}
_lowerCamelCase : List[str] = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
_lowerCamelCase : List[str] = re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )
@property
def A_ ( self ):
return len(self.encoder )
def A_ ( self ):
return dict(self.encoder , **self.added_tokens_encoder )
def A_ ( self , lowercase ):
if token in self.cache:
return self.cache[token]
_lowerCamelCase : Optional[int] = tuple(lowercase )
_lowerCamelCase : Tuple = get_pairs(lowercase )
if not pairs:
return token
while True:
_lowerCamelCase : List[str] = min(lowercase , key=lambda lowercase : self.bpe_ranks.get(lowercase , float('inf' ) ) )
if bigram not in self.bpe_ranks:
break
_lowerCamelCase, _lowerCamelCase : Optional[Any] = bigram
_lowerCamelCase : Tuple = []
_lowerCamelCase : Union[str, Any] = 0
while i < len(lowercase ):
try:
_lowerCamelCase : Tuple = word.index(lowercase , lowercase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_lowerCamelCase : Any = j
if word[i] == first and i < len(lowercase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_lowerCamelCase : Any = tuple(lowercase )
_lowerCamelCase : Tuple = new_word
if len(lowercase ) == 1:
break
else:
_lowerCamelCase : Optional[Any] = get_pairs(lowercase )
_lowerCamelCase : List[Any] = ' '.join(lowercase )
_lowerCamelCase : Any = word
return word
def A_ ( self , lowercase ):
_lowerCamelCase : Optional[Any] = []
for token in re.findall(self.pat , lowercase ):
_lowerCamelCase : Dict = ''.join(
self.byte_encoder[b] for b in token.encode('utf-8' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowercase ).split(' ' ) )
return bpe_tokens
def A_ ( self , lowercase ):
return self.encoder.get(lowercase , self.encoder.get(self.unk_token ) )
def A_ ( self , lowercase ):
return self.decoder.get(lowercase )
def A_ ( self , lowercase ):
_lowerCamelCase : List[Any] = ''.join(lowercase )
_lowerCamelCase : Optional[int] = bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8' , errors=self.errors )
return text
def A_ ( self , lowercase , lowercase = None ):
if not os.path.isdir(lowercase ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
_lowerCamelCase : Optional[int] = os.path.join(
lowercase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
_lowerCamelCase : Union[str, Any] = os.path.join(
lowercase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )
with open(lowercase , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=lowercase , ensure_ascii=lowercase ) + '\n' )
_lowerCamelCase : str = 0
with open(lowercase , 'w' , encoding='utf-8' ) as writer:
writer.write('#version: 0.2\n' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda lowercase : kv[1] ):
if index != token_index:
logger.warning(
F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
' Please check that the tokenizer is not corrupted!' )
_lowerCamelCase : List[Any] = token_index
writer.write(' '.join(lowercase ) + '\n' )
index += 1
return vocab_file, merge_file
def A_ ( self , lowercase , lowercase = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_lowerCamelCase : Optional[Any] = [self.cls_token_id]
_lowerCamelCase : Union[str, Any] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def A_ ( self , lowercase , lowercase = None , lowercase = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowercase , token_ids_a=lowercase , already_has_special_tokens=lowercase )
if token_ids_a is None:
return [1] + ([0] * len(lowercase )) + [1]
return [1] + ([0] * len(lowercase )) + [1, 1] + ([0] * len(lowercase )) + [1]
def A_ ( self , lowercase , lowercase = None ):
_lowerCamelCase : Tuple = [self.sep_token_id]
_lowerCamelCase : Dict = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def A_ ( self , lowercase , lowercase=False , **lowercase ):
_lowerCamelCase : Tuple = kwargs.pop('add_prefix_space' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(lowercase ) > 0 and not text[0].isspace()):
_lowerCamelCase : Dict = ' ' + text
return (text, kwargs) | 96 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ : int = {
'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ : Tuple = [
'MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'MegatronBertForCausalLM',
'MegatronBertForMaskedLM',
'MegatronBertForMultipleChoice',
'MegatronBertForNextSentencePrediction',
'MegatronBertForPreTraining',
'MegatronBertForQuestionAnswering',
'MegatronBertForSequenceClassification',
'MegatronBertForTokenClassification',
'MegatronBertModel',
'MegatronBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_megatron_bert import (
MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
MegatronBertPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_ : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 335 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowercase ( _lowerCamelCase , unittest.TestCase):
__lowerCAmelCase : Dict = BioGptTokenizer
__lowerCAmelCase : Optional[int] = False
def a_ ( self : Optional[Any] ):
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
A_ : List[str] = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''w</w>''',
'''r</w>''',
'''t</w>''',
'''lo''',
'''low''',
'''er</w>''',
'''low</w>''',
'''lowest</w>''',
'''newer</w>''',
'''wider</w>''',
'''<unk>''',
]
A_ : Optional[int] = dict(zip(_lowerCamelCase , range(len(_lowerCamelCase ) ) ) )
A_ : Optional[Any] = ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']
A_ : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
A_ : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' ) as fp:
fp.write(json.dumps(_lowerCamelCase ) )
with open(self.merges_file , '''w''' ) as fp:
fp.write('''\n'''.join(_lowerCamelCase ) )
def a_ ( self : int , _lowerCamelCase : Any ):
"""simple docstring"""
A_ : int = '''lower newer'''
A_ : str = '''lower newer'''
return input_text, output_text
def a_ ( self : int ):
"""simple docstring"""
A_ : Tuple = BioGptTokenizer(self.vocab_file , self.merges_file )
A_ : List[str] = '''lower'''
A_ : Optional[Any] = ['''low''', '''er</w>''']
A_ : Optional[int] = tokenizer.tokenize(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
A_ : Union[str, Any] = tokens + ['''<unk>''']
A_ : List[Any] = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase ) , _lowerCamelCase )
@slow
def a_ ( self : Any ):
"""simple docstring"""
A_ : Optional[Any] = BioGptTokenizer.from_pretrained('''microsoft/biogpt''' )
A_ : List[Any] = tokenizer.encode('''sequence builders''' , add_special_tokens=_lowerCamelCase )
A_ : Dict = tokenizer.encode('''multi-sequence build''' , add_special_tokens=_lowerCamelCase )
A_ : Optional[Any] = tokenizer.build_inputs_with_special_tokens(_lowerCamelCase )
A_ : str = tokenizer.build_inputs_with_special_tokens(_lowerCamelCase , _lowerCamelCase )
self.assertTrue(encoded_sentence == [2] + text )
self.assertTrue(encoded_pair == [2] + text + [2] + text_a )
| 167 |
"""simple docstring"""
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"""kwargs, expected""" , [
({"""num_shards""": 0, """max_num_jobs""": 1}, []),
({"""num_shards""": 10, """max_num_jobs""": 1}, [range(10 )]),
({"""num_shards""": 10, """max_num_jobs""": 10}, [range(UpperCAmelCase_ , i + 1 ) for i in range(10 )]),
({"""num_shards""": 1, """max_num_jobs""": 10}, [range(1 )]),
({"""num_shards""": 10, """max_num_jobs""": 3}, [range(0 , 4 ), range(4 , 7 ), range(7 , 10 )]),
({"""num_shards""": 3, """max_num_jobs""": 10}, [range(0 , 1 ), range(1 , 2 ), range(2 , 3 )]),
] , )
def _snake_case ( UpperCAmelCase_ : List[str] , UpperCAmelCase_ : int ):
A__ = _distribute_shards(**UpperCAmelCase_ )
assert out == expected
@pytest.mark.parametrize(
"""gen_kwargs, max_num_jobs, expected""" , [
({"""foo""": 0}, 10, [{"""foo""": 0}]),
({"""shards""": [0, 1, 2, 3]}, 1, [{"""shards""": [0, 1, 2, 3]}]),
({"""shards""": [0, 1, 2, 3]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}, {"""shards""": [2]}, {"""shards""": [3]}]),
({"""shards""": [0, 1]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}]),
({"""shards""": [0, 1, 2, 3]}, 2, [{"""shards""": [0, 1]}, {"""shards""": [2, 3]}]),
] , )
def _snake_case ( UpperCAmelCase_ : str , UpperCAmelCase_ : Dict , UpperCAmelCase_ : List[Any] ):
A__ = _split_gen_kwargs(UpperCAmelCase_ , UpperCAmelCase_ )
assert out == expected
@pytest.mark.parametrize(
"""gen_kwargs, expected""" , [
({"""foo""": 0}, 1),
({"""shards""": [0]}, 1),
({"""shards""": [0, 1, 2, 3]}, 4),
({"""shards""": [0, 1, 2, 3], """foo""": 0}, 4),
({"""shards""": [0, 1, 2, 3], """other""": (0, 1)}, 4),
({"""shards""": [0, 1, 2, 3], """shards2""": [0, 1]}, RuntimeError),
] , )
def _snake_case ( UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Optional[int] ):
if expected is RuntimeError:
with pytest.raises(UpperCAmelCase_ ):
_number_of_shards_in_gen_kwargs(UpperCAmelCase_ )
else:
A__ = _number_of_shards_in_gen_kwargs(UpperCAmelCase_ )
assert out == expected
| 335 | 0 |
'''simple docstring'''
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
ROBERTA_INPUTS_DOCSTRING,
ROBERTA_START_DOCSTRING,
RobertaEmbeddings,
)
from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy
@add_start_docstrings(
"The RoBERTa Model transformer with early exiting (DeeRoBERTa). " ,_lowerCamelCase ,)
class lowerCAmelCase_ ( _lowerCamelCase ):
__lowerCamelCase : List[Any] = RobertaConfig
__lowerCamelCase : List[str] = "roberta"
def __init__( self , _lowerCAmelCase ) -> Any:
super().__init__(_lowerCAmelCase )
_lowerCAmelCase = RobertaEmbeddings(_lowerCAmelCase )
self.init_weights()
@add_start_docstrings(
"RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n also takes care of multi-layer training. " ,_lowerCamelCase ,)
class lowerCAmelCase_ ( _lowerCamelCase ):
__lowerCamelCase : List[str] = RobertaConfig
__lowerCamelCase : Tuple = "roberta"
def __init__( self , _lowerCAmelCase ) -> Optional[int]:
super().__init__(_lowerCAmelCase )
_lowerCAmelCase = config.num_labels
_lowerCAmelCase = config.num_hidden_layers
_lowerCAmelCase = DeeRobertaModel(_lowerCAmelCase )
_lowerCAmelCase = nn.Dropout(config.hidden_dropout_prob )
_lowerCAmelCase = nn.Linear(config.hidden_size , self.config.num_labels )
@add_start_docstrings_to_model_forward(_lowerCAmelCase )
def _snake_case ( self , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase=-1 , _lowerCAmelCase=False , ) -> Dict:
_lowerCAmelCase = self.num_layers
try:
_lowerCAmelCase = self.roberta(
_lowerCAmelCase , attention_mask=_lowerCAmelCase , token_type_ids=_lowerCAmelCase , position_ids=_lowerCAmelCase , head_mask=_lowerCAmelCase , inputs_embeds=_lowerCAmelCase , )
_lowerCAmelCase = outputs[1]
_lowerCAmelCase = self.dropout(_lowerCAmelCase )
_lowerCAmelCase = self.classifier(_lowerCAmelCase )
_lowerCAmelCase = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
_lowerCAmelCase = e.message
_lowerCAmelCase = e.exit_layer
_lowerCAmelCase = outputs[0]
if not self.training:
_lowerCAmelCase = entropy(_lowerCAmelCase )
_lowerCAmelCase = []
_lowerCAmelCase = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
_lowerCAmelCase = MSELoss()
_lowerCAmelCase = loss_fct(logits.view(-1 ) , labels.view(-1 ) )
else:
_lowerCAmelCase = CrossEntropyLoss()
_lowerCAmelCase = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
# work with highway exits
_lowerCAmelCase = []
for highway_exit in outputs[-1]:
_lowerCAmelCase = highway_exit[0]
if not self.training:
highway_logits_all.append(_lowerCAmelCase )
highway_entropy.append(highway_exit[2] )
if self.num_labels == 1:
# We are doing regression
_lowerCAmelCase = MSELoss()
_lowerCAmelCase = loss_fct(highway_logits.view(-1 ) , labels.view(-1 ) )
else:
_lowerCAmelCase = CrossEntropyLoss()
_lowerCAmelCase = loss_fct(highway_logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
highway_losses.append(_lowerCAmelCase )
if train_highway:
_lowerCAmelCase = (sum(highway_losses[:-1] ),) + outputs
# exclude the final highway, of course
else:
_lowerCAmelCase = (loss,) + outputs
if not self.training:
_lowerCAmelCase = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
_lowerCAmelCase = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), entropy
| 158 |
"""simple docstring"""
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
SCREAMING_SNAKE_CASE_ : str = parse(importlib.metadata.version('torch'))
def _snake_case ( UpperCAmelCase_ : Union[str, Version] , UpperCAmelCase_ : str , UpperCAmelCase_ : str ):
if operation not in STR_OPERATION_TO_FUNC.keys():
raise ValueError(F"""`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys() )}, received {operation}""" )
A__ = STR_OPERATION_TO_FUNC[operation]
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
A__ = parse(importlib.metadata.version(UpperCAmelCase_ ) )
return operation(UpperCAmelCase_ , parse(UpperCAmelCase_ ) )
def _snake_case ( UpperCAmelCase_ : str , UpperCAmelCase_ : str ):
return compare_versions(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
| 335 | 0 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
lowercase__ =[
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
['memory_attention', 'encoder_attn'],
['attention', 'attn'],
['/', '.'],
['.LayerNorm.gamma', '_layer_norm.weight'],
['.LayerNorm.beta', '_layer_norm.bias'],
['r.layer_', 'r.layers.'],
['output_proj', 'out_proj'],
['ffn.dense_1.', 'fc2.'],
['ffn.dense.', 'fc1.'],
['ffn_layer_norm', 'final_layer_norm'],
['kernel', 'weight'],
['encoder_layer_norm.', 'encoder.layer_norm.'],
['decoder_layer_norm.', 'decoder.layer_norm.'],
['embeddings.weights', 'shared.weight'],
]
def __UpperCamelCase ( lowerCAmelCase__ : Dict ):
for pegasus_name, hf_name in PATTERNS:
__a : str = k.replace(UpperCAmelCase_ , UpperCAmelCase_ )
return k
def __UpperCamelCase ( lowerCAmelCase__ : dict , lowerCAmelCase__ : dict ):
__a : Optional[Any] = DEFAULTS.copy()
cfg_kwargs.update(UpperCAmelCase_ )
__a : Union[str, Any] = PegasusConfig(**UpperCAmelCase_ )
__a : Dict = PegasusForConditionalGeneration(UpperCAmelCase_ )
__a : List[Any] = torch_model.model.state_dict()
__a : Optional[int] = {}
for k, v in tf_weights.items():
__a : int = rename_state_dict_key(UpperCAmelCase_ )
if new_k not in sd:
raise ValueError(f"could not find new key {new_k} in state dict. (converted from {k})" )
if "dense" in k or "proj" in new_k:
__a : Any = v.T
__a : Dict = torch.tensor(UpperCAmelCase_ , dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, f"{new_k}, {k}, {v.shape}, {sd[new_k].shape}"
# make sure embedding.padding_idx is respected
__a : str = torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1] )
__a : List[str] = mapping['''shared.weight''']
__a : Optional[Any] = mapping['''shared.weight''']
__a : Optional[int] = {k: torch.zeros_like(UpperCAmelCase_ ) for k, v in sd.items() if k.endswith('''bias''' ) and k not in mapping}
mapping.update(**UpperCAmelCase_ )
__a , __a : int = torch_model.model.load_state_dict(UpperCAmelCase_ , strict=UpperCAmelCase_ )
__a : Union[str, Any] = [
k for k in missing if k not in ['''encoder.embed_positions.weight''', '''decoder.embed_positions.weight''']
]
assert unexpected_missing == [], f"no matches found for the following torch keys {unexpected_missing}"
assert extra == [], f"no matches found for the following tf keys {extra}"
return torch_model
def __UpperCamelCase ( lowerCAmelCase__ : Optional[int]="./ckpt/aeslc/model.ckpt-32000" ):
__a : Union[str, Any] = tf.train.list_variables(UpperCAmelCase_ )
__a : List[Any] = {}
__a : List[str] = ['''Adafactor''', '''global_step''']
for name, shape in tqdm(UpperCAmelCase_ , desc='''converting tf checkpoint to dict''' ):
__a : Tuple = any(pat in name for pat in ignore_name )
if skip_key:
continue
__a : str = tf.train.load_variable(UpperCAmelCase_ , UpperCAmelCase_ )
__a : Union[str, Any] = array
return tf_weights
def __UpperCamelCase ( lowerCAmelCase__ : str , lowerCAmelCase__ : str ):
# save tokenizer first
__a : Optional[Any] = Path(UpperCAmelCase_ ).parent.name
__a : Tuple = task_specific_params[f"summarization_{dataset}"]['''max_position_embeddings''']
__a : Any = PegasusTokenizer.from_pretrained('''sshleifer/pegasus''' , model_max_length=UpperCAmelCase_ )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(UpperCAmelCase_ )
# convert model
__a : int = get_tf_weights_as_numpy(UpperCAmelCase_ )
__a : str = task_specific_params[f"summarization_{dataset}"]
if dataset == "large":
__a : str = task_specific_params
__a : Optional[Any] = convert_pegasus(UpperCAmelCase_ , UpperCAmelCase_ )
torch_model.save_pretrained(UpperCAmelCase_ )
__a : Optional[int] = torch_model.state_dict()
sd.pop('''model.decoder.embed_positions.weight''' )
sd.pop('''model.encoder.embed_positions.weight''' )
torch.save(UpperCAmelCase_ , Path(UpperCAmelCase_ ) / '''pytorch_model.bin''' )
if __name__ == "__main__":
lowercase__ =argparse.ArgumentParser()
# Required parameters
parser.add_argument('tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
parser.add_argument('save_dir', default=None, type=str, help='Path to the output PyTorch model.')
lowercase__ =parser.parse_args()
if args.save_dir is None:
lowercase__ =Path(args.tf_ckpt_path).parent.name
lowercase__ =os.path.join('pegasus', dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 216 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
SCREAMING_SNAKE_CASE_ : Dict = '\\n@inproceedings{popovic-2015-chrf,\n title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n author = "Popovi{\'c}, Maja",\n booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",\n month = sep,\n year = "2015",\n address = "Lisbon, Portugal",\n publisher = "Association for Computational Linguistics",\n url = "https://aclanthology.org/W15-3049",\n doi = "10.18653/v1/W15-3049",\n pages = "392--395",\n}\n@inproceedings{popovic-2017-chrf,\n title = "chr{F}++: words helping character n-grams",\n author = "Popovi{\'c}, Maja",\n booktitle = "Proceedings of the Second Conference on Machine Translation",\n month = sep,\n year = "2017",\n address = "Copenhagen, Denmark",\n publisher = "Association for Computational Linguistics",\n url = "https://aclanthology.org/W17-4770",\n doi = "10.18653/v1/W17-4770",\n pages = "612--618",\n}\n@inproceedings{post-2018-call,\n title = "A Call for Clarity in Reporting {BLEU} Scores",\n author = "Post, Matt",\n booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n month = oct,\n year = "2018",\n address = "Belgium, Brussels",\n publisher = "Association for Computational Linguistics",\n url = "https://www.aclweb.org/anthology/W18-6319",\n pages = "186--191",\n}\n'
SCREAMING_SNAKE_CASE_ : str = '\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n'
SCREAMING_SNAKE_CASE_ : List[str] = '\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n predictions (list of str): The predicted sentences.\n references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n char_order (int): Character n-gram order. Defaults to `6`.\n word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n to reference chrF++.py, NLTK and Moses implementations. If `False`,\n it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n \'score\' (float): The chrF (chrF++) score,\n \'char_order\' (int): The character n-gram order,\n \'word_order\' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n \'beta\' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n Example 1--a simple example of calculating chrF:\n >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n >>> chrf = datasets.load_metric("chrf")\n >>> results = chrf.compute(predictions=prediction, references=reference)\n >>> print(results)\n {\'score\': 84.64214891738334, \'char_order\': 6, \'word_order\': 0, \'beta\': 2}\n\n Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n >>> chrf = datasets.load_metric("chrf")\n >>> results = chrf.compute(predictions=prediction,\n ... references=reference,\n ... word_order=2)\n >>> print(results)\n {\'score\': 82.87263732906315, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n\n Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n >>> chrf = datasets.load_metric("chrf")\n >>> results = chrf.compute(predictions=prediction,\n ... references=reference,\n ... word_order=2,\n ... lowercase=True)\n >>> print(results)\n {\'score\': 92.12853119829202, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class a ( datasets.Metric ):
"""simple docstring"""
def UpperCamelCase ( self: List[str] ):
"""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="""https://github.com/mjpost/sacreBLEU#chrf--chrf""" , 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#chrf--chrf"""] , reference_urls=[
"""https://github.com/m-popovic/chrF""",
] , )
def UpperCamelCase ( self: List[Any] , UpperCamelCase: Union[str, Any] , UpperCamelCase: Optional[Any] , UpperCamelCase: int = CHRF.CHAR_ORDER , UpperCamelCase: int = CHRF.WORD_ORDER , UpperCamelCase: int = CHRF.BETA , UpperCamelCase: bool = False , UpperCamelCase: bool = False , UpperCamelCase: bool = False , ):
"""simple docstring"""
A__ = len(references[0] )
if any(len(UpperCamelCase ) != references_per_prediction for refs in references ):
raise ValueError("""Sacrebleu requires the same number of references for each prediction""" )
A__ = [[refs[i] for refs in references] for i in range(UpperCamelCase )]
A__ = CHRF(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
A__ = sb_chrf.corpus_score(UpperCamelCase , UpperCamelCase )
return {
"score": output.score,
"char_order": output.char_order,
"word_order": output.word_order,
"beta": output.beta,
}
| 335 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowercase (snake_case__ : int = 4 ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase = abs(UpperCAmelCase_ ) or 4
return [[1 + x + y * row_size for x in range(UpperCAmelCase_ )] for y in range(UpperCAmelCase_ )]
def lowercase (snake_case__ : list[list[int]] ) -> Union[str, Any]:
'''simple docstring'''
return reverse_row(transpose(UpperCAmelCase_ ) )
# OR.. transpose(reverse_column(matrix))
def lowercase (snake_case__ : list[list[int]] ) -> int:
'''simple docstring'''
return reverse_row(reverse_column(UpperCAmelCase_ ) )
# OR.. reverse_column(reverse_row(matrix))
def lowercase (snake_case__ : list[list[int]] ) -> Dict:
'''simple docstring'''
return reverse_column(transpose(UpperCAmelCase_ ) )
# OR.. transpose(reverse_row(matrix))
def lowercase (snake_case__ : list[list[int]] ) -> Dict:
'''simple docstring'''
lowerCAmelCase = [list(UpperCAmelCase_ ) for x in zip(*UpperCAmelCase_ )]
return matrix
def lowercase (snake_case__ : list[list[int]] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase = matrix[::-1]
return matrix
def lowercase (snake_case__ : list[list[int]] ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase = [x[::-1] for x in matrix]
return matrix
def lowercase (snake_case__ : list[list[int]] ) -> Optional[Any]:
'''simple docstring'''
for i in matrix:
print(*UpperCAmelCase_ )
if __name__ == "__main__":
a = make_matrix()
print('\norigin:\n')
print_matrix(matrix)
print('\nrotate 90 counterclockwise:\n')
print_matrix(rotate_aa(matrix))
a = make_matrix()
print('\norigin:\n')
print_matrix(matrix)
print('\nrotate 180:\n')
print_matrix(rotate_aaa(matrix))
a = make_matrix()
print('\norigin:\n')
print_matrix(matrix)
print('\nrotate 270 counterclockwise:\n')
print_matrix(rotate_aaa(matrix))
| 155 |
"""simple docstring"""
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class a ( _lowerCamelCase ):
"""simple docstring"""
def __init__( self: Optional[int] , *UpperCamelCase: Optional[Any] , UpperCamelCase: Tuple=None , UpperCamelCase: Tuple=None , **UpperCamelCase: Dict ):
"""simple docstring"""
super().__init__(*UpperCamelCase , **UpperCamelCase )
A__ = eval_examples
A__ = post_process_function
def UpperCamelCase ( self: Optional[Any] , UpperCamelCase: Optional[Dataset] = None , UpperCamelCase: List[Any]=None , UpperCamelCase: Optional[List[str]] = None , UpperCamelCase: str = "eval" , **UpperCamelCase: Optional[int] , ):
"""simple docstring"""
A__ = gen_kwargs.copy()
A__ = (
gen_kwargs["""max_length"""] if gen_kwargs.get("""max_length""" ) is not None else self.args.generation_max_length
)
A__ = (
gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams""" ) is not None else self.args.generation_num_beams
)
A__ = gen_kwargs
A__ = self.eval_dataset if eval_dataset is None else eval_dataset
A__ = self.get_eval_dataloader(UpperCamelCase )
A__ = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
A__ = self.compute_metrics
A__ = None
A__ = time.time()
A__ = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
A__ = eval_loop(
UpperCamelCase , description="""Evaluation""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=UpperCamelCase , metric_key_prefix=UpperCamelCase , )
finally:
A__ = compute_metrics
A__ = self.args.eval_batch_size * self.args.world_size
if f"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
start_time += output.metrics[f"""{metric_key_prefix}_jit_compilation_time"""]
output.metrics.update(
speed_metrics(
UpperCamelCase , UpperCamelCase , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
A__ = self.post_process_function(UpperCamelCase , UpperCamelCase , UpperCamelCase )
A__ = self.compute_metrics(UpperCamelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
A__ = metrics.pop(UpperCamelCase )
metrics.update(output.metrics )
else:
A__ = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(UpperCamelCase )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
A__ = self.callback_handler.on_evaluate(self.args , self.state , self.control , UpperCamelCase )
return metrics
def UpperCamelCase ( self: List[Any] , UpperCamelCase: Dict , UpperCamelCase: List[str] , UpperCamelCase: Dict=None , UpperCamelCase: str = "test" , **UpperCamelCase: Optional[int] ):
"""simple docstring"""
A__ = gen_kwargs.copy()
A__ = self.get_test_dataloader(UpperCamelCase )
# Temporarily disable metric computation, we will do it in the loop here.
A__ = self.compute_metrics
A__ = None
A__ = time.time()
A__ = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
A__ = eval_loop(
UpperCamelCase , description="""Prediction""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=UpperCamelCase , metric_key_prefix=UpperCamelCase , )
finally:
A__ = compute_metrics
A__ = self.args.eval_batch_size * self.args.world_size
if f"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
start_time += output.metrics[f"""{metric_key_prefix}_jit_compilation_time"""]
output.metrics.update(
speed_metrics(
UpperCamelCase , UpperCamelCase , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
A__ = self.post_process_function(UpperCamelCase , UpperCamelCase , UpperCamelCase , """predict""" )
A__ = self.compute_metrics(UpperCamelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
A__ = metrics.pop(UpperCamelCase )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=UpperCamelCase )
| 335 | 0 |
"""simple docstring"""
__SCREAMING_SNAKE_CASE ='0.18.2'
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_note_seq_available,
is_onnx_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
is_transformers_available,
is_transformers_version,
is_unidecode_available,
logging,
)
try:
if not is_onnx_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_onnx_objects import * # noqa F403
else:
from .pipelines import OnnxRuntimeModel
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_pt_objects import * # noqa F403
else:
from .models import (
AutoencoderKL,
ControlNetModel,
ModelMixin,
PriorTransformer,
TaFilmDecoder,
TransformeraDModel,
UNetaDModel,
UNetaDConditionModel,
UNetaDModel,
UNetaDConditionModel,
VQModel,
)
from .optimization import (
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
get_scheduler,
)
from .pipelines import (
AudioPipelineOutput,
ConsistencyModelPipeline,
DanceDiffusionPipeline,
DDIMPipeline,
DDPMPipeline,
DiffusionPipeline,
DiTPipeline,
ImagePipelineOutput,
KarrasVePipeline,
LDMPipeline,
LDMSuperResolutionPipeline,
PNDMPipeline,
RePaintPipeline,
ScoreSdeVePipeline,
)
from .schedulers import (
CMStochasticIterativeScheduler,
DDIMInverseScheduler,
DDIMParallelScheduler,
DDIMScheduler,
DDPMParallelScheduler,
DDPMScheduler,
DEISMultistepScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler,
IPNDMScheduler,
KarrasVeScheduler,
KDPMaAncestralDiscreteScheduler,
KDPMaDiscreteScheduler,
PNDMScheduler,
RePaintScheduler,
SchedulerMixin,
ScoreSdeVeScheduler,
UnCLIPScheduler,
UniPCMultistepScheduler,
VQDiffusionScheduler,
)
from .training_utils import EMAModel
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .schedulers import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .schedulers import DPMSolverSDEScheduler
try:
if not (is_torch_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
AltDiffusionImgaImgPipeline,
AltDiffusionPipeline,
AudioLDMPipeline,
CycleDiffusionPipeline,
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
ImageTextPipelineOutput,
KandinskyImgaImgPipeline,
KandinskyInpaintPipeline,
KandinskyPipeline,
KandinskyPriorPipeline,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaControlnetPipeline,
KandinskyVaaImgaImgPipeline,
KandinskyVaaInpaintPipeline,
KandinskyVaaPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
KandinskyVaaPriorPipeline,
LDMTextToImagePipeline,
PaintByExamplePipeline,
SemanticStableDiffusionPipeline,
ShapEImgaImgPipeline,
ShapEPipeline,
StableDiffusionAttendAndExcitePipeline,
StableDiffusionControlNetImgaImgPipeline,
StableDiffusionControlNetInpaintPipeline,
StableDiffusionControlNetPipeline,
StableDiffusionDepthaImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionImageVariationPipeline,
StableDiffusionImgaImgPipeline,
StableDiffusionInpaintPipeline,
StableDiffusionInpaintPipelineLegacy,
StableDiffusionInstructPixaPixPipeline,
StableDiffusionLatentUpscalePipeline,
StableDiffusionLDMaDPipeline,
StableDiffusionModelEditingPipeline,
StableDiffusionPanoramaPipeline,
StableDiffusionParadigmsPipeline,
StableDiffusionPipeline,
StableDiffusionPipelineSafe,
StableDiffusionPixaPixZeroPipeline,
StableDiffusionSAGPipeline,
StableDiffusionUpscalePipeline,
StableUnCLIPImgaImgPipeline,
StableUnCLIPPipeline,
TextToVideoSDPipeline,
TextToVideoZeroPipeline,
UnCLIPImageVariationPipeline,
UnCLIPPipeline,
UniDiffuserModel,
UniDiffuserPipeline,
UniDiffuserTextDecoder,
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
VideoToVideoSDPipeline,
VQDiffusionPipeline,
)
try:
if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import * # noqa F403
else:
from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipelines import StableDiffusionKDiffusionPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_onnx_objects import * # noqa F403
else:
from .pipelines import (
OnnxStableDiffusionImgaImgPipeline,
OnnxStableDiffusionInpaintPipeline,
OnnxStableDiffusionInpaintPipelineLegacy,
OnnxStableDiffusionPipeline,
OnnxStableDiffusionUpscalePipeline,
StableDiffusionOnnxPipeline,
)
try:
if not (is_torch_available() and is_librosa_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_librosa_objects import * # noqa F403
else:
from .pipelines import AudioDiffusionPipeline, Mel
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .pipelines import SpectrogramDiffusionPipeline
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_objects import * # noqa F403
else:
from .models.controlnet_flax import FlaxControlNetModel
from .models.modeling_flax_utils import FlaxModelMixin
from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
from .models.vae_flax import FlaxAutoencoderKL
from .pipelines import FlaxDiffusionPipeline
from .schedulers import (
FlaxDDIMScheduler,
FlaxDDPMScheduler,
FlaxDPMSolverMultistepScheduler,
FlaxKarrasVeScheduler,
FlaxLMSDiscreteScheduler,
FlaxPNDMScheduler,
FlaxSchedulerMixin,
FlaxScoreSdeVeScheduler,
)
try:
if not (is_flax_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
FlaxStableDiffusionControlNetPipeline,
FlaxStableDiffusionImgaImgPipeline,
FlaxStableDiffusionInpaintPipeline,
FlaxStableDiffusionPipeline,
)
try:
if not (is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_note_seq_objects import * # noqa F403
else:
from .pipelines import MidiProcessor
| 213 |
"""simple docstring"""
class a :
"""simple docstring"""
def __init__( self: Dict ):
"""simple docstring"""
A__ = {}
def UpperCamelCase ( self: List[str] ):
"""simple docstring"""
print(self.vertex )
for i in self.vertex:
print(UpperCamelCase , """ -> """ , """ -> """.join([str(UpperCamelCase ) for j in self.vertex[i]] ) )
def UpperCamelCase ( self: Any , UpperCamelCase: int , UpperCamelCase: int ):
"""simple docstring"""
if from_vertex in self.vertex:
self.vertex[from_vertex].append(UpperCamelCase )
else:
# else make a new vertex
A__ = [to_vertex]
def UpperCamelCase ( self: Union[str, Any] ):
"""simple docstring"""
A__ = [False] * len(self.vertex )
# call the recursive helper function
for i in range(len(self.vertex ) ):
if not visited[i]:
self.dfs_recursive(UpperCamelCase , UpperCamelCase )
def UpperCamelCase ( self: str , UpperCamelCase: int , UpperCamelCase: list ):
"""simple docstring"""
A__ = True
print(UpperCamelCase , end=""" """ )
# Recur for all the vertices that are adjacent to this node
for i in self.vertex:
if not visited[i]:
self.dfs_recursive(UpperCamelCase , UpperCamelCase )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ : Optional[int] = Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
g.print_graph()
print('DFS:')
g.dfs()
# OUTPUT:
# 0 -> 1 -> 2
# 1 -> 2
# 2 -> 0 -> 3
# 3 -> 3
# DFS:
# 0 1 2 3
| 335 | 0 |
"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging
_SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__) # pylint: disable=invalid-name
class a ( _lowerCamelCase ):
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : AutoencoderKL , __SCREAMING_SNAKE_CASE : CLIPTextModel , __SCREAMING_SNAKE_CASE : CLIPTokenizer , __SCREAMING_SNAKE_CASE : UNetaDConditionModel , __SCREAMING_SNAKE_CASE : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , __SCREAMING_SNAKE_CASE : StableDiffusionSafetyChecker , __SCREAMING_SNAKE_CASE : CLIPImageProcessor , ) -> Any:
super().__init__()
self.register_modules(
vae=__SCREAMING_SNAKE_CASE , text_encoder=__SCREAMING_SNAKE_CASE , tokenizer=__SCREAMING_SNAKE_CASE , unet=__SCREAMING_SNAKE_CASE , scheduler=__SCREAMING_SNAKE_CASE , safety_checker=__SCREAMING_SNAKE_CASE , feature_extractor=__SCREAMING_SNAKE_CASE , )
def UpperCamelCase ( self : Dict , __SCREAMING_SNAKE_CASE : Optional[Union[str, int]] = "auto" ) -> List[Any]:
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
lowerCamelCase_ = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(__SCREAMING_SNAKE_CASE )
def UpperCamelCase ( self : Any ) -> Optional[Any]:
self.enable_attention_slicing(__SCREAMING_SNAKE_CASE )
@torch.no_grad()
def __call__( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Union[str, List[str]] , __SCREAMING_SNAKE_CASE : int = 512 , __SCREAMING_SNAKE_CASE : int = 512 , __SCREAMING_SNAKE_CASE : int = 50 , __SCREAMING_SNAKE_CASE : float = 7.5 , __SCREAMING_SNAKE_CASE : Optional[Union[str, List[str]]] = None , __SCREAMING_SNAKE_CASE : Optional[int] = 1 , __SCREAMING_SNAKE_CASE : float = 0.0 , __SCREAMING_SNAKE_CASE : Optional[torch.Generator] = None , __SCREAMING_SNAKE_CASE : Optional[torch.FloatTensor] = None , __SCREAMING_SNAKE_CASE : Optional[str] = "pil" , __SCREAMING_SNAKE_CASE : bool = True , __SCREAMING_SNAKE_CASE : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , __SCREAMING_SNAKE_CASE : int = 1 , __SCREAMING_SNAKE_CASE : Optional[torch.FloatTensor] = None , **__SCREAMING_SNAKE_CASE : Tuple , ) -> Any:
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowerCamelCase_ = 1
elif isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowerCamelCase_ = len(__SCREAMING_SNAKE_CASE )
else:
raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(__SCREAMING_SNAKE_CASE )}''' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''' )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) or callback_steps <= 0)
):
raise ValueError(
F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
F''' {type(__SCREAMING_SNAKE_CASE )}.''' )
# get prompt text embeddings
lowerCamelCase_ = self.tokenizer(
__SCREAMING_SNAKE_CASE , padding='max_length' , max_length=self.tokenizer.model_max_length , return_tensors='pt' , )
lowerCamelCase_ = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
lowerCamelCase_ = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
'The following part of your input was truncated because CLIP can only handle sequences up to'
F''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
lowerCamelCase_ = text_input_ids[:, : self.tokenizer.model_max_length]
if text_embeddings is None:
lowerCamelCase_ = self.text_encoder(text_input_ids.to(self.device ) )[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = text_embeddings.shape
lowerCamelCase_ = text_embeddings.repeat(1 , __SCREAMING_SNAKE_CASE , 1 )
lowerCamelCase_ = text_embeddings.view(bs_embed * num_images_per_prompt , __SCREAMING_SNAKE_CASE , -1 )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
lowerCamelCase_ = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
lowerCamelCase_ = 42
if negative_prompt is None:
lowerCamelCase_ = ['']
elif type(__SCREAMING_SNAKE_CASE ) is not type(__SCREAMING_SNAKE_CASE ):
raise TypeError(
F'''`negative_prompt` should be the same type to `prompt`, but got {type(__SCREAMING_SNAKE_CASE )} !='''
F''' {type(__SCREAMING_SNAKE_CASE )}.''' )
elif isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowerCamelCase_ = [negative_prompt]
elif batch_size != len(__SCREAMING_SNAKE_CASE ):
raise ValueError(
F'''`negative_prompt`: {negative_prompt} has batch size {len(__SCREAMING_SNAKE_CASE )}, but `prompt`:'''
F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
' the batch size of `prompt`.' )
else:
lowerCamelCase_ = negative_prompt
lowerCamelCase_ = text_input_ids.shape[-1]
lowerCamelCase_ = self.tokenizer(
__SCREAMING_SNAKE_CASE , padding='max_length' , max_length=__SCREAMING_SNAKE_CASE , truncation=__SCREAMING_SNAKE_CASE , return_tensors='pt' , )
lowerCamelCase_ = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
lowerCamelCase_ = uncond_embeddings.shape[1]
lowerCamelCase_ = uncond_embeddings.repeat(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , 1 )
lowerCamelCase_ = uncond_embeddings.view(batch_size * num_images_per_prompt , __SCREAMING_SNAKE_CASE , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
lowerCamelCase_ = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
lowerCamelCase_ = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
lowerCamelCase_ = (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
lowerCamelCase_ = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
lowerCamelCase_ = torch.randn(
__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , device='cpu' , dtype=__SCREAMING_SNAKE_CASE ).to(self.device )
lowerCamelCase_ = torch.randn(__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , device='cpu' , dtype=__SCREAMING_SNAKE_CASE ).to(
self.device )
else:
lowerCamelCase_ = torch.randn(
__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , device=self.device , dtype=__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = torch.randn(__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , device=self.device , dtype=__SCREAMING_SNAKE_CASE )
else:
if latents_reference.shape != latents_shape:
raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
lowerCamelCase_ = latents_reference.to(self.device )
lowerCamelCase_ = latents.to(self.device )
# This is the key part of the pipeline where we
# try to ensure that the generated images w/ the same seed
# but different sizes actually result in similar images
lowerCamelCase_ = (latents_shape[3] - latents_shape_reference[3]) // 2
lowerCamelCase_ = (latents_shape[2] - latents_shape_reference[2]) // 2
lowerCamelCase_ = latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
lowerCamelCase_ = latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
lowerCamelCase_ = 0 if dx < 0 else dx
lowerCamelCase_ = 0 if dy < 0 else dy
lowerCamelCase_ = max(-dx , 0 )
lowerCamelCase_ = max(-dy , 0 )
# import pdb
# pdb.set_trace()
lowerCamelCase_ = latents_reference[:, :, dy : dy + h, dx : dx + w]
# set timesteps
self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
lowerCamelCase_ = self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
lowerCamelCase_ = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
lowerCamelCase_ = 'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
lowerCamelCase_ = {}
if accepts_eta:
lowerCamelCase_ = eta
for i, t in enumerate(self.progress_bar(__SCREAMING_SNAKE_CASE ) ):
# expand the latents if we are doing classifier free guidance
lowerCamelCase_ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowerCamelCase_ = self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# predict the noise residual
lowerCamelCase_ = self.unet(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , encoder_hidden_states=__SCREAMING_SNAKE_CASE ).sample
# perform guidance
if do_classifier_free_guidance:
lowerCamelCase_ , lowerCamelCase_ = noise_pred.chunk(2 )
lowerCamelCase_ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
lowerCamelCase_ = self.scheduler.step(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
lowerCamelCase_ = 1 / 0.18_215 * latents
lowerCamelCase_ = self.vae.decode(__SCREAMING_SNAKE_CASE ).sample
lowerCamelCase_ = (image / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
lowerCamelCase_ = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if self.safety_checker is not None:
lowerCamelCase_ = self.feature_extractor(self.numpy_to_pil(__SCREAMING_SNAKE_CASE ) , return_tensors='pt' ).to(
self.device )
lowerCamelCase_ , lowerCamelCase_ = self.safety_checker(
images=__SCREAMING_SNAKE_CASE , clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype ) )
else:
lowerCamelCase_ = None
if output_type == "pil":
lowerCamelCase_ = self.numpy_to_pil(__SCREAMING_SNAKE_CASE )
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=__SCREAMING_SNAKE_CASE , nsfw_content_detected=__SCREAMING_SNAKE_CASE )
| 183 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : int = 10 ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or n < 0:
raise ValueError("""Invalid input""" )
A__ = 10**n
A__ = 2_8433 * (pow(2 , 783_0457 , UpperCAmelCase_ )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(f"""{solution(1_0) = }""")
| 335 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__A ={
'configuration_longt5': ['LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongT5Config', 'LongT5OnnxConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A =[
'LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST',
'LongT5EncoderModel',
'LongT5ForConditionalGeneration',
'LongT5Model',
'LongT5PreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A =[
'FlaxLongT5ForConditionalGeneration',
'FlaxLongT5Model',
'FlaxLongT5PreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longta import (
LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
LongTaEncoderModel,
LongTaForConditionalGeneration,
LongTaModel,
LongTaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_longta import (
FlaxLongTaForConditionalGeneration,
FlaxLongTaModel,
FlaxLongTaPreTrainedModel,
)
else:
import sys
__A =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 226 |
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = tuple[float, float, float]
SCREAMING_SNAKE_CASE_ : Optional[int] = tuple[float, float, float]
def _snake_case ( UpperCAmelCase_ : Pointad , UpperCAmelCase_ : Pointad ):
A__ = end_pointa[0] - end_pointa[0]
A__ = end_pointa[1] - end_pointa[1]
A__ = end_pointa[2] - end_pointa[2]
return (x, y, z)
def _snake_case ( UpperCAmelCase_ : Vectorad , UpperCAmelCase_ : Vectorad ):
A__ = ab[1] * ac[2] - ab[2] * ac[1] # *i
A__ = (ab[0] * ac[2] - ab[2] * ac[0]) * -1 # *j
A__ = ab[0] * ac[1] - ab[1] * ac[0] # *k
return (x, y, z)
def _snake_case ( UpperCAmelCase_ : Vectorad , UpperCAmelCase_ : int ):
return tuple(round(UpperCAmelCase_ , UpperCAmelCase_ ) for x in vector ) == (0, 0, 0)
def _snake_case ( UpperCAmelCase_ : Pointad , UpperCAmelCase_ : Pointad , UpperCAmelCase_ : Pointad , UpperCAmelCase_ : int = 10 ):
A__ = create_vector(UpperCAmelCase_ , UpperCAmelCase_ )
A__ = create_vector(UpperCAmelCase_ , UpperCAmelCase_ )
return is_zero_vector(get_ad_vectors_cross(UpperCAmelCase_ , UpperCAmelCase_ ) , UpperCAmelCase_ )
| 335 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :Union[str, Any] = {'configuration_mbart': ['MBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MBartConfig', 'MBartOnnxConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE :Any = ['MBartTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE :Optional[Any] = ['MBartTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE :int = [
'MBART_PRETRAINED_MODEL_ARCHIVE_LIST',
'MBartForCausalLM',
'MBartForConditionalGeneration',
'MBartForQuestionAnswering',
'MBartForSequenceClassification',
'MBartModel',
'MBartPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE :Optional[int] = [
'TFMBartForConditionalGeneration',
'TFMBartModel',
'TFMBartPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE :Optional[int] = [
'FlaxMBartForConditionalGeneration',
'FlaxMBartForQuestionAnswering',
'FlaxMBartForSequenceClassification',
'FlaxMBartModel',
'FlaxMBartPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart import MBartTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart_fast import MBartTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mbart import (
MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
MBartForCausalLM,
MBartForConditionalGeneration,
MBartForQuestionAnswering,
MBartForSequenceClassification,
MBartModel,
MBartPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mbart import (
FlaxMBartForConditionalGeneration,
FlaxMBartForQuestionAnswering,
FlaxMBartForSequenceClassification,
FlaxMBartModel,
FlaxMBartPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE :Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 15 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ : Optional[int] = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ : Any = [
'PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST',
'PegasusXForConditionalGeneration',
'PegasusXModel',
'PegasusXPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pegasus_x import (
PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
PegasusXForConditionalGeneration,
PegasusXModel,
PegasusXPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_ : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 335 | 0 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class _lowercase ( _lowerCamelCase ):
"""simple docstring"""
def __init__(self , lowerCamelCase_ , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = False , lowerCamelCase_ = False , lowerCamelCase_ = None , lowerCamelCase_ = None , **lowerCamelCase_ , ):
"""simple docstring"""
super().__init__(
features=lowerCamelCase_ , cache_dir=lowerCamelCase_ , keep_in_memory=lowerCamelCase_ , streaming=lowerCamelCase_ , num_proc=lowerCamelCase_ , **lowerCamelCase_ , )
a = Generator(
cache_dir=lowerCamelCase_ , features=lowerCamelCase_ , generator=lowerCamelCase_ , gen_kwargs=lowerCamelCase_ , **lowerCamelCase_ , )
def UpperCamelCase_ (self ):
"""simple docstring"""
if self.streaming:
a = self.builder.as_streaming_dataset(split="train" )
# Build regular (map-style) dataset
else:
a = None
a = None
a = None
a = None
self.builder.download_and_prepare(
download_config=lowerCamelCase_ , download_mode=lowerCamelCase_ , verification_mode=lowerCamelCase_ , base_path=lowerCamelCase_ , num_proc=self.num_proc , )
a = self.builder.as_dataset(
split="train" , verification_mode=lowerCamelCase_ , in_memory=self.keep_in_memory )
return dataset
| 227 |
"""simple docstring"""
import math
class a :
"""simple docstring"""
def __init__( self: List[Any] , UpperCamelCase: List[str]=0 ): # a graph with Node 0,1,...,N-1
"""simple docstring"""
A__ = n
A__ = [
[math.inf for j in range(0 , UpperCamelCase )] for i in range(0 , UpperCamelCase )
] # adjacency matrix for weight
A__ = [
[math.inf for j in range(0 , UpperCamelCase )] for i in range(0 , UpperCamelCase )
] # dp[i][j] stores minimum distance from i to j
def UpperCamelCase ( self: Union[str, Any] , UpperCamelCase: Optional[int] , UpperCamelCase: Union[str, Any] , UpperCamelCase: Tuple ):
"""simple docstring"""
A__ = w
def UpperCamelCase ( self: int ):
"""simple docstring"""
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
A__ = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def UpperCamelCase ( self: int , UpperCamelCase: List[str] , UpperCamelCase: Dict ):
"""simple docstring"""
return self.dp[u][v]
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ : List[Any] = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 1_0)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 1_0)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 335 | 0 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
)
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__=13 , lowerCamelCase__=7 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=99 , lowerCamelCase__=64 , 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__=3 , lowerCamelCase__=4 , lowerCamelCase__=None , ) -> Dict:
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_input_mask
__lowerCamelCase = use_token_type_ids
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = embedding_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = type_sequence_label_size
__lowerCamelCase = initializer_range
__lowerCamelCase = num_labels
__lowerCamelCase = num_choices
__lowerCamelCase = scope
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCamelCase = None
if self.use_input_mask:
__lowerCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCamelCase = None
if self.use_token_type_ids:
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCamelCase = None
__lowerCamelCase = None
__lowerCamelCase = None
if self.use_labels:
__lowerCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCamelCase = ids_tensor([self.batch_size] , self.num_choices )
__lowerCamelCase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowercase_ ( self ) -> int:
'''simple docstring'''
return MegatronBertConfig(
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 , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCamelCase__ , initializer_range=self.initializer_range , )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = MegatronBertModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = model(lowerCamelCase__ , attention_mask=lowerCamelCase__ , token_type_ids=lowerCamelCase__ )
__lowerCamelCase = model(lowerCamelCase__ , token_type_ids=lowerCamelCase__ )
__lowerCamelCase = model(lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Any:
'''simple docstring'''
__lowerCamelCase = MegatronBertForMaskedLM(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = model(lowerCamelCase__ , attention_mask=lowerCamelCase__ , token_type_ids=lowerCamelCase__ , labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> str:
'''simple docstring'''
__lowerCamelCase = MegatronBertForCausalLM(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = model(lowerCamelCase__ , attention_mask=lowerCamelCase__ , token_type_ids=lowerCamelCase__ , labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Optional[int]:
'''simple docstring'''
__lowerCamelCase = MegatronBertForNextSentencePrediction(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = model(
lowerCamelCase__ , attention_mask=lowerCamelCase__ , token_type_ids=lowerCamelCase__ , labels=lowerCamelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = MegatronBertForPreTraining(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = model(
lowerCamelCase__ , attention_mask=lowerCamelCase__ , token_type_ids=lowerCamelCase__ , labels=lowerCamelCase__ , next_sentence_label=lowerCamelCase__ , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Dict:
'''simple docstring'''
__lowerCamelCase = MegatronBertForQuestionAnswering(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = model(
lowerCamelCase__ , attention_mask=lowerCamelCase__ , token_type_ids=lowerCamelCase__ , start_positions=lowerCamelCase__ , end_positions=lowerCamelCase__ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Any:
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = MegatronBertForSequenceClassification(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = model(lowerCamelCase__ , attention_mask=lowerCamelCase__ , token_type_ids=lowerCamelCase__ , labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Optional[int]:
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = MegatronBertForTokenClassification(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = model(lowerCamelCase__ , attention_mask=lowerCamelCase__ , token_type_ids=lowerCamelCase__ , labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> str:
'''simple docstring'''
__lowerCamelCase = self.num_choices
__lowerCamelCase = MegatronBertForMultipleChoice(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase = model(
lowerCamelCase__ , attention_mask=lowerCamelCase__ , token_type_ids=lowerCamelCase__ , labels=lowerCamelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
(
(
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) ,
) = config_and_inputs
__lowerCamelCase = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
snake_case_ = (
(
MegatronBertModel,
MegatronBertForMaskedLM,
MegatronBertForCausalLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
)
if is_torch_available()
else ()
)
snake_case_ = (
{
'''feature-extraction''': MegatronBertModel,
'''fill-mask''': MegatronBertForMaskedLM,
'''question-answering''': MegatronBertForQuestionAnswering,
'''text-classification''': MegatronBertForSequenceClassification,
'''text-generation''': MegatronBertForCausalLM,
'''token-classification''': MegatronBertForTokenClassification,
'''zero-shot''': MegatronBertForSequenceClassification,
}
if is_torch_available()
else {}
)
snake_case_ = True
# test_resize_embeddings = False
snake_case_ = False
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=False ) -> Optional[int]:
'''simple docstring'''
__lowerCamelCase = super()._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ , return_labels=lowerCamelCase__ )
if return_labels:
if model_class in get_values(lowerCamelCase__ ):
__lowerCamelCase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=lowerCamelCase__ )
__lowerCamelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCamelCase__ )
return inputs_dict
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = MegatronBertModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=lowerCamelCase__ , hidden_size=37 )
def lowercase_ ( self ) -> int:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowercase_ ( self ) -> int:
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_model(*lowerCamelCase__ )
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_masked_lm(*lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*lowerCamelCase__ )
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*lowerCamelCase__ )
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_pretraining(*lowerCamelCase__ )
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_question_answering(*lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*lowerCamelCase__ )
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_token_classification(*lowerCamelCase__ )
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ) -> List[str]:
"""simple docstring"""
return torch.tensor(
UpperCAmelCase_ , dtype=torch.long , device=UpperCAmelCase_ , )
__A = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
@unittest.skip('Model is not available.' )
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = 'nvidia/megatron-bert-uncased-345m'
if "MYDIR" in os.environ:
__lowerCamelCase = os.path.join(os.environ['MYDIR'] , lowerCamelCase__ )
__lowerCamelCase = MegatronBertModel.from_pretrained(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.half()
__lowerCamelCase = _long_tensor([[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]] )
with torch.no_grad():
__lowerCamelCase = model(lowerCamelCase__ )[0]
__lowerCamelCase = torch.Size((1, 9, 1_024) )
self.assertEqual(output.shape , lowerCamelCase__ )
__lowerCamelCase = [-0.60_40, -0.25_17, -0.10_25, 0.34_20, -0.67_58, -0.00_17, -0.10_89, -0.19_90, 0.57_28]
for ii in range(3 ):
for jj in range(3 ):
__lowerCamelCase = output[0, ii, jj]
__lowerCamelCase = expected[3 * ii + jj]
__lowerCamelCase = 'ii={} jj={} a={} b={}'.format(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
self.assertTrue(math.isclose(lowerCamelCase__ , lowerCamelCase__ , rel_tol=lowerCamelCase__ , abs_tol=lowerCamelCase__ ) , msg=lowerCamelCase__ )
| 90 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class a ( unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = ViTImageProcessor if is_vision_available() else None
@property
def UpperCamelCase ( self: str ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase ( self: List[Any] ):
"""simple docstring"""
A__ = (3, 32, 1_28)
A__ = tempfile.mkdtemp()
# fmt: off
A__ = ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
# fmt: on
A__ = dict(zip(UpperCamelCase , range(len(UpperCamelCase ) ) ) )
A__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(UpperCamelCase ) + """\n""" )
A__ = {
"""do_normalize""": False,
"""do_resize""": True,
"""image_processor_type""": """ViTImageProcessor""",
"""resample""": 3,
"""size""": {"""height""": 32, """width""": 1_28},
}
A__ = os.path.join(self.tmpdirname , UpperCamelCase )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(UpperCamelCase , UpperCamelCase )
def UpperCamelCase ( self: List[str] , **UpperCamelCase: Union[str, Any] ):
"""simple docstring"""
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **UpperCamelCase )
def UpperCamelCase ( self: List[Any] , **UpperCamelCase: str ):
"""simple docstring"""
return ViTImageProcessor.from_pretrained(self.tmpdirname , **UpperCamelCase )
def UpperCamelCase ( self: Optional[Any] ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def UpperCamelCase ( self: Union[str, Any] ):
"""simple docstring"""
A__ = np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )
A__ = Image.fromarray(np.moveaxis(UpperCamelCase , 0 , -1 ) )
return image_input
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
A__ = self.get_tokenizer()
A__ = self.get_image_processor()
A__ = MgpstrProcessor(tokenizer=UpperCamelCase , image_processor=UpperCamelCase )
processor.save_pretrained(self.tmpdirname )
A__ = MgpstrProcessor.from_pretrained(self.tmpdirname , use_fast=UpperCamelCase )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , UpperCamelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , UpperCamelCase )
def UpperCamelCase ( self: Dict ):
"""simple docstring"""
A__ = self.get_tokenizer()
A__ = self.get_image_processor()
A__ = MgpstrProcessor(tokenizer=UpperCamelCase , image_processor=UpperCamelCase )
processor.save_pretrained(self.tmpdirname )
A__ = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
A__ = self.get_image_processor(do_normalize=UpperCamelCase , padding_value=1.0 )
A__ = MgpstrProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=UpperCamelCase , padding_value=1.0 )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , UpperCamelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , UpperCamelCase )
def UpperCamelCase ( self: List[Any] ):
"""simple docstring"""
A__ = self.get_image_processor()
A__ = self.get_tokenizer()
A__ = MgpstrProcessor(tokenizer=UpperCamelCase , image_processor=UpperCamelCase )
A__ = self.prepare_image_inputs()
A__ = image_processor(UpperCamelCase , return_tensors="""np""" )
A__ = processor(images=UpperCamelCase , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1e-2 )
def UpperCamelCase ( self: Union[str, Any] ):
"""simple docstring"""
A__ = self.get_image_processor()
A__ = self.get_tokenizer()
A__ = MgpstrProcessor(tokenizer=UpperCamelCase , image_processor=UpperCamelCase )
A__ = """test"""
A__ = processor(text=UpperCamelCase )
A__ = tokenizer(UpperCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def UpperCamelCase ( self: Optional[Any] ):
"""simple docstring"""
A__ = self.get_image_processor()
A__ = self.get_tokenizer()
A__ = MgpstrProcessor(tokenizer=UpperCamelCase , image_processor=UpperCamelCase )
A__ = """test"""
A__ = self.prepare_image_inputs()
A__ = processor(text=UpperCamelCase , images=UpperCamelCase )
self.assertListEqual(list(inputs.keys() ) , ["""pixel_values""", """labels"""] )
# test if it raises when no input is passed
with pytest.raises(UpperCamelCase ):
processor()
def UpperCamelCase ( self: List[str] ):
"""simple docstring"""
A__ = self.get_image_processor()
A__ = self.get_tokenizer()
A__ = MgpstrProcessor(tokenizer=UpperCamelCase , image_processor=UpperCamelCase )
A__ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]
A__ = processor.char_decode(UpperCamelCase )
A__ = tokenizer.batch_decode(UpperCamelCase )
A__ = [seq.replace(""" """ , """""" ) for seq in decoded_tok]
self.assertListEqual(UpperCamelCase , UpperCamelCase )
def UpperCamelCase ( self: List[Any] ):
"""simple docstring"""
A__ = self.get_image_processor()
A__ = self.get_tokenizer()
A__ = MgpstrProcessor(tokenizer=UpperCamelCase , image_processor=UpperCamelCase )
A__ = None
A__ = self.prepare_image_inputs()
A__ = processor(text=UpperCamelCase , images=UpperCamelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
def UpperCamelCase ( self: Optional[Any] ):
"""simple docstring"""
A__ = self.get_image_processor()
A__ = self.get_tokenizer()
A__ = MgpstrProcessor(tokenizer=UpperCamelCase , image_processor=UpperCamelCase )
A__ = torch.randn(1 , 27 , 38 )
A__ = torch.randn(1 , 27 , 5_02_57 )
A__ = torch.randn(1 , 27 , 3_05_22 )
A__ = processor.batch_decode([char_input, bpe_input, wp_input] )
self.assertListEqual(list(results.keys() ) , ["""generated_text""", """scores""", """char_preds""", """bpe_preds""", """wp_preds"""] )
| 335 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
@dataclass
class lowerCAmelCase__ ( _lowerCamelCase ):
'''simple docstring'''
lowerCamelCase__ = 42
class lowerCAmelCase__ ( _lowerCamelCase, _lowerCamelCase ):
'''simple docstring'''
@register_to_config
def __init__( self , lowercase = 3 , lowercase = 3 , lowercase = ("DownEncoderBlock2D",) , lowercase = ("UpDecoderBlock2D",) , lowercase = (64,) , lowercase = 1 , lowercase = "silu" , lowercase = 3 , lowercase = 32 , lowercase = 256 , lowercase = 32 , lowercase = None , lowercase = 0.1_82_15 , lowercase = "group" , ):
super().__init__()
# pass init params to Encoder
_lowerCamelCase : Dict = Encoder(
in_channels=lowercase , out_channels=lowercase , down_block_types=lowercase , block_out_channels=lowercase , layers_per_block=lowercase , act_fn=lowercase , norm_num_groups=lowercase , double_z=lowercase , )
_lowerCamelCase : Any = vq_embed_dim if vq_embed_dim is not None else latent_channels
_lowerCamelCase : Dict = nn.Convad(lowercase , lowercase , 1 )
_lowerCamelCase : str = VectorQuantizer(lowercase , lowercase , beta=0.25 , remap=lowercase , sane_index_shape=lowercase )
_lowerCamelCase : Union[str, Any] = nn.Convad(lowercase , lowercase , 1 )
# pass init params to Decoder
_lowerCamelCase : Union[str, Any] = Decoder(
in_channels=lowercase , out_channels=lowercase , up_block_types=lowercase , block_out_channels=lowercase , layers_per_block=lowercase , act_fn=lowercase , norm_num_groups=lowercase , norm_type=lowercase , )
@apply_forward_hook
def A_ ( self , lowercase , lowercase = True ):
_lowerCamelCase : Any = self.encoder(lowercase )
_lowerCamelCase : Optional[int] = self.quant_conv(lowercase )
if not return_dict:
return (h,)
return VQEncoderOutput(latents=lowercase )
@apply_forward_hook
def A_ ( self , lowercase , lowercase = False , lowercase = True ):
if not force_not_quantize:
_lowerCamelCase, _lowerCamelCase, _lowerCamelCase : str = self.quantize(lowercase )
else:
_lowerCamelCase : List[Any] = h
_lowerCamelCase : Tuple = self.post_quant_conv(lowercase )
_lowerCamelCase : str = self.decoder(lowercase , quant if self.config.norm_type == 'spatial' else None )
if not return_dict:
return (dec,)
return DecoderOutput(sample=lowercase )
def A_ ( self , lowercase , lowercase = True ):
_lowerCamelCase : List[Any] = sample
_lowerCamelCase : Dict = self.encode(lowercase ).latents
_lowerCamelCase : Any = self.decode(lowercase ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=lowercase ) | 96 |
"""simple docstring"""
import math
def _snake_case ( UpperCAmelCase_ : float , UpperCAmelCase_ : float ):
if initial_intensity < 0:
raise ValueError("""The value of intensity cannot be negative""" )
# handling of negative values of initial intensity
if angle < 0 or angle > 360:
raise ValueError("""In Malus Law, the angle is in the range 0-360 degrees""" )
# handling of values out of allowed range
return initial_intensity * (math.cos(math.radians(UpperCAmelCase_ ) ) ** 2)
if __name__ == "__main__":
import doctest
doctest.testmod(name='malus_law')
| 335 | 0 |
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
_lowerCamelCase : Any = logging.getLogger(__name__)
@dataclass
@add_start_docstrings(TrainingArguments.__doc__)
class lowercase ( _lowerCamelCase):
__lowerCAmelCase : int = field(default=_lowerCamelCase , metadata={"""help""": """Whether to use SortishSampler or not."""})
__lowerCAmelCase : int = field(
default=_lowerCamelCase , metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""})
__lowerCAmelCase : Optional[int] = field(
default=_lowerCamelCase , metadata={
"""help""": (
"""The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default """
"""to the `max_length` value of the model configuration."""
)
} , )
__lowerCAmelCase : Dict = field(
default=_lowerCamelCase , metadata={
"""help""": (
"""The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default """
"""to the `num_beams` value of the model configuration."""
)
} , )
__lowerCAmelCase : Optional[Any] = field(
default=_lowerCamelCase , metadata={
"""help""": """Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."""
} , )
def a_ ( self : int ):
"""simple docstring"""
A_ : int = super().to_dict()
for k, v in d.items():
if isinstance(_lowerCamelCase , _lowerCamelCase ):
A_ : List[str] = v.to_dict()
return d
| 167 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
RobertaSeriesConfig,
RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
class a ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self: Any ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def UpperCamelCase ( self: Dict ):
"""simple docstring"""
A__ = 1
A__ = 3
A__ = (32, 32)
A__ = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(UpperCamelCase )
return image
@property
def UpperCamelCase ( self: int ):
"""simple docstring"""
torch.manual_seed(0 )
A__ = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
return model
@property
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
torch.manual_seed(0 )
A__ = 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 , )
return model
@property
def UpperCamelCase ( self: List[Any] ):
"""simple docstring"""
torch.manual_seed(0 )
A__ = RobertaSeriesConfig(
hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=50_06 , )
return RobertaSeriesModelWithTransformation(UpperCamelCase )
@property
def UpperCamelCase ( self: str ):
"""simple docstring"""
def extract(*UpperCamelCase: List[str] , **UpperCamelCase: Any ):
class a :
"""simple docstring"""
def __init__( self: Any ):
"""simple docstring"""
A__ = torch.ones([0] )
def UpperCamelCase ( self: Dict , UpperCamelCase: Optional[Any] ):
"""simple docstring"""
self.pixel_values.to(UpperCamelCase )
return self
return Out()
return extract
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = """cpu""" # ensure determinism for the device-dependent torch.Generator
A__ = self.dummy_cond_unet
A__ = PNDMScheduler(skip_prk_steps=UpperCamelCase )
A__ = self.dummy_vae
A__ = self.dummy_text_encoder
A__ = XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta""" )
A__ = 77
A__ = self.dummy_image.to(UpperCamelCase )
A__ = init_image / 2 + 0.5
# make sure here that pndm scheduler skips prk
A__ = AltDiffusionImgaImgPipeline(
unet=UpperCamelCase , scheduler=UpperCamelCase , vae=UpperCamelCase , text_encoder=UpperCamelCase , tokenizer=UpperCamelCase , safety_checker=UpperCamelCase , feature_extractor=self.dummy_extractor , )
A__ = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor , do_normalize=UpperCamelCase )
A__ = alt_pipe.to(UpperCamelCase )
alt_pipe.set_progress_bar_config(disable=UpperCamelCase )
A__ = """A painting of a squirrel eating a burger"""
A__ = torch.Generator(device=UpperCamelCase ).manual_seed(0 )
A__ = alt_pipe(
[prompt] , generator=UpperCamelCase , guidance_scale=6.0 , num_inference_steps=2 , output_type="""np""" , image=UpperCamelCase , )
A__ = output.images
A__ = torch.Generator(device=UpperCamelCase ).manual_seed(0 )
A__ = alt_pipe(
[prompt] , generator=UpperCamelCase , guidance_scale=6.0 , num_inference_steps=2 , output_type="""np""" , image=UpperCamelCase , return_dict=UpperCamelCase , )[0]
A__ = image[0, -3:, -3:, -1]
A__ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
A__ = np.array([0.4_427, 0.3_731, 0.4_249, 0.4_941, 0.4_546, 0.4_148, 0.4_193, 0.4_666, 0.4_499] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 5e-3
@unittest.skipIf(torch_device != """cuda""" , """This test requires a GPU""" )
def UpperCamelCase ( self: int ):
"""simple docstring"""
A__ = self.dummy_cond_unet
A__ = PNDMScheduler(skip_prk_steps=UpperCamelCase )
A__ = self.dummy_vae
A__ = self.dummy_text_encoder
A__ = XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta""" )
A__ = 77
A__ = self.dummy_image.to(UpperCamelCase )
# put models in fp16
A__ = unet.half()
A__ = vae.half()
A__ = bert.half()
# make sure here that pndm scheduler skips prk
A__ = AltDiffusionImgaImgPipeline(
unet=UpperCamelCase , scheduler=UpperCamelCase , vae=UpperCamelCase , text_encoder=UpperCamelCase , tokenizer=UpperCamelCase , safety_checker=UpperCamelCase , feature_extractor=self.dummy_extractor , )
A__ = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor , do_normalize=UpperCamelCase )
A__ = alt_pipe.to(UpperCamelCase )
alt_pipe.set_progress_bar_config(disable=UpperCamelCase )
A__ = """A painting of a squirrel eating a burger"""
A__ = torch.manual_seed(0 )
A__ = alt_pipe(
[prompt] , generator=UpperCamelCase , num_inference_steps=2 , output_type="""np""" , image=UpperCamelCase , ).images
assert image.shape == (1, 32, 32, 3)
@unittest.skipIf(torch_device != """cuda""" , """This test requires a GPU""" )
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
# resize to resolution that is divisible by 8 but not 16 or 32
A__ = init_image.resize((7_60, 5_04) )
A__ = """BAAI/AltDiffusion"""
A__ = AltDiffusionImgaImgPipeline.from_pretrained(
UpperCamelCase , safety_checker=UpperCamelCase , )
pipe.to(UpperCamelCase )
pipe.set_progress_bar_config(disable=UpperCamelCase )
pipe.enable_attention_slicing()
A__ = """A fantasy landscape, trending on artstation"""
A__ = torch.manual_seed(0 )
A__ = pipe(
prompt=UpperCamelCase , image=UpperCamelCase , strength=0.75 , guidance_scale=7.5 , generator=UpperCamelCase , output_type="""np""" , )
A__ = output.images[0]
A__ = image[2_55:2_58, 3_83:3_86, -1]
assert image.shape == (5_04, 7_60, 3)
A__ = np.array([0.9_358, 0.9_397, 0.9_599, 0.9_901, 1.0_000, 1.0_000, 0.9_882, 1.0_000, 1.0_000] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch_gpu
class a ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self: Any ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase ( self: List[str] ):
"""simple docstring"""
A__ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
A__ = init_image.resize((7_68, 5_12) )
A__ = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy""" )
A__ = """BAAI/AltDiffusion"""
A__ = AltDiffusionImgaImgPipeline.from_pretrained(
UpperCamelCase , safety_checker=UpperCamelCase , )
pipe.to(UpperCamelCase )
pipe.set_progress_bar_config(disable=UpperCamelCase )
pipe.enable_attention_slicing()
A__ = """A fantasy landscape, trending on artstation"""
A__ = torch.manual_seed(0 )
A__ = pipe(
prompt=UpperCamelCase , image=UpperCamelCase , strength=0.75 , guidance_scale=7.5 , generator=UpperCamelCase , output_type="""np""" , )
A__ = output.images[0]
assert image.shape == (5_12, 7_68, 3)
# img2img is flaky across GPUs even in fp32, so using MAE here
assert np.abs(expected_image - image ).max() < 1e-2
| 335 | 0 |
'''simple docstring'''
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig
from transformers import WavLMConfig, WavLMModel, logging
logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
'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.grep_linear': 'encoder.layers.*.attention.gru_rel_pos_linear',
'self_attn.relative_attention_bias': 'encoder.layers.*.attention.rel_attn_embed',
'self_attn.grep_a': 'encoder.layers.*.attention.gru_rel_pos_const',
'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',
'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': 'ctc_proj',
'mask_emb': 'masked_spec_embed',
}
_SCREAMING_SNAKE_CASE = [
'ctc_proj',
'quantizer.weight_proj',
'quantizer.codevectors',
'project_q',
'project_hid',
]
def __a(SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Optional[Any] ):
'''simple docstring'''
for attribute in key.split("." ):
_lowerCAmelCase = getattr(UpperCAmelCase_ , UpperCAmelCase_ )
if weight_type is not None:
_lowerCAmelCase = getattr(UpperCAmelCase_ , UpperCAmelCase_ ).shape
else:
_lowerCAmelCase = hf_pointer.shape
assert hf_shape == value.shape, (
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":
_lowerCAmelCase = value
elif weight_type == "weight_g":
_lowerCAmelCase = value
elif weight_type == "weight_v":
_lowerCAmelCase = value
elif weight_type == "bias":
_lowerCAmelCase = value
else:
_lowerCAmelCase = value
logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' )
def __a(SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Dict ):
'''simple docstring'''
_lowerCAmelCase = []
_lowerCAmelCase = fairseq_model.state_dict()
_lowerCAmelCase = hf_model.feature_extractor
for name, value in fairseq_dict.items():
_lowerCAmelCase = False
if "conv_layers" in name:
load_conv_layer(
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , hf_model.config.feat_extract_norm == "group" , )
_lowerCAmelCase = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
_lowerCAmelCase = True
if "*" in mapped_key:
_lowerCAmelCase = name.split(UpperCAmelCase_ )[0].split("." )[-2]
_lowerCAmelCase = mapped_key.replace("*" , UpperCAmelCase_ )
if "weight_g" in name:
_lowerCAmelCase = "weight_g"
elif "weight_v" in name:
_lowerCAmelCase = "weight_v"
elif "bias" in name and "relative_attention_bias" not in name:
_lowerCAmelCase = "bias"
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
_lowerCAmelCase = "weight"
else:
_lowerCAmelCase = None
set_recursively(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
continue
if not is_used:
unused_weights.append(UpperCAmelCase_ )
logger.warning(F'''Unused weights: {unused_weights}''' )
def __a(SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Dict ):
'''simple docstring'''
_lowerCAmelCase = full_name.split("conv_layers." )[-1]
_lowerCAmelCase = name.split("." )
_lowerCAmelCase = int(items[0] )
_lowerCAmelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
)
_lowerCAmelCase = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
)
_lowerCAmelCase = 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:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
" found."
)
_lowerCAmelCase = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
)
_lowerCAmelCase = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(UpperCAmelCase_ )
@torch.no_grad()
def __a(SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Optional[int]=None ):
'''simple docstring'''
_lowerCAmelCase = torch.load(UpperCAmelCase_ )
_lowerCAmelCase = WavLMConfigOrig(checkpoint["cfg"] )
_lowerCAmelCase = WavLMOrig(UpperCAmelCase_ )
model.load_state_dict(checkpoint["model"] )
model.eval()
if config_path is not None:
_lowerCAmelCase = WavLMConfig.from_pretrained(UpperCAmelCase_ )
else:
_lowerCAmelCase = WavLMConfig()
_lowerCAmelCase = WavLMModel(UpperCAmelCase_ )
recursively_load_weights(UpperCAmelCase_ , UpperCAmelCase_ )
hf_wavlm.save_pretrained(UpperCAmelCase_ )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = 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("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
_SCREAMING_SNAKE_CASE = parser.parse_args()
convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 158 |
"""simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def _snake_case ( UpperCAmelCase_ : List[Any] ):
A__ = FileLock(str(tmpdir / """foo.lock""" ) )
A__ = FileLock(str(tmpdir / """foo.lock""" ) )
A__ = 0.01
with locka.acquire():
with pytest.raises(UpperCAmelCase_ ):
A__ = time.time()
locka.acquire(UpperCAmelCase_ )
assert time.time() - _start > timeout
def _snake_case ( UpperCAmelCase_ : List[Any] ):
A__ = """a""" * 1000 + """.lock"""
A__ = FileLock(str(tmpdir / filename ) )
assert locka._lock_file.endswith(""".lock""" )
assert not locka._lock_file.endswith(UpperCAmelCase_ )
assert len(os.path.basename(locka._lock_file ) ) <= 255
A__ = FileLock(tmpdir / filename )
with locka.acquire():
with pytest.raises(UpperCAmelCase_ ):
locka.acquire(0 )
| 335 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DeformableDetrImageProcessor
class UpperCamelCase__ ( unittest.TestCase ):
def __init__(self : Optional[int] , snake_case_ : List[str] , snake_case_ : List[Any]=7 , snake_case_ : Tuple=3 , snake_case_ : Any=3_0 , snake_case_ : Union[str, Any]=4_0_0 , snake_case_ : Optional[int]=True , snake_case_ : int=None , snake_case_ : List[str]=True , snake_case_ : Tuple=[0.5, 0.5, 0.5] , snake_case_ : Any=[0.5, 0.5, 0.5] , snake_case_ : int=True , snake_case_ : Optional[Any]=1 / 2_5_5 , snake_case_ : str=True , ):
__a : Optional[int] = size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
__a : Dict = parent
__a : Optional[int] = batch_size
__a : str = num_channels
__a : Optional[int] = min_resolution
__a : str = max_resolution
__a : Optional[Any] = do_resize
__a : Any = size
__a : str = do_normalize
__a : List[Any] = image_mean
__a : List[Any] = image_std
__a : List[Any] = do_rescale
__a : Tuple = rescale_factor
__a : Union[str, Any] = do_pad
def lowerCAmelCase (self : Tuple ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def lowerCAmelCase (self : Union[str, Any] , snake_case_ : Union[str, Any] , snake_case_ : int=False ):
if not batched:
__a : List[Any] = image_inputs[0]
if isinstance(snake_case_ , Image.Image ):
__a , __a : int = image.size
else:
__a , __a : Union[str, Any] = image.shape[1], image.shape[2]
if w < h:
__a : Union[str, Any] = int(self.size['''shortest_edge'''] * h / w )
__a : Union[str, Any] = self.size['''shortest_edge''']
elif w > h:
__a : Tuple = self.size['''shortest_edge''']
__a : Tuple = int(self.size['''shortest_edge'''] * w / h )
else:
__a : List[str] = self.size['''shortest_edge''']
__a : Tuple = self.size['''shortest_edge''']
else:
__a : Optional[Any] = []
for image in image_inputs:
__a , __a : Optional[Any] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
__a : Union[str, Any] = max(snake_case_ , key=lambda snake_case_ : item[0] )[0]
__a : Any = max(snake_case_ , key=lambda snake_case_ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class UpperCamelCase__ ( _lowerCamelCase ,unittest.TestCase ):
_SCREAMING_SNAKE_CASE : str = DeformableDetrImageProcessor if is_vision_available() else None
def lowerCAmelCase (self : Any ):
__a : Dict = DeformableDetrImageProcessingTester(self )
@property
def lowerCAmelCase (self : Any ):
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCAmelCase (self : Optional[int] ):
__a : List[str] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(snake_case_ , '''image_mean''' ) )
self.assertTrue(hasattr(snake_case_ , '''image_std''' ) )
self.assertTrue(hasattr(snake_case_ , '''do_normalize''' ) )
self.assertTrue(hasattr(snake_case_ , '''do_resize''' ) )
self.assertTrue(hasattr(snake_case_ , '''do_rescale''' ) )
self.assertTrue(hasattr(snake_case_ , '''do_pad''' ) )
self.assertTrue(hasattr(snake_case_ , '''size''' ) )
def lowerCAmelCase (self : int ):
__a : Any = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3} )
self.assertEqual(image_processor.do_pad , snake_case_ )
__a : Optional[Any] = self.image_processing_class.from_dict(
self.image_processor_dict , size=4_2 , max_size=8_4 , pad_and_return_pixel_mask=snake_case_ )
self.assertEqual(image_processor.size , {'''shortest_edge''': 4_2, '''longest_edge''': 8_4} )
self.assertEqual(image_processor.do_pad , snake_case_ )
def lowerCAmelCase (self : Tuple ):
pass
def lowerCAmelCase (self : List[Any] ):
__a : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__a : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case_ )
for image in image_inputs:
self.assertIsInstance(snake_case_ , Image.Image )
# Test not batched input
__a : Tuple = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__a , __a : Union[str, Any] = self.image_processor_tester.get_expected_values(snake_case_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__a , __a : List[Any] = self.image_processor_tester.get_expected_values(snake_case_ , batched=snake_case_ )
__a : Dict = image_processing(snake_case_ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCAmelCase (self : Union[str, Any] ):
__a : int = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__a : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case_ , numpify=snake_case_ )
for image in image_inputs:
self.assertIsInstance(snake_case_ , np.ndarray )
# Test not batched input
__a : Tuple = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__a , __a : str = self.image_processor_tester.get_expected_values(snake_case_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__a : Any = image_processing(snake_case_ , return_tensors='''pt''' ).pixel_values
__a , __a : str = self.image_processor_tester.get_expected_values(snake_case_ , batched=snake_case_ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCAmelCase (self : Any ):
__a : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__a : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case_ , torchify=snake_case_ )
for image in image_inputs:
self.assertIsInstance(snake_case_ , torch.Tensor )
# Test not batched input
__a : Any = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__a , __a : Optional[int] = self.image_processor_tester.get_expected_values(snake_case_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__a : str = image_processing(snake_case_ , return_tensors='''pt''' ).pixel_values
__a , __a : str = self.image_processor_tester.get_expected_values(snake_case_ , batched=snake_case_ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def lowerCAmelCase (self : Union[str, Any] ):
__a : Optional[Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
__a : str = json.loads(f.read() )
__a : List[Any] = {'''image_id''': 3_9_7_6_9, '''annotations''': target}
# encode them
__a : Dict = DeformableDetrImageProcessor()
__a : Dict = image_processing(images=snake_case_ , annotations=snake_case_ , return_tensors='''pt''' )
# verify pixel values
__a : List[str] = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['''pixel_values'''].shape , snake_case_ )
__a : Tuple = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , snake_case_ , atol=1E-4 ) )
# verify area
__a : int = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , snake_case_ ) )
# verify boxes
__a : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , snake_case_ )
__a : List[str] = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , snake_case_ , atol=1E-3 ) )
# verify image_id
__a : Any = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , snake_case_ ) )
# verify is_crowd
__a : str = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , snake_case_ ) )
# verify class_labels
__a : int = torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , snake_case_ ) )
# verify orig_size
__a : Any = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , snake_case_ ) )
# verify size
__a : str = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , snake_case_ ) )
@slow
def lowerCAmelCase (self : int ):
__a : str = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
__a : Optional[Any] = json.loads(f.read() )
__a : int = {'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}
__a : int = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
__a : Tuple = DeformableDetrImageProcessor(format='''coco_panoptic''' )
__a : Tuple = image_processing(images=snake_case_ , annotations=snake_case_ , masks_path=snake_case_ , return_tensors='''pt''' )
# verify pixel values
__a : Tuple = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['''pixel_values'''].shape , snake_case_ )
__a : Dict = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , snake_case_ , atol=1E-4 ) )
# verify area
__a : str = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , snake_case_ ) )
# verify boxes
__a : Union[str, Any] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , snake_case_ )
__a : List[Any] = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , snake_case_ , atol=1E-3 ) )
# verify image_id
__a : Union[str, Any] = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , snake_case_ ) )
# verify is_crowd
__a : int = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , snake_case_ ) )
# verify class_labels
__a : List[str] = torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , snake_case_ ) )
# verify masks
__a : Optional[Any] = 8_2_2_8_7_3
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , snake_case_ )
# verify orig_size
__a : Tuple = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , snake_case_ ) )
# verify size
__a : Optional[int] = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , snake_case_ ) )
| 216 |
"""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.
from typing import TYPE_CHECKING
import torch
from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class a ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = "dandelin/vilt-b32-finetuned-vqa"
UpperCAmelCase = (
"This is a tool that answers a question about an image. It takes an input named `image` which should be the "
"image containing the information, as well as a `question` which should be the question in English. It "
"returns a text that is the answer to the question."
)
UpperCAmelCase = "image_qa"
UpperCAmelCase = AutoProcessor
UpperCAmelCase = AutoModelForVisualQuestionAnswering
UpperCAmelCase = ["image", "text"]
UpperCAmelCase = ["text"]
def __init__( self: List[str] , *UpperCamelCase: Dict , **UpperCamelCase: List[str] ):
"""simple docstring"""
requires_backends(self , ["""vision"""] )
super().__init__(*UpperCamelCase , **UpperCamelCase )
def UpperCamelCase ( self: str , UpperCamelCase: "Image" , UpperCamelCase: str ):
"""simple docstring"""
return self.pre_processor(UpperCamelCase , UpperCamelCase , return_tensors="""pt""" )
def UpperCamelCase ( self: str , UpperCamelCase: str ):
"""simple docstring"""
with torch.no_grad():
return self.model(**UpperCamelCase ).logits
def UpperCamelCase ( self: Union[str, Any] , UpperCamelCase: int ):
"""simple docstring"""
A__ = outputs.argmax(-1 ).item()
return self.model.config.idalabel[idx]
| 335 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
import torch
from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class SCREAMING_SNAKE_CASE__ ( _lowerCamelCase ):
_a = 'dandelin/vilt-b32-finetuned-vqa'
_a = (
'This is a tool that answers a question about an image. It takes an input named `image` which should be the '
'image containing the information, as well as a `question` which should be the question in English. It '
'returns a text that is the answer to the question.'
)
_a = 'image_qa'
_a = AutoProcessor
_a = AutoModelForVisualQuestionAnswering
_a = ['image', 'text']
_a = ['text']
def __init__( self : List[str] , *lowerCAmelCase : Dict , **lowerCAmelCase : List[str] ):
requires_backends(self , ["""vision"""] )
super().__init__(*lowerCAmelCase , **lowerCAmelCase )
def __lowercase ( self : str , lowerCAmelCase : "Image" , lowerCAmelCase : str ):
return self.pre_processor(lowerCAmelCase , lowerCAmelCase , return_tensors="""pt""" )
def __lowercase ( self : str , lowerCAmelCase : str ):
with torch.no_grad():
return self.model(**lowerCAmelCase ).logits
def __lowercase ( self : Union[str, Any] , lowerCAmelCase : int ):
lowerCAmelCase = outputs.argmax(-1 ).item()
return self.model.config.idalabel[idx]
| 155 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import YolosImageProcessor
class a ( unittest.TestCase ):
"""simple docstring"""
def __init__( self: Optional[Any] , UpperCamelCase: Any , UpperCamelCase: Optional[int]=7 , UpperCamelCase: str=3 , UpperCamelCase: int=30 , UpperCamelCase: int=4_00 , UpperCamelCase: Union[str, Any]=True , UpperCamelCase: Tuple=None , UpperCamelCase: Any=True , UpperCamelCase: int=[0.5, 0.5, 0.5] , UpperCamelCase: Any=[0.5, 0.5, 0.5] , UpperCamelCase: Optional[Any]=True , UpperCamelCase: List[Any]=1 / 2_55 , UpperCamelCase: Tuple=True , ):
"""simple docstring"""
A__ = size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
A__ = parent
A__ = batch_size
A__ = num_channels
A__ = min_resolution
A__ = max_resolution
A__ = do_resize
A__ = size
A__ = do_normalize
A__ = image_mean
A__ = image_std
A__ = do_rescale
A__ = rescale_factor
A__ = do_pad
def UpperCamelCase ( self: Optional[Any] ):
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def UpperCamelCase ( self: Any , UpperCamelCase: List[str] , UpperCamelCase: int=False ):
"""simple docstring"""
if not batched:
A__ = image_inputs[0]
if isinstance(UpperCamelCase , Image.Image ):
A__ , A__ = image.size
else:
A__ , A__ = image.shape[1], image.shape[2]
if w < h:
A__ = int(self.size["""shortest_edge"""] * h / w )
A__ = self.size["""shortest_edge"""]
elif w > h:
A__ = self.size["""shortest_edge"""]
A__ = int(self.size["""shortest_edge"""] * w / h )
else:
A__ = self.size["""shortest_edge"""]
A__ = self.size["""shortest_edge"""]
else:
A__ = []
for image in image_inputs:
A__ , A__ = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
A__ = max(UpperCamelCase , key=lambda UpperCamelCase : item[0] )[0]
A__ = max(UpperCamelCase , key=lambda UpperCamelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class a ( _lowerCamelCase, unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = YolosImageProcessor if is_vision_available() else None
def UpperCamelCase ( self: Optional[int] ):
"""simple docstring"""
A__ = YolosImageProcessingTester(self )
@property
def UpperCamelCase ( self: Optional[int] ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase ( self: Union[str, Any] ):
"""simple docstring"""
A__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCamelCase , """image_mean""" ) )
self.assertTrue(hasattr(UpperCamelCase , """image_std""" ) )
self.assertTrue(hasattr(UpperCamelCase , """do_normalize""" ) )
self.assertTrue(hasattr(UpperCamelCase , """do_resize""" ) )
self.assertTrue(hasattr(UpperCamelCase , """size""" ) )
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
A__ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 18, """longest_edge""": 13_33} )
self.assertEqual(image_processor.do_pad , UpperCamelCase )
A__ = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=UpperCamelCase )
self.assertEqual(image_processor.size , {"""shortest_edge""": 42, """longest_edge""": 84} )
self.assertEqual(image_processor.do_pad , UpperCamelCase )
def UpperCamelCase ( self: str ):
"""simple docstring"""
pass
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
A__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase , Image.Image )
# Test not batched input
A__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase , batched=UpperCamelCase )
A__ = image_processing(UpperCamelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
A__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
A__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase , numpify=UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase , np.ndarray )
# Test not batched input
A__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
A__ = image_processing(UpperCamelCase , return_tensors="""pt""" ).pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase , batched=UpperCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
A__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase , torchify=UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase , torch.Tensor )
# Test not batched input
A__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
A__ = image_processing(UpperCamelCase , return_tensors="""pt""" ).pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase , batched=UpperCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = self.image_processing_class(**self.image_processor_dict )
A__ = self.image_processing_class(do_resize=UpperCamelCase , do_normalize=UpperCamelCase , do_rescale=UpperCamelCase )
# create random PyTorch tensors
A__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase , torchify=UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
A__ = image_processing_a.pad(UpperCamelCase , return_tensors="""pt""" )
A__ = image_processing_a(UpperCamelCase , return_tensors="""pt""" )
self.assertTrue(
torch.allclose(encoded_images_with_method["""pixel_values"""] , encoded_images["""pixel_values"""] , atol=1e-4 ) )
@slow
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt""" , """r""" ) as f:
A__ = json.loads(f.read() )
A__ = {"""image_id""": 3_97_69, """annotations""": target}
# encode them
A__ = YolosImageProcessor.from_pretrained("""hustvl/yolos-small""" )
A__ = image_processing(images=UpperCamelCase , annotations=UpperCamelCase , return_tensors="""pt""" )
# verify pixel values
A__ = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding["""pixel_values"""].shape , UpperCamelCase )
A__ = torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , UpperCamelCase , atol=1e-4 ) )
# verify area
A__ = torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , UpperCamelCase ) )
# verify boxes
A__ = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , UpperCamelCase )
A__ = torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , UpperCamelCase , atol=1e-3 ) )
# verify image_id
A__ = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , UpperCamelCase ) )
# verify is_crowd
A__ = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , UpperCamelCase ) )
# verify class_labels
A__ = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , UpperCamelCase ) )
# verify orig_size
A__ = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , UpperCamelCase ) )
# verify size
A__ = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , UpperCamelCase ) )
@slow
def UpperCamelCase ( self: int ):
"""simple docstring"""
A__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt""" , """r""" ) as f:
A__ = json.loads(f.read() )
A__ = {"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}
A__ = pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""" )
# encode them
A__ = YolosImageProcessor(format="""coco_panoptic""" )
A__ = image_processing(images=UpperCamelCase , annotations=UpperCamelCase , masks_path=UpperCamelCase , return_tensors="""pt""" )
# verify pixel values
A__ = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding["""pixel_values"""].shape , UpperCamelCase )
A__ = torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , UpperCamelCase , atol=1e-4 ) )
# verify area
A__ = torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , UpperCamelCase ) )
# verify boxes
A__ = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , UpperCamelCase )
A__ = torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , UpperCamelCase , atol=1e-3 ) )
# verify image_id
A__ = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , UpperCamelCase ) )
# verify is_crowd
A__ = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , UpperCamelCase ) )
# verify class_labels
A__ = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , UpperCamelCase ) )
# verify masks
A__ = 82_28_73
self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item() , UpperCamelCase )
# verify orig_size
A__ = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , UpperCamelCase ) )
# verify size
A__ = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , UpperCamelCase ) )
| 335 | 0 |
"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
logging.basicConfig(level=logging.DEBUG)
__SCREAMING_SNAKE_CASE =logging.getLogger()
def lowercase__( __SCREAMING_SNAKE_CASE : Path , __SCREAMING_SNAKE_CASE : list ):
lowercase_ : List[str] = '\n'.join(UpperCAmelCase_ )
Path(UpperCAmelCase_ ).open('w' ).writelines(UpperCAmelCase_ )
__SCREAMING_SNAKE_CASE ='patrickvonplaten/t5-tiny-random'
__SCREAMING_SNAKE_CASE ='sshleifer/bart-tiny-random'
__SCREAMING_SNAKE_CASE ='sshleifer/tiny-mbart'
__SCREAMING_SNAKE_CASE =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks
class UpperCamelCase ( _lowerCamelCase ):
def _UpperCAmelCase ( self ,__UpperCamelCase ) -> int:
'''simple docstring'''
lowercase_ : Tuple = Path(self.get_auto_remove_tmp_dir() ) / 'utest_input.source'
lowercase_ : List[str] = input_file_name.parent / 'utest_output.txt'
assert not output_file_name.exists()
lowercase_ : Optional[Any] = [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
_dump_articles(__UpperCamelCase ,__UpperCamelCase )
lowercase_ : Optional[Any] = str(Path(self.get_auto_remove_tmp_dir() ) / 'scores.json' )
lowercase_ : Union[str, Any] = 'translation_en_to_de' if model == T5_TINY else 'summarization'
lowercase_ : Optional[int] = f'''
run_eval_search.py
{model}
{input_file_name}
{output_file_name}
--score_path {score_path}
--task {task}
--num_beams 2
--length_penalty 2.0
'''.split()
with patch.object(__UpperCamelCase ,'argv' ,__UpperCamelCase ):
run_generate()
assert Path(__UpperCamelCase ).exists()
# os.remove(Path(output_file_name))
def _UpperCAmelCase ( self ) -> Tuple:
'''simple docstring'''
self.run_eval_tester(__UpperCamelCase )
@parameterized.expand([BART_TINY, MBART_TINY] )
@slow
def _UpperCAmelCase ( self ,__UpperCamelCase ) -> Tuple:
'''simple docstring'''
self.run_eval_tester(__UpperCamelCase )
@parameterized.expand([T5_TINY, MBART_TINY] )
@slow
def _UpperCAmelCase ( self ,__UpperCamelCase ) -> Dict:
'''simple docstring'''
lowercase_ : Any = Path(self.get_auto_remove_tmp_dir() ) / 'utest_input.source'
lowercase_ : str = input_file_name.parent / 'utest_output.txt'
assert not output_file_name.exists()
lowercase_ : Union[str, Any] = {
'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
'de': [
'Maschinelles Lernen ist großartig, oder?',
'Ich esse gerne Bananen',
'Morgen ist wieder ein toller Tag!',
],
}
lowercase_ : Any = Path(self.get_auto_remove_tmp_dir() )
lowercase_ : Tuple = str(tmp_dir / 'scores.json' )
lowercase_ : List[str] = str(tmp_dir / 'val.target' )
_dump_articles(__UpperCamelCase ,text['en'] )
_dump_articles(__UpperCamelCase ,text['de'] )
lowercase_ : Union[str, Any] = 'translation_en_to_de' if model == T5_TINY else 'summarization'
lowercase_ : str = f'''
run_eval_search.py
{model}
{str(__UpperCamelCase )}
{str(__UpperCamelCase )}
--score_path {score_path}
--reference_path {reference_path}
--task {task}
'''.split()
testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0'] )
with patch.object(__UpperCamelCase ,'argv' ,__UpperCamelCase ):
with CaptureStdout() as cs:
run_search()
lowercase_ : int = [' num_beams | length_penalty', model, 'Best score args']
lowercase_ : int = ['Info']
if "translation" in task:
expected_strings.append('bleu' )
else:
expected_strings.extend(__UpperCamelCase )
for w in expected_strings:
assert w in cs.out
for w in un_expected_strings:
assert w not in cs.out
assert Path(__UpperCamelCase ).exists()
os.remove(Path(__UpperCamelCase ) )
| 213 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : Dict ): # noqa: E741
A__ = len(UpperCAmelCase_ )
A__ = 0
A__ = [0] * n
A__ = [False] * n
A__ = [False] * n
def dfs(UpperCAmelCase_ : List[str] , UpperCAmelCase_ : int , UpperCAmelCase_ : Dict , UpperCAmelCase_ : List[Any] ):
if parent == root:
out_edge_count += 1
A__ = True
A__ = at
for to in l[at]:
if to == parent:
pass
elif not visited[to]:
A__ = dfs(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
A__ = min(low[at] , low[to] )
# AP found via bridge
if at < low[to]:
A__ = True
# AP found via cycle
if at == low[to]:
A__ = True
else:
A__ = min(low[at] , UpperCAmelCase_ )
return out_edge_count
for i in range(UpperCAmelCase_ ):
if not visited[i]:
A__ = 0
A__ = dfs(UpperCAmelCase_ , UpperCAmelCase_ , -1 , UpperCAmelCase_ )
A__ = out_edge_count > 1
for x in range(len(UpperCAmelCase_ ) ):
if is_art[x] is True:
print(UpperCAmelCase_ )
# Adjacency list of graph
SCREAMING_SNAKE_CASE_ : Optional[int] = {
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
}
compute_ap(data)
| 335 | 0 |
"""simple docstring"""
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def lowerCamelCase__ ( _lowerCamelCase : Dict ) -> int:
if not is_accelerate_available():
return method
lowerCamelCase_ = version.parse(accelerate.__version__ ).base_version
if version.parse(UpperCAmelCase_ ) < version.parse('0.17.0' ):
return method
def wrapper(self : int , *_lowerCamelCase : Tuple , **_lowerCamelCase : Dict ):
if hasattr(self , '_hf_hook' ) and hasattr(self._hf_hook , 'pre_forward' ):
self._hf_hook.pre_forward(self )
return method(self , *UpperCAmelCase_ , **UpperCAmelCase_ )
return wrapper
| 183 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class a ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = get_activation("""swish""" )
self.assertIsInstance(UpperCamelCase , nn.SiLU )
self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def UpperCamelCase ( self: Any ):
"""simple docstring"""
A__ = get_activation("""silu""" )
self.assertIsInstance(UpperCamelCase , nn.SiLU )
self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def UpperCamelCase ( self: Optional[int] ):
"""simple docstring"""
A__ = get_activation("""mish""" )
self.assertIsInstance(UpperCamelCase , nn.Mish )
self.assertEqual(act(torch.tensor(-2_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def UpperCamelCase ( self: Any ):
"""simple docstring"""
A__ = get_activation("""gelu""" )
self.assertIsInstance(UpperCamelCase , nn.GELU )
self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
| 335 | 0 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
from .text_encoder import MultilingualCLIP
__A =logging.get_logger(__name__) # pylint: disable=invalid-name
__A ='\n Examples:\n ```py\n >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n >>> import torch\n\n >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")\n >>> pipe_prior.to("cuda")\n\n >>> prompt = "red cat, 4k photo"\n >>> out = pipe_prior(prompt)\n >>> image_emb = out.image_embeds\n >>> negative_image_emb = out.negative_image_embeds\n\n >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")\n >>> pipe.to("cuda")\n\n >>> image = pipe(\n ... prompt,\n ... image_embeds=image_emb,\n ... negative_image_embeds=negative_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=100,\n ... ).images\n\n >>> image[0].save("cat.png")\n ```\n'
def a ( _UpperCAmelCase : Tuple , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[int]=8 ):
'''simple docstring'''
__UpperCAmelCase : List[Any] = h // scale_factor**2
if h % scale_factor**2 != 0:
new_h += 1
__UpperCAmelCase : Union[str, Any] = w // scale_factor**2
if w % scale_factor**2 != 0:
new_w += 1
return new_h * scale_factor, new_w * scale_factor
class UpperCAmelCase__ ( _lowerCamelCase ):
'''simple docstring'''
def __init__( self : Any , a_ : MultilingualCLIP , a_ : XLMRobertaTokenizer , a_ : UNetaDConditionModel , a_ : Union[DDIMScheduler, DDPMScheduler] , a_ : VQModel , ):
'''simple docstring'''
super().__init__()
self.register_modules(
text_encoder=a_ , tokenizer=a_ , unet=a_ , scheduler=a_ , movq=a_ , )
__UpperCAmelCase : Optional[int] = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def snake_case__ ( self : Any , a_ : Optional[int] , a_ : str , a_ : Optional[Any] , a_ : List[str] , a_ : int , a_ : Optional[Any] ):
'''simple docstring'''
if latents is None:
__UpperCAmelCase : Optional[int] = randn_tensor(a_ , generator=a_ , device=a_ , dtype=a_ )
else:
if latents.shape != shape:
raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}' )
__UpperCAmelCase : int = latents.to(a_ )
__UpperCAmelCase : Union[str, Any] = latents * scheduler.init_noise_sigma
return latents
def snake_case__ ( self : Tuple , a_ : Union[str, Any] , a_ : List[str] , a_ : Optional[Any] , a_ : Any , a_ : List[str]=None , ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = len(a_ ) if isinstance(a_ , a_ ) else 1
# get prompt text embeddings
__UpperCAmelCase : Dict = self.tokenizer(
a_ , padding='''max_length''' , truncation=a_ , max_length=77 , return_attention_mask=a_ , add_special_tokens=a_ , return_tensors='''pt''' , )
__UpperCAmelCase : Dict = text_inputs.input_ids
__UpperCAmelCase : Union[str, Any] = self.tokenizer(a_ , padding='''longest''' , return_tensors='''pt''' ).input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(a_ , a_ ):
__UpperCAmelCase : Dict = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] )
logger.warning(
'''The following part of your input was truncated because CLIP can only handle sequences up to'''
F' {self.tokenizer.model_max_length} tokens: {removed_text}' )
__UpperCAmelCase : str = text_input_ids.to(a_ )
__UpperCAmelCase : Optional[int] = text_inputs.attention_mask.to(a_ )
__UpperCAmelCase , __UpperCAmelCase : Optional[Any] = self.text_encoder(
input_ids=a_ , attention_mask=a_ )
__UpperCAmelCase : Union[str, Any] = prompt_embeds.repeat_interleave(a_ , dim=0 )
__UpperCAmelCase : str = text_encoder_hidden_states.repeat_interleave(a_ , dim=0 )
__UpperCAmelCase : int = text_mask.repeat_interleave(a_ , dim=0 )
if do_classifier_free_guidance:
__UpperCAmelCase : Optional[Any] = 42
if negative_prompt is None:
__UpperCAmelCase : int = [''''''] * batch_size
elif type(a_ ) is not type(a_ ):
raise TypeError(
F'`negative_prompt` should be the same type to `prompt`, but got {type(a_ )} !='
F' {type(a_ )}.' )
elif isinstance(a_ , a_ ):
__UpperCAmelCase : int = [negative_prompt]
elif batch_size != len(a_ ):
raise ValueError(
F'`negative_prompt`: {negative_prompt} has batch size {len(a_ )}, but `prompt`:'
F' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
''' the batch size of `prompt`.''' )
else:
__UpperCAmelCase : Dict = negative_prompt
__UpperCAmelCase : int = self.tokenizer(
a_ , padding='''max_length''' , max_length=77 , truncation=a_ , return_attention_mask=a_ , add_special_tokens=a_ , return_tensors='''pt''' , )
__UpperCAmelCase : List[str] = uncond_input.input_ids.to(a_ )
__UpperCAmelCase : Optional[int] = uncond_input.attention_mask.to(a_ )
__UpperCAmelCase , __UpperCAmelCase : int = self.text_encoder(
input_ids=a_ , attention_mask=a_ )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
__UpperCAmelCase : Optional[int] = negative_prompt_embeds.shape[1]
__UpperCAmelCase : Dict = negative_prompt_embeds.repeat(1 , a_ )
__UpperCAmelCase : Tuple = negative_prompt_embeds.view(batch_size * num_images_per_prompt , a_ )
__UpperCAmelCase : str = uncond_text_encoder_hidden_states.shape[1]
__UpperCAmelCase : Union[str, Any] = uncond_text_encoder_hidden_states.repeat(1 , a_ , 1 )
__UpperCAmelCase : Tuple = uncond_text_encoder_hidden_states.view(
batch_size * num_images_per_prompt , a_ , -1 )
__UpperCAmelCase : Optional[int] = uncond_text_mask.repeat_interleave(a_ , dim=0 )
# done duplicates
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
__UpperCAmelCase : Tuple = torch.cat([negative_prompt_embeds, prompt_embeds] )
__UpperCAmelCase : str = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states] )
__UpperCAmelCase : List[Any] = torch.cat([uncond_text_mask, text_mask] )
return prompt_embeds, text_encoder_hidden_states, text_mask
def snake_case__ ( self : List[str] , a_ : Dict=0 ):
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('''Please install accelerate via `pip install accelerate`''' )
__UpperCAmelCase : List[Any] = torch.device(F'cuda:{gpu_id}' )
__UpperCAmelCase : int = [
self.unet,
self.text_encoder,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(a_ , a_ )
def snake_case__ ( self : List[Any] , a_ : Union[str, Any]=0 ):
'''simple docstring'''
if is_accelerate_available() and is_accelerate_version('''>=''' , '''0.17.0.dev0''' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.''' )
__UpperCAmelCase : Tuple = torch.device(F'cuda:{gpu_id}' )
if self.device.type != "cpu":
self.to('''cpu''' , silence_dtype_warnings=a_ )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
__UpperCAmelCase : int = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
__UpperCAmelCase , __UpperCAmelCase : str = cpu_offload_with_hook(a_ , a_ , prev_module_hook=a_ )
if self.safety_checker is not None:
__UpperCAmelCase , __UpperCAmelCase : Optional[Any] = cpu_offload_with_hook(self.safety_checker , a_ , prev_module_hook=a_ )
# We'll offload the last model manually.
__UpperCAmelCase : List[Any] = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def snake_case__ ( self : Dict ):
'''simple docstring'''
if not hasattr(self.unet , '''_hf_hook''' ):
return self.device
for module in self.unet.modules():
if (
hasattr(a_ , '''_hf_hook''' )
and hasattr(module._hf_hook , '''execution_device''' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(a_ )
def __call__( self : Optional[int] , a_ : Union[str, List[str]] , a_ : Union[torch.FloatTensor, List[torch.FloatTensor]] , a_ : Union[torch.FloatTensor, List[torch.FloatTensor]] , a_ : Optional[Union[str, List[str]]] = None , a_ : int = 5_12 , a_ : int = 5_12 , a_ : int = 1_00 , a_ : float = 4.0 , a_ : int = 1 , a_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , a_ : Optional[torch.FloatTensor] = None , a_ : Optional[str] = "pil" , a_ : bool = True , ):
'''simple docstring'''
if isinstance(a_ , a_ ):
__UpperCAmelCase : int = 1
elif isinstance(a_ , a_ ):
__UpperCAmelCase : Dict = len(a_ )
else:
raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(a_ )}' )
__UpperCAmelCase : Optional[int] = self._execution_device
__UpperCAmelCase : int = batch_size * num_images_per_prompt
__UpperCAmelCase : Optional[int] = guidance_scale > 1.0
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Tuple = self._encode_prompt(
a_ , a_ , a_ , a_ , a_ )
if isinstance(a_ , a_ ):
__UpperCAmelCase : Optional[Any] = torch.cat(a_ , dim=0 )
if isinstance(a_ , a_ ):
__UpperCAmelCase : Any = torch.cat(a_ , dim=0 )
if do_classifier_free_guidance:
__UpperCAmelCase : Optional[Any] = image_embeds.repeat_interleave(a_ , dim=0 )
__UpperCAmelCase : Dict = negative_image_embeds.repeat_interleave(a_ , dim=0 )
__UpperCAmelCase : int = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(
dtype=prompt_embeds.dtype , device=a_ )
self.scheduler.set_timesteps(a_ , device=a_ )
__UpperCAmelCase : Any = self.scheduler.timesteps
__UpperCAmelCase : Dict = self.unet.config.in_channels
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = get_new_h_w(a_ , a_ , self.movq_scale_factor )
# create initial latent
__UpperCAmelCase : Union[str, Any] = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , text_encoder_hidden_states.dtype , a_ , a_ , a_ , self.scheduler , )
for i, t in enumerate(self.progress_bar(a_ ) ):
# expand the latents if we are doing classifier free guidance
__UpperCAmelCase : str = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
__UpperCAmelCase : int = {'''text_embeds''': prompt_embeds, '''image_embeds''': image_embeds}
__UpperCAmelCase : List[str] = self.unet(
sample=a_ , timestep=a_ , encoder_hidden_states=a_ , added_cond_kwargs=a_ , return_dict=a_ , )[0]
if do_classifier_free_guidance:
__UpperCAmelCase , __UpperCAmelCase : Dict = noise_pred.split(latents.shape[1] , dim=1 )
__UpperCAmelCase , __UpperCAmelCase : Optional[int] = noise_pred.chunk(2 )
__UpperCAmelCase , __UpperCAmelCase : Any = variance_pred.chunk(2 )
__UpperCAmelCase : Any = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
__UpperCAmelCase : Optional[Any] = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , '''variance_type''' )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
__UpperCAmelCase , __UpperCAmelCase : int = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
__UpperCAmelCase : Tuple = self.scheduler.step(
a_ , a_ , a_ , generator=a_ , ).prev_sample
# post-processing
__UpperCAmelCase : str = self.movq.decode(a_ , force_not_quantize=a_ )['''sample''']
if output_type not in ["pt", "np", "pil"]:
raise ValueError(F'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}' )
if output_type in ["np", "pil"]:
__UpperCAmelCase : Union[str, Any] = image * 0.5 + 0.5
__UpperCAmelCase : Dict = image.clamp(0 , 1 )
__UpperCAmelCase : List[Any] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
__UpperCAmelCase : Optional[Any] = self.numpy_to_pil(a_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=a_ )
| 226 |
"""simple docstring"""
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
ROBERTA_INPUTS_DOCSTRING,
ROBERTA_START_DOCSTRING,
RobertaEmbeddings,
)
from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy
@add_start_docstrings(
"The RoBERTa Model transformer with early exiting (DeeRoBERTa). ", _lowerCamelCase, )
class a ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = RobertaConfig
UpperCAmelCase = "roberta"
def __init__( self: Union[str, Any] , UpperCamelCase: Any ):
"""simple docstring"""
super().__init__(UpperCamelCase )
A__ = RobertaEmbeddings(UpperCamelCase )
self.init_weights()
@add_start_docstrings(
"RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n also takes care of multi-layer training. ", _lowerCamelCase, )
class a ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = RobertaConfig
UpperCAmelCase = "roberta"
def __init__( self: Optional[Any] , UpperCamelCase: int ):
"""simple docstring"""
super().__init__(UpperCamelCase )
A__ = config.num_labels
A__ = config.num_hidden_layers
A__ = DeeRobertaModel(UpperCamelCase )
A__ = nn.Dropout(config.hidden_dropout_prob )
A__ = nn.Linear(config.hidden_size , self.config.num_labels )
@add_start_docstrings_to_model_forward(UpperCamelCase )
def UpperCamelCase ( self: Union[str, Any] , UpperCamelCase: Optional[int]=None , UpperCamelCase: str=None , UpperCamelCase: str=None , UpperCamelCase: List[str]=None , UpperCamelCase: Dict=None , UpperCamelCase: List[Any]=None , UpperCamelCase: Tuple=None , UpperCamelCase: Optional[int]=-1 , UpperCamelCase: Optional[Any]=False , ):
"""simple docstring"""
A__ = self.num_layers
try:
A__ = self.roberta(
UpperCamelCase , attention_mask=UpperCamelCase , token_type_ids=UpperCamelCase , position_ids=UpperCamelCase , head_mask=UpperCamelCase , inputs_embeds=UpperCamelCase , )
A__ = outputs[1]
A__ = self.dropout(UpperCamelCase )
A__ = self.classifier(UpperCamelCase )
A__ = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
A__ = e.message
A__ = e.exit_layer
A__ = outputs[0]
if not self.training:
A__ = entropy(UpperCamelCase )
A__ = []
A__ = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
A__ = MSELoss()
A__ = loss_fct(logits.view(-1 ) , labels.view(-1 ) )
else:
A__ = CrossEntropyLoss()
A__ = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
# work with highway exits
A__ = []
for highway_exit in outputs[-1]:
A__ = highway_exit[0]
if not self.training:
highway_logits_all.append(UpperCamelCase )
highway_entropy.append(highway_exit[2] )
if self.num_labels == 1:
# We are doing regression
A__ = MSELoss()
A__ = loss_fct(highway_logits.view(-1 ) , labels.view(-1 ) )
else:
A__ = CrossEntropyLoss()
A__ = loss_fct(highway_logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
highway_losses.append(UpperCamelCase )
if train_highway:
A__ = (sum(highway_losses[:-1] ),) + outputs
# exclude the final highway, of course
else:
A__ = (loss,) + outputs
if not self.training:
A__ = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
A__ = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), entropy
| 335 | 0 |
from collections import defaultdict
def UpperCAmelCase ( a_ , a_ ) -> Any:
"""simple docstring"""
__A = first_str.lower().strip()
__A = second_str.lower().strip()
# Remove whitespace
__A = first_str.replace(" " , "" )
__A = second_str.replace(" " , "" )
# Strings of different lengths are not anagrams
if len(UpperCAmelCase_ ) != len(UpperCAmelCase_ ):
return False
# Default values for count should be 0
__A = defaultdict(UpperCAmelCase_ )
# For each character in input strings,
# increment count in the corresponding
for i in range(len(UpperCAmelCase_ ) ):
count[first_str[i]] += 1
count[second_str[i]] -= 1
return all(_count == 0 for _count in count.values() )
if __name__ == "__main__":
from doctest import testmod
testmod()
SCREAMING_SNAKE_CASE :Tuple = input('Enter the first string ').strip()
SCREAMING_SNAKE_CASE :Tuple = input('Enter the second string ').strip()
SCREAMING_SNAKE_CASE :List[Any] = check_anagrams(input_a, input_b)
print(f'''{input_a} and {input_b} are {"" if status else "not "}anagrams.''')
| 15 |
"""simple docstring"""
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
SCREAMING_SNAKE_CASE_ : int = [
'Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'
' final seconds on board Flight 9525. The Germanwings co-pilot says he had a "previous episode of severe'
' depression\" German airline confirms it knew of Andreas Lubitz\'s depression years before he took control.',
'The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal'
' accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC\'s'
' founding Rome Statute in January. Israel and the United States opposed the Palestinians\' efforts to join the'
' body.',
'Amnesty International releases its annual report on the death penalty. The report catalogs the use of'
' state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the'
' world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital'
' punishment.',
]
SCREAMING_SNAKE_CASE_ : List[Any] = [
'Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .'
' Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz'
' had informed his Lufthansa training school of an episode of severe depression, airline says .',
'Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June .'
' Israel and the United States opposed the move, which could open the door to war crimes investigations against'
' Israelis .',
'Amnesty\'s annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to'
' death . Organization claims that governments around the world are using the threat of terrorism to advance'
' executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death'
' sentences up by 28% .',
]
def _snake_case ( ):
A__ = calculate_rouge(UpperCAmelCase_ , UpperCAmelCase_ , bootstrap_aggregation=UpperCAmelCase_ , rouge_keys=["""rouge2""", """rougeL"""] )
assert isinstance(UpperCAmelCase_ , UpperCAmelCase_ )
A__ = calculate_rouge(UpperCAmelCase_ , UpperCAmelCase_ , bootstrap_aggregation=UpperCAmelCase_ , rouge_keys=["""rouge2"""] )
assert (
pd.DataFrame(no_aggregation["""rouge2"""] ).fmeasure.mean()
== pd.DataFrame(no_aggregation_just_ra["""rouge2"""] ).fmeasure.mean()
)
def _snake_case ( ):
A__ = """rougeLsum"""
A__ = calculate_rouge(UpperCAmelCase_ , UpperCAmelCase_ , newline_sep=UpperCAmelCase_ , rouge_keys=[k] )[k]
A__ = calculate_rouge(UpperCAmelCase_ , UpperCAmelCase_ , newline_sep=UpperCAmelCase_ , rouge_keys=[k] )[k]
assert score > score_no_sep
def _snake_case ( ):
A__ = ["""rouge1""", """rouge2""", """rougeL"""]
A__ = calculate_rouge(UpperCAmelCase_ , UpperCAmelCase_ , newline_sep=UpperCAmelCase_ , rouge_keys=UpperCAmelCase_ )
A__ = calculate_rouge(UpperCAmelCase_ , UpperCAmelCase_ , newline_sep=UpperCAmelCase_ , rouge_keys=UpperCAmelCase_ )
assert score_sep == score_no_sep
def _snake_case ( ):
A__ = [
"""Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.""",
"""Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .""",
]
A__ = [
"""Margot Frank, died in 1945, a month earlier than previously thought.""",
"""Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"""
""" the final seconds on board Flight 9525.""",
]
assert calculate_rouge(UpperCAmelCase_ , UpperCAmelCase_ , newline_sep=UpperCAmelCase_ ) == calculate_rouge(UpperCAmelCase_ , UpperCAmelCase_ , newline_sep=UpperCAmelCase_ )
def _snake_case ( ):
A__ = [
"""\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" """
]
A__ = [
""" Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."""
]
A__ = calculate_rouge(UpperCAmelCase_ , UpperCAmelCase_ , rouge_keys=["""rougeLsum"""] , newline_sep=UpperCAmelCase_ )["""rougeLsum"""]
A__ = calculate_rouge(UpperCAmelCase_ , UpperCAmelCase_ , rouge_keys=["""rougeLsum"""] )["""rougeLsum"""]
assert new_score > prev_score
def _snake_case ( ):
A__ = Path("""examples/seq2seq/test_data/wmt_en_ro""" )
A__ = calculate_rouge_path(data_dir.joinpath("""test.source""" ) , data_dir.joinpath("""test.target""" ) )
assert isinstance(UpperCAmelCase_ , UpperCAmelCase_ )
A__ = calculate_rouge_path(
data_dir.joinpath("""test.source""" ) , data_dir.joinpath("""test.target""" ) , bootstrap_aggregation=UpperCAmelCase_ )
assert isinstance(UpperCAmelCase_ , UpperCAmelCase_ )
| 335 | 0 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def a( ) -> List[Any]:
"""simple docstring"""
a = HfArgumentParser(UpperCAmelCase_ )
a = parser.parse_args_into_dataclasses()[0]
a = TensorFlowBenchmark(args=UpperCAmelCase_ )
try:
a = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
a = "Arg --no_{0} is no longer used, please use --no-{0} instead."
a = " ".join(str(UpperCAmelCase_ ).split(" " )[:-1] )
a = ""
a = eval(str(UpperCAmelCase_ ).split(" " )[-1] )
a = []
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(UpperCAmelCase_ )
if len(UpperCAmelCase_ ) > 0:
a = full_error_msg + begin_error_msg + str(UpperCAmelCase_ )
raise ValueError(UpperCAmelCase_ )
benchmark.run()
if __name__ == "__main__":
main()
| 227 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, 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_mobilenet_va import MobileNetVaConfig
SCREAMING_SNAKE_CASE_ : Union[str, Any] = logging.get_logger(__name__)
# General docstring
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 'MobileNetV1Config'
# Base docstring
SCREAMING_SNAKE_CASE_ : str = 'google/mobilenet_v1_1.0_224'
SCREAMING_SNAKE_CASE_ : List[str] = [1, 1_0_2_4, 7, 7]
# Image classification docstring
SCREAMING_SNAKE_CASE_ : Optional[Any] = 'google/mobilenet_v1_1.0_224'
SCREAMING_SNAKE_CASE_ : Tuple = 'tabby, tabby cat'
SCREAMING_SNAKE_CASE_ : Union[str, Any] = [
'google/mobilenet_v1_1.0_224',
'google/mobilenet_v1_0.75_192',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def _snake_case ( UpperCAmelCase_ : int , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Dict=None ):
A__ = {}
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
A__ = model.mobilenet_va
else:
A__ = model
A__ = """MobilenetV1/Conv2d_0/"""
A__ = backbone.conv_stem.convolution.weight
A__ = backbone.conv_stem.normalization.bias
A__ = backbone.conv_stem.normalization.weight
A__ = backbone.conv_stem.normalization.running_mean
A__ = backbone.conv_stem.normalization.running_var
for i in range(13 ):
A__ = i + 1
A__ = i * 2
A__ = backbone.layer[pt_index]
A__ = F"""MobilenetV1/Conv2d_{tf_index}_depthwise/"""
A__ = pointer.convolution.weight
A__ = pointer.normalization.bias
A__ = pointer.normalization.weight
A__ = pointer.normalization.running_mean
A__ = pointer.normalization.running_var
A__ = backbone.layer[pt_index + 1]
A__ = F"""MobilenetV1/Conv2d_{tf_index}_pointwise/"""
A__ = pointer.convolution.weight
A__ = pointer.normalization.bias
A__ = pointer.normalization.weight
A__ = pointer.normalization.running_mean
A__ = pointer.normalization.running_var
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
A__ = """MobilenetV1/Logits/Conv2d_1c_1x1/"""
A__ = model.classifier.weight
A__ = model.classifier.bias
return tf_to_pt_map
def _snake_case ( UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : List[Any] ):
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
"""Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see """
"""https://www.tensorflow.org/install/ for installation instructions.""" )
raise
# Load weights from TF model
A__ = tf.train.list_variables(UpperCAmelCase_ )
A__ = {}
for name, shape in init_vars:
logger.info(F"""Loading TF weight {name} with shape {shape}""" )
A__ = tf.train.load_variable(UpperCAmelCase_ , UpperCAmelCase_ )
A__ = array
# Build TF to PyTorch weights loading map
A__ = _build_tf_to_pytorch_map(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
for name, pointer in tf_to_pt_map.items():
logger.info(F"""Importing {name}""" )
if name not in tf_weights:
logger.info(F"""{name} not in tf pre-trained weights, skipping""" )
continue
A__ = tf_weights[name]
if "depthwise_weights" in name:
logger.info("""Transposing depthwise""" )
A__ = np.transpose(UpperCAmelCase_ , (2, 3, 0, 1) )
elif "weights" in name:
logger.info("""Transposing""" )
if len(pointer.shape ) == 2: # copying into linear layer
A__ = array.squeeze().transpose()
else:
A__ = np.transpose(UpperCAmelCase_ , (3, 2, 0, 1) )
if pointer.shape != array.shape:
raise ValueError(F"""Pointer shape {pointer.shape} and array shape {array.shape} mismatched""" )
logger.info(F"""Initialize PyTorch weight {name} {array.shape}""" )
A__ = torch.from_numpy(UpperCAmelCase_ )
tf_weights.pop(UpperCAmelCase_ , UpperCAmelCase_ )
tf_weights.pop(name + """/RMSProp""" , UpperCAmelCase_ )
tf_weights.pop(name + """/RMSProp_1""" , UpperCAmelCase_ )
tf_weights.pop(name + """/ExponentialMovingAverage""" , UpperCAmelCase_ )
logger.info(F"""Weights not copied to PyTorch model: {', '.join(tf_weights.keys() )}""" )
return model
def _snake_case ( UpperCAmelCase_ : torch.Tensor , UpperCAmelCase_ : nn.Convad ):
A__ , A__ = features.shape[-2:]
A__ , A__ = conv_layer.stride
A__ , A__ = conv_layer.kernel_size
if in_height % stride_height == 0:
A__ = max(kernel_height - stride_height , 0 )
else:
A__ = max(kernel_height - (in_height % stride_height) , 0 )
if in_width % stride_width == 0:
A__ = max(kernel_width - stride_width , 0 )
else:
A__ = max(kernel_width - (in_width % stride_width) , 0 )
A__ = pad_along_width // 2
A__ = pad_along_width - pad_left
A__ = pad_along_height // 2
A__ = pad_along_height - pad_top
A__ = (pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(UpperCAmelCase_ , UpperCAmelCase_ , """constant""" , 0.0 )
class a ( nn.Module ):
"""simple docstring"""
def __init__( self: Union[str, Any] , UpperCamelCase: MobileNetVaConfig , UpperCamelCase: int , UpperCamelCase: int , UpperCamelCase: int , UpperCamelCase: Optional[int] = 1 , UpperCamelCase: Optional[int] = 1 , UpperCamelCase: bool = False , UpperCamelCase: Optional[bool] = True , UpperCamelCase: Optional[bool or str] = True , ):
"""simple docstring"""
super().__init__()
A__ = config
if in_channels % groups != 0:
raise ValueError(f"""Input channels ({in_channels}) are not divisible by {groups} groups.""" )
if out_channels % groups != 0:
raise ValueError(f"""Output channels ({out_channels}) are not divisible by {groups} groups.""" )
A__ = 0 if config.tf_padding else int((kernel_size - 1) / 2 )
A__ = nn.Convad(
in_channels=UpperCamelCase , out_channels=UpperCamelCase , kernel_size=UpperCamelCase , stride=UpperCamelCase , padding=UpperCamelCase , groups=UpperCamelCase , bias=UpperCamelCase , padding_mode="""zeros""" , )
if use_normalization:
A__ = nn.BatchNormad(
num_features=UpperCamelCase , eps=config.layer_norm_eps , momentum=0.9_997 , affine=UpperCamelCase , track_running_stats=UpperCamelCase , )
else:
A__ = None
if use_activation:
if isinstance(UpperCamelCase , UpperCamelCase ):
A__ = ACTaFN[use_activation]
elif isinstance(config.hidden_act , UpperCamelCase ):
A__ = ACTaFN[config.hidden_act]
else:
A__ = config.hidden_act
else:
A__ = None
def UpperCamelCase ( self: List[Any] , UpperCamelCase: torch.Tensor ):
"""simple docstring"""
if self.config.tf_padding:
A__ = apply_tf_padding(UpperCamelCase , self.convolution )
A__ = self.convolution(UpperCamelCase )
if self.normalization is not None:
A__ = self.normalization(UpperCamelCase )
if self.activation is not None:
A__ = self.activation(UpperCamelCase )
return features
class a ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = MobileNetVaConfig
UpperCAmelCase = load_tf_weights_in_mobilenet_va
UpperCAmelCase = "mobilenet_v1"
UpperCAmelCase = "pixel_values"
UpperCAmelCase = False
def UpperCamelCase ( self: Any , UpperCamelCase: Union[nn.Linear, nn.Convad] ):
"""simple docstring"""
if isinstance(UpperCamelCase , (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(UpperCamelCase , nn.BatchNormad ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
SCREAMING_SNAKE_CASE_ : Optional[Any] = r'\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
SCREAMING_SNAKE_CASE_ : Optional[Any] = r'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`MobileNetV1ImageProcessor.__call__`] for details.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.", _lowerCamelCase, )
class a ( _lowerCamelCase ):
"""simple docstring"""
def __init__( self: Any , UpperCamelCase: MobileNetVaConfig , UpperCamelCase: bool = True ):
"""simple docstring"""
super().__init__(UpperCamelCase )
A__ = config
A__ = 32
A__ = max(int(depth * config.depth_multiplier ) , config.min_depth )
A__ = MobileNetVaConvLayer(
UpperCamelCase , in_channels=config.num_channels , out_channels=UpperCamelCase , kernel_size=3 , stride=2 , )
A__ = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
A__ = nn.ModuleList()
for i in range(13 ):
A__ = out_channels
if strides[i] == 2 or i == 0:
depth *= 2
A__ = max(int(depth * config.depth_multiplier ) , config.min_depth )
self.layer.append(
MobileNetVaConvLayer(
UpperCamelCase , in_channels=UpperCamelCase , out_channels=UpperCamelCase , kernel_size=3 , stride=strides[i] , groups=UpperCamelCase , ) )
self.layer.append(
MobileNetVaConvLayer(
UpperCamelCase , in_channels=UpperCamelCase , out_channels=UpperCamelCase , kernel_size=1 , ) )
A__ = nn.AdaptiveAvgPoolad((1, 1) ) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def UpperCamelCase ( self: Dict , UpperCamelCase: Optional[Any] ):
"""simple docstring"""
raise NotImplementedError
@add_start_docstrings_to_model_forward(UpperCamelCase )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=UpperCamelCase , config_class=_CONFIG_FOR_DOC , modality="""vision""" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def UpperCamelCase ( self: Tuple , UpperCamelCase: Optional[torch.Tensor] = None , UpperCamelCase: Optional[bool] = None , UpperCamelCase: Optional[bool] = None , ):
"""simple docstring"""
A__ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
A__ = 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""" )
A__ = self.conv_stem(UpperCamelCase )
A__ = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer ):
A__ = layer_module(UpperCamelCase )
if output_hidden_states:
A__ = all_hidden_states + (hidden_states,)
A__ = hidden_states
if self.pooler is not None:
A__ = torch.flatten(self.pooler(UpperCamelCase ) , start_dim=1 )
else:
A__ = None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None )
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=UpperCamelCase , pooler_output=UpperCamelCase , hidden_states=UpperCamelCase , )
@add_start_docstrings(
"\n MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n ", _lowerCamelCase, )
class a ( _lowerCamelCase ):
"""simple docstring"""
def __init__( self: Union[str, Any] , UpperCamelCase: MobileNetVaConfig ):
"""simple docstring"""
super().__init__(UpperCamelCase )
A__ = config.num_labels
A__ = MobileNetVaModel(UpperCamelCase )
A__ = self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
A__ = nn.Dropout(config.classifier_dropout_prob , inplace=UpperCamelCase )
A__ = nn.Linear(UpperCamelCase , 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(UpperCamelCase )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=UpperCamelCase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def UpperCamelCase ( self: Union[str, Any] , UpperCamelCase: Optional[torch.Tensor] = None , UpperCamelCase: Optional[bool] = None , UpperCamelCase: Optional[torch.Tensor] = None , UpperCamelCase: Optional[bool] = None , ):
"""simple docstring"""
A__ = return_dict if return_dict is not None else self.config.use_return_dict
A__ = self.mobilenet_va(UpperCamelCase , output_hidden_states=UpperCamelCase , return_dict=UpperCamelCase )
A__ = outputs.pooler_output if return_dict else outputs[1]
A__ = self.classifier(self.dropout(UpperCamelCase ) )
A__ = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
A__ = """regression"""
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
A__ = """single_label_classification"""
else:
A__ = """multi_label_classification"""
if self.config.problem_type == "regression":
A__ = MSELoss()
if self.num_labels == 1:
A__ = loss_fct(logits.squeeze() , labels.squeeze() )
else:
A__ = loss_fct(UpperCamelCase , UpperCamelCase )
elif self.config.problem_type == "single_label_classification":
A__ = CrossEntropyLoss()
A__ = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
A__ = BCEWithLogitsLoss()
A__ = loss_fct(UpperCamelCase , UpperCamelCase )
if not return_dict:
A__ = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=UpperCamelCase , logits=UpperCamelCase , hidden_states=outputs.hidden_states , )
| 335 | 0 |
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
__A = TypeVar("T")
class __lowerCAmelCase ( Generic[T] ):
"""simple docstring"""
def __init__( self , lowerCamelCase__ = True ) -> str:
'''simple docstring'''
__lowerCamelCase = {} # dictionary of lists
__lowerCamelCase = directed
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ ) -> int:
'''simple docstring'''
if not self.directed: # For undirected graphs
# if both source vertex and destination vertex are both present in the
# adjacency list, add destination vertex to source vertex list of adjacent
# vertices and add source vertex to destination vertex list of adjacent
# vertices.
if source_vertex in self.adj_list and destination_vertex in self.adj_list:
self.adj_list[source_vertex].append(lowerCamelCase__ )
self.adj_list[destination_vertex].append(lowerCamelCase__ )
# if only source vertex is present in adjacency list, add destination vertex
# to source vertex list of adjacent vertices, then create a new vertex with
# destination vertex as key and assign a list containing the source vertex
# as it's first adjacent vertex.
elif source_vertex in self.adj_list:
self.adj_list[source_vertex].append(lowerCamelCase__ )
__lowerCamelCase = [source_vertex]
# if only destination vertex is present in adjacency list, add source vertex
# to destination vertex list of adjacent vertices, then create a new vertex
# with source vertex as key and assign a list containing the source vertex
# as it's first adjacent vertex.
elif destination_vertex in self.adj_list:
self.adj_list[destination_vertex].append(lowerCamelCase__ )
__lowerCamelCase = [destination_vertex]
# if both source vertex and destination vertex are not present in adjacency
# list, create a new vertex with source vertex as key and assign a list
# containing the destination vertex as it's first adjacent vertex also
# create a new vertex with destination vertex as key and assign a list
# containing the source vertex as it's first adjacent vertex.
else:
__lowerCamelCase = [destination_vertex]
__lowerCamelCase = [source_vertex]
else: # For directed graphs
# if both source vertex and destination vertex are present in adjacency
# list, add destination vertex to source vertex list of adjacent vertices.
if source_vertex in self.adj_list and destination_vertex in self.adj_list:
self.adj_list[source_vertex].append(lowerCamelCase__ )
# if only source vertex is present in adjacency list, add destination
# vertex to source vertex list of adjacent vertices and create a new vertex
# with destination vertex as key, which has no adjacent vertex
elif source_vertex in self.adj_list:
self.adj_list[source_vertex].append(lowerCamelCase__ )
__lowerCamelCase = []
# if only destination vertex is present in adjacency list, create a new
# vertex with source vertex as key and assign a list containing destination
# vertex as first adjacent vertex
elif destination_vertex in self.adj_list:
__lowerCamelCase = [destination_vertex]
# if both source vertex and destination vertex are not present in adjacency
# list, create a new vertex with source vertex as key and a list containing
# destination vertex as it's first adjacent vertex. Then create a new vertex
# with destination vertex as key, which has no adjacent vertex
else:
__lowerCamelCase = [destination_vertex]
__lowerCamelCase = []
return self
def __repr__( self ) -> Union[str, Any]:
'''simple docstring'''
return pformat(self.adj_list )
| 90 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : list[list[int]] , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : set ):
A__ , A__ = len(UpperCAmelCase_ ), len(grid[0] )
if (
min(UpperCAmelCase_ , UpperCAmelCase_ ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
A__ = 0
count += depth_first_search(UpperCAmelCase_ , row + 1 , UpperCAmelCase_ , UpperCAmelCase_ )
count += depth_first_search(UpperCAmelCase_ , row - 1 , UpperCAmelCase_ , UpperCAmelCase_ )
count += depth_first_search(UpperCAmelCase_ , UpperCAmelCase_ , col + 1 , UpperCAmelCase_ )
count += depth_first_search(UpperCAmelCase_ , UpperCAmelCase_ , col - 1 , UpperCAmelCase_ )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 335 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
""" Distillation"""
)
)
parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
parser.add_argument("""--vocab_transform""", action="""store_true""")
lowercase__ = parser.parse_args()
if args.model_type == "bert":
lowercase__ = BertForMaskedLM.from_pretrained(args.model_name)
lowercase__ = 'bert'
else:
raise ValueError("""args.model_type should be \"bert\".""")
lowercase__ = model.state_dict()
lowercase__ = {}
for w in ["word_embeddings", "position_embeddings"]:
lowercase__ = state_dict[F"{prefix}.embeddings.{w}.weight"]
for w in ["weight", "bias"]:
lowercase__ = state_dict[F"{prefix}.embeddings.LayerNorm.{w}"]
lowercase__ = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
for w in ["weight", "bias"]:
lowercase__ = state_dict[
F"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"
]
lowercase__ = state_dict[
F"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"
]
lowercase__ = state_dict[
F"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"
]
lowercase__ = state_dict[
F"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"
]
lowercase__ = state_dict[
F"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"
]
lowercase__ = state_dict[
F"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"
]
lowercase__ = state_dict[
F"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"
]
lowercase__ = state_dict[
F"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"
]
std_idx += 1
lowercase__ = state_dict['cls.predictions.decoder.weight']
lowercase__ = state_dict['cls.predictions.bias']
if args.vocab_transform:
for w in ["weight", "bias"]:
lowercase__ = state_dict[F"cls.predictions.transform.dense.{w}"]
lowercase__ = state_dict[F"cls.predictions.transform.LayerNorm.{w}"]
print(F"N layers selected for distillation: {std_idx}")
print(F"Number of params transferred for distillation: {len(compressed_sd.keys())}")
print(F"Save transferred checkpoint to {args.dump_checkpoint}.")
torch.save(compressed_sd, args.dump_checkpoint) | 96 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ : int = {
'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ : Tuple = [
'MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'MegatronBertForCausalLM',
'MegatronBertForMaskedLM',
'MegatronBertForMultipleChoice',
'MegatronBertForNextSentencePrediction',
'MegatronBertForPreTraining',
'MegatronBertForQuestionAnswering',
'MegatronBertForSequenceClassification',
'MegatronBertForTokenClassification',
'MegatronBertModel',
'MegatronBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_megatron_bert import (
MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
MegatronBertPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_ : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 335 | 0 |
"""simple docstring"""
_lowerCamelCase : int = {
'a': 'AAAAA',
'b': 'AAAAB',
'c': 'AAABA',
'd': 'AAABB',
'e': 'AABAA',
'f': 'AABAB',
'g': 'AABBA',
'h': 'AABBB',
'i': 'ABAAA',
'j': 'BBBAA',
'k': 'ABAAB',
'l': 'ABABA',
'm': 'ABABB',
'n': 'ABBAA',
'o': 'ABBAB',
'p': 'ABBBA',
'q': 'ABBBB',
'r': 'BAAAA',
's': 'BAAAB',
't': 'BAABA',
'u': 'BAABB',
'v': 'BBBAB',
'w': 'BABAA',
'x': 'BABAB',
'y': 'BABBA',
'z': 'BABBB',
' ': ' ',
}
_lowerCamelCase : str = {value: key for key, value in encode_dict.items()}
def lowercase_ ( _UpperCAmelCase ):
"""simple docstring"""
A_ : Union[str, Any] = ''''''
for letter in word.lower():
if letter.isalpha() or letter == " ":
encoded += encode_dict[letter]
else:
raise Exception('''encode() accepts only letters of the alphabet and spaces''' )
return encoded
def lowercase_ ( _UpperCAmelCase ):
"""simple docstring"""
if set(UpperCAmelCase_ ) - {"A", "B", " "} != set():
raise Exception('''decode() accepts only \'A\', \'B\' and spaces''' )
A_ : int = ''''''
for word in coded.split():
while len(UpperCAmelCase_ ) != 0:
decoded += decode_dict[word[:5]]
A_ : int = word[5:]
decoded += " "
return decoded.strip()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 167 |
"""simple docstring"""
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"""kwargs, expected""" , [
({"""num_shards""": 0, """max_num_jobs""": 1}, []),
({"""num_shards""": 10, """max_num_jobs""": 1}, [range(10 )]),
({"""num_shards""": 10, """max_num_jobs""": 10}, [range(UpperCAmelCase_ , i + 1 ) for i in range(10 )]),
({"""num_shards""": 1, """max_num_jobs""": 10}, [range(1 )]),
({"""num_shards""": 10, """max_num_jobs""": 3}, [range(0 , 4 ), range(4 , 7 ), range(7 , 10 )]),
({"""num_shards""": 3, """max_num_jobs""": 10}, [range(0 , 1 ), range(1 , 2 ), range(2 , 3 )]),
] , )
def _snake_case ( UpperCAmelCase_ : List[str] , UpperCAmelCase_ : int ):
A__ = _distribute_shards(**UpperCAmelCase_ )
assert out == expected
@pytest.mark.parametrize(
"""gen_kwargs, max_num_jobs, expected""" , [
({"""foo""": 0}, 10, [{"""foo""": 0}]),
({"""shards""": [0, 1, 2, 3]}, 1, [{"""shards""": [0, 1, 2, 3]}]),
({"""shards""": [0, 1, 2, 3]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}, {"""shards""": [2]}, {"""shards""": [3]}]),
({"""shards""": [0, 1]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}]),
({"""shards""": [0, 1, 2, 3]}, 2, [{"""shards""": [0, 1]}, {"""shards""": [2, 3]}]),
] , )
def _snake_case ( UpperCAmelCase_ : str , UpperCAmelCase_ : Dict , UpperCAmelCase_ : List[Any] ):
A__ = _split_gen_kwargs(UpperCAmelCase_ , UpperCAmelCase_ )
assert out == expected
@pytest.mark.parametrize(
"""gen_kwargs, expected""" , [
({"""foo""": 0}, 1),
({"""shards""": [0]}, 1),
({"""shards""": [0, 1, 2, 3]}, 4),
({"""shards""": [0, 1, 2, 3], """foo""": 0}, 4),
({"""shards""": [0, 1, 2, 3], """other""": (0, 1)}, 4),
({"""shards""": [0, 1, 2, 3], """shards2""": [0, 1]}, RuntimeError),
] , )
def _snake_case ( UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Optional[int] ):
if expected is RuntimeError:
with pytest.raises(UpperCAmelCase_ ):
_number_of_shards_in_gen_kwargs(UpperCAmelCase_ )
else:
A__ = _number_of_shards_in_gen_kwargs(UpperCAmelCase_ )
assert out == expected
| 335 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE = {
'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = ['RemBertTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = ['RemBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = [
'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'RemBertForCausalLM',
'RemBertForMaskedLM',
'RemBertForMultipleChoice',
'RemBertForQuestionAnswering',
'RemBertForSequenceClassification',
'RemBertForTokenClassification',
'RemBertLayer',
'RemBertModel',
'RemBertPreTrainedModel',
'load_tf_weights_in_rembert',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = [
'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFRemBertForCausalLM',
'TFRemBertForMaskedLM',
'TFRemBertForMultipleChoice',
'TFRemBertForQuestionAnswering',
'TFRemBertForSequenceClassification',
'TFRemBertForTokenClassification',
'TFRemBertLayer',
'TFRemBertModel',
'TFRemBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert import RemBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert_fast import RemBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rembert import (
REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RemBertForCausalLM,
RemBertForMaskedLM,
RemBertForMultipleChoice,
RemBertForQuestionAnswering,
RemBertForSequenceClassification,
RemBertForTokenClassification,
RemBertLayer,
RemBertModel,
RemBertPreTrainedModel,
load_tf_weights_in_rembert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rembert import (
TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRemBertForCausalLM,
TFRemBertForMaskedLM,
TFRemBertForMultipleChoice,
TFRemBertForQuestionAnswering,
TFRemBertForSequenceClassification,
TFRemBertForTokenClassification,
TFRemBertLayer,
TFRemBertModel,
TFRemBertPreTrainedModel,
)
else:
import sys
_SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 158 |
"""simple docstring"""
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
SCREAMING_SNAKE_CASE_ : str = parse(importlib.metadata.version('torch'))
def _snake_case ( UpperCAmelCase_ : Union[str, Version] , UpperCAmelCase_ : str , UpperCAmelCase_ : str ):
if operation not in STR_OPERATION_TO_FUNC.keys():
raise ValueError(F"""`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys() )}, received {operation}""" )
A__ = STR_OPERATION_TO_FUNC[operation]
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
A__ = parse(importlib.metadata.version(UpperCAmelCase_ ) )
return operation(UpperCAmelCase_ , parse(UpperCAmelCase_ ) )
def _snake_case ( UpperCAmelCase_ : str , UpperCAmelCase_ : str ):
return compare_versions(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
| 335 | 0 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCamelCase__ :
def __init__(self : Optional[int] , snake_case_ : Tuple , snake_case_ : Optional[int]=1_3 , snake_case_ : List[Any]=3_2 , snake_case_ : str=2 , snake_case_ : List[str]=3 , snake_case_ : Dict=1_6 , snake_case_ : Dict=[1, 2, 1] , snake_case_ : List[Any]=[2, 2, 4] , snake_case_ : Optional[int]=2 , snake_case_ : str=2.0 , snake_case_ : Tuple=True , snake_case_ : Union[str, Any]=0.0 , snake_case_ : List[str]=0.0 , snake_case_ : Optional[int]=0.1 , snake_case_ : int="gelu" , snake_case_ : Any=False , snake_case_ : str=True , snake_case_ : List[Any]=0.02 , snake_case_ : str=1E-5 , snake_case_ : Optional[int]=True , snake_case_ : Dict=None , snake_case_ : Dict=True , snake_case_ : Tuple=1_0 , snake_case_ : Any=8 , ):
__a : str = parent
__a : Union[str, Any] = batch_size
__a : Tuple = image_size
__a : List[Any] = patch_size
__a : Optional[int] = num_channels
__a : Tuple = embed_dim
__a : Tuple = depths
__a : Optional[int] = num_heads
__a : Any = window_size
__a : List[str] = mlp_ratio
__a : Union[str, Any] = qkv_bias
__a : Union[str, Any] = hidden_dropout_prob
__a : str = attention_probs_dropout_prob
__a : Tuple = drop_path_rate
__a : int = hidden_act
__a : Optional[int] = use_absolute_embeddings
__a : str = patch_norm
__a : int = layer_norm_eps
__a : Dict = initializer_range
__a : int = is_training
__a : Union[str, Any] = scope
__a : Optional[int] = use_labels
__a : List[str] = type_sequence_label_size
__a : str = encoder_stride
def lowerCAmelCase (self : int ):
__a : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__a : List[Any] = None
if self.use_labels:
__a : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__a : Dict = self.get_config()
return config, pixel_values, labels
def lowerCAmelCase (self : Any ):
return SwinvaConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def lowerCAmelCase (self : Any , snake_case_ : Any , snake_case_ : Union[str, Any] , snake_case_ : Optional[Any] ):
__a : Dict = SwinvaModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
__a : Optional[Any] = model(snake_case_ )
__a : Union[str, Any] = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
__a : Tuple = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def lowerCAmelCase (self : str , snake_case_ : Optional[Any] , snake_case_ : Any , snake_case_ : List[Any] ):
__a : Dict = SwinvaForMaskedImageModeling(config=snake_case_ )
model.to(snake_case_ )
model.eval()
__a : int = model(snake_case_ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
__a : Dict = 1
__a : Any = SwinvaForMaskedImageModeling(snake_case_ )
model.to(snake_case_ )
model.eval()
__a : int = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__a : Any = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def lowerCAmelCase (self : int , snake_case_ : Any , snake_case_ : List[str] , snake_case_ : Tuple ):
__a : List[Any] = self.type_sequence_label_size
__a : Dict = SwinvaForImageClassification(snake_case_ )
model.to(snake_case_ )
model.eval()
__a : Union[str, Any] = model(snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCAmelCase (self : str ):
__a : Any = self.prepare_config_and_inputs()
__a , __a , __a : str = config_and_inputs
__a : Union[str, Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class UpperCamelCase__ ( _lowerCamelCase ,_lowerCamelCase ,unittest.TestCase ):
_SCREAMING_SNAKE_CASE : List[Any] = (
(SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
)
_SCREAMING_SNAKE_CASE : Dict = (
{"feature-extraction": SwinvaModel, "image-classification": SwinvaForImageClassification}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE : Any = False
_SCREAMING_SNAKE_CASE : Union[str, Any] = False
_SCREAMING_SNAKE_CASE : List[Any] = False
_SCREAMING_SNAKE_CASE : Tuple = False
def lowerCAmelCase (self : Any ):
__a : Optional[Any] = SwinvaModelTester(self )
__a : str = ConfigTester(self , config_class=snake_case_ , embed_dim=3_7 )
def lowerCAmelCase (self : List[str] ):
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCAmelCase (self : str ):
__a : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
@unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.''' )
def lowerCAmelCase (self : int ):
pass
@unittest.skip(reason='''Swinv2 does not use inputs_embeds''' )
def lowerCAmelCase (self : Optional[int] ):
pass
def lowerCAmelCase (self : Any ):
__a , __a : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a : List[str] = model_class(snake_case_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__a : Optional[Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(snake_case_ , nn.Linear ) )
def lowerCAmelCase (self : Union[str, Any] ):
__a , __a : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a : Tuple = model_class(snake_case_ )
__a : Optional[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__a : List[Any] = [*signature.parameters.keys()]
__a : Optional[int] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , snake_case_ )
def lowerCAmelCase (self : Optional[int] ):
__a , __a : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
__a : Union[str, Any] = True
for model_class in self.all_model_classes:
__a : str = True
__a : str = False
__a : str = True
__a : Optional[int] = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
__a : Optional[Any] = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
__a : Tuple = outputs.attentions
__a : Any = len(self.model_tester.depths )
self.assertEqual(len(snake_case_ ) , snake_case_ )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__a : str = True
__a : Dict = config.window_size**2
__a : Union[str, Any] = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
__a : Optional[Any] = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
__a : List[Any] = outputs.attentions
self.assertEqual(len(snake_case_ ) , snake_case_ )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
__a : str = len(snake_case_ )
# Check attention is always last and order is fine
__a : Optional[Any] = True
__a : List[str] = True
__a : Optional[Any] = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
__a : Dict = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
if hasattr(self.model_tester , '''num_hidden_states_types''' ):
__a : Union[str, Any] = self.model_tester.num_hidden_states_types
else:
# also another +1 for reshaped_hidden_states
__a : Optional[Any] = 2
self.assertEqual(out_len + added_hidden_states , len(snake_case_ ) )
__a : List[str] = outputs.attentions
self.assertEqual(len(snake_case_ ) , snake_case_ )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
def lowerCAmelCase (self : Optional[int] , snake_case_ : Tuple , snake_case_ : Union[str, Any] , snake_case_ : int , snake_case_ : str ):
__a : int = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
__a : Tuple = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
__a : Tuple = outputs.hidden_states
__a : str = getattr(
self.model_tester , '''expected_num_hidden_layers''' , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(snake_case_ ) , snake_case_ )
# Swinv2 has a different seq_length
__a : Optional[int] = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__a : List[str] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
__a : Tuple = outputs.reshaped_hidden_states
self.assertEqual(len(snake_case_ ) , snake_case_ )
__a , __a , __a , __a : Optional[int] = reshaped_hidden_states[0].shape
__a : Dict = (
reshaped_hidden_states[0].view(snake_case_ , snake_case_ , height * width ).permute(0 , 2 , 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def lowerCAmelCase (self : str ):
__a , __a : Any = self.model_tester.prepare_config_and_inputs_for_common()
__a : List[str] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
__a : Optional[int] = True
self.check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__a : int = True
self.check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
def lowerCAmelCase (self : Optional[Any] ):
__a , __a : Dict = self.model_tester.prepare_config_and_inputs_for_common()
__a : str = 3
__a : Optional[Any] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
__a : List[Any] = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__a : str = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
__a : int = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
__a : int = True
self.check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__a : int = True
self.check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ , (padded_height, padded_width) )
def lowerCAmelCase (self : List[str] ):
__a : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*snake_case_ )
def lowerCAmelCase (self : Optional[int] ):
__a : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case_ )
@slow
def lowerCAmelCase (self : Any ):
for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a : List[str] = SwinvaModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
def lowerCAmelCase (self : Tuple ):
__a , __a : int = self.model_tester.prepare_config_and_inputs_for_common()
__a : Optional[int] = _config_zero_init(snake_case_ )
for model_class in self.all_model_classes:
__a : str = model_class(config=snake_case_ )
for name, param in model.named_parameters():
if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=f"Parameter {name} of model {model_class} seems not properly initialized" , )
@require_vision
@require_torch
class UpperCamelCase__ ( unittest.TestCase ):
@cached_property
def lowerCAmelCase (self : Optional[int] ):
return (
AutoImageProcessor.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256''' )
if is_vision_available()
else None
)
@slow
def lowerCAmelCase (self : Optional[Any] ):
__a : List[Any] = SwinvaForImageClassification.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256''' ).to(
snake_case_ )
__a : List[Any] = self.default_image_processor
__a : List[str] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
__a : List[str] = image_processor(images=snake_case_ , return_tensors='''pt''' ).to(snake_case_ )
# forward pass
with torch.no_grad():
__a : List[str] = model(**snake_case_ )
# verify the logits
__a : str = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , snake_case_ )
__a : List[Any] = torch.tensor([-0.3947, -0.4306, 0.0026] ).to(snake_case_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case_ , atol=1E-4 ) )
| 216 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
SCREAMING_SNAKE_CASE_ : Dict = '\\n@inproceedings{popovic-2015-chrf,\n title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n author = "Popovi{\'c}, Maja",\n booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",\n month = sep,\n year = "2015",\n address = "Lisbon, Portugal",\n publisher = "Association for Computational Linguistics",\n url = "https://aclanthology.org/W15-3049",\n doi = "10.18653/v1/W15-3049",\n pages = "392--395",\n}\n@inproceedings{popovic-2017-chrf,\n title = "chr{F}++: words helping character n-grams",\n author = "Popovi{\'c}, Maja",\n booktitle = "Proceedings of the Second Conference on Machine Translation",\n month = sep,\n year = "2017",\n address = "Copenhagen, Denmark",\n publisher = "Association for Computational Linguistics",\n url = "https://aclanthology.org/W17-4770",\n doi = "10.18653/v1/W17-4770",\n pages = "612--618",\n}\n@inproceedings{post-2018-call,\n title = "A Call for Clarity in Reporting {BLEU} Scores",\n author = "Post, Matt",\n booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n month = oct,\n year = "2018",\n address = "Belgium, Brussels",\n publisher = "Association for Computational Linguistics",\n url = "https://www.aclweb.org/anthology/W18-6319",\n pages = "186--191",\n}\n'
SCREAMING_SNAKE_CASE_ : str = '\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n'
SCREAMING_SNAKE_CASE_ : List[str] = '\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n predictions (list of str): The predicted sentences.\n references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n char_order (int): Character n-gram order. Defaults to `6`.\n word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n to reference chrF++.py, NLTK and Moses implementations. If `False`,\n it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n \'score\' (float): The chrF (chrF++) score,\n \'char_order\' (int): The character n-gram order,\n \'word_order\' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n \'beta\' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n Example 1--a simple example of calculating chrF:\n >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n >>> chrf = datasets.load_metric("chrf")\n >>> results = chrf.compute(predictions=prediction, references=reference)\n >>> print(results)\n {\'score\': 84.64214891738334, \'char_order\': 6, \'word_order\': 0, \'beta\': 2}\n\n Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n >>> chrf = datasets.load_metric("chrf")\n >>> results = chrf.compute(predictions=prediction,\n ... references=reference,\n ... word_order=2)\n >>> print(results)\n {\'score\': 82.87263732906315, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n\n Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n >>> chrf = datasets.load_metric("chrf")\n >>> results = chrf.compute(predictions=prediction,\n ... references=reference,\n ... word_order=2,\n ... lowercase=True)\n >>> print(results)\n {\'score\': 92.12853119829202, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class a ( datasets.Metric ):
"""simple docstring"""
def UpperCamelCase ( self: List[str] ):
"""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="""https://github.com/mjpost/sacreBLEU#chrf--chrf""" , 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#chrf--chrf"""] , reference_urls=[
"""https://github.com/m-popovic/chrF""",
] , )
def UpperCamelCase ( self: List[Any] , UpperCamelCase: Union[str, Any] , UpperCamelCase: Optional[Any] , UpperCamelCase: int = CHRF.CHAR_ORDER , UpperCamelCase: int = CHRF.WORD_ORDER , UpperCamelCase: int = CHRF.BETA , UpperCamelCase: bool = False , UpperCamelCase: bool = False , UpperCamelCase: bool = False , ):
"""simple docstring"""
A__ = len(references[0] )
if any(len(UpperCamelCase ) != references_per_prediction for refs in references ):
raise ValueError("""Sacrebleu requires the same number of references for each prediction""" )
A__ = [[refs[i] for refs in references] for i in range(UpperCamelCase )]
A__ = CHRF(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
A__ = sb_chrf.corpus_score(UpperCamelCase , UpperCamelCase )
return {
"score": output.score,
"char_order": output.char_order,
"word_order": output.word_order,
"beta": output.beta,
}
| 335 | 0 |
"""simple docstring"""
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
a = 5_0_0_0_0_0
a = os.path.split(__file__)
a = os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def lowercase (snake_case__ : datasets.Dataset , **snake_case__ : str ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase = dataset.map(**UpperCAmelCase_ )
@get_duration
def lowercase (snake_case__ : datasets.Dataset , **snake_case__ : Any ) -> List[str]:
'''simple docstring'''
lowerCAmelCase = dataset.filter(**UpperCAmelCase_ )
def lowercase () -> Dict:
'''simple docstring'''
lowerCAmelCase = {"""num examples""": SPEED_TEST_N_EXAMPLES}
with tempfile.TemporaryDirectory() as tmp_dir:
lowerCAmelCase = datasets.Features({"""text""": datasets.Value("""string""" ), """numbers""": datasets.Value("""float32""" )} )
lowerCAmelCase = generate_example_dataset(
os.path.join(UpperCAmelCase_ , """dataset.arrow""" ) , UpperCAmelCase_ , num_examples=UpperCAmelCase_ )
lowerCAmelCase = transformers.AutoTokenizer.from_pretrained("""bert-base-cased""" , use_fast=UpperCAmelCase_ )
def tokenize(snake_case__ : Optional[int] ):
return tokenizer(examples["""text"""] )
lowerCAmelCase = map(UpperCAmelCase_ )
lowerCAmelCase = map(UpperCAmelCase_ , batched=UpperCAmelCase_ )
lowerCAmelCase = map(UpperCAmelCase_ , function=lambda snake_case__ : None , batched=UpperCAmelCase_ )
with dataset.formatted_as(type="""numpy""" ):
lowerCAmelCase = map(UpperCAmelCase_ , function=lambda snake_case__ : None , batched=UpperCAmelCase_ )
with dataset.formatted_as(type="""pandas""" ):
lowerCAmelCase = map(UpperCAmelCase_ , function=lambda snake_case__ : None , batched=UpperCAmelCase_ )
with dataset.formatted_as(type="""torch""" , columns="""numbers""" ):
lowerCAmelCase = map(UpperCAmelCase_ , function=lambda snake_case__ : None , batched=UpperCAmelCase_ )
with dataset.formatted_as(type="""tensorflow""" , columns="""numbers""" ):
lowerCAmelCase = map(UpperCAmelCase_ , function=lambda snake_case__ : None , batched=UpperCAmelCase_ )
lowerCAmelCase = map(UpperCAmelCase_ , function=UpperCAmelCase_ , batched=UpperCAmelCase_ )
lowerCAmelCase = filter(UpperCAmelCase_ )
# 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(UpperCAmelCase_ , """wb""" ) as f:
f.write(json.dumps(UpperCAmelCase_ ).encode("""utf-8""" ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_map_filter()
| 155 |
"""simple docstring"""
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class a ( _lowerCamelCase ):
"""simple docstring"""
def __init__( self: Optional[int] , *UpperCamelCase: Optional[Any] , UpperCamelCase: Tuple=None , UpperCamelCase: Tuple=None , **UpperCamelCase: Dict ):
"""simple docstring"""
super().__init__(*UpperCamelCase , **UpperCamelCase )
A__ = eval_examples
A__ = post_process_function
def UpperCamelCase ( self: Optional[Any] , UpperCamelCase: Optional[Dataset] = None , UpperCamelCase: List[Any]=None , UpperCamelCase: Optional[List[str]] = None , UpperCamelCase: str = "eval" , **UpperCamelCase: Optional[int] , ):
"""simple docstring"""
A__ = gen_kwargs.copy()
A__ = (
gen_kwargs["""max_length"""] if gen_kwargs.get("""max_length""" ) is not None else self.args.generation_max_length
)
A__ = (
gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams""" ) is not None else self.args.generation_num_beams
)
A__ = gen_kwargs
A__ = self.eval_dataset if eval_dataset is None else eval_dataset
A__ = self.get_eval_dataloader(UpperCamelCase )
A__ = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
A__ = self.compute_metrics
A__ = None
A__ = time.time()
A__ = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
A__ = eval_loop(
UpperCamelCase , description="""Evaluation""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=UpperCamelCase , metric_key_prefix=UpperCamelCase , )
finally:
A__ = compute_metrics
A__ = self.args.eval_batch_size * self.args.world_size
if f"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
start_time += output.metrics[f"""{metric_key_prefix}_jit_compilation_time"""]
output.metrics.update(
speed_metrics(
UpperCamelCase , UpperCamelCase , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
A__ = self.post_process_function(UpperCamelCase , UpperCamelCase , UpperCamelCase )
A__ = self.compute_metrics(UpperCamelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
A__ = metrics.pop(UpperCamelCase )
metrics.update(output.metrics )
else:
A__ = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(UpperCamelCase )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
A__ = self.callback_handler.on_evaluate(self.args , self.state , self.control , UpperCamelCase )
return metrics
def UpperCamelCase ( self: List[Any] , UpperCamelCase: Dict , UpperCamelCase: List[str] , UpperCamelCase: Dict=None , UpperCamelCase: str = "test" , **UpperCamelCase: Optional[int] ):
"""simple docstring"""
A__ = gen_kwargs.copy()
A__ = self.get_test_dataloader(UpperCamelCase )
# Temporarily disable metric computation, we will do it in the loop here.
A__ = self.compute_metrics
A__ = None
A__ = time.time()
A__ = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
A__ = eval_loop(
UpperCamelCase , description="""Prediction""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=UpperCamelCase , metric_key_prefix=UpperCamelCase , )
finally:
A__ = compute_metrics
A__ = self.args.eval_batch_size * self.args.world_size
if f"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
start_time += output.metrics[f"""{metric_key_prefix}_jit_compilation_time"""]
output.metrics.update(
speed_metrics(
UpperCamelCase , UpperCamelCase , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
A__ = self.post_process_function(UpperCamelCase , UpperCamelCase , UpperCamelCase , """predict""" )
A__ = self.compute_metrics(UpperCamelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
A__ = metrics.pop(UpperCamelCase )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=UpperCamelCase )
| 335 | 0 |
"""simple docstring"""
import re
def lowercase__( __SCREAMING_SNAKE_CASE : str ):
lowercase_ : Dict = re.compile(R'^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$' )
if match := re.search(UpperCAmelCase_ , UpperCAmelCase_ ):
return match.string == phone
return False
if __name__ == "__main__":
print(indian_phone_validator("+918827897895"))
| 213 |
"""simple docstring"""
class a :
"""simple docstring"""
def __init__( self: Dict ):
"""simple docstring"""
A__ = {}
def UpperCamelCase ( self: List[str] ):
"""simple docstring"""
print(self.vertex )
for i in self.vertex:
print(UpperCamelCase , """ -> """ , """ -> """.join([str(UpperCamelCase ) for j in self.vertex[i]] ) )
def UpperCamelCase ( self: Any , UpperCamelCase: int , UpperCamelCase: int ):
"""simple docstring"""
if from_vertex in self.vertex:
self.vertex[from_vertex].append(UpperCamelCase )
else:
# else make a new vertex
A__ = [to_vertex]
def UpperCamelCase ( self: Union[str, Any] ):
"""simple docstring"""
A__ = [False] * len(self.vertex )
# call the recursive helper function
for i in range(len(self.vertex ) ):
if not visited[i]:
self.dfs_recursive(UpperCamelCase , UpperCamelCase )
def UpperCamelCase ( self: str , UpperCamelCase: int , UpperCamelCase: list ):
"""simple docstring"""
A__ = True
print(UpperCamelCase , end=""" """ )
# Recur for all the vertices that are adjacent to this node
for i in self.vertex:
if not visited[i]:
self.dfs_recursive(UpperCamelCase , UpperCamelCase )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ : Optional[int] = Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
g.print_graph()
print('DFS:')
g.dfs()
# OUTPUT:
# 0 -> 1 -> 2
# 1 -> 2
# 2 -> 0 -> 3
# 3 -> 3
# DFS:
# 0 1 2 3
| 335 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Optional[int] = {
'microsoft/swin-tiny-patch4-window7-224': (
'https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class a ( _lowerCamelCase , _lowerCamelCase ):
SCREAMING_SNAKE_CASE : List[Any] = """swin"""
SCREAMING_SNAKE_CASE : Dict = {
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self : int , __SCREAMING_SNAKE_CASE : str=224 , __SCREAMING_SNAKE_CASE : Tuple=4 , __SCREAMING_SNAKE_CASE : Tuple=3 , __SCREAMING_SNAKE_CASE : Optional[Any]=96 , __SCREAMING_SNAKE_CASE : Optional[Any]=[2, 2, 6, 2] , __SCREAMING_SNAKE_CASE : Dict=[3, 6, 12, 24] , __SCREAMING_SNAKE_CASE : Dict=7 , __SCREAMING_SNAKE_CASE : Tuple=4.0 , __SCREAMING_SNAKE_CASE : Optional[Any]=True , __SCREAMING_SNAKE_CASE : Optional[Any]=0.0 , __SCREAMING_SNAKE_CASE : List[str]=0.0 , __SCREAMING_SNAKE_CASE : Optional[int]=0.1 , __SCREAMING_SNAKE_CASE : Optional[int]="gelu" , __SCREAMING_SNAKE_CASE : Dict=False , __SCREAMING_SNAKE_CASE : Tuple=0.02 , __SCREAMING_SNAKE_CASE : Dict=1e-5 , __SCREAMING_SNAKE_CASE : Dict=32 , __SCREAMING_SNAKE_CASE : List[str]=None , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , **__SCREAMING_SNAKE_CASE : List[str] , ) -> List[Any]:
super().__init__(**__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = image_size
lowerCamelCase_ = patch_size
lowerCamelCase_ = num_channels
lowerCamelCase_ = embed_dim
lowerCamelCase_ = depths
lowerCamelCase_ = len(__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = num_heads
lowerCamelCase_ = window_size
lowerCamelCase_ = mlp_ratio
lowerCamelCase_ = qkv_bias
lowerCamelCase_ = hidden_dropout_prob
lowerCamelCase_ = attention_probs_dropout_prob
lowerCamelCase_ = drop_path_rate
lowerCamelCase_ = hidden_act
lowerCamelCase_ = use_absolute_embeddings
lowerCamelCase_ = layer_norm_eps
lowerCamelCase_ = initializer_range
lowerCamelCase_ = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
lowerCamelCase_ = int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE ) - 1) )
lowerCamelCase_ = ['stem'] + [F'''stage{idx}''' for idx in range(1 , len(__SCREAMING_SNAKE_CASE ) + 1 )]
lowerCamelCase_ , lowerCamelCase_ = get_aligned_output_features_output_indices(
out_features=__SCREAMING_SNAKE_CASE , out_indices=__SCREAMING_SNAKE_CASE , stage_names=self.stage_names )
class a ( _lowerCamelCase ):
SCREAMING_SNAKE_CASE : Optional[Any] = version.parse("""1.11""" )
@property
def UpperCamelCase ( self : str ) -> str:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def UpperCamelCase ( self : Union[str, Any] ) -> Optional[Any]:
return 1e-4
| 183 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : int = 10 ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or n < 0:
raise ValueError("""Invalid input""" )
A__ = 10**n
A__ = 2_8433 * (pow(2 , 783_0457 , UpperCAmelCase_ )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(f"""{solution(1_0) = }""")
| 335 | 0 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def a ( _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[Any] ):
'''simple docstring'''
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
# expert layer
__UpperCAmelCase : List[str] = flax_key_tuple[:-1] + ('''weight''',)
__UpperCAmelCase : Union[str, Any] = torch.permute(UpperCAmelCase_ , (0, 2, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(UpperCAmelCase_ ):
# linear layer
__UpperCAmelCase : Optional[int] = flax_key_tuple[:-1] + ('''weight''',)
__UpperCAmelCase : str = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
__UpperCAmelCase : int = flax_key_tuple[:-1] + ('''weight''',)
return flax_key_tuple, flax_tensor
def a ( _UpperCAmelCase : str , _UpperCAmelCase : List[str] , _UpperCAmelCase : List[str] ):
'''simple docstring'''
if "metadata" in layer:
__UpperCAmelCase : Union[str, Any] = layer.split('''metadata''' )
__UpperCAmelCase : Tuple = ''''''.join(split_layer[0] )[:-1]
__UpperCAmelCase : Optional[int] = [tuple(('''metadata''' + split_layer[1]).split('''/''' ) )]
elif "kvstore" in layer:
__UpperCAmelCase : Optional[int] = layer.split('''kvstore''' )
__UpperCAmelCase : Union[str, Any] = ''''''.join(split_layer[0] )[:-1]
__UpperCAmelCase : Any = [tuple(('''kvstore''' + split_layer[1]).split('''/''' ) )]
else:
__UpperCAmelCase : Optional[Any] = layer.split('''/''' )
__UpperCAmelCase : Dict = '''/'''.join(split_layer[:-1] )
__UpperCAmelCase : List[Any] = (split_layer[-1],)
if "kvstore/path" in layer:
__UpperCAmelCase : int = f'{switch_checkpoint_path}/{checkpoint_info[layer]}'
elif "kvstore/driver" in layer:
__UpperCAmelCase : List[str] = '''file'''
else:
__UpperCAmelCase : List[str] = checkpoint_info[layer]
return curr_real_layer_name, split_layer, content
def a ( _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[Any] ):
'''simple docstring'''
__UpperCAmelCase : int = rename_keys(UpperCAmelCase_ )
__UpperCAmelCase : Union[str, Any] = {}
for k, v in current_block.items():
__UpperCAmelCase : Optional[Any] = v
__UpperCAmelCase : List[str] = new_current_block
torch.save(UpperCAmelCase_ , UpperCAmelCase_ )
def a ( _UpperCAmelCase : int , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : str = WEIGHTS_NAME ):
'''simple docstring'''
__UpperCAmelCase : Tuple = convert_file_size_to_int(UpperCAmelCase_ )
__UpperCAmelCase : str = []
__UpperCAmelCase : int = {}
__UpperCAmelCase : Any = 0
__UpperCAmelCase : List[Any] = 0
os.makedirs(UpperCAmelCase_ , exist_ok=UpperCAmelCase_ )
with gfile.GFile(switch_checkpoint_path + '''/checkpoint''' , '''rb''' ) as fp:
__UpperCAmelCase : Union[str, Any] = serialization.msgpack_restore(fp.read() )['''optimizer''']['''target''']
__UpperCAmelCase : List[Any] = flatten_dict(UpperCAmelCase_ , sep='''/''' )
__UpperCAmelCase : Optional[Any] = {}
for layer in checkpoint_info.keys():
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Any = get_key_and_tensorstore_dict(
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
if curr_real_layer_name in all_layers:
__UpperCAmelCase : List[Any] = content
else:
__UpperCAmelCase : Any = {split_layer[-1]: content}
for key in all_layers.keys():
# open tensorstore file
__UpperCAmelCase : List[Any] = ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
__UpperCAmelCase : str = torch.tensor(UpperCAmelCase_ )
__UpperCAmelCase : Any = raw_weights.numel() * dtype_byte_size(raw_weights.dtype )
# use the renaming pattern from the small conversion scripts
__UpperCAmelCase , __UpperCAmelCase : List[str] = rename_base_flax_keys(tuple(key.split('''/''' ) ) , UpperCAmelCase_ )
__UpperCAmelCase : Dict = '''/'''.join(UpperCAmelCase_ )
# If this weight is going to tip up over the maximal size, we split.
if current_block_size + weight_size > max_shard_size:
__UpperCAmelCase : Optional[Any] = os.path.join(
UpperCAmelCase_ , weights_name.replace('''.bin''' , f'-{len(UpperCAmelCase_ )+1:05d}-of-???.bin' ) )
rename_and_save_block(UpperCAmelCase_ , UpperCAmelCase_ )
sharded_state_dicts.append(current_block.keys() )
del current_block
__UpperCAmelCase : Tuple = {}
__UpperCAmelCase : str = 0
__UpperCAmelCase : List[str] = raw_weights.to(getattr(UpperCAmelCase_ , UpperCAmelCase_ ) )
current_block_size += weight_size
total_size += weight_size
# Add the last block
__UpperCAmelCase : Dict = os.path.join(UpperCAmelCase_ , weights_name.replace('''.bin''' , f'-{len(UpperCAmelCase_ )+1:05d}-of-???.bin' ) )
rename_and_save_block(UpperCAmelCase_ , UpperCAmelCase_ )
sharded_state_dicts.append(current_block.keys() )
# If we only have one shard, we return it
if len(UpperCAmelCase_ ) == 1:
return {weights_name: sharded_state_dicts[0]}, None
# Otherwise, let's build the index
__UpperCAmelCase : Dict = {}
__UpperCAmelCase : Optional[Any] = {}
for idx, shard in enumerate(UpperCAmelCase_ ):
__UpperCAmelCase : Optional[int] = weights_name.replace(
'''.bin''' , f'-{idx+1:05d}-of-{len(UpperCAmelCase_ ):05d}.bin' ) # len(sharded_state_dicts):05d}
__UpperCAmelCase : Union[str, Any] = os.path.join(UpperCAmelCase_ , weights_name.replace('''.bin''' , f'-{idx+1:05d}-of-???.bin' ) )
os.rename(UpperCAmelCase_ , os.path.join(UpperCAmelCase_ , UpperCAmelCase_ ) )
__UpperCAmelCase : Any = shard
for key in shard:
__UpperCAmelCase : Dict = shard_file
# Add the metadata
__UpperCAmelCase : List[Any] = {'''total_size''': total_size}
__UpperCAmelCase : List[Any] = {'''metadata''': metadata, '''weight_map''': weight_map}
with open(os.path.join(UpperCAmelCase_ , UpperCAmelCase_ ) , '''w''' , encoding='''utf-8''' ) as f:
__UpperCAmelCase : Any = json.dumps(UpperCAmelCase_ , indent=2 , sort_keys=UpperCAmelCase_ ) + '''\n'''
f.write(UpperCAmelCase_ )
return metadata, index
if __name__ == "__main__":
__A =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--switch_t5x_checkpoint_path",
default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600",
type=str,
required=False,
help="Path to a directory containing a folder per layer. Follows the original Google format.",
)
parser.add_argument("--max_shard_size", default="10GB", required=False, help="Max shard size")
parser.add_argument("--dtype", default="bfloat16", type=str, required=False, help="dtype of the saved model")
parser.add_argument(
"--pytorch_dump_folder_path",
default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted",
type=str,
required=False,
help="Path to the output pytorch model.",
)
__A =parser.parse_args()
shard_on_the_fly(
args.switch_tax_checkpoint_path,
args.pytorch_dump_folder_path,
args.max_shard_size,
args.dtype,
)
def a ( ):
'''simple docstring'''
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer
__UpperCAmelCase : Any = SwitchTransformersConfig.from_pretrained('''google/switch-base-8''' )
config.save_pretrained('''/home/arthur_huggingface_co/transformers/switch_converted''' )
__UpperCAmelCase : Any = SwitchTransformersForConditionalGeneration.from_pretrained(
'''/home/arthur_huggingface_co/transformers/switch_converted''' , device_map='''auto''' )
__UpperCAmelCase : str = TaTokenizer.from_pretrained('''t5-small''' )
__UpperCAmelCase : Tuple = '''A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'''
__UpperCAmelCase : List[str] = tokenizer(UpperCAmelCase_ , return_tensors='''pt''' ).input_ids
__UpperCAmelCase : Tuple = model.generate(UpperCAmelCase_ , decoder_start_token_id=0 )
print(tokenizer.decode(out[0] ) )
| 226 |
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = tuple[float, float, float]
SCREAMING_SNAKE_CASE_ : Optional[int] = tuple[float, float, float]
def _snake_case ( UpperCAmelCase_ : Pointad , UpperCAmelCase_ : Pointad ):
A__ = end_pointa[0] - end_pointa[0]
A__ = end_pointa[1] - end_pointa[1]
A__ = end_pointa[2] - end_pointa[2]
return (x, y, z)
def _snake_case ( UpperCAmelCase_ : Vectorad , UpperCAmelCase_ : Vectorad ):
A__ = ab[1] * ac[2] - ab[2] * ac[1] # *i
A__ = (ab[0] * ac[2] - ab[2] * ac[0]) * -1 # *j
A__ = ab[0] * ac[1] - ab[1] * ac[0] # *k
return (x, y, z)
def _snake_case ( UpperCAmelCase_ : Vectorad , UpperCAmelCase_ : int ):
return tuple(round(UpperCAmelCase_ , UpperCAmelCase_ ) for x in vector ) == (0, 0, 0)
def _snake_case ( UpperCAmelCase_ : Pointad , UpperCAmelCase_ : Pointad , UpperCAmelCase_ : Pointad , UpperCAmelCase_ : int = 10 ):
A__ = create_vector(UpperCAmelCase_ , UpperCAmelCase_ )
A__ = create_vector(UpperCAmelCase_ , UpperCAmelCase_ )
return is_zero_vector(get_ad_vectors_cross(UpperCAmelCase_ , UpperCAmelCase_ ) , UpperCAmelCase_ )
| 335 | 0 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : int ):
__A = ""
__A = ""
__A = []
__A = 0
__A = 2_56
__A = 0
__A = 0
__A = 0
__A = 0
def UpperCamelCase_ ( self : List[Any] ,A : int ):
__A = cva.imread(A ,0 )
__A = copy.deepcopy(self.img )
__A , __A , __A = plt.hist(self.img.ravel() ,2_56 ,[0, 2_56] ,label="x" )
__A = np.sum(A )
for i in range(len(A ) ):
__A = x[i] / self.k
self.sk += prk
__A = (self.L - 1) * self.sk
if self.rem != 0:
__A = int(last % last )
__A = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(A )
__A = int(np.ma.count(self.img ) / self.img[1].size )
__A = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
__A = self.img[j][i]
if num != self.last_list[num]:
__A = self.last_list[num]
cva.imwrite("output_data/output.jpg" ,self.img )
def UpperCamelCase_ ( self : Optional[Any] ):
plt.hist(self.img.ravel() ,2_56 ,[0, 2_56] )
def UpperCamelCase_ ( self : Optional[Any] ):
cva.imshow("Output-Image" ,self.img )
cva.imshow("Input-Image" ,self.original_image )
cva.waitKey(50_00 )
cva.destroyAllWindows()
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :str = os.path.join(os.path.basename(__file__), 'image_data/input.jpg')
SCREAMING_SNAKE_CASE :str = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 15 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ : Optional[int] = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ : Any = [
'PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST',
'PegasusXForConditionalGeneration',
'PegasusXModel',
'PegasusXPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pegasus_x import (
PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
PegasusXForConditionalGeneration,
PegasusXModel,
PegasusXPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_ : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 335 | 0 |
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 a( A : Optional[int] ) -> List[Any]: # picklable for multiprocessing
"""simple docstring"""
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def a( ) -> str:
"""simple docstring"""
with parallel_backend("spark" ):
assert ParallelBackendConfig.backend_name == "spark"
a = [1, 2, 3]
with pytest.raises(UpperCAmelCase_ ):
with parallel_backend("unsupported backend" ):
map_nested(UpperCAmelCase_ , UpperCAmelCase_ , num_proc=2 )
with pytest.raises(UpperCAmelCase_ ):
with parallel_backend("unsupported backend" ):
map_nested(UpperCAmelCase_ , UpperCAmelCase_ , num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("num_proc" , [2, -1] )
def a( A : List[str] ) -> int:
"""simple docstring"""
a = [1, 2]
a = {"a": 1, "b": 2}
a = {"a": [1, 2], "b": [3, 4]}
a = {"a": {"1": 1}, "b": 2}
a = {"a": 1, "b": 2, "c": 3, "d": 4}
a = [2, 3]
a = {"a": 2, "b": 3}
a = {"a": [2, 3], "b": [4, 5]}
a = {"a": {"1": 2}, "b": 3}
a = {"a": 2, "b": 3, "c": 4, "d": 5}
with parallel_backend("spark" ):
assert map_nested(UpperCAmelCase_ , UpperCAmelCase_ , num_proc=UpperCAmelCase_ ) == expected_map_nested_sa
assert map_nested(UpperCAmelCase_ , UpperCAmelCase_ , num_proc=UpperCAmelCase_ ) == expected_map_nested_sa
assert map_nested(UpperCAmelCase_ , UpperCAmelCase_ , num_proc=UpperCAmelCase_ ) == expected_map_nested_sa
assert map_nested(UpperCAmelCase_ , UpperCAmelCase_ , num_proc=UpperCAmelCase_ ) == expected_map_nested_sa
assert map_nested(UpperCAmelCase_ , UpperCAmelCase_ , num_proc=UpperCAmelCase_ ) == expected_map_nested_sa
| 227 |
"""simple docstring"""
import math
class a :
"""simple docstring"""
def __init__( self: List[Any] , UpperCamelCase: List[str]=0 ): # a graph with Node 0,1,...,N-1
"""simple docstring"""
A__ = n
A__ = [
[math.inf for j in range(0 , UpperCamelCase )] for i in range(0 , UpperCamelCase )
] # adjacency matrix for weight
A__ = [
[math.inf for j in range(0 , UpperCamelCase )] for i in range(0 , UpperCamelCase )
] # dp[i][j] stores minimum distance from i to j
def UpperCamelCase ( self: Union[str, Any] , UpperCamelCase: Optional[int] , UpperCamelCase: Union[str, Any] , UpperCamelCase: Tuple ):
"""simple docstring"""
A__ = w
def UpperCamelCase ( self: int ):
"""simple docstring"""
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
A__ = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def UpperCamelCase ( self: int , UpperCamelCase: List[str] , UpperCamelCase: Dict ):
"""simple docstring"""
return self.dp[u][v]
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ : List[Any] = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 1_0)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 1_0)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 335 | 0 |
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self , lowerCamelCase__ ) -> int:
'''simple docstring'''
__lowerCamelCase = val
__lowerCamelCase = None
__lowerCamelCase = None
def lowercase_ ( self , lowerCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
if self.val:
if val < self.val:
if self.left is None:
__lowerCamelCase = Node(lowerCamelCase__ )
else:
self.left.insert(lowerCamelCase__ )
elif val > self.val:
if self.right is None:
__lowerCamelCase = Node(lowerCamelCase__ )
else:
self.right.insert(lowerCamelCase__ )
else:
__lowerCamelCase = val
def lowerCamelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : Tuple ) -> List[Any]:
"""simple docstring"""
if root:
inorder(root.left , UpperCAmelCase_ )
res.append(root.val )
inorder(root.right , UpperCAmelCase_ )
def lowerCamelCase_ ( UpperCamelCase__ : Optional[Any] ) -> int:
"""simple docstring"""
if len(UpperCAmelCase_ ) == 0:
return arr
__lowerCamelCase = Node(arr[0] )
for i in range(1 , len(UpperCAmelCase_ ) ):
root.insert(arr[i] )
# Traverse BST in order.
__lowerCamelCase = []
inorder(UpperCAmelCase_ , UpperCAmelCase_ )
return res
if __name__ == "__main__":
print(tree_sort([10, 1, 3, 2, 9, 14, 13]))
| 90 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class a ( unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = ViTImageProcessor if is_vision_available() else None
@property
def UpperCamelCase ( self: str ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase ( self: List[Any] ):
"""simple docstring"""
A__ = (3, 32, 1_28)
A__ = tempfile.mkdtemp()
# fmt: off
A__ = ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
# fmt: on
A__ = dict(zip(UpperCamelCase , range(len(UpperCamelCase ) ) ) )
A__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(UpperCamelCase ) + """\n""" )
A__ = {
"""do_normalize""": False,
"""do_resize""": True,
"""image_processor_type""": """ViTImageProcessor""",
"""resample""": 3,
"""size""": {"""height""": 32, """width""": 1_28},
}
A__ = os.path.join(self.tmpdirname , UpperCamelCase )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(UpperCamelCase , UpperCamelCase )
def UpperCamelCase ( self: List[str] , **UpperCamelCase: Union[str, Any] ):
"""simple docstring"""
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **UpperCamelCase )
def UpperCamelCase ( self: List[Any] , **UpperCamelCase: str ):
"""simple docstring"""
return ViTImageProcessor.from_pretrained(self.tmpdirname , **UpperCamelCase )
def UpperCamelCase ( self: Optional[Any] ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def UpperCamelCase ( self: Union[str, Any] ):
"""simple docstring"""
A__ = np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )
A__ = Image.fromarray(np.moveaxis(UpperCamelCase , 0 , -1 ) )
return image_input
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
A__ = self.get_tokenizer()
A__ = self.get_image_processor()
A__ = MgpstrProcessor(tokenizer=UpperCamelCase , image_processor=UpperCamelCase )
processor.save_pretrained(self.tmpdirname )
A__ = MgpstrProcessor.from_pretrained(self.tmpdirname , use_fast=UpperCamelCase )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , UpperCamelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , UpperCamelCase )
def UpperCamelCase ( self: Dict ):
"""simple docstring"""
A__ = self.get_tokenizer()
A__ = self.get_image_processor()
A__ = MgpstrProcessor(tokenizer=UpperCamelCase , image_processor=UpperCamelCase )
processor.save_pretrained(self.tmpdirname )
A__ = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
A__ = self.get_image_processor(do_normalize=UpperCamelCase , padding_value=1.0 )
A__ = MgpstrProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=UpperCamelCase , padding_value=1.0 )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , UpperCamelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , UpperCamelCase )
def UpperCamelCase ( self: List[Any] ):
"""simple docstring"""
A__ = self.get_image_processor()
A__ = self.get_tokenizer()
A__ = MgpstrProcessor(tokenizer=UpperCamelCase , image_processor=UpperCamelCase )
A__ = self.prepare_image_inputs()
A__ = image_processor(UpperCamelCase , return_tensors="""np""" )
A__ = processor(images=UpperCamelCase , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1e-2 )
def UpperCamelCase ( self: Union[str, Any] ):
"""simple docstring"""
A__ = self.get_image_processor()
A__ = self.get_tokenizer()
A__ = MgpstrProcessor(tokenizer=UpperCamelCase , image_processor=UpperCamelCase )
A__ = """test"""
A__ = processor(text=UpperCamelCase )
A__ = tokenizer(UpperCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def UpperCamelCase ( self: Optional[Any] ):
"""simple docstring"""
A__ = self.get_image_processor()
A__ = self.get_tokenizer()
A__ = MgpstrProcessor(tokenizer=UpperCamelCase , image_processor=UpperCamelCase )
A__ = """test"""
A__ = self.prepare_image_inputs()
A__ = processor(text=UpperCamelCase , images=UpperCamelCase )
self.assertListEqual(list(inputs.keys() ) , ["""pixel_values""", """labels"""] )
# test if it raises when no input is passed
with pytest.raises(UpperCamelCase ):
processor()
def UpperCamelCase ( self: List[str] ):
"""simple docstring"""
A__ = self.get_image_processor()
A__ = self.get_tokenizer()
A__ = MgpstrProcessor(tokenizer=UpperCamelCase , image_processor=UpperCamelCase )
A__ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]
A__ = processor.char_decode(UpperCamelCase )
A__ = tokenizer.batch_decode(UpperCamelCase )
A__ = [seq.replace(""" """ , """""" ) for seq in decoded_tok]
self.assertListEqual(UpperCamelCase , UpperCamelCase )
def UpperCamelCase ( self: List[Any] ):
"""simple docstring"""
A__ = self.get_image_processor()
A__ = self.get_tokenizer()
A__ = MgpstrProcessor(tokenizer=UpperCamelCase , image_processor=UpperCamelCase )
A__ = None
A__ = self.prepare_image_inputs()
A__ = processor(text=UpperCamelCase , images=UpperCamelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
def UpperCamelCase ( self: Optional[Any] ):
"""simple docstring"""
A__ = self.get_image_processor()
A__ = self.get_tokenizer()
A__ = MgpstrProcessor(tokenizer=UpperCamelCase , image_processor=UpperCamelCase )
A__ = torch.randn(1 , 27 , 38 )
A__ = torch.randn(1 , 27 , 5_02_57 )
A__ = torch.randn(1 , 27 , 3_05_22 )
A__ = processor.batch_decode([char_input, bpe_input, wp_input] )
self.assertListEqual(list(results.keys() ) , ["""generated_text""", """scores""", """char_preds""", """bpe_preds""", """wp_preds"""] )
| 335 | 0 |
"""simple docstring"""
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
lowercase__ = logging.get_logger(__name__)
class lowerCAmelCase__ ( _lowerCamelCase ):
'''simple docstring'''
def A_ ( self , lowercase ):
if isinstance(lowercase , lowercase ):
_lowerCamelCase : List[str] = [label.strip() for label in labels.split(',' ) if label.strip()]
return labels
def __call__( self , lowercase , lowercase , lowercase ):
if len(lowercase ) == 0 or len(lowercase ) == 0:
raise ValueError('You must include at least one label and at least one sequence.' )
if hypothesis_template.format(labels[0] ) == hypothesis_template:
raise ValueError(
(
'The provided hypothesis_template \"{}\" was not able to be formatted with the target labels. '
'Make sure the passed template includes formatting syntax such as {{}} where the label should go.'
).format(lowercase ) )
if isinstance(lowercase , lowercase ):
_lowerCamelCase : Optional[Any] = [sequences]
_lowerCamelCase : Union[str, Any] = []
for sequence in sequences:
sequence_pairs.extend([[sequence, hypothesis_template.format(lowercase )] for label in labels] )
return sequence_pairs, sequences
@add_end_docstrings(_lowerCamelCase )
class lowerCAmelCase__ ( _lowerCamelCase ):
'''simple docstring'''
def __init__( self , lowercase=ZeroShotClassificationArgumentHandler() , *lowercase , **lowercase ):
_lowerCamelCase : Union[str, Any] = args_parser
super().__init__(*lowercase , **lowercase )
if self.entailment_id == -1:
logger.warning(
'Failed to determine \'entailment\' label id from the label2id mapping in the model config. Setting to '
'-1. Define a descriptive label2id mapping in the model config to ensure correct outputs.' )
@property
def A_ ( self ):
for label, ind in self.model.config.labelaid.items():
if label.lower().startswith('entail' ):
return ind
return -1
def A_ ( self , lowercase , lowercase=True , lowercase=True , lowercase=TruncationStrategy.ONLY_FIRST , **lowercase ):
_lowerCamelCase : Union[str, Any] = self.framework
if self.tokenizer.pad_token is None:
# Override for tokenizers not supporting padding
logger.error(
'Tokenizer was not supporting padding necessary for zero-shot, attempting to use '
' `pad_token=eos_token`' )
_lowerCamelCase : int = self.tokenizer.eos_token
try:
_lowerCamelCase : Any = self.tokenizer(
lowercase , add_special_tokens=lowercase , return_tensors=lowercase , padding=lowercase , truncation=lowercase , )
except Exception as e:
if "too short" in str(lowercase ):
# tokenizers might yell that we want to truncate
# to a value that is not even reached by the input.
# In that case we don't want to truncate.
# It seems there's not a really better way to catch that
# exception.
_lowerCamelCase : Optional[Any] = self.tokenizer(
lowercase , add_special_tokens=lowercase , return_tensors=lowercase , padding=lowercase , truncation=TruncationStrategy.DO_NOT_TRUNCATE , )
else:
raise e
return inputs
def A_ ( self , **lowercase ):
if kwargs.get('multi_class' , lowercase ) is not None:
_lowerCamelCase : Tuple = kwargs['multi_class']
logger.warning(
'The `multi_class` argument has been deprecated and renamed to `multi_label`. '
'`multi_class` will be removed in a future version of Transformers.' )
_lowerCamelCase : int = {}
if "candidate_labels" in kwargs:
_lowerCamelCase : List[str] = self._args_parser._parse_labels(kwargs['candidate_labels'] )
if "hypothesis_template" in kwargs:
_lowerCamelCase : Optional[Any] = kwargs['hypothesis_template']
_lowerCamelCase : List[str] = {}
if "multi_label" in kwargs:
_lowerCamelCase : Optional[int] = kwargs['multi_label']
return preprocess_params, {}, postprocess_params
def __call__( self , lowercase , *lowercase , **lowercase , ):
if len(lowercase ) == 0:
pass
elif len(lowercase ) == 1 and "candidate_labels" not in kwargs:
_lowerCamelCase : Union[str, Any] = args[0]
else:
raise ValueError(F'''Unable to understand extra arguments {args}''' )
return super().__call__(lowercase , **lowercase )
def A_ ( self , lowercase , lowercase=None , lowercase="This example is {}." ):
_lowerCamelCase, _lowerCamelCase : List[Any] = self._args_parser(lowercase , lowercase , lowercase )
for i, (candidate_label, sequence_pair) in enumerate(zip(lowercase , lowercase ) ):
_lowerCamelCase : Any = self._parse_and_tokenize([sequence_pair] )
yield {
"candidate_label": candidate_label,
"sequence": sequences[0],
"is_last": i == len(lowercase ) - 1,
**model_input,
}
def A_ ( self , lowercase ):
_lowerCamelCase : Dict = inputs['candidate_label']
_lowerCamelCase : Dict = inputs['sequence']
_lowerCamelCase : Union[str, Any] = {k: inputs[k] for k in self.tokenizer.model_input_names}
_lowerCamelCase : str = self.model(**lowercase )
_lowerCamelCase : int = {
'candidate_label': candidate_label,
'sequence': sequence,
'is_last': inputs['is_last'],
**outputs,
}
return model_outputs
def A_ ( self , lowercase , lowercase=False ):
_lowerCamelCase : Optional[int] = [outputs['candidate_label'] for outputs in model_outputs]
_lowerCamelCase : Tuple = [outputs['sequence'] for outputs in model_outputs]
_lowerCamelCase : Dict = np.concatenate([output['logits'].numpy() for output in model_outputs] )
_lowerCamelCase : List[str] = logits.shape[0]
_lowerCamelCase : Any = len(lowercase )
_lowerCamelCase : List[Any] = N // n
_lowerCamelCase : int = logits.reshape((num_sequences, n, -1) )
if multi_label or len(lowercase ) == 1:
# softmax over the entailment vs. contradiction dim for each label independently
_lowerCamelCase : Optional[Any] = self.entailment_id
_lowerCamelCase : Dict = -1 if entailment_id == 0 else 0
_lowerCamelCase : int = reshaped_outputs[..., [contradiction_id, entailment_id]]
_lowerCamelCase : List[str] = np.exp(lowercase ) / np.exp(lowercase ).sum(-1 , keepdims=lowercase )
_lowerCamelCase : Any = scores[..., 1]
else:
# softmax the "entailment" logits over all candidate labels
_lowerCamelCase : int = reshaped_outputs[..., self.entailment_id]
_lowerCamelCase : Optional[int] = np.exp(lowercase ) / np.exp(lowercase ).sum(-1 , keepdims=lowercase )
_lowerCamelCase : int = list(reversed(scores[0].argsort() ) )
return {
"sequence": sequences[0],
"labels": [candidate_labels[i] for i in top_inds],
"scores": scores[0, top_inds].tolist(),
} | 96 |
"""simple docstring"""
import math
def _snake_case ( UpperCAmelCase_ : float , UpperCAmelCase_ : float ):
if initial_intensity < 0:
raise ValueError("""The value of intensity cannot be negative""" )
# handling of negative values of initial intensity
if angle < 0 or angle > 360:
raise ValueError("""In Malus Law, the angle is in the range 0-360 degrees""" )
# handling of values out of allowed range
return initial_intensity * (math.cos(math.radians(UpperCAmelCase_ ) ) ** 2)
if __name__ == "__main__":
import doctest
doctest.testmod(name='malus_law')
| 335 | 0 |
"""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()
_lowerCamelCase : List[str] = {
'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 lowercase_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=False , _UpperCAmelCase=True ):
"""simple docstring"""
if model_type not in MODEL_CLASSES:
raise ValueError(f"""Unrecognized model type, should be one of {list(MODEL_CLASSES.keys() )}.""" )
A_ , A_ , A_ , A_ : Dict = MODEL_CLASSES[model_type]
# Initialise TF model
if config_file in aws_config_map:
A_ : Any = cached_file(UpperCAmelCase_ , UpperCAmelCase_ , force_download=not use_cached_models )
A_ : Optional[Any] = config_class.from_json_file(UpperCAmelCase_ )
A_ : int = True
A_ : Dict = True
print(f"""Building TensorFlow model from configuration: {config}""" )
A_ : Tuple = model_class(UpperCAmelCase_ )
# Load weights from tf checkpoint
if pytorch_checkpoint_path in aws_config_map.keys():
A_ : int = cached_file(
UpperCAmelCase_ , UpperCAmelCase_ , force_download=not use_cached_models )
# Load PyTorch checkpoint in tf2 model:
A_ : Tuple = load_pytorch_checkpoint_in_tfa_model(UpperCAmelCase_ , UpperCAmelCase_ )
if compare_with_pt_model:
A_ : Optional[Any] = tf_model(tf_model.dummy_inputs , training=UpperCAmelCase_ ) # build the network
A_ : Any = torch.load(UpperCAmelCase_ , map_location='''cpu''' )
A_ : Dict = pt_model_class.from_pretrained(
pretrained_model_name_or_path=UpperCAmelCase_ , config=UpperCAmelCase_ , state_dict=UpperCAmelCase_ )
with torch.no_grad():
A_ : Optional[int] = pt_model(**pt_model.dummy_inputs )
A_ : Optional[int] = pto[0].numpy()
A_ : Dict = tfo[0].numpy()
A_ : int = 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(UpperCAmelCase_ , save_format='''h5''' )
def lowercase_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=False , _UpperCAmelCase=False , _UpperCAmelCase=False , _UpperCAmelCase=False , ):
"""simple docstring"""
if args_model_type is None:
A_ : Tuple = list(MODEL_CLASSES.keys() )
else:
A_ : Tuple = [args_model_type]
for j, model_type in enumerate(UpperCAmelCase_ , start=1 ):
print('''=''' * 100 )
print(f""" Converting model type {j}/{len(UpperCAmelCase_ )}: {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() )}.""" )
A_ , A_ , A_ , A_ , A_ : Optional[Any] = MODEL_CLASSES[model_type]
if model_shortcut_names_or_path is None:
A_ : List[str] = list(aws_model_maps.keys() )
if config_shortcut_names_or_path is None:
A_ : Tuple = model_shortcut_names_or_path
for i, (model_shortcut_name, config_shortcut_name) in enumerate(
zip(UpperCAmelCase_ , UpperCAmelCase_ ) , 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
A_ : List[Any] = model_shortcut_name
elif only_convert_finetuned_models:
print(f""" Skipping not finetuned checkpoint {model_shortcut_name}""" )
continue
print(
f""" Converting checkpoint {i}/{len(UpperCAmelCase_ )}: {model_shortcut_name} - model_type {model_type}""" )
print('''-''' * 100 )
if config_shortcut_name in aws_config_map:
A_ : Tuple = cached_file(UpperCAmelCase_ , UpperCAmelCase_ , force_download=not use_cached_models )
else:
A_ : Dict = config_shortcut_name
if model_shortcut_name in aws_model_maps:
A_ : Union[str, Any] = cached_file(UpperCAmelCase_ , UpperCAmelCase_ , force_download=not use_cached_models )
else:
A_ : Optional[Any] = model_shortcut_name
if os.path.isfile(UpperCAmelCase_ ):
A_ : Any = '''converted_model'''
convert_pt_checkpoint_to_tf(
model_type=UpperCAmelCase_ , pytorch_checkpoint_path=UpperCAmelCase_ , config_file=UpperCAmelCase_ , tf_dump_path=os.path.join(UpperCAmelCase_ , model_shortcut_name + '''-tf_model.h5''' ) , compare_with_pt_model=UpperCAmelCase_ , )
if remove_cached_files:
os.remove(UpperCAmelCase_ )
os.remove(UpperCAmelCase_ )
if __name__ == "__main__":
_lowerCamelCase : 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.')
_lowerCamelCase : List[str] = 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,
)
| 167 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
RobertaSeriesConfig,
RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
class a ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self: Any ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def UpperCamelCase ( self: Dict ):
"""simple docstring"""
A__ = 1
A__ = 3
A__ = (32, 32)
A__ = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(UpperCamelCase )
return image
@property
def UpperCamelCase ( self: int ):
"""simple docstring"""
torch.manual_seed(0 )
A__ = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
return model
@property
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
torch.manual_seed(0 )
A__ = 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 , )
return model
@property
def UpperCamelCase ( self: List[Any] ):
"""simple docstring"""
torch.manual_seed(0 )
A__ = RobertaSeriesConfig(
hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=50_06 , )
return RobertaSeriesModelWithTransformation(UpperCamelCase )
@property
def UpperCamelCase ( self: str ):
"""simple docstring"""
def extract(*UpperCamelCase: List[str] , **UpperCamelCase: Any ):
class a :
"""simple docstring"""
def __init__( self: Any ):
"""simple docstring"""
A__ = torch.ones([0] )
def UpperCamelCase ( self: Dict , UpperCamelCase: Optional[Any] ):
"""simple docstring"""
self.pixel_values.to(UpperCamelCase )
return self
return Out()
return extract
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = """cpu""" # ensure determinism for the device-dependent torch.Generator
A__ = self.dummy_cond_unet
A__ = PNDMScheduler(skip_prk_steps=UpperCamelCase )
A__ = self.dummy_vae
A__ = self.dummy_text_encoder
A__ = XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta""" )
A__ = 77
A__ = self.dummy_image.to(UpperCamelCase )
A__ = init_image / 2 + 0.5
# make sure here that pndm scheduler skips prk
A__ = AltDiffusionImgaImgPipeline(
unet=UpperCamelCase , scheduler=UpperCamelCase , vae=UpperCamelCase , text_encoder=UpperCamelCase , tokenizer=UpperCamelCase , safety_checker=UpperCamelCase , feature_extractor=self.dummy_extractor , )
A__ = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor , do_normalize=UpperCamelCase )
A__ = alt_pipe.to(UpperCamelCase )
alt_pipe.set_progress_bar_config(disable=UpperCamelCase )
A__ = """A painting of a squirrel eating a burger"""
A__ = torch.Generator(device=UpperCamelCase ).manual_seed(0 )
A__ = alt_pipe(
[prompt] , generator=UpperCamelCase , guidance_scale=6.0 , num_inference_steps=2 , output_type="""np""" , image=UpperCamelCase , )
A__ = output.images
A__ = torch.Generator(device=UpperCamelCase ).manual_seed(0 )
A__ = alt_pipe(
[prompt] , generator=UpperCamelCase , guidance_scale=6.0 , num_inference_steps=2 , output_type="""np""" , image=UpperCamelCase , return_dict=UpperCamelCase , )[0]
A__ = image[0, -3:, -3:, -1]
A__ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
A__ = np.array([0.4_427, 0.3_731, 0.4_249, 0.4_941, 0.4_546, 0.4_148, 0.4_193, 0.4_666, 0.4_499] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 5e-3
@unittest.skipIf(torch_device != """cuda""" , """This test requires a GPU""" )
def UpperCamelCase ( self: int ):
"""simple docstring"""
A__ = self.dummy_cond_unet
A__ = PNDMScheduler(skip_prk_steps=UpperCamelCase )
A__ = self.dummy_vae
A__ = self.dummy_text_encoder
A__ = XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta""" )
A__ = 77
A__ = self.dummy_image.to(UpperCamelCase )
# put models in fp16
A__ = unet.half()
A__ = vae.half()
A__ = bert.half()
# make sure here that pndm scheduler skips prk
A__ = AltDiffusionImgaImgPipeline(
unet=UpperCamelCase , scheduler=UpperCamelCase , vae=UpperCamelCase , text_encoder=UpperCamelCase , tokenizer=UpperCamelCase , safety_checker=UpperCamelCase , feature_extractor=self.dummy_extractor , )
A__ = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor , do_normalize=UpperCamelCase )
A__ = alt_pipe.to(UpperCamelCase )
alt_pipe.set_progress_bar_config(disable=UpperCamelCase )
A__ = """A painting of a squirrel eating a burger"""
A__ = torch.manual_seed(0 )
A__ = alt_pipe(
[prompt] , generator=UpperCamelCase , num_inference_steps=2 , output_type="""np""" , image=UpperCamelCase , ).images
assert image.shape == (1, 32, 32, 3)
@unittest.skipIf(torch_device != """cuda""" , """This test requires a GPU""" )
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
# resize to resolution that is divisible by 8 but not 16 or 32
A__ = init_image.resize((7_60, 5_04) )
A__ = """BAAI/AltDiffusion"""
A__ = AltDiffusionImgaImgPipeline.from_pretrained(
UpperCamelCase , safety_checker=UpperCamelCase , )
pipe.to(UpperCamelCase )
pipe.set_progress_bar_config(disable=UpperCamelCase )
pipe.enable_attention_slicing()
A__ = """A fantasy landscape, trending on artstation"""
A__ = torch.manual_seed(0 )
A__ = pipe(
prompt=UpperCamelCase , image=UpperCamelCase , strength=0.75 , guidance_scale=7.5 , generator=UpperCamelCase , output_type="""np""" , )
A__ = output.images[0]
A__ = image[2_55:2_58, 3_83:3_86, -1]
assert image.shape == (5_04, 7_60, 3)
A__ = np.array([0.9_358, 0.9_397, 0.9_599, 0.9_901, 1.0_000, 1.0_000, 0.9_882, 1.0_000, 1.0_000] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch_gpu
class a ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self: Any ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase ( self: List[str] ):
"""simple docstring"""
A__ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
A__ = init_image.resize((7_68, 5_12) )
A__ = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy""" )
A__ = """BAAI/AltDiffusion"""
A__ = AltDiffusionImgaImgPipeline.from_pretrained(
UpperCamelCase , safety_checker=UpperCamelCase , )
pipe.to(UpperCamelCase )
pipe.set_progress_bar_config(disable=UpperCamelCase )
pipe.enable_attention_slicing()
A__ = """A fantasy landscape, trending on artstation"""
A__ = torch.manual_seed(0 )
A__ = pipe(
prompt=UpperCamelCase , image=UpperCamelCase , strength=0.75 , guidance_scale=7.5 , generator=UpperCamelCase , output_type="""np""" , )
A__ = output.images[0]
assert image.shape == (5_12, 7_68, 3)
# img2img is flaky across GPUs even in fp32, so using MAE here
assert np.abs(expected_image - image ).max() < 1e-2
| 335 | 0 |
'''simple docstring'''
from math import pi
def __a(SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
'''simple docstring'''
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 158 |
"""simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def _snake_case ( UpperCAmelCase_ : List[Any] ):
A__ = FileLock(str(tmpdir / """foo.lock""" ) )
A__ = FileLock(str(tmpdir / """foo.lock""" ) )
A__ = 0.01
with locka.acquire():
with pytest.raises(UpperCAmelCase_ ):
A__ = time.time()
locka.acquire(UpperCAmelCase_ )
assert time.time() - _start > timeout
def _snake_case ( UpperCAmelCase_ : List[Any] ):
A__ = """a""" * 1000 + """.lock"""
A__ = FileLock(str(tmpdir / filename ) )
assert locka._lock_file.endswith(""".lock""" )
assert not locka._lock_file.endswith(UpperCAmelCase_ )
assert len(os.path.basename(locka._lock_file ) ) <= 255
A__ = FileLock(tmpdir / filename )
with locka.acquire():
with pytest.raises(UpperCAmelCase_ ):
locka.acquire(0 )
| 335 | 0 |
lowercase__ =tuple[float, float, float]
lowercase__ =tuple[float, float, float]
def __UpperCamelCase ( lowerCAmelCase__ : Pointad , lowerCAmelCase__ : Pointad ):
__a : Optional[int] = end_pointa[0] - end_pointa[0]
__a : Optional[int] = end_pointa[1] - end_pointa[1]
__a : Union[str, Any] = end_pointa[2] - end_pointa[2]
return (x, y, z)
def __UpperCamelCase ( lowerCAmelCase__ : Vectorad , lowerCAmelCase__ : Vectorad ):
__a : str = ab[1] * ac[2] - ab[2] * ac[1] # *i
__a : str = (ab[0] * ac[2] - ab[2] * ac[0]) * -1 # *j
__a : Optional[int] = ab[0] * ac[1] - ab[1] * ac[0] # *k
return (x, y, z)
def __UpperCamelCase ( lowerCAmelCase__ : Vectorad , lowerCAmelCase__ : int ):
return tuple(round(UpperCAmelCase_ , UpperCAmelCase_ ) for x in vector ) == (0, 0, 0)
def __UpperCamelCase ( lowerCAmelCase__ : Pointad , lowerCAmelCase__ : Pointad , lowerCAmelCase__ : Pointad , lowerCAmelCase__ : int = 1_0 ):
__a : Optional[int] = create_vector(UpperCAmelCase_ , UpperCAmelCase_ )
__a : Optional[int] = create_vector(UpperCAmelCase_ , UpperCAmelCase_ )
return is_zero_vector(get_ad_vectors_cross(UpperCAmelCase_ , UpperCAmelCase_ ) , UpperCAmelCase_ )
| 216 |
"""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.
from typing import TYPE_CHECKING
import torch
from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class a ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = "dandelin/vilt-b32-finetuned-vqa"
UpperCAmelCase = (
"This is a tool that answers a question about an image. It takes an input named `image` which should be the "
"image containing the information, as well as a `question` which should be the question in English. It "
"returns a text that is the answer to the question."
)
UpperCAmelCase = "image_qa"
UpperCAmelCase = AutoProcessor
UpperCAmelCase = AutoModelForVisualQuestionAnswering
UpperCAmelCase = ["image", "text"]
UpperCAmelCase = ["text"]
def __init__( self: List[str] , *UpperCamelCase: Dict , **UpperCamelCase: List[str] ):
"""simple docstring"""
requires_backends(self , ["""vision"""] )
super().__init__(*UpperCamelCase , **UpperCamelCase )
def UpperCamelCase ( self: str , UpperCamelCase: "Image" , UpperCamelCase: str ):
"""simple docstring"""
return self.pre_processor(UpperCamelCase , UpperCamelCase , return_tensors="""pt""" )
def UpperCamelCase ( self: str , UpperCamelCase: str ):
"""simple docstring"""
with torch.no_grad():
return self.model(**UpperCamelCase ).logits
def UpperCamelCase ( self: Union[str, Any] , UpperCamelCase: int ):
"""simple docstring"""
A__ = outputs.argmax(-1 ).item()
return self.model.config.idalabel[idx]
| 335 | 0 |
"""simple docstring"""
def lowercase (snake_case__ : int , snake_case__ : int ) -> Optional[Any]:
'''simple docstring'''
if number < 0 or shift_amount < 0:
raise ValueError("""both inputs must be positive integers""" )
lowerCAmelCase = str(bin(UpperCAmelCase_ ) )
binary_number += "0" * shift_amount
return binary_number
def lowercase (snake_case__ : int , snake_case__ : int ) -> List[str]:
'''simple docstring'''
if number < 0 or shift_amount < 0:
raise ValueError("""both inputs must be positive integers""" )
lowerCAmelCase = str(bin(UpperCAmelCase_ ) )[2:]
if shift_amount >= len(UpperCAmelCase_ ):
return "0b0"
lowerCAmelCase = binary_number[: len(UpperCAmelCase_ ) - shift_amount]
return "0b" + shifted_binary_number
def lowercase (snake_case__ : int , snake_case__ : int ) -> List[Any]:
'''simple docstring'''
if number >= 0: # Get binary representation of positive number
lowerCAmelCase = """0""" + str(bin(UpperCAmelCase_ ) ).strip("""-""" )[2:]
else: # Get binary (2's complement) representation of negative number
lowerCAmelCase = len(bin(UpperCAmelCase_ )[3:] ) # Find 2's complement of number
lowerCAmelCase = bin(abs(UpperCAmelCase_ ) - (1 << binary_number_length) )[3:]
lowerCAmelCase = (
"""1""" + """0""" * (binary_number_length - len(UpperCAmelCase_ )) + binary_number
)
if shift_amount >= len(UpperCAmelCase_ ):
return "0b" + binary_number[0] * len(UpperCAmelCase_ )
return (
"0b"
+ binary_number[0] * shift_amount
+ binary_number[: len(UpperCAmelCase_ ) - shift_amount]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 155 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import YolosImageProcessor
class a ( unittest.TestCase ):
"""simple docstring"""
def __init__( self: Optional[Any] , UpperCamelCase: Any , UpperCamelCase: Optional[int]=7 , UpperCamelCase: str=3 , UpperCamelCase: int=30 , UpperCamelCase: int=4_00 , UpperCamelCase: Union[str, Any]=True , UpperCamelCase: Tuple=None , UpperCamelCase: Any=True , UpperCamelCase: int=[0.5, 0.5, 0.5] , UpperCamelCase: Any=[0.5, 0.5, 0.5] , UpperCamelCase: Optional[Any]=True , UpperCamelCase: List[Any]=1 / 2_55 , UpperCamelCase: Tuple=True , ):
"""simple docstring"""
A__ = size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
A__ = parent
A__ = batch_size
A__ = num_channels
A__ = min_resolution
A__ = max_resolution
A__ = do_resize
A__ = size
A__ = do_normalize
A__ = image_mean
A__ = image_std
A__ = do_rescale
A__ = rescale_factor
A__ = do_pad
def UpperCamelCase ( self: Optional[Any] ):
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def UpperCamelCase ( self: Any , UpperCamelCase: List[str] , UpperCamelCase: int=False ):
"""simple docstring"""
if not batched:
A__ = image_inputs[0]
if isinstance(UpperCamelCase , Image.Image ):
A__ , A__ = image.size
else:
A__ , A__ = image.shape[1], image.shape[2]
if w < h:
A__ = int(self.size["""shortest_edge"""] * h / w )
A__ = self.size["""shortest_edge"""]
elif w > h:
A__ = self.size["""shortest_edge"""]
A__ = int(self.size["""shortest_edge"""] * w / h )
else:
A__ = self.size["""shortest_edge"""]
A__ = self.size["""shortest_edge"""]
else:
A__ = []
for image in image_inputs:
A__ , A__ = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
A__ = max(UpperCamelCase , key=lambda UpperCamelCase : item[0] )[0]
A__ = max(UpperCamelCase , key=lambda UpperCamelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class a ( _lowerCamelCase, unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = YolosImageProcessor if is_vision_available() else None
def UpperCamelCase ( self: Optional[int] ):
"""simple docstring"""
A__ = YolosImageProcessingTester(self )
@property
def UpperCamelCase ( self: Optional[int] ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase ( self: Union[str, Any] ):
"""simple docstring"""
A__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCamelCase , """image_mean""" ) )
self.assertTrue(hasattr(UpperCamelCase , """image_std""" ) )
self.assertTrue(hasattr(UpperCamelCase , """do_normalize""" ) )
self.assertTrue(hasattr(UpperCamelCase , """do_resize""" ) )
self.assertTrue(hasattr(UpperCamelCase , """size""" ) )
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
A__ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 18, """longest_edge""": 13_33} )
self.assertEqual(image_processor.do_pad , UpperCamelCase )
A__ = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=UpperCamelCase )
self.assertEqual(image_processor.size , {"""shortest_edge""": 42, """longest_edge""": 84} )
self.assertEqual(image_processor.do_pad , UpperCamelCase )
def UpperCamelCase ( self: str ):
"""simple docstring"""
pass
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
A__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase , Image.Image )
# Test not batched input
A__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase , batched=UpperCamelCase )
A__ = image_processing(UpperCamelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
A__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
A__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase , numpify=UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase , np.ndarray )
# Test not batched input
A__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
A__ = image_processing(UpperCamelCase , return_tensors="""pt""" ).pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase , batched=UpperCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
A__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase , torchify=UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase , torch.Tensor )
# Test not batched input
A__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
A__ = image_processing(UpperCamelCase , return_tensors="""pt""" ).pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase , batched=UpperCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = self.image_processing_class(**self.image_processor_dict )
A__ = self.image_processing_class(do_resize=UpperCamelCase , do_normalize=UpperCamelCase , do_rescale=UpperCamelCase )
# create random PyTorch tensors
A__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase , torchify=UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
A__ = image_processing_a.pad(UpperCamelCase , return_tensors="""pt""" )
A__ = image_processing_a(UpperCamelCase , return_tensors="""pt""" )
self.assertTrue(
torch.allclose(encoded_images_with_method["""pixel_values"""] , encoded_images["""pixel_values"""] , atol=1e-4 ) )
@slow
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt""" , """r""" ) as f:
A__ = json.loads(f.read() )
A__ = {"""image_id""": 3_97_69, """annotations""": target}
# encode them
A__ = YolosImageProcessor.from_pretrained("""hustvl/yolos-small""" )
A__ = image_processing(images=UpperCamelCase , annotations=UpperCamelCase , return_tensors="""pt""" )
# verify pixel values
A__ = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding["""pixel_values"""].shape , UpperCamelCase )
A__ = torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , UpperCamelCase , atol=1e-4 ) )
# verify area
A__ = torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , UpperCamelCase ) )
# verify boxes
A__ = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , UpperCamelCase )
A__ = torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , UpperCamelCase , atol=1e-3 ) )
# verify image_id
A__ = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , UpperCamelCase ) )
# verify is_crowd
A__ = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , UpperCamelCase ) )
# verify class_labels
A__ = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , UpperCamelCase ) )
# verify orig_size
A__ = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , UpperCamelCase ) )
# verify size
A__ = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , UpperCamelCase ) )
@slow
def UpperCamelCase ( self: int ):
"""simple docstring"""
A__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt""" , """r""" ) as f:
A__ = json.loads(f.read() )
A__ = {"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}
A__ = pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""" )
# encode them
A__ = YolosImageProcessor(format="""coco_panoptic""" )
A__ = image_processing(images=UpperCamelCase , annotations=UpperCamelCase , masks_path=UpperCamelCase , return_tensors="""pt""" )
# verify pixel values
A__ = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding["""pixel_values"""].shape , UpperCamelCase )
A__ = torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , UpperCamelCase , atol=1e-4 ) )
# verify area
A__ = torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , UpperCamelCase ) )
# verify boxes
A__ = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , UpperCamelCase )
A__ = torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , UpperCamelCase , atol=1e-3 ) )
# verify image_id
A__ = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , UpperCamelCase ) )
# verify is_crowd
A__ = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , UpperCamelCase ) )
# verify class_labels
A__ = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , UpperCamelCase ) )
# verify masks
A__ = 82_28_73
self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item() , UpperCamelCase )
# verify orig_size
A__ = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , UpperCamelCase ) )
# verify size
A__ = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , UpperCamelCase ) )
| 335 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[Any]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Any:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Dict:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Any:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Tuple:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Dict:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
def lowercase__( *__SCREAMING_SNAKE_CASE : Optional[Any] , **__SCREAMING_SNAKE_CASE : int ):
requires_backends(UpperCAmelCase_ , ['torch'] )
def lowercase__( *__SCREAMING_SNAKE_CASE : List[Any] , **__SCREAMING_SNAKE_CASE : Union[str, Any] ):
requires_backends(UpperCAmelCase_ , ['torch'] )
def lowercase__( *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : int ):
requires_backends(UpperCAmelCase_ , ['torch'] )
def lowercase__( *__SCREAMING_SNAKE_CASE : Optional[Any] , **__SCREAMING_SNAKE_CASE : int ):
requires_backends(UpperCAmelCase_ , ['torch'] )
def lowercase__( *__SCREAMING_SNAKE_CASE : List[str] , **__SCREAMING_SNAKE_CASE : Tuple ):
requires_backends(UpperCAmelCase_ , ['torch'] )
def lowercase__( *__SCREAMING_SNAKE_CASE : List[str] , **__SCREAMING_SNAKE_CASE : int ):
requires_backends(UpperCAmelCase_ , ['torch'] )
def lowercase__( *__SCREAMING_SNAKE_CASE : List[Any] , **__SCREAMING_SNAKE_CASE : int ):
requires_backends(UpperCAmelCase_ , ['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Any:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Tuple:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Dict:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Any:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Tuple:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Any:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Any:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Dict:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Tuple:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Tuple:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[Any]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Any:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Tuple:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Any:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Dict:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[Any]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Dict:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[Any]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Tuple:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Any:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Any:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Any:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Dict:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Any:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Tuple:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Any:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Tuple:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Any:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Tuple:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Dict:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Tuple:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[Any]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Dict:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Tuple:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Any:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Tuple:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Dict:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_lowerCamelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Tuple:
'''simple docstring'''
requires_backends(cls ,['torch'] )
| 213 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : Dict ): # noqa: E741
A__ = len(UpperCAmelCase_ )
A__ = 0
A__ = [0] * n
A__ = [False] * n
A__ = [False] * n
def dfs(UpperCAmelCase_ : List[str] , UpperCAmelCase_ : int , UpperCAmelCase_ : Dict , UpperCAmelCase_ : List[Any] ):
if parent == root:
out_edge_count += 1
A__ = True
A__ = at
for to in l[at]:
if to == parent:
pass
elif not visited[to]:
A__ = dfs(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
A__ = min(low[at] , low[to] )
# AP found via bridge
if at < low[to]:
A__ = True
# AP found via cycle
if at == low[to]:
A__ = True
else:
A__ = min(low[at] , UpperCAmelCase_ )
return out_edge_count
for i in range(UpperCAmelCase_ ):
if not visited[i]:
A__ = 0
A__ = dfs(UpperCAmelCase_ , UpperCAmelCase_ , -1 , UpperCAmelCase_ )
A__ = out_edge_count > 1
for x in range(len(UpperCAmelCase_ ) ):
if is_art[x] is True:
print(UpperCAmelCase_ )
# Adjacency list of graph
SCREAMING_SNAKE_CASE_ : Optional[int] = {
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
}
compute_ap(data)
| 335 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : List[str] = logging.get_logger(__name__)
_lowerCamelCase : int = {
"SCUT-DLVCLab/lilt-roberta-en-base": (
"https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
),
}
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """lilt"""
def __init__( self : List[str], __A : Dict=3_0_5_2_2, __A : Tuple=7_6_8, __A : int=1_2, __A : Any=1_2, __A : Dict=3_0_7_2, __A : Tuple="gelu", __A : Optional[int]=0.1, __A : Tuple=0.1, __A : Any=5_1_2, __A : Any=2, __A : Optional[Any]=0.0_2, __A : Tuple=1E-12, __A : Optional[Any]=0, __A : Dict="absolute", __A : Any=None, __A : Union[str, Any]=4, __A : Dict=1_0_2_4, **__A : Tuple, ):
super().__init__(pad_token_id=__A, **__A )
UpperCAmelCase : List[Any] = vocab_size
UpperCAmelCase : List[Any] = hidden_size
UpperCAmelCase : Union[str, Any] = num_hidden_layers
UpperCAmelCase : Tuple = num_attention_heads
UpperCAmelCase : Tuple = hidden_act
UpperCAmelCase : Optional[Any] = intermediate_size
UpperCAmelCase : Optional[int] = hidden_dropout_prob
UpperCAmelCase : Dict = attention_probs_dropout_prob
UpperCAmelCase : Optional[int] = max_position_embeddings
UpperCAmelCase : List[str] = type_vocab_size
UpperCAmelCase : int = initializer_range
UpperCAmelCase : Dict = layer_norm_eps
UpperCAmelCase : Any = position_embedding_type
UpperCAmelCase : int = classifier_dropout
UpperCAmelCase : Union[str, Any] = channel_shrink_ratio
UpperCAmelCase : Union[str, Any] = max_ad_position_embeddings
| 336 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __UpperCAmelCase ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
UpperCamelCase = [("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]
def a__ ( ) -> Dict:
if os.name == "nt":
UpperCAmelCase : List[str] = CursorInfo()
UpperCAmelCase : List[Any] = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
UpperCAmelCase : Dict = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
elif os.name == "posix":
sys.stdout.write('''\033[?25l''' )
sys.stdout.flush()
def a__ ( ) -> Optional[int]:
if os.name == "nt":
UpperCAmelCase : int = CursorInfo()
UpperCAmelCase : int = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
UpperCAmelCase : Any = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
elif os.name == "posix":
sys.stdout.write('''\033[?25h''' )
sys.stdout.flush()
@contextmanager
def a__ ( ) -> Optional[Any]:
try:
hide_cursor()
yield
finally:
show_cursor()
| 336 | 1 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __UpperCAmelCase :
def __init__( self : Tuple, __A : List[str], __A : Optional[int]=3, __A : Any=3_2, __A : Tuple=3, __A : List[Any]=1_0, __A : List[Any]=[1_0, 2_0, 3_0, 4_0], __A : Tuple=[1, 1, 2, 1], __A : Dict=True, __A : int=True, __A : Dict="relu", __A : List[str]=3, __A : Tuple=None, ):
UpperCAmelCase : List[Any] = parent
UpperCAmelCase : List[Any] = batch_size
UpperCAmelCase : Dict = image_size
UpperCAmelCase : Tuple = num_channels
UpperCAmelCase : int = embeddings_size
UpperCAmelCase : Tuple = hidden_sizes
UpperCAmelCase : str = depths
UpperCAmelCase : Optional[Any] = is_training
UpperCAmelCase : Any = use_labels
UpperCAmelCase : int = hidden_act
UpperCAmelCase : Dict = num_labels
UpperCAmelCase : int = scope
UpperCAmelCase : Tuple = len(__A )
def __magic_name__ ( self : Dict ):
UpperCAmelCase : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : Tuple = None
if self.use_labels:
UpperCAmelCase : Dict = ids_tensor([self.batch_size], self.num_labels )
UpperCAmelCase : Optional[Any] = self.get_config()
return config, pixel_values, labels
def __magic_name__ ( self : Any ):
return ResNetConfig(
num_channels=self.num_channels, embeddings_size=self.embeddings_size, hidden_sizes=self.hidden_sizes, depths=self.depths, hidden_act=self.hidden_act, num_labels=self.num_labels, image_size=self.image_size, )
def __magic_name__ ( self : List[Any], __A : Any, __A : str, __A : Union[str, Any] ):
UpperCAmelCase : List[Any] = TFResNetModel(config=__A )
UpperCAmelCase : List[str] = model(__A )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2), )
def __magic_name__ ( self : List[str], __A : Dict, __A : Any, __A : Any ):
UpperCAmelCase : Any = self.num_labels
UpperCAmelCase : Optional[Any] = TFResNetForImageClassification(__A )
UpperCAmelCase : Dict = model(__A, labels=__A )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : List[Any] = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Any = config_and_inputs
UpperCAmelCase : Tuple = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
UpperCamelCase = (
{"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
if is_tf_available()
else {}
)
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
def __magic_name__ ( self : int ):
UpperCAmelCase : List[str] = TFResNetModelTester(self )
UpperCAmelCase : Optional[int] = ConfigTester(self, config_class=__A, has_text_modality=__A )
def __magic_name__ ( self : int ):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def __magic_name__ ( self : Union[str, Any] ):
return
@unittest.skip(reason='''ResNet does not use inputs_embeds''' )
def __magic_name__ ( self : List[str] ):
pass
@unittest.skip(reason='''ResNet does not support input and output embeddings''' )
def __magic_name__ ( self : List[Any] ):
pass
def __magic_name__ ( self : Tuple ):
UpperCAmelCase , UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Dict = model_class(__A )
UpperCAmelCase : List[str] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase : Optional[int] = [*signature.parameters.keys()]
UpperCAmelCase : List[str] = ['''pixel_values''']
self.assertListEqual(arg_names[:1], __A )
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __magic_name__ ( self : Dict ):
def check_hidden_states_output(__A : Any, __A : Optional[int], __A : str ):
UpperCAmelCase : List[Any] = model_class(__A )
UpperCAmelCase : int = model(**self._prepare_for_class(__A, __A ) )
UpperCAmelCase : Optional[int] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
UpperCAmelCase : Any = self.model_tester.num_stages
self.assertEqual(len(__A ), expected_num_stages + 1 )
# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ), [self.model_tester.image_size // 4, self.model_tester.image_size // 4], )
UpperCAmelCase , UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Any = ['''basic''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
UpperCAmelCase : Tuple = layer_type
UpperCAmelCase : List[Any] = True
check_hidden_states_output(__A, __A, __A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase : Optional[Any] = True
check_hidden_states_output(__A, __A, __A )
def __magic_name__ ( self : Tuple ):
UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__A )
@slow
def __magic_name__ ( self : str ):
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase : int = TFResNetModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def a__ ( ) -> Tuple:
UpperCAmelCase : Any = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : int ):
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : Any = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
UpperCAmelCase : List[Any] = self.default_image_processor
UpperCAmelCase : Optional[Any] = prepare_img()
UpperCAmelCase : List[Any] = image_processor(images=__A, return_tensors='''tf''' )
# forward pass
UpperCAmelCase : List[Any] = model(**__A )
# verify the logits
UpperCAmelCase : List[Any] = tf.TensorShape((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape, __A )
UpperCAmelCase : List[str] = tf.constant([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy(), __A, atol=1E-4 ) )
| 336 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowerCamelCase : Tuple = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"feature_extraction_encodec": ["EncodecFeatureExtractor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[Any] = [
"ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST",
"EncodecModel",
"EncodecPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
_lowerCamelCase : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 336 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowerCamelCase : List[str] = {"configuration_vit_mae": ["VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMAEConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Tuple = [
"VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST",
"ViTMAEForPreTraining",
"ViTMAELayer",
"ViTMAEModel",
"ViTMAEPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : List[Any] = [
"TFViTMAEForPreTraining",
"TFViTMAEModel",
"TFViTMAEPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_mae import (
VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMAEForPreTraining,
ViTMAELayer,
ViTMAEModel,
ViTMAEPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel
else:
import sys
_lowerCamelCase : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 336 |
from __future__ import annotations
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int ) -> list[str]:
if partitions <= 0:
raise ValueError('''partitions must be a positive number!''' )
if partitions > number_of_bytes:
raise ValueError('''partitions can not > number_of_bytes!''' )
UpperCAmelCase : str = number_of_bytes // partitions
UpperCAmelCase : Dict = []
for i in range(UpperCAmelCase ):
UpperCAmelCase : int = i * bytes_per_partition + 1
UpperCAmelCase : Optional[int] = (
number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
)
allocation_list.append(f'''{start_bytes}-{end_bytes}''' )
return allocation_list
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 | 1 |
import math
def a__ ( UpperCAmelCase : int ) -> bool:
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(UpperCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def a__ ( UpperCAmelCase : int = 10_001 ) -> int:
try:
UpperCAmelCase : Any = int(UpperCAmelCase )
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.''' )
UpperCAmelCase : list[int] = []
UpperCAmelCase : int = 2
while len(UpperCAmelCase ) < nth:
if is_prime(UpperCAmelCase ):
primes.append(UpperCAmelCase )
num += 1
else:
num += 1
return primes[len(UpperCAmelCase ) - 1]
if __name__ == "__main__":
print(f"""{solution() = }""")
| 336 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
_lowerCamelCase : Union[str, Any] = "Run commands across TPU VMs for initial setup before running `accelerate launch`."
def a__ ( UpperCAmelCase : Dict=None ) -> Optional[int]:
if subparsers is not None:
UpperCAmelCase : Tuple = subparsers.add_parser('''tpu-config''' , description=_description )
else:
UpperCAmelCase : Dict = argparse.ArgumentParser('''Accelerate tpu-config command''' , description=_description )
# Core arguments
UpperCAmelCase : Optional[int] = parser.add_argument_group(
'''Config Arguments''' , '''Arguments that can be configured through `accelerate config`.''' )
config_args.add_argument(
'''--config_file''' , type=UpperCAmelCase , default=UpperCAmelCase , help='''Path to the config file to use for accelerate.''' , )
config_args.add_argument(
'''--tpu_name''' , default=UpperCAmelCase , help='''The name of the TPU to use. If not specified, will use the TPU specified in the config file.''' , )
config_args.add_argument(
'''--tpu_zone''' , default=UpperCAmelCase , help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.''' , )
UpperCAmelCase : Union[str, Any] = parser.add_argument_group('''TPU Arguments''' , '''Arguments for options ran inside the TPU.''' )
pod_args.add_argument(
'''--use_alpha''' , action='''store_true''' , help='''Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.''' , )
pod_args.add_argument(
'''--command_file''' , default=UpperCAmelCase , help='''The path to the file containing the commands to run on the pod on startup.''' , )
pod_args.add_argument(
'''--command''' , action='''append''' , nargs='''+''' , help='''A command to run on the pod. Can be passed multiple times.''' , )
pod_args.add_argument(
'''--install_accelerate''' , action='''store_true''' , help='''Whether to install accelerate on the pod. Defaults to False.''' , )
pod_args.add_argument(
'''--accelerate_version''' , default='''latest''' , help='''The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.''' , )
pod_args.add_argument(
'''--debug''' , action='''store_true''' , help='''If set, will print the command that would be run instead of running it.''' )
if subparsers is not None:
parser.set_defaults(func=UpperCAmelCase )
return parser
def a__ ( UpperCAmelCase : Optional[int] ) -> Union[str, Any]:
UpperCAmelCase : Union[str, Any] = None
# Get the default from the config file if it exists.
if args.config_file is not None or os.path.isfile(UpperCAmelCase ):
UpperCAmelCase : Union[str, Any] = load_config_from_file(args.config_file )
if not args.command_file and defaults.command_file is not None and not args.command:
UpperCAmelCase : List[Any] = defaults.command_file
if not args.command and defaults.commands is not None:
UpperCAmelCase : List[str] = defaults.commands
if not args.tpu_name:
UpperCAmelCase : Tuple = defaults.tpu_name
if not args.tpu_zone:
UpperCAmelCase : int = defaults.tpu_zone
if args.accelerate_version == "dev":
UpperCAmelCase : Tuple = '''git+https://github.com/huggingface/accelerate.git'''
elif args.accelerate_version == "latest":
UpperCAmelCase : Dict = '''accelerate -U'''
elif isinstance(parse(args.accelerate_version ) , UpperCAmelCase ):
UpperCAmelCase : Optional[int] = f'''accelerate=={args.accelerate_version}'''
if not args.command_file and not args.command:
raise ValueError('''You must specify either a command file or a command to run on the pod.''' )
if args.command_file:
with open(args.command_file , '''r''' ) as f:
UpperCAmelCase : int = [f.read().splitlines()]
# To turn list of lists into list of strings
if isinstance(args.command[0] , UpperCAmelCase ):
UpperCAmelCase : int = [line for cmd in args.command for line in cmd]
# Default to the shared folder and install accelerate
UpperCAmelCase : Optional[int] = ['''cd /usr/share''']
if args.install_accelerate:
new_cmd += [f'''pip install {args.accelerate_version}''']
new_cmd += args.command
UpperCAmelCase : int = '''; '''.join(UpperCAmelCase )
# Then send it to gcloud
# Eventually try to use google-api-core to do this instead of subprocess
UpperCAmelCase : Any = ['''gcloud''']
if args.use_alpha:
cmd += ["alpha"]
cmd += [
"compute",
"tpus",
"tpu-vm",
"ssh",
args.tpu_name,
"--zone",
args.tpu_zone,
"--command",
args.command,
"--worker",
"all",
]
if args.debug:
print(f'''Running {" ".join(UpperCAmelCase )}''' )
return
subprocess.run(UpperCAmelCase )
print('''Successfully setup pod.''' )
def a__ ( ) -> Any:
UpperCAmelCase : Any = tpu_command_parser()
UpperCAmelCase : Tuple = parser.parse_args()
tpu_command_launcher(UpperCAmelCase )
| 336 | 1 |
from __future__ import annotations
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int ) -> list[str]:
if partitions <= 0:
raise ValueError('''partitions must be a positive number!''' )
if partitions > number_of_bytes:
raise ValueError('''partitions can not > number_of_bytes!''' )
UpperCAmelCase : str = number_of_bytes // partitions
UpperCAmelCase : Dict = []
for i in range(UpperCAmelCase ):
UpperCAmelCase : int = i * bytes_per_partition + 1
UpperCAmelCase : Optional[int] = (
number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
)
allocation_list.append(f'''{start_bytes}-{end_bytes}''' )
return allocation_list
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 |
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase : Optional[int] = logging.get_logger(__name__)
def a__ ( UpperCAmelCase : Union[str, Any] ) -> Optional[Any]:
print('''Loading config file...''' )
def flatten_yaml_as_dict(UpperCAmelCase : Tuple , UpperCAmelCase : Any="" , UpperCAmelCase : Dict="." ):
UpperCAmelCase : List[str] = []
for k, v in d.items():
UpperCAmelCase : List[Any] = parent_key + sep + k if parent_key else k
if isinstance(UpperCAmelCase , collections.abc.MutableMapping ):
items.extend(flatten_yaml_as_dict(UpperCAmelCase , UpperCAmelCase , sep=UpperCAmelCase ).items() )
else:
items.append((new_key, v) )
return dict(UpperCAmelCase )
UpperCAmelCase : List[str] = argparse.Namespace()
with open(UpperCAmelCase , '''r''' ) as yaml_file:
try:
UpperCAmelCase : List[str] = yaml.load(UpperCAmelCase , Loader=yaml.FullLoader )
UpperCAmelCase : Optional[int] = flatten_yaml_as_dict(UpperCAmelCase )
for k, v in flat_cfg.items():
setattr(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
except yaml.YAMLError as exc:
logger.error('''Error while loading config file: {}. Error message: {}'''.format(UpperCAmelCase , str(UpperCAmelCase ) ) )
return config
def a__ ( UpperCAmelCase : List[str] , UpperCAmelCase : int ) -> List[Any]:
UpperCAmelCase : int = MobileViTVaConfig()
UpperCAmelCase : str = False
# dataset
if task_name.startswith('''imagenet1k_''' ):
UpperCAmelCase : Any = 1_000
if int(task_name.strip().split('''_''' )[-1] ) == 384:
UpperCAmelCase : Any = 384
else:
UpperCAmelCase : Tuple = 256
UpperCAmelCase : int = '''imagenet-1k-id2label.json'''
elif task_name.startswith('''imagenet21k_to_1k_''' ):
UpperCAmelCase : Optional[Any] = 21_000
if int(task_name.strip().split('''_''' )[-1] ) == 384:
UpperCAmelCase : str = 384
else:
UpperCAmelCase : Dict = 256
UpperCAmelCase : List[Any] = '''imagenet-22k-id2label.json'''
elif task_name.startswith('''ade20k_''' ):
UpperCAmelCase : Optional[Any] = 151
UpperCAmelCase : Tuple = 512
UpperCAmelCase : Tuple = '''ade20k-id2label.json'''
UpperCAmelCase : Tuple = True
elif task_name.startswith('''voc_''' ):
UpperCAmelCase : Dict = 21
UpperCAmelCase : str = 512
UpperCAmelCase : Union[str, Any] = '''pascal-voc-id2label.json'''
UpperCAmelCase : Dict = True
# orig_config
UpperCAmelCase : List[Any] = load_orig_config_file(UpperCAmelCase )
assert getattr(UpperCAmelCase , '''model.classification.name''' , -1 ) == "mobilevit_v2", "Invalid model"
UpperCAmelCase : Tuple = getattr(UpperCAmelCase , '''model.classification.mitv2.width_multiplier''' , 1.0 )
assert (
getattr(UpperCAmelCase , '''model.classification.mitv2.attn_norm_layer''' , -1 ) == "layer_norm_2d"
), "Norm layers other than layer_norm_2d is not supported"
UpperCAmelCase : int = getattr(UpperCAmelCase , '''model.classification.activation.name''' , '''swish''' )
# config.image_size == getattr(orig_config, 'sampler.bs.crop_size_width', 256)
if is_segmentation_model:
UpperCAmelCase : str = getattr(UpperCAmelCase , '''model.segmentation.output_stride''' , 16 )
if "_deeplabv3" in task_name:
UpperCAmelCase : int = getattr(UpperCAmelCase , '''model.segmentation.deeplabv3.aspp_rates''' , [12, 24, 36] )
UpperCAmelCase : Any = getattr(UpperCAmelCase , '''model.segmentation.deeplabv3.aspp_out_channels''' , 512 )
UpperCAmelCase : Optional[Any] = getattr(UpperCAmelCase , '''model.segmentation.deeplabv3.aspp_dropout''' , 0.1 )
# id2label
UpperCAmelCase : Union[str, Any] = '''huggingface/label-files'''
UpperCAmelCase : List[Any] = json.load(open(hf_hub_download(UpperCAmelCase , UpperCAmelCase , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase : Any = {int(UpperCAmelCase ): v for k, v in idalabel.items()}
UpperCAmelCase : int = idalabel
UpperCAmelCase : Optional[int] = {v: k for k, v in idalabel.items()}
return config
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Optional[int] ) -> List[str]:
UpperCAmelCase : Union[str, Any] = dct.pop(UpperCAmelCase )
UpperCAmelCase : List[str] = val
def a__ ( UpperCAmelCase : Union[str, Any] , UpperCAmelCase : int=False ) -> Union[str, Any]:
if base_model:
UpperCAmelCase : Dict = ''''''
else:
UpperCAmelCase : Dict = '''mobilevitv2.'''
UpperCAmelCase : Optional[int] = []
for k in state_dict.keys():
if k[:8] == "encoder.":
UpperCAmelCase : List[str] = k[8:]
else:
UpperCAmelCase : Dict = k
if ".block." in k:
UpperCAmelCase : List[Any] = k_new.replace('''.block.''' , '''.''' )
if ".conv." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''.conv.''' , '''.convolution.''' )
if ".norm." in k:
UpperCAmelCase : List[str] = k_new.replace('''.norm.''' , '''.normalization.''' )
if "conv_1." in k:
UpperCAmelCase : Union[str, Any] = k_new.replace('''conv_1.''' , f'''{model_prefix}conv_stem.''' )
for i in [1, 2]:
if f'''layer_{i}.''' in k:
UpperCAmelCase : Union[str, Any] = k_new.replace(f'''layer_{i}.''' , f'''{model_prefix}encoder.layer.{i-1}.layer.''' )
if ".exp_1x1." in k:
UpperCAmelCase : Optional[Any] = k_new.replace('''.exp_1x1.''' , '''.expand_1x1.''' )
if ".red_1x1." in k:
UpperCAmelCase : int = k_new.replace('''.red_1x1.''' , '''.reduce_1x1.''' )
for i in [3, 4, 5]:
if f'''layer_{i}.0.''' in k:
UpperCAmelCase : Any = k_new.replace(f'''layer_{i}.0.''' , f'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.''' )
if f'''layer_{i}.1.local_rep.0.''' in k:
UpperCAmelCase : str = k_new.replace(f'''layer_{i}.1.local_rep.0.''' , f'''{model_prefix}encoder.layer.{i-1}.conv_kxk.''' )
if f'''layer_{i}.1.local_rep.1.''' in k:
UpperCAmelCase : int = k_new.replace(f'''layer_{i}.1.local_rep.1.''' , f'''{model_prefix}encoder.layer.{i-1}.conv_1x1.''' )
for i in [3, 4, 5]:
if i == 3:
UpperCAmelCase : Dict = [0, 1]
elif i == 4:
UpperCAmelCase : Dict = [0, 1, 2, 3]
elif i == 5:
UpperCAmelCase : int = [0, 1, 2]
for j in j_in:
if f'''layer_{i}.1.global_rep.{j}.''' in k:
UpperCAmelCase : Optional[Any] = k_new.replace(
f'''layer_{i}.1.global_rep.{j}.''' , f'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.''' )
if f'''layer_{i}.1.global_rep.{j+1}.''' in k:
UpperCAmelCase : Any = k_new.replace(
f'''layer_{i}.1.global_rep.{j+1}.''' , f'''{model_prefix}encoder.layer.{i-1}.layernorm.''' )
if f'''layer_{i}.1.conv_proj.''' in k:
UpperCAmelCase : Union[str, Any] = k_new.replace(f'''layer_{i}.1.conv_proj.''' , f'''{model_prefix}encoder.layer.{i-1}.conv_projection.''' )
if "pre_norm_attn.0." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''pre_norm_attn.0.''' , '''layernorm_before.''' )
if "pre_norm_attn.1." in k:
UpperCAmelCase : Optional[Any] = k_new.replace('''pre_norm_attn.1.''' , '''attention.''' )
if "pre_norm_ffn.0." in k:
UpperCAmelCase : List[Any] = k_new.replace('''pre_norm_ffn.0.''' , '''layernorm_after.''' )
if "pre_norm_ffn.1." in k:
UpperCAmelCase : List[Any] = k_new.replace('''pre_norm_ffn.1.''' , '''ffn.conv1.''' )
if "pre_norm_ffn.3." in k:
UpperCAmelCase : Any = k_new.replace('''pre_norm_ffn.3.''' , '''ffn.conv2.''' )
if "classifier.1." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''classifier.1.''' , '''classifier.''' )
if "seg_head." in k:
UpperCAmelCase : Union[str, Any] = k_new.replace('''seg_head.''' , '''segmentation_head.''' )
if ".aspp_layer." in k:
UpperCAmelCase : Tuple = k_new.replace('''.aspp_layer.''' , '''.''' )
if ".aspp_pool." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''.aspp_pool.''' , '''.''' )
rename_keys.append((k, k_new) )
return rename_keys
def a__ ( UpperCAmelCase : Union[str, Any] ) -> Any:
UpperCAmelCase : str = []
for k in state_dict.keys():
if k.startswith('''seg_head.aux_head.''' ):
keys_to_ignore.append(UpperCAmelCase )
for k in keys_to_ignore:
state_dict.pop(UpperCAmelCase , UpperCAmelCase )
def a__ ( ) -> Union[str, Any]:
UpperCAmelCase : int = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
UpperCAmelCase : List[str] = Image.open(requests.get(UpperCAmelCase , stream=UpperCAmelCase ).raw )
return im
@torch.no_grad()
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[Any] ) -> Union[str, Any]:
UpperCAmelCase : Union[str, Any] = get_mobilevitva_config(UpperCAmelCase , UpperCAmelCase )
# load original state_dict
UpperCAmelCase : List[str] = torch.load(UpperCAmelCase , map_location='''cpu''' )
# load huggingface model
if task_name.startswith('''ade20k_''' ) or task_name.startswith('''voc_''' ):
UpperCAmelCase : str = MobileViTVaForSemanticSegmentation(UpperCAmelCase ).eval()
UpperCAmelCase : str = False
else:
UpperCAmelCase : Union[str, Any] = MobileViTVaForImageClassification(UpperCAmelCase ).eval()
UpperCAmelCase : Any = False
# remove and rename some keys of load the original model
UpperCAmelCase : Optional[Any] = checkpoint
remove_unused_keys(UpperCAmelCase )
UpperCAmelCase : Optional[Any] = create_rename_keys(UpperCAmelCase , base_model=UpperCAmelCase )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# load modified state_dict
model.load_state_dict(UpperCAmelCase )
# Check outputs on an image, prepared by MobileViTImageProcessor
UpperCAmelCase : Dict = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
UpperCAmelCase : Any = image_processor(images=prepare_img() , return_tensors='''pt''' )
UpperCAmelCase : Union[str, Any] = model(**UpperCAmelCase )
# verify classification model
if task_name.startswith('''imagenet''' ):
UpperCAmelCase : Optional[Any] = outputs.logits
UpperCAmelCase : int = logits.argmax(-1 ).item()
print('''Predicted class:''' , model.config.idalabel[predicted_class_idx] )
if task_name.startswith('''imagenet1k_256''' ) and config.width_multiplier == 1.0:
# expected_logits for base variant
UpperCAmelCase : str = torch.tensor([-1.6_336E00, -7.3_204E-02, -5.1_883E-01] )
assert torch.allclose(logits[0, :3] , UpperCAmelCase , atol=1E-4 )
Path(UpperCAmelCase ).mkdir(exist_ok=UpperCAmelCase )
print(f'''Saving model {task_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(UpperCAmelCase )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(UpperCAmelCase )
if __name__ == "__main__":
_lowerCamelCase : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--task",
default="imagenet1k_256",
type=str,
help=(
"Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
"\n Classification (ImageNet-1k)\n - MobileViTV2 (256x256) : imagenet1k_256\n - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n imagenet21k_to_1k_256\n - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n ImageNet-1k 384x384) : imagenet21k_to_1k_384\n Segmentation\n - ADE20K Dataset : ade20k_deeplabv3\n - Pascal VOC 2012 Dataset: voc_deeplabv3\n "
),
choices=[
"imagenet1k_256",
"imagenet1k_384",
"imagenet21k_to_1k_256",
"imagenet21k_to_1k_384",
"ade20k_deeplabv3",
"voc_deeplabv3",
],
)
parser.add_argument(
"--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
)
parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
)
_lowerCamelCase : Optional[int] = parser.parse_args()
convert_mobilevitva_checkpoint(
args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
)
| 336 | 1 |
def a__ ( UpperCAmelCase : str , UpperCAmelCase : int ) -> list[str]:
return [sentence[i : i + ngram_size] for i in range(len(UpperCAmelCase ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 336 |
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
if is_flax_available():
import jax.numpy as jnp
class __UpperCAmelCase ( lowerCamelCase__ ):
def __get__( self : Tuple, __A : Optional[Any], __A : Optional[int]=None ):
# See docs.python.org/3/howto/descriptor.html#properties
if obj is None:
return self
if self.fget is None:
raise AttributeError('''unreadable attribute''' )
UpperCAmelCase : str = '''__cached_''' + self.fget.__name__
UpperCAmelCase : int = getattr(__A, __A, __A )
if cached is None:
UpperCAmelCase : Any = self.fget(__A )
setattr(__A, __A, __A )
return cached
def a__ ( UpperCAmelCase : Optional[Any] ) -> Any:
UpperCAmelCase : Any = val.lower()
if val in {"y", "yes", "t", "true", "on", "1"}:
return 1
if val in {"n", "no", "f", "false", "off", "0"}:
return 0
raise ValueError(f'''invalid truth value {val!r}''' )
def a__ ( UpperCAmelCase : Dict ) -> List[str]:
if is_torch_fx_proxy(UpperCAmelCase ):
return True
if is_torch_available():
import torch
if isinstance(UpperCAmelCase , torch.Tensor ):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(UpperCAmelCase , tf.Tensor ):
return True
if is_flax_available():
import jax.numpy as jnp
from jax.core import Tracer
if isinstance(UpperCAmelCase , (jnp.ndarray, Tracer) ):
return True
return isinstance(UpperCAmelCase , np.ndarray )
def a__ ( UpperCAmelCase : List[Any] ) -> Union[str, Any]:
return isinstance(UpperCAmelCase , np.ndarray )
def a__ ( UpperCAmelCase : str ) -> Tuple:
return _is_numpy(UpperCAmelCase )
def a__ ( UpperCAmelCase : str ) -> List[Any]:
import torch
return isinstance(UpperCAmelCase , torch.Tensor )
def a__ ( UpperCAmelCase : str ) -> List[Any]:
return False if not is_torch_available() else _is_torch(UpperCAmelCase )
def a__ ( UpperCAmelCase : Tuple ) -> List[str]:
import torch
return isinstance(UpperCAmelCase , torch.device )
def a__ ( UpperCAmelCase : Any ) -> Any:
return False if not is_torch_available() else _is_torch_device(UpperCAmelCase )
def a__ ( UpperCAmelCase : Dict ) -> List[str]:
import torch
if isinstance(UpperCAmelCase , UpperCAmelCase ):
if hasattr(UpperCAmelCase , UpperCAmelCase ):
UpperCAmelCase : Union[str, Any] = getattr(UpperCAmelCase , UpperCAmelCase )
else:
return False
return isinstance(UpperCAmelCase , torch.dtype )
def a__ ( UpperCAmelCase : Optional[Any] ) -> Union[str, Any]:
return False if not is_torch_available() else _is_torch_dtype(UpperCAmelCase )
def a__ ( UpperCAmelCase : Any ) -> str:
import tensorflow as tf
return isinstance(UpperCAmelCase , tf.Tensor )
def a__ ( UpperCAmelCase : int ) -> Union[str, Any]:
return False if not is_tf_available() else _is_tensorflow(UpperCAmelCase )
def a__ ( UpperCAmelCase : List[str] ) -> Tuple:
import tensorflow as tf
# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
if hasattr(UpperCAmelCase , '''is_symbolic_tensor''' ):
return tf.is_symbolic_tensor(UpperCAmelCase )
return type(UpperCAmelCase ) == tf.Tensor
def a__ ( UpperCAmelCase : int ) -> List[Any]:
return False if not is_tf_available() else _is_tf_symbolic_tensor(UpperCAmelCase )
def a__ ( UpperCAmelCase : List[Any] ) -> Dict:
import jax.numpy as jnp # noqa: F811
return isinstance(UpperCAmelCase , jnp.ndarray )
def a__ ( UpperCAmelCase : List[Any] ) -> Optional[int]:
return False if not is_flax_available() else _is_jax(UpperCAmelCase )
def a__ ( UpperCAmelCase : int ) -> Tuple:
if isinstance(UpperCAmelCase , (dict, UserDict) ):
return {k: to_py_obj(UpperCAmelCase ) for k, v in obj.items()}
elif isinstance(UpperCAmelCase , (list, tuple) ):
return [to_py_obj(UpperCAmelCase ) for o in obj]
elif is_tf_tensor(UpperCAmelCase ):
return obj.numpy().tolist()
elif is_torch_tensor(UpperCAmelCase ):
return obj.detach().cpu().tolist()
elif is_jax_tensor(UpperCAmelCase ):
return np.asarray(UpperCAmelCase ).tolist()
elif isinstance(UpperCAmelCase , (np.ndarray, np.number) ): # tolist also works on 0d np arrays
return obj.tolist()
else:
return obj
def a__ ( UpperCAmelCase : Any ) -> List[str]:
if isinstance(UpperCAmelCase , (dict, UserDict) ):
return {k: to_numpy(UpperCAmelCase ) for k, v in obj.items()}
elif isinstance(UpperCAmelCase , (list, tuple) ):
return np.array(UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
return obj.numpy()
elif is_torch_tensor(UpperCAmelCase ):
return obj.detach().cpu().numpy()
elif is_jax_tensor(UpperCAmelCase ):
return np.asarray(UpperCAmelCase )
else:
return obj
class __UpperCAmelCase ( lowerCamelCase__ ):
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : Optional[Any] = fields(self )
# Safety and consistency checks
if not len(__A ):
raise ValueError(F'''{self.__class__.__name__} has no fields.''' )
if not all(field.default is None for field in class_fields[1:] ):
raise ValueError(F'''{self.__class__.__name__} should not have more than one required field.''' )
UpperCAmelCase : int = getattr(self, class_fields[0].name )
UpperCAmelCase : str = all(getattr(self, field.name ) is None for field in class_fields[1:] )
if other_fields_are_none and not is_tensor(__A ):
if isinstance(__A, __A ):
UpperCAmelCase : Tuple = first_field.items()
UpperCAmelCase : Any = True
else:
try:
UpperCAmelCase : Optional[Any] = iter(__A )
UpperCAmelCase : Optional[Any] = True
except TypeError:
UpperCAmelCase : Optional[int] = False
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for idx, element in enumerate(__A ):
if (
not isinstance(__A, (list, tuple) )
or not len(__A ) == 2
or not isinstance(element[0], __A )
):
if idx == 0:
# If we do not have an iterator of key/values, set it as attribute
UpperCAmelCase : Any = first_field
else:
# If we have a mixed iterator, raise an error
raise ValueError(
F'''Cannot set key/value for {element}. It needs to be a tuple (key, value).''' )
break
setattr(self, element[0], element[1] )
if element[1] is not None:
UpperCAmelCase : Union[str, Any] = element[1]
elif first_field is not None:
UpperCAmelCase : Union[str, Any] = first_field
else:
for field in class_fields:
UpperCAmelCase : Optional[Any] = getattr(self, field.name )
if v is not None:
UpperCAmelCase : Optional[int] = v
def __delitem__( self : Union[str, Any], *__A : str, **__A : Tuple ):
raise Exception(F'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.''' )
def __magic_name__ ( self : List[str], *__A : Union[str, Any], **__A : Optional[Any] ):
raise Exception(F'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.''' )
def __magic_name__ ( self : Any, *__A : Dict, **__A : str ):
raise Exception(F'''You cannot use ``pop`` on a {self.__class__.__name__} instance.''' )
def __magic_name__ ( self : Dict, *__A : int, **__A : Dict ):
raise Exception(F'''You cannot use ``update`` on a {self.__class__.__name__} instance.''' )
def __getitem__( self : List[str], __A : List[str] ):
if isinstance(__A, __A ):
UpperCAmelCase : int = dict(self.items() )
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__( self : Optional[Any], __A : Dict, __A : Union[str, Any] ):
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(__A, __A )
super().__setattr__(__A, __A )
def __setitem__( self : Dict, __A : List[Any], __A : Union[str, Any] ):
# Will raise a KeyException if needed
super().__setitem__(__A, __A )
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(__A, __A )
def __magic_name__ ( self : List[str] ):
return tuple(self[k] for k in self.keys() )
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@classmethod
def __magic_name__ ( cls : List[Any], __A : Tuple ):
raise ValueError(
F'''{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys() )}''' )
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """longest"""
UpperCamelCase = """max_length"""
UpperCamelCase = """do_not_pad"""
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """pt"""
UpperCamelCase = """tf"""
UpperCamelCase = """np"""
UpperCamelCase = """jax"""
class __UpperCAmelCase :
def __init__( self : Any, __A : List[ContextManager] ):
UpperCAmelCase : Tuple = context_managers
UpperCAmelCase : Tuple = ExitStack()
def __enter__( self : Any ):
for context_manager in self.context_managers:
self.stack.enter_context(__A )
def __exit__( self : List[Any], *__A : Union[str, Any], **__A : Dict ):
self.stack.__exit__(*__A, **__A )
def a__ ( UpperCAmelCase : Union[str, Any] ) -> str:
UpperCAmelCase : int = infer_framework(UpperCAmelCase )
if framework == "tf":
UpperCAmelCase : List[str] = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
UpperCAmelCase : List[Any] = inspect.signature(model_class.forward ) # PyTorch models
else:
UpperCAmelCase : Tuple = inspect.signature(model_class.__call__ ) # Flax models
for p in signature.parameters:
if p == "return_loss" and signature.parameters[p].default is True:
return True
return False
def a__ ( UpperCAmelCase : Dict ) -> Any:
UpperCAmelCase : List[Any] = model_class.__name__
UpperCAmelCase : Union[str, Any] = infer_framework(UpperCAmelCase )
if framework == "tf":
UpperCAmelCase : Tuple = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
UpperCAmelCase : Dict = inspect.signature(model_class.forward ) # PyTorch models
else:
UpperCAmelCase : Dict = inspect.signature(model_class.__call__ ) # Flax models
if "QuestionAnswering" in model_name:
return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
else:
return [p for p in signature.parameters if "label" in p]
def a__ ( UpperCAmelCase : MutableMapping , UpperCAmelCase : str = "" , UpperCAmelCase : str = "." ) -> Union[str, Any]:
def _flatten_dict(UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[str]="" , UpperCAmelCase : Any="." ):
for k, v in d.items():
UpperCAmelCase : List[str] = str(UpperCAmelCase ) + delimiter + str(UpperCAmelCase ) if parent_key else k
if v and isinstance(UpperCAmelCase , UpperCAmelCase ):
yield from flatten_dict(UpperCAmelCase , UpperCAmelCase , delimiter=UpperCAmelCase ).items()
else:
yield key, v
return dict(_flatten_dict(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) )
@contextmanager
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : bool = False ) -> Optional[Any]:
if use_temp_dir:
with tempfile.TemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield working_dir
def a__ ( UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[str]=None ) -> Optional[Any]:
if is_numpy_array(UpperCAmelCase ):
return np.transpose(UpperCAmelCase , axes=UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.T if axes is None else array.permute(*UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.transpose(UpperCAmelCase , perm=UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.transpose(UpperCAmelCase , axes=UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for transpose: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : str , UpperCAmelCase : Optional[int] ) -> List[str]:
if is_numpy_array(UpperCAmelCase ):
return np.reshape(UpperCAmelCase , UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.reshape(*UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.reshape(UpperCAmelCase , UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.reshape(UpperCAmelCase , UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for reshape: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : Tuple , UpperCAmelCase : Optional[int]=None ) -> Any:
if is_numpy_array(UpperCAmelCase ):
return np.squeeze(UpperCAmelCase , axis=UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.squeeze() if axis is None else array.squeeze(dim=UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.squeeze(UpperCAmelCase , axis=UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.squeeze(UpperCAmelCase , axis=UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for squeeze: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : str , UpperCAmelCase : int ) -> str:
if is_numpy_array(UpperCAmelCase ):
return np.expand_dims(UpperCAmelCase , UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.unsqueeze(dim=UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.expand_dims(UpperCAmelCase , axis=UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.expand_dims(UpperCAmelCase , axis=UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for expand_dims: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : Dict ) -> List[str]:
if is_numpy_array(UpperCAmelCase ):
return np.size(UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.numel()
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.size(UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return array.size
else:
raise ValueError(f'''Type not supported for expand_dims: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : List[str] , UpperCAmelCase : List[str] ) -> Dict:
for key, value in auto_map.items():
if isinstance(UpperCAmelCase , (tuple, list) ):
UpperCAmelCase : List[Any] = [f'''{repo_id}--{v}''' if (v is not None and '''--''' not in v) else v for v in value]
elif value is not None and "--" not in value:
UpperCAmelCase : List[Any] = f'''{repo_id}--{value}'''
return auto_map
def a__ ( UpperCAmelCase : Tuple ) -> Union[str, Any]:
for base_class in inspect.getmro(UpperCAmelCase ):
UpperCAmelCase : Any = base_class.__module__
UpperCAmelCase : Dict = base_class.__name__
if module.startswith('''tensorflow''' ) or module.startswith('''keras''' ) or name == "TFPreTrainedModel":
return "tf"
elif module.startswith('''torch''' ) or name == "PreTrainedModel":
return "pt"
elif module.startswith('''flax''' ) or module.startswith('''jax''' ) or name == "FlaxPreTrainedModel":
return "flax"
else:
raise TypeError(f'''Could not infer framework from class {model_class}.''' )
| 336 | 1 |
import warnings
from .generation import TFGenerationMixin
class __UpperCAmelCase ( lowerCamelCase__ ):
# warning at import time
warnings.warn(
"""Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will """
"""be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead.""" , lowerCamelCase__ , )
| 336 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __UpperCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = LayoutLMTokenizer
UpperCamelCase = LayoutLMTokenizerFast
UpperCamelCase = True
UpperCamelCase = True
def __magic_name__ ( self : Any ):
super().setUp()
UpperCAmelCase : Dict = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
UpperCAmelCase : 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] ) )
def __magic_name__ ( self : Union[str, Any], **__A : List[str] ):
return LayoutLMTokenizer.from_pretrained(self.tmpdirname, **__A )
def __magic_name__ ( self : Optional[int], __A : int ):
UpperCAmelCase : Optional[Any] = '''UNwant\u00E9d,running'''
UpperCAmelCase : Optional[int] = '''unwanted, running'''
return input_text, output_text
def __magic_name__ ( self : Any ):
UpperCAmelCase : Union[str, Any] = self.tokenizer_class(self.vocab_file )
UpperCAmelCase : Optional[Any] = tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(__A, ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ), [7, 4, 5, 1_0, 8, 9] )
def __magic_name__ ( self : Optional[int] ):
pass
| 336 | 1 |
_lowerCamelCase : Any = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
_lowerCamelCase : int = [{"type": "code", "content": INSTALL_CONTENT}]
_lowerCamelCase : Optional[Any] = {
"{processor_class}": "FakeProcessorClass",
"{model_class}": "FakeModelClass",
"{object_class}": "FakeObjectClass",
}
| 336 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTMAEForPreTraining, TFViTMAEModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __UpperCAmelCase :
def __init__( self : Any, __A : str, __A : Dict=1_3, __A : int=3_0, __A : Tuple=2, __A : Union[str, Any]=3, __A : Any=True, __A : str=True, __A : Dict=3_2, __A : List[Any]=2, __A : Optional[Any]=4, __A : Union[str, Any]=3_7, __A : int="gelu", __A : int=0.1, __A : List[Any]=0.1, __A : Tuple=1_0, __A : Tuple=0.0_2, __A : Any=3, __A : List[str]=0.6, __A : Any=None, ):
UpperCAmelCase : Union[str, Any] = parent
UpperCAmelCase : Dict = batch_size
UpperCAmelCase : List[str] = image_size
UpperCAmelCase : Dict = patch_size
UpperCAmelCase : int = num_channels
UpperCAmelCase : Union[str, Any] = is_training
UpperCAmelCase : Union[str, Any] = use_labels
UpperCAmelCase : Union[str, Any] = hidden_size
UpperCAmelCase : Optional[int] = num_hidden_layers
UpperCAmelCase : Union[str, Any] = num_attention_heads
UpperCAmelCase : List[str] = intermediate_size
UpperCAmelCase : Optional[int] = hidden_act
UpperCAmelCase : Tuple = hidden_dropout_prob
UpperCAmelCase : List[Any] = attention_probs_dropout_prob
UpperCAmelCase : Any = type_sequence_label_size
UpperCAmelCase : Tuple = initializer_range
UpperCAmelCase : Tuple = mask_ratio
UpperCAmelCase : Any = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
UpperCAmelCase : Tuple = (image_size // patch_size) ** 2
UpperCAmelCase : List[Any] = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : Any = None
if self.use_labels:
UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size], self.type_sequence_label_size )
UpperCAmelCase : str = self.get_config()
return config, pixel_values, labels
def __magic_name__ ( self : Optional[Any] ):
return ViTMAEConfig(
image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, decoder_hidden_size=self.hidden_size, decoder_num_hidden_layers=self.num_hidden_layers, decoder_num_attention_heads=self.num_attention_heads, decoder_intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, is_decoder=__A, initializer_range=self.initializer_range, mask_ratio=self.mask_ratio, )
def __magic_name__ ( self : str, __A : List[Any], __A : Any, __A : Any ):
UpperCAmelCase : Optional[Any] = TFViTMAEModel(config=__A )
UpperCAmelCase : Tuple = model(__A, training=__A )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self : Tuple, __A : str, __A : int, __A : str ):
UpperCAmelCase : Dict = TFViTMAEForPreTraining(__A )
UpperCAmelCase : int = model(__A, training=__A )
# expected sequence length = num_patches
UpperCAmelCase : int = (self.image_size // self.patch_size) ** 2
UpperCAmelCase : Optional[Any] = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
UpperCAmelCase : Tuple = 1
UpperCAmelCase : List[Any] = TFViTMAEForPreTraining(__A )
UpperCAmelCase : Union[str, Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCAmelCase : List[Any] = model(__A, training=__A )
UpperCAmelCase : Union[str, Any] = self.patch_size**2
self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels) )
def __magic_name__ ( self : List[Any] ):
UpperCAmelCase : Dict = self.prepare_config_and_inputs()
((UpperCAmelCase) , (UpperCAmelCase) , (UpperCAmelCase)) : Union[str, Any] = config_and_inputs
UpperCAmelCase : Optional[Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
UpperCamelCase = {"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : List[Any] = TFViTMAEModelTester(self )
UpperCAmelCase : int = ConfigTester(self, config_class=__A, has_text_modality=__A, hidden_size=3_7 )
def __magic_name__ ( self : List[str] ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMAE does not use inputs_embeds''' )
def __magic_name__ ( self : List[Any] ):
pass
def __magic_name__ ( self : List[str] ):
UpperCAmelCase , UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : List[str] = model_class(__A )
self.assertIsInstance(model.get_input_embeddings(), (tf.keras.layers.Layer) )
UpperCAmelCase : Union[str, Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__A, tf.keras.layers.Layer ) )
def __magic_name__ ( self : str ):
UpperCAmelCase , UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Any = model_class(__A )
UpperCAmelCase : Any = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase : int = [*signature.parameters.keys()]
UpperCAmelCase : Tuple = ['''pixel_values''']
self.assertListEqual(arg_names[:1], __A )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __magic_name__ ( self : str ):
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__A )
def __magic_name__ ( self : int ):
# make the mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Tuple = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : List[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCAmelCase : str = model_class(__A )
UpperCAmelCase : int = self._prepare_for_class(__A, __A )
UpperCAmelCase : Dict = model(__A, noise=__A )
UpperCAmelCase : Any = copy.deepcopy(self._prepare_for_class(__A, __A ) )
UpperCAmelCase : Union[str, Any] = model(**__A, noise=__A )
UpperCAmelCase : Dict = outputs_dict[0].numpy()
UpperCAmelCase : Tuple = outputs_keywords[0].numpy()
self.assertLess(np.sum(np.abs(output_dict - output_keywords ) ), 1E-6 )
def __magic_name__ ( self : Optional[Any] ):
# make the mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : str = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Union[str, Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
def prepare_numpy_arrays(__A : Union[str, Any] ):
UpperCAmelCase : str = {}
for k, v in inputs_dict.items():
if tf.is_tensor(__A ):
UpperCAmelCase : Tuple = v.numpy()
else:
UpperCAmelCase : str = np.array(__A )
return inputs_np_dict
for model_class in self.all_model_classes:
UpperCAmelCase : Dict = model_class(__A )
UpperCAmelCase : Any = self._prepare_for_class(__A, __A )
UpperCAmelCase : Optional[int] = prepare_numpy_arrays(__A )
UpperCAmelCase : str = model(__A, noise=__A )
UpperCAmelCase : str = model(**__A, noise=__A )
self.assert_outputs_same(__A, __A )
def __magic_name__ ( self : int, __A : str, __A : Union[str, Any], __A : Optional[Any] ):
# make masks reproducible
np.random.seed(2 )
UpperCAmelCase : Any = int((tf_model.config.image_size // tf_model.config.patch_size) ** 2 )
UpperCAmelCase : Tuple = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCAmelCase : int = tf.constant(__A )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
UpperCAmelCase : List[Any] = tf_noise
super().check_pt_tf_models(__A, __A, __A )
def __magic_name__ ( self : str ):
# make mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Union[str, Any] = {
module_member
for model_class in self.all_model_classes
for module in (import_module(model_class.__module__ ),)
for module_member_name in dir(__A )
if module_member_name.endswith('''MainLayer''' )
# This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
and module_member_name[: -len('''MainLayer''' )] == model_class.__name__[: -len('''Model''' )]
for module_member in (getattr(__A, __A ),)
if isinstance(__A, __A )
and tf.keras.layers.Layer in module_member.__bases__
and getattr(__A, '''_keras_serializable''', __A )
}
UpperCAmelCase : Union[str, Any] = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Optional[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCAmelCase : str = tf.convert_to_tensor(__A )
inputs_dict.update({'''noise''': noise} )
for main_layer_class in tf_main_layer_classes:
UpperCAmelCase : Tuple = main_layer_class(__A )
UpperCAmelCase : int = {
name: tf.keras.Input(tensor.shape[1:], dtype=tensor.dtype ) for name, tensor in inputs_dict.items()
}
UpperCAmelCase : List[Any] = tf.keras.Model(__A, outputs=main_layer(__A ) )
UpperCAmelCase : List[Any] = model(__A )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase : Any = os.path.join(__A, '''keras_model.h5''' )
model.save(__A )
UpperCAmelCase : List[str] = tf.keras.models.load_model(
__A, custom_objects={main_layer_class.__name__: main_layer_class} )
assert isinstance(__A, tf.keras.Model )
UpperCAmelCase : Tuple = model(__A )
self.assert_outputs_same(__A, __A )
@slow
def __magic_name__ ( self : Dict ):
# make mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Optional[Any] = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Optional[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCAmelCase : int = model_class(__A )
UpperCAmelCase : List[str] = self._prepare_for_class(__A, __A )
UpperCAmelCase : Union[str, Any] = model(__A, noise=__A )
if model_class.__name__ == "TFViTMAEModel":
UpperCAmelCase : Optional[int] = outputs.last_hidden_state.numpy()
UpperCAmelCase : Union[str, Any] = 0
else:
UpperCAmelCase : Optional[int] = outputs.logits.numpy()
UpperCAmelCase : int = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__A, saved_model=__A )
UpperCAmelCase : Dict = model_class.from_pretrained(__A )
UpperCAmelCase : str = model(__A, noise=__A )
if model_class.__name__ == "TFViTMAEModel":
UpperCAmelCase : int = after_outputs['''last_hidden_state'''].numpy()
UpperCAmelCase : Dict = 0
else:
UpperCAmelCase : Any = after_outputs['''logits'''].numpy()
UpperCAmelCase : Dict = 0
UpperCAmelCase : Union[str, Any] = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(__A, 1E-5 )
def __magic_name__ ( self : Optional[Any] ):
# make mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : List[Any] = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Tuple = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCAmelCase : Dict = model_class(__A )
UpperCAmelCase : int = self._prepare_for_class(__A, __A )
UpperCAmelCase : List[Any] = model(__A, noise=__A )
UpperCAmelCase : str = model.get_config()
# make sure that returned config is jsonifiable, which is required by keras
json.dumps(__A )
UpperCAmelCase : int = model_class.from_config(model.get_config() )
# make sure it also accepts a normal config
UpperCAmelCase : str = model_class.from_config(model.config )
UpperCAmelCase : List[str] = new_model(__A ) # Build model
new_model.set_weights(model.get_weights() )
UpperCAmelCase : Tuple = new_model(__A, noise=__A )
self.assert_outputs_same(__A, __A )
@unittest.skip(
reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.''' )
def __magic_name__ ( self : Optional[int] ):
pass
@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load''' )
def __magic_name__ ( self : Tuple ):
pass
@slow
def __magic_name__ ( self : str ):
UpperCAmelCase : Tuple = TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224''' )
self.assertIsNotNone(__A )
def a__ ( ) -> Dict:
UpperCAmelCase : int = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : List[str] ):
return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base''' ) if is_vision_available() else None
@slow
def __magic_name__ ( self : str ):
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
UpperCAmelCase : Tuple = TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base''' )
UpperCAmelCase : List[str] = self.default_image_processor
UpperCAmelCase : Any = prepare_img()
UpperCAmelCase : str = image_processor(images=__A, return_tensors='''tf''' )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
UpperCAmelCase : Optional[int] = ViTMAEConfig()
UpperCAmelCase : int = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
UpperCAmelCase : Tuple = np.random.uniform(size=(1, num_patches) )
# forward pass
UpperCAmelCase : Optional[int] = model(**__A, noise=__A )
# verify the logits
UpperCAmelCase : Union[str, Any] = tf.convert_to_tensor([1, 1_9_6, 7_6_8] )
self.assertEqual(outputs.logits.shape, __A )
UpperCAmelCase : List[str] = tf.convert_to_tensor(
[[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]] )
tf.debugging.assert_near(outputs.logits[0, :3, :3], __A, atol=1E-4 )
| 336 | 1 |
import warnings
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 ( lowerCamelCase__ ):
UpperCamelCase = ["""image_processor""", """tokenizer"""]
UpperCamelCase = """LayoutLMv3ImageProcessor"""
UpperCamelCase = ("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")
def __init__( self : Optional[Any], __A : Any=None, __A : Dict=None, **__A : Dict ):
UpperCAmelCase : Tuple = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''', __A, )
UpperCAmelCase : Optional[Any] = kwargs.pop('''feature_extractor''' )
UpperCAmelCase : Optional[Any] = 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__(__A, __A )
def __call__( self : Optional[Any], __A : str, __A : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, __A : Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None, __A : Union[List[List[int]], List[List[List[int]]]] = None, __A : Optional[Union[List[int], List[List[int]]]] = None, __A : bool = True, __A : Union[bool, str, PaddingStrategy] = False, __A : Union[bool, str, TruncationStrategy] = None, __A : Optional[int] = None, __A : int = 0, __A : Optional[int] = None, __A : Optional[bool] = None, __A : Optional[bool] = None, __A : bool = False, __A : bool = False, __A : bool = False, __A : bool = False, __A : bool = True, __A : Optional[Union[str, TensorType]] = None, **__A : Tuple, ):
# verify input
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
'''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.''' )
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
'''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.''' )
# first, apply the image processor
UpperCAmelCase : int = self.image_processor(images=__A, return_tensors=__A )
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(__A, __A ):
UpperCAmelCase : Any = [text] # add batch dimension (as the image processor always adds a batch dimension)
UpperCAmelCase : List[str] = features['''words''']
UpperCAmelCase : Union[str, Any] = self.tokenizer(
text=text if text is not None else features['''words'''], text_pair=text_pair if text_pair is not None else None, boxes=boxes if boxes is not None else features['''boxes'''], word_labels=__A, add_special_tokens=__A, padding=__A, truncation=__A, max_length=__A, stride=__A, pad_to_multiple_of=__A, return_token_type_ids=__A, return_attention_mask=__A, return_overflowing_tokens=__A, return_special_tokens_mask=__A, return_offsets_mapping=__A, return_length=__A, verbose=__A, return_tensors=__A, **__A, )
# add pixel values
UpperCAmelCase : List[Any] = features.pop('''pixel_values''' )
if return_overflowing_tokens is True:
UpperCAmelCase : Tuple = self.get_overflowing_images(__A, encoded_inputs['''overflow_to_sample_mapping'''] )
UpperCAmelCase : int = images
return encoded_inputs
def __magic_name__ ( self : str, __A : List[Any], __A : Any ):
# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
UpperCAmelCase : Tuple = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx] )
if len(__A ) != len(__A ):
raise ValueError(
'''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
F''' {len(__A )} and {len(__A )}''' )
return images_with_overflow
def __magic_name__ ( self : Any, *__A : List[Any], **__A : Tuple ):
return self.tokenizer.batch_decode(*__A, **__A )
def __magic_name__ ( self : Any, *__A : Optional[int], **__A : int ):
return self.tokenizer.decode(*__A, **__A )
@property
def __magic_name__ ( self : List[Any] ):
return ["input_ids", "bbox", "attention_mask", "pixel_values"]
@property
def __magic_name__ ( self : Optional[Any] ):
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''', __A, )
return self.image_processor_class
@property
def __magic_name__ ( self : str ):
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''', __A, )
return self.image_processor
| 336 |
def a__ ( UpperCAmelCase : int ) -> int:
UpperCAmelCase : list[list[int]] = [[0 for _ in range(UpperCAmelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
UpperCAmelCase : Optional[Any] = 1
for n in range(m + 1 ):
for k in range(1 , UpperCAmelCase ):
memo[n][k] += memo[n][k - 1]
if n - k > 0:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1]
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
try:
_lowerCamelCase : List[Any] = int(input("Enter a number: ").strip())
print(partition(n))
except ValueError:
print("Please enter a number.")
else:
try:
_lowerCamelCase : str = int(sys.argv[1])
print(partition(n))
except ValueError:
print("Please pass a number.")
| 336 | 1 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info
def a__ ( UpperCAmelCase : int ) -> Any:
return {key.lstrip('''-''' ): value for key, value in zip(unknown_args[::2] , unknown_args[1::2] )}
def a__ ( ) -> Union[str, Any]:
UpperCAmelCase : Optional[int] = ArgumentParser(
'''HuggingFace Datasets CLI tool''' , usage='''datasets-cli <command> [<args>]''' , allow_abbrev=UpperCAmelCase )
UpperCAmelCase : Optional[Any] = parser.add_subparsers(help='''datasets-cli command helpers''' )
set_verbosity_info()
# Register commands
ConvertCommand.register_subcommand(UpperCAmelCase )
EnvironmentCommand.register_subcommand(UpperCAmelCase )
TestCommand.register_subcommand(UpperCAmelCase )
RunBeamCommand.register_subcommand(UpperCAmelCase )
DummyDataCommand.register_subcommand(UpperCAmelCase )
# Parse args
UpperCAmelCase , UpperCAmelCase : Dict = parser.parse_known_args()
if not hasattr(UpperCAmelCase , '''func''' ):
parser.print_help()
exit(1 )
UpperCAmelCase : str = parse_unknown_args(UpperCAmelCase )
# Run
UpperCAmelCase : Dict = args.func(UpperCAmelCase , **UpperCAmelCase )
service.run()
if __name__ == "__main__":
main()
| 336 |
from __future__ import annotations
def a__ ( UpperCAmelCase : list[list[int]] ) -> bool:
UpperCAmelCase : Union[str, Any] = len(UpperCAmelCase )
# We need to create solution object to save path.
UpperCAmelCase : int = [[0 for _ in range(UpperCAmelCase )] for _ in range(UpperCAmelCase )]
UpperCAmelCase : Union[str, Any] = run_maze(UpperCAmelCase , 0 , 0 , UpperCAmelCase )
if solved:
print('''\n'''.join(str(UpperCAmelCase ) for row in solutions ) )
else:
print('''No solution exists!''' )
return solved
def a__ ( UpperCAmelCase : list[list[int]] , UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : list[list[int]] ) -> bool:
UpperCAmelCase : Dict = len(UpperCAmelCase )
# Final check point.
if i == j == (size - 1):
UpperCAmelCase : Dict = 1
return True
UpperCAmelCase : Union[str, Any] = (not i < 0) and (not j < 0) # Check lower bounds
UpperCAmelCase : List[Any] = (i < size) and (j < size) # Check upper bounds
if lower_flag and upper_flag:
# check for already visited and block points.
UpperCAmelCase : Any = (not solutions[i][j]) and (not maze[i][j])
if block_flag:
# check visited
UpperCAmelCase : str = 1
# check for directions
if (
run_maze(UpperCAmelCase , i + 1 , UpperCAmelCase , UpperCAmelCase )
or run_maze(UpperCAmelCase , UpperCAmelCase , j + 1 , UpperCAmelCase )
or run_maze(UpperCAmelCase , i - 1 , UpperCAmelCase , UpperCAmelCase )
or run_maze(UpperCAmelCase , UpperCAmelCase , j - 1 , UpperCAmelCase )
):
return True
UpperCAmelCase : Any = 0
return False
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 | 1 |
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class __UpperCAmelCase :
def __magic_name__ ( self : int, __A : Dict ):
raise NotImplementedError()
def __magic_name__ ( self : int ):
raise NotImplementedError()
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : str, __A : "AutoTokenizer", __A : bool = False, **__A : str ):
UpperCAmelCase : List[str] = tokenizer
UpperCAmelCase : str = skip_prompt
UpperCAmelCase : List[str] = decode_kwargs
# variables used in the streaming process
UpperCAmelCase : Dict = []
UpperCAmelCase : List[str] = 0
UpperCAmelCase : Union[str, Any] = True
def __magic_name__ ( self : Dict, __A : Optional[int] ):
if len(value.shape ) > 1 and value.shape[0] > 1:
raise ValueError('''TextStreamer only supports batch size 1''' )
elif len(value.shape ) > 1:
UpperCAmelCase : Union[str, Any] = value[0]
if self.skip_prompt and self.next_tokens_are_prompt:
UpperCAmelCase : Optional[int] = False
return
# Add the new token to the cache and decodes the entire thing.
self.token_cache.extend(value.tolist() )
UpperCAmelCase : Any = self.tokenizer.decode(self.token_cache, **self.decode_kwargs )
# After the symbol for a new line, we flush the cache.
if text.endswith('''\n''' ):
UpperCAmelCase : Union[str, Any] = text[self.print_len :]
UpperCAmelCase : int = []
UpperCAmelCase : int = 0
# If the last token is a CJK character, we print the characters.
elif len(__A ) > 0 and self._is_chinese_char(ord(text[-1] ) ):
UpperCAmelCase : Union[str, Any] = text[self.print_len :]
self.print_len += len(__A )
# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
# which may change with the subsequent token -- there are probably smarter ways to do this!)
else:
UpperCAmelCase : Optional[Any] = text[self.print_len : text.rfind(''' ''' ) + 1]
self.print_len += len(__A )
self.on_finalized_text(__A )
def __magic_name__ ( self : str ):
# Flush the cache, if it exists
if len(self.token_cache ) > 0:
UpperCAmelCase : int = self.tokenizer.decode(self.token_cache, **self.decode_kwargs )
UpperCAmelCase : Dict = text[self.print_len :]
UpperCAmelCase : List[Any] = []
UpperCAmelCase : List[Any] = 0
else:
UpperCAmelCase : Dict = ''''''
UpperCAmelCase : str = True
self.on_finalized_text(__A, stream_end=__A )
def __magic_name__ ( self : List[str], __A : str, __A : bool = False ):
print(__A, flush=__A, end='''''' if not stream_end else None )
def __magic_name__ ( self : List[Any], __A : Optional[int] ):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0X4E00 and cp <= 0X9FFF)
or (cp >= 0X3400 and cp <= 0X4DBF) #
or (cp >= 0X20000 and cp <= 0X2A6DF) #
or (cp >= 0X2A700 and cp <= 0X2B73F) #
or (cp >= 0X2B740 and cp <= 0X2B81F) #
or (cp >= 0X2B820 and cp <= 0X2CEAF) #
or (cp >= 0XF900 and cp <= 0XFAFF)
or (cp >= 0X2F800 and cp <= 0X2FA1F) #
): #
return True
return False
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : Dict, __A : "AutoTokenizer", __A : bool = False, __A : Optional[float] = None, **__A : str ):
super().__init__(__A, __A, **__A )
UpperCAmelCase : Dict = Queue()
UpperCAmelCase : Any = None
UpperCAmelCase : Any = timeout
def __magic_name__ ( self : Dict, __A : str, __A : bool = False ):
self.text_queue.put(__A, timeout=self.timeout )
if stream_end:
self.text_queue.put(self.stop_signal, timeout=self.timeout )
def __iter__( self : int ):
return self
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : List[Any] = self.text_queue.get(timeout=self.timeout )
if value == self.stop_signal:
raise StopIteration()
else:
return value
| 336 |
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class __UpperCAmelCase :
def __init__( self : List[Any], __A : List[str], __A : List[str]=1_3, __A : Any=6_4, __A : Optional[Any]=2, __A : str=3, __A : str=True, __A : str=True, __A : Optional[Any]=3_2, __A : List[str]=5, __A : int=4, __A : str=3_7, __A : str="gelu", __A : Dict=0.1, __A : List[Any]=0.1, __A : Dict=1_0, __A : int=0.0_2, __A : Any=[1, 1_6, 4, 4], __A : Optional[int]=None, ):
UpperCAmelCase : Union[str, Any] = parent
UpperCAmelCase : Any = batch_size
UpperCAmelCase : List[str] = image_size
UpperCAmelCase : List[str] = patch_size
UpperCAmelCase : Dict = num_channels
UpperCAmelCase : List[Any] = is_training
UpperCAmelCase : Dict = use_labels
UpperCAmelCase : Optional[int] = hidden_size
UpperCAmelCase : Union[str, Any] = num_hidden_layers
UpperCAmelCase : Optional[Any] = num_attention_heads
UpperCAmelCase : Any = intermediate_size
UpperCAmelCase : Any = hidden_act
UpperCAmelCase : Any = hidden_dropout_prob
UpperCAmelCase : Optional[int] = attention_probs_dropout_prob
UpperCAmelCase : str = type_sequence_label_size
UpperCAmelCase : Any = initializer_range
UpperCAmelCase : int = scope
UpperCAmelCase : List[str] = backbone_featmap_shape
# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
# the number of patches is based on the feature map of the backbone, which by default uses an output stride
# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
UpperCAmelCase : str = (self.image_size // 3_2) ** 2
UpperCAmelCase : List[str] = num_patches + 1
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : str = None
if self.use_labels:
UpperCAmelCase : Any = ids_tensor([self.batch_size], self.type_sequence_label_size )
UpperCAmelCase : Optional[int] = self.get_config()
return config, pixel_values, labels
def __magic_name__ ( self : Any ):
UpperCAmelCase : Dict = {
'''global_padding''': '''same''',
'''layer_type''': '''bottleneck''',
'''depths''': [3, 4, 9],
'''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
'''embedding_dynamic_padding''': True,
'''hidden_sizes''': [4, 8, 1_6, 3_2],
'''num_groups''': 2,
}
return ViTHybridConfig(
image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, is_decoder=__A, initializer_range=self.initializer_range, backbone_featmap_shape=self.backbone_featmap_shape, backbone_config=__A, )
def __magic_name__ ( self : Optional[int], __A : Optional[int], __A : int, __A : Tuple ):
UpperCAmelCase : int = ViTHybridModel(config=__A )
model.to(__A )
model.eval()
UpperCAmelCase : Tuple = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self : Tuple, __A : Dict, __A : str, __A : List[str] ):
UpperCAmelCase : str = self.type_sequence_label_size
UpperCAmelCase : List[Any] = ViTHybridForImageClassification(__A )
model.to(__A )
model.eval()
UpperCAmelCase : Dict = model(__A, labels=__A )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size) )
def __magic_name__ ( self : int ):
UpperCAmelCase : Union[str, Any] = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[str] = config_and_inputs
UpperCAmelCase : int = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
UpperCamelCase = (
{"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
if is_torch_available()
else {}
)
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : Any = ViTHybridModelTester(self )
UpperCAmelCase : List[Any] = ConfigTester(self, config_class=__A, has_text_modality=__A, hidden_size=3_7 )
def __magic_name__ ( self : int ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViT does not use inputs_embeds''' )
def __magic_name__ ( self : List[Any] ):
pass
def __magic_name__ ( self : int ):
UpperCAmelCase , UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Dict = model_class(__A )
self.assertIsInstance(model.get_input_embeddings(), (nn.Module) )
UpperCAmelCase : Optional[int] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__A, nn.Linear ) )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase , UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : List[Any] = model_class(__A )
UpperCAmelCase : Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase : str = [*signature.parameters.keys()]
UpperCAmelCase : Optional[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1], __A )
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__A )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase , UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Dict = _config_zero_init(__A )
for model_class in self.all_model_classes:
UpperCAmelCase : Optional[Any] = model_class(config=__A )
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
UpperCAmelCase : Union[str, Any] = [F'''{name}.{key}''' for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item(), [0.0, 1.0], msg=F'''Parameter {name} of model {model_class} seems not properly initialized''', )
@slow
def __magic_name__ ( self : List[str] ):
for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase : Union[str, Any] = ViTHybridModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def a__ ( ) -> Tuple:
UpperCAmelCase : Any = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : str ):
return (
ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : int = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
__A )
UpperCAmelCase : Tuple = self.default_image_processor
UpperCAmelCase : int = prepare_img()
UpperCAmelCase : Union[str, Any] = image_processor(images=__A, return_tensors='''pt''' ).to(__A )
# forward pass
with torch.no_grad():
UpperCAmelCase : Optional[Any] = model(**__A )
# verify the logits
UpperCAmelCase : str = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape, __A )
UpperCAmelCase : Optional[Any] = torch.tensor([-1.9_0_9_0, -0.4_9_9_3, -0.2_3_8_9] ).to(__A )
self.assertTrue(torch.allclose(outputs.logits[0, :3], __A, atol=1E-4 ) )
@slow
@require_accelerate
def __magic_name__ ( self : Dict ):
UpperCAmelCase : Union[str, Any] = ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384''' )
UpperCAmelCase : int = ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384''', device_map='''auto''' )
UpperCAmelCase : Tuple = prepare_img()
UpperCAmelCase : Optional[int] = image_processor(images=__A, return_tensors='''pt''' )
UpperCAmelCase : Dict = model(**__A )
UpperCAmelCase : Any = outputs.logits
# model predicts one of the 1000 ImageNet classes
UpperCAmelCase : Dict = logits.argmax(-1 ).item()
self.assertTrue(model.config.idalabel[predicted_class_idx], '''tabby, tabby cat''' )
| 336 | 1 |
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
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
def a__ ( UpperCAmelCase : str ) -> List[List[ImageInput]]:
if isinstance(UpperCAmelCase , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(UpperCAmelCase , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(UpperCAmelCase ):
return [[videos]]
raise ValueError(f'''Could not make batched video from {videos}''' )
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = ["""pixel_values"""]
def __init__( self : Optional[Any], __A : bool = True, __A : Dict[str, int] = None, __A : PILImageResampling = PILImageResampling.BILINEAR, __A : bool = True, __A : Dict[str, int] = None, __A : bool = True, __A : Union[int, float] = 1 / 2_5_5, __A : bool = True, __A : bool = True, __A : Optional[Union[float, List[float]]] = None, __A : Optional[Union[float, List[float]]] = None, **__A : Any, ):
super().__init__(**__A )
UpperCAmelCase : Tuple = size if size is not None else {'''shortest_edge''': 2_5_6}
UpperCAmelCase : Tuple = get_size_dict(__A, default_to_square=__A )
UpperCAmelCase : int = crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
UpperCAmelCase : int = get_size_dict(__A, param_name='''crop_size''' )
UpperCAmelCase : Tuple = do_resize
UpperCAmelCase : Any = size
UpperCAmelCase : int = do_center_crop
UpperCAmelCase : List[str] = crop_size
UpperCAmelCase : List[str] = resample
UpperCAmelCase : Optional[int] = do_rescale
UpperCAmelCase : Optional[int] = rescale_factor
UpperCAmelCase : int = offset
UpperCAmelCase : Union[str, Any] = do_normalize
UpperCAmelCase : Dict = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCAmelCase : str = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __magic_name__ ( self : int, __A : np.ndarray, __A : Dict[str, int], __A : PILImageResampling = PILImageResampling.BILINEAR, __A : Optional[Union[str, ChannelDimension]] = None, **__A : Union[str, Any], ):
UpperCAmelCase : Tuple = get_size_dict(__A, default_to_square=__A )
if "shortest_edge" in size:
UpperCAmelCase : Dict = get_resize_output_image_size(__A, size['''shortest_edge'''], default_to_square=__A )
elif "height" in size and "width" in size:
UpperCAmelCase : List[Any] = (size['''height'''], size['''width'''])
else:
raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''' )
return resize(__A, size=__A, resample=__A, data_format=__A, **__A )
def __magic_name__ ( self : List[str], __A : np.ndarray, __A : Dict[str, int], __A : Optional[Union[str, ChannelDimension]] = None, **__A : Optional[int], ):
UpperCAmelCase : Optional[Any] = get_size_dict(__A )
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(__A, size=(size['''height'''], size['''width''']), data_format=__A, **__A )
def __magic_name__ ( self : Optional[Any], __A : np.ndarray, __A : Union[int, float], __A : bool = True, __A : Optional[Union[str, ChannelDimension]] = None, **__A : Optional[Any], ):
UpperCAmelCase : Dict = image.astype(np.floataa )
if offset:
UpperCAmelCase : Union[str, Any] = image - (scale / 2)
return rescale(__A, scale=__A, data_format=__A, **__A )
def __magic_name__ ( self : int, __A : np.ndarray, __A : Union[float, List[float]], __A : Union[float, List[float]], __A : Optional[Union[str, ChannelDimension]] = None, **__A : Dict, ):
return normalize(__A, mean=__A, std=__A, data_format=__A, **__A )
def __magic_name__ ( self : int, __A : ImageInput, __A : bool = None, __A : Dict[str, int] = None, __A : PILImageResampling = None, __A : bool = None, __A : Dict[str, int] = None, __A : bool = None, __A : float = None, __A : bool = None, __A : bool = None, __A : Optional[Union[float, List[float]]] = None, __A : Optional[Union[float, List[float]]] = None, __A : Optional[ChannelDimension] = ChannelDimension.FIRST, ):
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.
UpperCAmelCase : str = to_numpy_array(__A )
if do_resize:
UpperCAmelCase : List[Any] = self.resize(image=__A, size=__A, resample=__A )
if do_center_crop:
UpperCAmelCase : Optional[Any] = self.center_crop(__A, size=__A )
if do_rescale:
UpperCAmelCase : List[str] = self.rescale(image=__A, scale=__A, offset=__A )
if do_normalize:
UpperCAmelCase : Optional[int] = self.normalize(image=__A, mean=__A, std=__A )
UpperCAmelCase : int = to_channel_dimension_format(__A, __A )
return image
def __magic_name__ ( self : Union[str, Any], __A : ImageInput, __A : bool = None, __A : Dict[str, int] = None, __A : PILImageResampling = None, __A : bool = None, __A : Dict[str, int] = None, __A : bool = None, __A : float = None, __A : bool = None, __A : bool = None, __A : Optional[Union[float, List[float]]] = None, __A : Optional[Union[float, List[float]]] = None, __A : Optional[Union[str, TensorType]] = None, __A : ChannelDimension = ChannelDimension.FIRST, **__A : int, ):
UpperCAmelCase : List[Any] = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase : Union[str, Any] = resample if resample is not None else self.resample
UpperCAmelCase : str = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase : Optional[Any] = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase : str = offset if offset is not None else self.offset
UpperCAmelCase : List[str] = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase : Tuple = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase : Any = image_std if image_std is not None else self.image_std
UpperCAmelCase : Union[str, Any] = size if size is not None else self.size
UpperCAmelCase : Dict = get_size_dict(__A, default_to_square=__A )
UpperCAmelCase : str = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase : Any = get_size_dict(__A, param_name='''crop_size''' )
if not valid_images(__A ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
UpperCAmelCase : Optional[Any] = make_batched(__A )
UpperCAmelCase : str = [
[
self._preprocess_image(
image=__A, do_resize=__A, size=__A, resample=__A, do_center_crop=__A, crop_size=__A, do_rescale=__A, rescale_factor=__A, offset=__A, do_normalize=__A, image_mean=__A, image_std=__A, data_format=__A, )
for img in video
]
for video in videos
]
UpperCAmelCase : List[str] = {'''pixel_values''': videos}
return BatchFeature(data=__A, tensor_type=__A )
| 336 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def a__ ( ) -> tuple[list[int], int]:
UpperCAmelCase : str = [randint(-1_000 , 1_000 ) for i in range(10 )]
UpperCAmelCase : Any = randint(-5_000 , 5_000 )
return (arr, r)
_lowerCamelCase : Any = make_dataset()
def a__ ( UpperCAmelCase : list[int] , UpperCAmelCase : int ) -> tuple[int, ...]:
for triplet in permutations(UpperCAmelCase , 3 ):
if sum(UpperCAmelCase ) == target:
return tuple(sorted(UpperCAmelCase ) )
return (0, 0, 0)
def a__ ( UpperCAmelCase : list[int] , UpperCAmelCase : int ) -> tuple[int, int, int]:
arr.sort()
UpperCAmelCase : Tuple = len(UpperCAmelCase )
for i in range(n - 1 ):
UpperCAmelCase , UpperCAmelCase : int = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def a__ ( ) -> tuple[float, float]:
UpperCAmelCase : Union[str, Any] = '''
from __main__ import dataset, triplet_sum1, triplet_sum2
'''
UpperCAmelCase : Tuple = '''
triplet_sum1(*dataset)
'''
UpperCAmelCase : List[str] = '''
triplet_sum2(*dataset)
'''
UpperCAmelCase : Tuple = repeat(setup=UpperCAmelCase , stmt=UpperCAmelCase , repeat=5 , number=10_000 )
UpperCAmelCase : str = repeat(setup=UpperCAmelCase , stmt=UpperCAmelCase , repeat=5 , number=10_000 )
return (min(UpperCAmelCase ), min(UpperCAmelCase ))
if __name__ == "__main__":
from doctest import testmod
testmod()
_lowerCamelCase : int = solution_times()
print(f"""The time for naive implementation is {times[0]}.""")
print(f"""The time for optimized implementation is {times[1]}.""")
| 336 | 1 |
from __future__ import annotations
import math
def a__ ( UpperCAmelCase : int ) -> bool:
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(UpperCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
_lowerCamelCase : int = [num for num in range(3, 1_0_0_0_0_1, 2) if not is_prime(num)]
def a__ ( UpperCAmelCase : int ) -> list[int]:
if not isinstance(UpperCAmelCase , UpperCAmelCase ):
raise ValueError('''n must be an integer''' )
if n <= 0:
raise ValueError('''n must be >= 0''' )
UpperCAmelCase : Dict = []
for num in range(len(UpperCAmelCase ) ):
UpperCAmelCase : List[str] = 0
while 2 * i * i <= odd_composites[num]:
UpperCAmelCase : int = odd_composites[num] - 2 * i * i
if is_prime(UpperCAmelCase ):
break
i += 1
else:
list_nums.append(odd_composites[num] )
if len(UpperCAmelCase ) == n:
return list_nums
return []
def a__ ( ) -> int:
return compute_nums(1 )[0]
if __name__ == "__main__":
print(f"""{solution() = }""")
| 336 |
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class __UpperCAmelCase :
def __magic_name__ ( self : int, __A : Dict ):
raise NotImplementedError()
def __magic_name__ ( self : int ):
raise NotImplementedError()
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : str, __A : "AutoTokenizer", __A : bool = False, **__A : str ):
UpperCAmelCase : List[str] = tokenizer
UpperCAmelCase : str = skip_prompt
UpperCAmelCase : List[str] = decode_kwargs
# variables used in the streaming process
UpperCAmelCase : Dict = []
UpperCAmelCase : List[str] = 0
UpperCAmelCase : Union[str, Any] = True
def __magic_name__ ( self : Dict, __A : Optional[int] ):
if len(value.shape ) > 1 and value.shape[0] > 1:
raise ValueError('''TextStreamer only supports batch size 1''' )
elif len(value.shape ) > 1:
UpperCAmelCase : Union[str, Any] = value[0]
if self.skip_prompt and self.next_tokens_are_prompt:
UpperCAmelCase : Optional[int] = False
return
# Add the new token to the cache and decodes the entire thing.
self.token_cache.extend(value.tolist() )
UpperCAmelCase : Any = self.tokenizer.decode(self.token_cache, **self.decode_kwargs )
# After the symbol for a new line, we flush the cache.
if text.endswith('''\n''' ):
UpperCAmelCase : Union[str, Any] = text[self.print_len :]
UpperCAmelCase : int = []
UpperCAmelCase : int = 0
# If the last token is a CJK character, we print the characters.
elif len(__A ) > 0 and self._is_chinese_char(ord(text[-1] ) ):
UpperCAmelCase : Union[str, Any] = text[self.print_len :]
self.print_len += len(__A )
# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
# which may change with the subsequent token -- there are probably smarter ways to do this!)
else:
UpperCAmelCase : Optional[Any] = text[self.print_len : text.rfind(''' ''' ) + 1]
self.print_len += len(__A )
self.on_finalized_text(__A )
def __magic_name__ ( self : str ):
# Flush the cache, if it exists
if len(self.token_cache ) > 0:
UpperCAmelCase : int = self.tokenizer.decode(self.token_cache, **self.decode_kwargs )
UpperCAmelCase : Dict = text[self.print_len :]
UpperCAmelCase : List[Any] = []
UpperCAmelCase : List[Any] = 0
else:
UpperCAmelCase : Dict = ''''''
UpperCAmelCase : str = True
self.on_finalized_text(__A, stream_end=__A )
def __magic_name__ ( self : List[str], __A : str, __A : bool = False ):
print(__A, flush=__A, end='''''' if not stream_end else None )
def __magic_name__ ( self : List[Any], __A : Optional[int] ):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0X4E00 and cp <= 0X9FFF)
or (cp >= 0X3400 and cp <= 0X4DBF) #
or (cp >= 0X20000 and cp <= 0X2A6DF) #
or (cp >= 0X2A700 and cp <= 0X2B73F) #
or (cp >= 0X2B740 and cp <= 0X2B81F) #
or (cp >= 0X2B820 and cp <= 0X2CEAF) #
or (cp >= 0XF900 and cp <= 0XFAFF)
or (cp >= 0X2F800 and cp <= 0X2FA1F) #
): #
return True
return False
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : Dict, __A : "AutoTokenizer", __A : bool = False, __A : Optional[float] = None, **__A : str ):
super().__init__(__A, __A, **__A )
UpperCAmelCase : Dict = Queue()
UpperCAmelCase : Any = None
UpperCAmelCase : Any = timeout
def __magic_name__ ( self : Dict, __A : str, __A : bool = False ):
self.text_queue.put(__A, timeout=self.timeout )
if stream_end:
self.text_queue.put(self.stop_signal, timeout=self.timeout )
def __iter__( self : int ):
return self
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : List[Any] = self.text_queue.get(timeout=self.timeout )
if value == self.stop_signal:
raise StopIteration()
else:
return value
| 336 | 1 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
_lowerCamelCase : Dict = logging.get_logger(__name__)
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = ["""input_features"""]
def __init__( self : List[str], __A : Union[str, Any]=8_0, __A : Optional[int]=1_6_0_0_0, __A : Optional[Any]=1_6_0, __A : Tuple=3_0, __A : Dict=4_0_0, __A : Any=0.0, __A : Any=False, **__A : Dict, ):
super().__init__(
feature_size=__A, sampling_rate=__A, padding_value=__A, return_attention_mask=__A, **__A, )
UpperCAmelCase : List[str] = n_fft
UpperCAmelCase : Union[str, Any] = hop_length
UpperCAmelCase : Tuple = chunk_length
UpperCAmelCase : Union[str, Any] = chunk_length * sampling_rate
UpperCAmelCase : List[Any] = self.n_samples // hop_length
UpperCAmelCase : Union[str, Any] = sampling_rate
UpperCAmelCase : str = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2, num_mel_filters=__A, min_frequency=0.0, max_frequency=8_0_0_0.0, sampling_rate=__A, norm='''slaney''', mel_scale='''slaney''', )
def __magic_name__ ( self : Dict, __A : np.array ):
UpperCAmelCase : Tuple = spectrogram(
__A, window_function(self.n_fft, '''hann''' ), frame_length=self.n_fft, hop_length=self.hop_length, power=2.0, mel_filters=self.mel_filters, log_mel='''log10''', )
UpperCAmelCase : Optional[Any] = log_spec[:, :-1]
UpperCAmelCase : Union[str, Any] = np.maximum(__A, log_spec.max() - 8.0 )
UpperCAmelCase : Tuple = (log_spec + 4.0) / 4.0
return log_spec
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def __magic_name__ ( __A : List[np.ndarray], __A : List[np.ndarray], __A : float = 0.0 ):
if attention_mask is not None:
UpperCAmelCase : Union[str, Any] = np.array(__A, np.intaa )
UpperCAmelCase : int = []
for vector, length in zip(__A, attention_mask.sum(-1 ) ):
UpperCAmelCase : int = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
if length < normed_slice.shape[0]:
UpperCAmelCase : int = padding_value
normed_input_values.append(__A )
else:
UpperCAmelCase : Any = [(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]
return normed_input_values
def __call__( self : Optional[Any], __A : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]], __A : bool = True, __A : Optional[int] = None, __A : Optional[Union[str, TensorType]] = None, __A : Optional[bool] = None, __A : Optional[str] = "max_length", __A : Optional[int] = None, __A : Optional[int] = None, __A : Optional[bool] = None, **__A : Union[str, Any], ):
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
F''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
F''' was sampled with {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
'''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
'''Failing to do so can result in silent errors that might be hard to debug.''' )
UpperCAmelCase : List[str] = isinstance(__A, np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(F'''Only mono-channel audio is supported for input to {self}''' )
UpperCAmelCase : List[str] = is_batched_numpy or (
isinstance(__A, (list, tuple) ) and (isinstance(raw_speech[0], (np.ndarray, tuple, list) ))
)
if is_batched:
UpperCAmelCase : Optional[int] = [np.asarray([speech], dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(__A, np.ndarray ):
UpperCAmelCase : Optional[Any] = np.asarray(__A, dtype=np.floataa )
elif isinstance(__A, np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
UpperCAmelCase : str = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
UpperCAmelCase : List[Any] = [np.asarray([raw_speech] ).T]
UpperCAmelCase : Any = BatchFeature({'''input_features''': raw_speech} )
# convert into correct format for padding
UpperCAmelCase : Union[str, Any] = self.pad(
__A, padding=__A, max_length=max_length if max_length else self.n_samples, truncation=__A, pad_to_multiple_of=__A, return_attention_mask=return_attention_mask or do_normalize, )
# zero-mean and unit-variance normalization
if do_normalize:
UpperCAmelCase : Any = self.zero_mean_unit_var_norm(
padded_inputs['''input_features'''], attention_mask=padded_inputs['''attention_mask'''], padding_value=self.padding_value, )
UpperCAmelCase : Optional[Any] = np.stack(padded_inputs['''input_features'''], axis=0 )
# make sure list is in array format
UpperCAmelCase : Optional[Any] = padded_inputs.get('''input_features''' ).transpose(2, 0, 1 )
UpperCAmelCase : Dict = [self._np_extract_fbank_features(__A ) for waveform in input_features[0]]
if isinstance(input_features[0], __A ):
UpperCAmelCase : Dict = [np.asarray(__A, dtype=np.floataa ) for feature in input_features]
else:
UpperCAmelCase : List[str] = input_features
if return_attention_mask:
# rescale from sample (48000) to feature (3000)
UpperCAmelCase : int = padded_inputs['''attention_mask'''][:, :: self.hop_length]
if return_tensors is not None:
UpperCAmelCase : Optional[int] = padded_inputs.convert_to_tensors(__A )
return padded_inputs
def __magic_name__ ( self : str ):
UpperCAmelCase : Any = copy.deepcopy(self.__dict__ )
UpperCAmelCase : Optional[int] = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
return output
| 336 |
import numpy
# List of input, output pairs
_lowerCamelCase : Dict = (
((5, 2, 3), 1_5),
((6, 5, 9), 2_5),
((1_1, 1_2, 1_3), 4_1),
((1, 1, 1), 8),
((1_1, 1_2, 1_3), 4_1),
)
_lowerCamelCase : str = (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
_lowerCamelCase : Dict = [2, 4, 1, 5]
_lowerCamelCase : Dict = len(train_data)
_lowerCamelCase : int = 0.0_0_9
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : Optional[int]="train" ) -> Dict:
return calculate_hypothesis_value(UpperCAmelCase , UpperCAmelCase ) - output(
UpperCAmelCase , UpperCAmelCase )
def a__ ( UpperCAmelCase : int ) -> Any:
UpperCAmelCase : str = 0
for i in range(len(UpperCAmelCase ) - 1 ):
hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
hyp_val += parameter_vector[0]
return hyp_val
def a__ ( UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[Any] ) -> Optional[int]:
if data_set == "train":
return train_data[example_no][1]
elif data_set == "test":
return test_data[example_no][1]
return None
def a__ ( UpperCAmelCase : int , UpperCAmelCase : Optional[Any] ) -> List[str]:
if data_set == "train":
return _hypothesis_value(train_data[example_no][0] )
elif data_set == "test":
return _hypothesis_value(test_data[example_no][0] )
return None
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : str=m ) -> Dict:
UpperCAmelCase : Optional[int] = 0
for i in range(UpperCAmelCase ):
if index == -1:
summation_value += _error(UpperCAmelCase )
else:
summation_value += _error(UpperCAmelCase ) * train_data[i][0][index]
return summation_value
def a__ ( UpperCAmelCase : Dict ) -> Dict:
UpperCAmelCase : Dict = summation_of_cost_derivative(UpperCAmelCase , UpperCAmelCase ) / m
return cost_derivative_value
def a__ ( ) -> List[Any]:
global parameter_vector
# Tune these values to set a tolerance value for predicted output
UpperCAmelCase : List[str] = 0.000002
UpperCAmelCase : Any = 0
UpperCAmelCase : Dict = 0
while True:
j += 1
UpperCAmelCase : List[Any] = [0, 0, 0, 0]
for i in range(0 , len(UpperCAmelCase ) ):
UpperCAmelCase : List[str] = get_cost_derivative(i - 1 )
UpperCAmelCase : Tuple = (
parameter_vector[i] - LEARNING_RATE * cost_derivative
)
if numpy.allclose(
UpperCAmelCase , UpperCAmelCase , atol=UpperCAmelCase , rtol=UpperCAmelCase , ):
break
UpperCAmelCase : int = temp_parameter_vector
print(('''Number of iterations:''', j) )
def a__ ( ) -> List[Any]:
for i in range(len(UpperCAmelCase ) ):
print(('''Actual output value:''', output(UpperCAmelCase , '''test''' )) )
print(('''Hypothesis output:''', calculate_hypothesis_value(UpperCAmelCase , '''test''' )) )
if __name__ == "__main__":
run_gradient_descent()
print("\nTesting gradient descent for a linear hypothesis function.\n")
test_gradient_descent()
| 336 | 1 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
_lowerCamelCase : Any = np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
_lowerCamelCase : Optional[int] = [0, 2_5, 5_0]
_lowerCamelCase : Union[str, Any] = [2_5, 5_0, 7_5]
_lowerCamelCase : Any = fuzz.membership.trimf(X, abca)
_lowerCamelCase : Union[str, Any] = fuzz.membership.trimf(X, abca)
# Compute the different operations using inbuilt functions.
_lowerCamelCase : str = np.ones(7_5)
_lowerCamelCase : str = np.zeros((7_5,))
# 1. Union = max(µA(x), µB(x))
_lowerCamelCase : Optional[int] = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
_lowerCamelCase : Optional[Any] = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
_lowerCamelCase : Any = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
_lowerCamelCase : List[str] = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
_lowerCamelCase : Union[str, Any] = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
_lowerCamelCase : Any = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
_lowerCamelCase : Any = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
_lowerCamelCase : Dict = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# max-min composition
# max-product composition
# Plot each set A, set B and each operation result using plot() and subplot().
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title("Young")
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title("Middle aged")
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title("union")
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title("intersection")
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title("complement_a")
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title("difference a/b")
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title("alg_sum")
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title("alg_product")
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title("bdd_sum")
plt.grid(True)
plt.subplot(4, 3, 1_0)
plt.plot(X, bdd_difference)
plt.title("bdd_difference")
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
| 336 |
def a__ ( UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[int] ) -> Optional[Any]:
UpperCAmelCase : List[str] = 0
UpperCAmelCase : List[Any] = len(UpperCAmelCase ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
UpperCAmelCase : Optional[int] = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(UpperCAmelCase ):
return None
UpperCAmelCase : Optional[Any] = sorted_collection[point]
if current_item == item:
return point
else:
if point < left:
UpperCAmelCase : Any = left
UpperCAmelCase : List[str] = point
elif point > right:
UpperCAmelCase : Any = right
UpperCAmelCase : List[str] = point
else:
if item < current_item:
UpperCAmelCase : Optional[int] = point - 1
else:
UpperCAmelCase : str = point + 1
return None
def a__ ( UpperCAmelCase : Optional[Any] , UpperCAmelCase : int , UpperCAmelCase : str , UpperCAmelCase : Union[str, Any] ) -> Dict:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
UpperCAmelCase : List[str] = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(UpperCAmelCase ):
return None
if sorted_collection[point] == item:
return point
elif point < left:
return interpolation_search_by_recursion(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
elif point > right:
return interpolation_search_by_recursion(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
else:
if sorted_collection[point] > item:
return interpolation_search_by_recursion(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , point - 1 )
else:
return interpolation_search_by_recursion(
UpperCAmelCase , UpperCAmelCase , point + 1 , UpperCAmelCase )
def a__ ( UpperCAmelCase : Union[str, Any] ) -> int:
if collection != sorted(UpperCAmelCase ):
raise ValueError('''Collection must be ascending sorted''' )
return True
if __name__ == "__main__":
import sys
_lowerCamelCase : Optional[int] = 0
if debug == 1:
_lowerCamelCase : Dict = [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
try:
__assert_sorted(collection)
except ValueError:
sys.exit("Sequence must be ascending sorted to apply interpolation search")
_lowerCamelCase : List[Any] = 6_7
_lowerCamelCase : Optional[Any] = interpolation_search(collection, target)
if result is not None:
print(f"""{target} found at positions: {result}""")
else:
print("Not found")
| 336 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : List[str] = {
"configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
"tokenization_luke": ["LukeTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[int] = [
"LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
"LukeForEntityClassification",
"LukeForEntityPairClassification",
"LukeForEntitySpanClassification",
"LukeForMultipleChoice",
"LukeForQuestionAnswering",
"LukeForSequenceClassification",
"LukeForTokenClassification",
"LukeForMaskedLM",
"LukeModel",
"LukePreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
from .tokenization_luke import LukeTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_luke import (
LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
LukeForEntityClassification,
LukeForEntityPairClassification,
LukeForEntitySpanClassification,
LukeForMaskedLM,
LukeForMultipleChoice,
LukeForQuestionAnswering,
LukeForSequenceClassification,
LukeForTokenClassification,
LukeModel,
LukePreTrainedModel,
)
else:
import sys
_lowerCamelCase : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 336 |
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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase : Any = logging.get_logger(__name__)
def a__ ( UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[Any]=False , UpperCAmelCase : List[str]=False ) -> Any:
UpperCAmelCase : Optional[int] = '''backbone.''' if is_semantic else ''''''
UpperCAmelCase : Dict = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f'''{prefix}blocks.{i}.norm1.weight''', f'''beit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((f'''{prefix}blocks.{i}.norm1.bias''', f'''beit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(f'''{prefix}blocks.{i}.attn.proj.weight''', f'''beit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append(
(f'''{prefix}blocks.{i}.attn.proj.bias''', f'''beit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((f'''{prefix}blocks.{i}.norm2.weight''', f'''beit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((f'''{prefix}blocks.{i}.norm2.bias''', f'''beit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.weight''', f'''beit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.bias''', f'''beit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.weight''', f'''beit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.bias''', f'''beit.encoder.layer.{i}.output.dense.bias''') )
# projection layer + position embeddings
rename_keys.extend(
[
(f'''{prefix}cls_token''', '''beit.embeddings.cls_token'''),
(f'''{prefix}patch_embed.proj.weight''', '''beit.embeddings.patch_embeddings.projection.weight'''),
(f'''{prefix}patch_embed.proj.bias''', '''beit.embeddings.patch_embeddings.projection.bias'''),
(f'''{prefix}pos_embed''', '''beit.embeddings.position_embeddings'''),
] )
if has_lm_head:
# mask token + layernorm
rename_keys.extend(
[
('''mask_token''', '''beit.embeddings.mask_token'''),
('''norm.weight''', '''layernorm.weight'''),
('''norm.bias''', '''layernorm.bias'''),
] )
else:
# layernorm + classification head
rename_keys.extend(
[
('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
('''head.weight''', '''classifier.weight'''),
('''head.bias''', '''classifier.bias'''),
] )
return rename_keys
def a__ ( UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str , UpperCAmelCase : str=False , UpperCAmelCase : Dict=False ) -> Any:
for i in range(config.num_hidden_layers ):
UpperCAmelCase : Tuple = '''backbone.''' if is_semantic else ''''''
# queries, keys and values
UpperCAmelCase : Optional[Any] = state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight''' )
UpperCAmelCase : Optional[Any] = state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias''' )
UpperCAmelCase : List[Any] = state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias''' )
UpperCAmelCase : Union[str, Any] = in_proj_weight[
: config.hidden_size, :
]
UpperCAmelCase : str = q_bias
UpperCAmelCase : List[str] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
UpperCAmelCase : List[str] = in_proj_weight[
-config.hidden_size :, :
]
UpperCAmelCase : int = v_bias
# gamma_1 and gamma_2
# we call them lambda because otherwise they are renamed when using .from_pretrained
UpperCAmelCase : int = state_dict.pop(f'''{prefix}blocks.{i}.gamma_1''' )
UpperCAmelCase : Optional[Any] = state_dict.pop(f'''{prefix}blocks.{i}.gamma_2''' )
UpperCAmelCase : str = gamma_a
UpperCAmelCase : Dict = gamma_a
def a__ ( UpperCAmelCase : Optional[int] , UpperCAmelCase : List[Any] , UpperCAmelCase : Tuple ) -> Optional[Any]:
UpperCAmelCase : Union[str, Any] = dct.pop(UpperCAmelCase )
UpperCAmelCase : str = val
def a__ ( ) -> Optional[int]:
UpperCAmelCase : List[Any] = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
UpperCAmelCase : Union[str, Any] = Image.open(requests.get(UpperCAmelCase , stream=UpperCAmelCase ).raw )
return im
@torch.no_grad()
def a__ ( UpperCAmelCase : Optional[int] , UpperCAmelCase : Tuple , UpperCAmelCase : List[Any]=False ) -> Union[str, Any]:
UpperCAmelCase : Dict = False if '''rvlcdip''' in checkpoint_url else True
UpperCAmelCase : Any = BeitConfig(use_absolute_position_embeddings=UpperCAmelCase , use_mask_token=UpperCAmelCase )
# size of the architecture
if "large" in checkpoint_url or "dit-l" in checkpoint_url:
UpperCAmelCase : List[Any] = 1_024
UpperCAmelCase : Optional[Any] = 4_096
UpperCAmelCase : Any = 24
UpperCAmelCase : Union[str, Any] = 16
# labels
if "rvlcdip" in checkpoint_url:
UpperCAmelCase : Optional[Any] = 16
UpperCAmelCase : List[Any] = '''huggingface/label-files'''
UpperCAmelCase : Any = '''rvlcdip-id2label.json'''
UpperCAmelCase : List[str] = json.load(open(hf_hub_download(UpperCAmelCase , UpperCAmelCase , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase : Dict = {int(UpperCAmelCase ): v for k, v in idalabel.items()}
UpperCAmelCase : Union[str, Any] = idalabel
UpperCAmelCase : Tuple = {v: k for k, v in idalabel.items()}
# load state_dict of original model, remove and rename some keys
UpperCAmelCase : Tuple = torch.hub.load_state_dict_from_url(UpperCAmelCase , map_location='''cpu''' )['''model''']
UpperCAmelCase : List[str] = create_rename_keys(UpperCAmelCase , has_lm_head=UpperCAmelCase )
for src, dest in rename_keys:
rename_key(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
read_in_q_k_v(UpperCAmelCase , UpperCAmelCase , has_lm_head=UpperCAmelCase )
# load HuggingFace model
UpperCAmelCase : Tuple = BeitForMaskedImageModeling(UpperCAmelCase ) if has_lm_head else BeitForImageClassification(UpperCAmelCase )
model.eval()
model.load_state_dict(UpperCAmelCase )
# Check outputs on an image
UpperCAmelCase : Dict = BeitImageProcessor(
size=config.image_size , resample=PILImageResampling.BILINEAR , do_center_crop=UpperCAmelCase )
UpperCAmelCase : List[str] = prepare_img()
UpperCAmelCase : Optional[Any] = image_processor(images=UpperCAmelCase , return_tensors='''pt''' )
UpperCAmelCase : str = encoding['''pixel_values''']
UpperCAmelCase : Any = model(UpperCAmelCase )
UpperCAmelCase : Optional[Any] = outputs.logits
# verify logits
UpperCAmelCase : List[Any] = [1, 16] if '''rvlcdip''' in checkpoint_url else [1, 196, 8_192]
assert logits.shape == torch.Size(UpperCAmelCase ), "Shape of logits not as expected"
Path(UpperCAmelCase ).mkdir(exist_ok=UpperCAmelCase )
print(f'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(UpperCAmelCase )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(UpperCAmelCase )
if push_to_hub:
if has_lm_head:
UpperCAmelCase : List[Any] = '''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
else:
UpperCAmelCase : Any = '''dit-base-finetuned-rvlcdip''' if '''dit-b''' in checkpoint_url else '''dit-large-finetuned-rvlcdip'''
image_processor.push_to_hub(
repo_path_or_name=Path(UpperCAmelCase , UpperCAmelCase ) , organization='''nielsr''' , commit_message='''Add image processor''' , use_temp_dir=UpperCAmelCase , )
model.push_to_hub(
repo_path_or_name=Path(UpperCAmelCase , UpperCAmelCase ) , organization='''nielsr''' , commit_message='''Add model''' , use_temp_dir=UpperCAmelCase , )
if __name__ == "__main__":
_lowerCamelCase : Tuple = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_url",
default="https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth",
type=str,
help="URL to the original PyTorch checkpoint (.pth file).",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
parser.add_argument(
"--push_to_hub",
action="store_true",
)
_lowerCamelCase : Optional[int] = parser.parse_args()
convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 336 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPSegProcessor, ViTImageProcessor
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
def __magic_name__ ( self : Dict ):
UpperCAmelCase : Union[str, Any] = tempfile.mkdtemp()
# fmt: off
UpperCAmelCase : Optional[Any] = ['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
# fmt: on
UpperCAmelCase : Any = dict(zip(__A, range(len(__A ) ) ) )
UpperCAmelCase : Optional[int] = ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
UpperCAmelCase : List[str] = {'''unk_token''': '''<unk>'''}
UpperCAmelCase : str = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['''vocab_file'''] )
UpperCAmelCase : List[Any] = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file, '''w''', encoding='''utf-8''' ) as fp:
fp.write(json.dumps(__A ) + '''\n''' )
with open(self.merges_file, '''w''', encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__A ) )
UpperCAmelCase : List[Any] = {
'''do_resize''': True,
'''size''': 2_0,
'''do_center_crop''': True,
'''crop_size''': 1_8,
'''do_normalize''': True,
'''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
'''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
}
UpperCAmelCase : str = os.path.join(self.tmpdirname, __A )
with open(self.image_processor_file, '''w''', encoding='''utf-8''' ) as fp:
json.dump(__A, __A )
def __magic_name__ ( self : Optional[int], **__A : Optional[int] ):
return CLIPTokenizer.from_pretrained(self.tmpdirname, **__A )
def __magic_name__ ( self : Optional[int], **__A : Optional[int] ):
return CLIPTokenizerFast.from_pretrained(self.tmpdirname, **__A )
def __magic_name__ ( self : Optional[Any], **__A : List[Any] ):
return ViTImageProcessor.from_pretrained(self.tmpdirname, **__A )
def __magic_name__ ( self : Union[str, Any] ):
shutil.rmtree(self.tmpdirname )
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : Optional[Any] = [np.random.randint(2_5_5, size=(3, 3_0, 4_0_0), dtype=np.uinta )]
UpperCAmelCase : Tuple = [Image.fromarray(np.moveaxis(__A, 0, -1 ) ) for x in image_inputs]
return image_inputs
def __magic_name__ ( self : Optional[Any] ):
UpperCAmelCase : Optional[int] = self.get_tokenizer()
UpperCAmelCase : Optional[Any] = self.get_rust_tokenizer()
UpperCAmelCase : Any = self.get_image_processor()
UpperCAmelCase : Union[str, Any] = CLIPSegProcessor(tokenizer=__A, image_processor=__A )
processor_slow.save_pretrained(self.tmpdirname )
UpperCAmelCase : Tuple = CLIPSegProcessor.from_pretrained(self.tmpdirname, use_fast=__A )
UpperCAmelCase : str = CLIPSegProcessor(tokenizer=__A, image_processor=__A )
processor_fast.save_pretrained(self.tmpdirname )
UpperCAmelCase : Any = CLIPSegProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab(), tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab(), tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab(), tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer, __A )
self.assertIsInstance(processor_fast.tokenizer, __A )
self.assertEqual(processor_slow.image_processor.to_json_string(), image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string(), image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor, __A )
self.assertIsInstance(processor_fast.image_processor, __A )
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : Optional[int] = CLIPSegProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
UpperCAmelCase : str = self.get_tokenizer(bos_token='''(BOS)''', eos_token='''(EOS)''' )
UpperCAmelCase : Any = self.get_image_processor(do_normalize=__A, padding_value=1.0 )
UpperCAmelCase : Optional[int] = CLIPSegProcessor.from_pretrained(
self.tmpdirname, bos_token='''(BOS)''', eos_token='''(EOS)''', do_normalize=__A, padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer, __A )
self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor, __A )
def __magic_name__ ( self : Tuple ):
UpperCAmelCase : int = self.get_image_processor()
UpperCAmelCase : Any = self.get_tokenizer()
UpperCAmelCase : Any = CLIPSegProcessor(tokenizer=__A, image_processor=__A )
UpperCAmelCase : List[str] = self.prepare_image_inputs()
UpperCAmelCase : Dict = image_processor(__A, return_tensors='''np''' )
UpperCAmelCase : List[str] = processor(images=__A, return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1E-2 )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : Dict = self.get_image_processor()
UpperCAmelCase : int = self.get_tokenizer()
UpperCAmelCase : Any = CLIPSegProcessor(tokenizer=__A, image_processor=__A )
UpperCAmelCase : Optional[int] = '''lower newer'''
UpperCAmelCase : List[str] = processor(text=__A )
UpperCAmelCase : Union[str, Any] = tokenizer(__A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key] )
def __magic_name__ ( self : Dict ):
UpperCAmelCase : List[str] = self.get_image_processor()
UpperCAmelCase : List[Any] = self.get_tokenizer()
UpperCAmelCase : str = CLIPSegProcessor(tokenizer=__A, image_processor=__A )
UpperCAmelCase : Optional[int] = '''lower newer'''
UpperCAmelCase : str = self.prepare_image_inputs()
UpperCAmelCase : List[Any] = processor(text=__A, images=__A )
self.assertListEqual(list(inputs.keys() ), ['''input_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(__A ):
processor()
def __magic_name__ ( self : List[Any] ):
UpperCAmelCase : List[str] = self.get_image_processor()
UpperCAmelCase : Any = self.get_tokenizer()
UpperCAmelCase : Optional[Any] = CLIPSegProcessor(tokenizer=__A, image_processor=__A )
UpperCAmelCase : Dict = self.prepare_image_inputs()
UpperCAmelCase : Optional[int] = self.prepare_image_inputs()
UpperCAmelCase : List[Any] = processor(images=__A, visual_prompt=__A )
self.assertListEqual(list(inputs.keys() ), ['''pixel_values''', '''conditional_pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(__A ):
processor()
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : int = self.get_image_processor()
UpperCAmelCase : int = self.get_tokenizer()
UpperCAmelCase : Any = CLIPSegProcessor(tokenizer=__A, image_processor=__A )
UpperCAmelCase : Optional[int] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
UpperCAmelCase : List[Any] = processor.batch_decode(__A )
UpperCAmelCase : Optional[Any] = tokenizer.batch_decode(__A )
self.assertListEqual(__A, __A )
| 336 |
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 : Optional[int], __A : Optional[int], __A : Any=1_3, __A : str=7, __A : Optional[int]=True, __A : Tuple=True, __A : Union[str, Any]=True, __A : Any=True, __A : Optional[int]=9_9, __A : Tuple=3_2, __A : str=5, __A : Union[str, Any]=4, __A : List[str]=3_7, __A : Tuple="gelu", __A : Optional[int]=0.1, __A : int=0.1, __A : Optional[Any]=5_1_2, __A : int=1_6, __A : Optional[Any]=2, __A : Union[str, Any]=0.0_2, __A : Optional[int]=4, ):
UpperCAmelCase : Any = parent
UpperCAmelCase : List[Any] = batch_size
UpperCAmelCase : Any = seq_length
UpperCAmelCase : Tuple = is_training
UpperCAmelCase : str = use_attention_mask
UpperCAmelCase : List[str] = use_token_type_ids
UpperCAmelCase : int = use_labels
UpperCAmelCase : List[Any] = vocab_size
UpperCAmelCase : Optional[int] = hidden_size
UpperCAmelCase : str = num_hidden_layers
UpperCAmelCase : Dict = num_attention_heads
UpperCAmelCase : Tuple = intermediate_size
UpperCAmelCase : List[str] = hidden_act
UpperCAmelCase : str = hidden_dropout_prob
UpperCAmelCase : int = attention_probs_dropout_prob
UpperCAmelCase : List[Any] = max_position_embeddings
UpperCAmelCase : Optional[Any] = type_vocab_size
UpperCAmelCase : Any = type_sequence_label_size
UpperCAmelCase : Optional[Any] = initializer_range
UpperCAmelCase : Any = num_choices
def __magic_name__ ( self : str ):
UpperCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
UpperCAmelCase : List[Any] = None
if self.use_attention_mask:
UpperCAmelCase : Any = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase : Any = None
if self.use_token_type_ids:
UpperCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size )
UpperCAmelCase : Union[str, Any] = 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=__A, initializer_range=self.initializer_range, )
return config, input_ids, token_type_ids, attention_mask
def __magic_name__ ( self : int ):
UpperCAmelCase : Any = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[Any] = config_and_inputs
UpperCAmelCase : Dict = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : List[Any] = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Dict = config_and_inputs
UpperCAmelCase : Any = True
UpperCAmelCase : Union[str, Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
UpperCAmelCase : List[Any] = 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 ( lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = True
UpperCamelCase = (
(
FlaxRobertaModel,
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __magic_name__ ( self : Optional[Any] ):
UpperCAmelCase : Dict = FlaxRobertaModelTester(self )
@slow
def __magic_name__ ( self : Any ):
for model_class_name in self.all_model_classes:
UpperCAmelCase : Dict = model_class_name.from_pretrained('''roberta-base''', from_pt=__A )
UpperCAmelCase : List[str] = model(np.ones((1, 1) ) )
self.assertIsNotNone(__A )
| 336 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCamelCase : Optional[int] = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class __UpperCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = XLNetTokenizer
UpperCamelCase = XLNetTokenizerFast
UpperCamelCase = True
UpperCamelCase = True
def __magic_name__ ( self : Any ):
super().setUp()
# We have a SentencePiece fixture for testing
UpperCAmelCase : Tuple = XLNetTokenizer(__A, keep_accents=__A )
tokenizer.sanitize_special_tokens()
tokenizer.save_pretrained(self.tmpdirname )
def __magic_name__ ( self : Any ):
UpperCAmelCase : List[Any] = '''<s>'''
UpperCAmelCase : Union[str, Any] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__A ), __A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__A ), __A )
def __magic_name__ ( self : Dict ):
UpperCAmelCase : Union[str, Any] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0], '''<unk>''' )
self.assertEqual(vocab_keys[1], '''<s>''' )
self.assertEqual(vocab_keys[-1], '''<eod>''' )
self.assertEqual(len(__A ), 1_0_0_6 )
def __magic_name__ ( self : Optional[Any] ):
self.assertEqual(self.get_tokenizer().vocab_size, 1_0_0_0 )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : Any = XLNetTokenizer(__A, keep_accents=__A )
UpperCAmelCase : Union[str, Any] = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__A, ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ), [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2] )
UpperCAmelCase : Tuple = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__A, [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
], )
UpperCAmelCase : Any = tokenizer.convert_tokens_to_ids(__A )
self.assertListEqual(__A, [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4] )
UpperCAmelCase : List[Any] = tokenizer.convert_ids_to_tokens(__A )
self.assertListEqual(
__A, [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
], )
def __magic_name__ ( self : Any ):
UpperCAmelCase : int = XLNetTokenizer(__A, do_lower_case=__A )
UpperCAmelCase : int = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__A, [
SPIECE_UNDERLINE + '''''',
'''i''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''se''',
'''.''',
], )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ), ['''▁he''', '''ll''', '''o'''] )
def __magic_name__ ( self : Any ):
UpperCAmelCase : List[str] = XLNetTokenizer(__A, do_lower_case=__A )
UpperCAmelCase : Dict = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__A, [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''se''',
'''.''',
], )
@slow
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : Optional[int] = XLNetTokenizer.from_pretrained('''xlnet-base-cased''' )
UpperCAmelCase : Tuple = tokenizer.encode('''sequence builders''', add_special_tokens=__A )
UpperCAmelCase : Any = tokenizer.encode('''multi-sequence build''', add_special_tokens=__A )
UpperCAmelCase : str = tokenizer.build_inputs_with_special_tokens(__A )
UpperCAmelCase : Tuple = tokenizer.build_inputs_with_special_tokens(__A, __A )
assert encoded_sentence == text + [4, 3]
assert encoded_pair == text + [4] + text_a + [4, 3]
@slow
def __magic_name__ ( self : str ):
# fmt: off
UpperCAmelCase : Optional[Any] = {'''input_ids''': [[1_7, 2_1_4_4_2, 2_7_0, 1_7, 1_0, 1_4_6_4_5, 3_1_8, 3_4, 1_7, 4_5_4_6, 3_1_4_5, 7_8_7, 1_3, 7_7_5_2, 2_2_0_1_8, 2_3, 2_1, 1_7, 4_5_4_6, 3_1_4_5, 7_8_7, 1_3, 3_3_5_2, 1_4_4_3_1, 1_3, 5_5_0_0, 1_1, 1_1_7_6, 5_8_0, 1_3, 1_6_8_1_9, 4_7_9_7, 2_3, 1_7, 1_0, 1_7_1_3_5, 6_5_8, 1_9, 4_5_7, 7_9_3_2, 1_3, 1_8_4, 1_9, 3_1_5_4, 1_7_1_3_5, 6_4_6_8, 1_9, 1_4_0_4, 1_2_2_6_9, 1_9, 4_2_2_9, 5_3_5_6, 1_6_2_6_4, 4_6, 1_9, 1_7, 2_0_5_4_5, 1_0_3_9_5, 9, 9, 9, 1_1, 2_8, 6_4_2_1, 9_5_3_1, 2_0_7_2_9, 1_7, 1_0, 3_5_3, 1_7_0_2_2, 1_1, 2_1, 6_4_2_1, 9_5_3_1, 1_6_9_4_9, 1_7, 1_0, 1_1_5_0_9, 7_5_3, 1_1, 3_3, 9_5, 2_4_2_1, 7_3_8_5, 9_5_6, 1_4_4_3_1, 2_6_2_6, 2_5, 8_4_2, 7_3_8_5, 4_8_3_6, 2_1, 1_4_2_9, 2_2_7_2, 9_8_5_5, 3_1_2_0, 1_6_1, 2_4_7_3_8, 1_9, 1_3_2_0_3, 6_5_8, 2_1_8, 7_8_7, 2_1, 4_3_0, 1_8_4_8_2, 8_4_7, 2_6_3_7, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_2_2, 2_2_1_7_8, 2_7, 1_0_6_4, 2_2, 9_5_6, 1_3, 1_1_1_0_1, 1_4_2_9, 5_8_5_4, 2_4_3_1_3, 1_8_9_5_3, 4_0, 4_2_2, 2_4_3_6_6, 6_8, 1_7_5_8, 3_7, 1_0_4_8_3, 1_4_2_5_7, 3_1, 2_0_7, 2_6_3, 2_1, 2_0_3, 3_7_7_3, 2_5, 7_1, 9_7_3_5, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_2, 2_0_4_9, 3_4_4_2, 1_7, 1_3_8_9_4, 3_3_8_0, 2_3, 9_5, 1_8, 1_7_6_3_4, 2_2_8_8, 9, 4, 3]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__A, model_name='''xlnet-base-cased''', revision='''c841166438c31ec7ca9a106dee7bb312b73ae511''', )
| 336 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCamelCase : Dict = {"vocab_file": "vocab.txt"}
_lowerCamelCase : List[str] = {
"vocab_file": {
"facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
"facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
},
}
_lowerCamelCase : List[Any] = {
"facebook/esm2_t6_8M_UR50D": 1_0_2_4,
"facebook/esm2_t12_35M_UR50D": 1_0_2_4,
}
def a__ ( UpperCAmelCase : List[str] ) -> Any:
with open(UpperCAmelCase , '''r''' ) as f:
UpperCAmelCase : Dict = f.read().splitlines()
return [l.strip() for l in lines]
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = VOCAB_FILES_NAMES
UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase = ["""input_ids""", """attention_mask"""]
def __init__( self : Any, __A : Dict, __A : List[Any]="<unk>", __A : List[str]="<cls>", __A : Any="<pad>", __A : Union[str, Any]="<mask>", __A : int="<eos>", **__A : Tuple, ):
super().__init__(**__A )
UpperCAmelCase : Tuple = load_vocab_file(__A )
UpperCAmelCase : List[Any] = dict(enumerate(self.all_tokens ) )
UpperCAmelCase : str = {tok: ind for ind, tok in enumerate(self.all_tokens )}
UpperCAmelCase : Union[str, Any] = unk_token
UpperCAmelCase : Optional[Any] = cls_token
UpperCAmelCase : Optional[int] = pad_token
UpperCAmelCase : Optional[int] = mask_token
UpperCAmelCase : List[str] = eos_token
UpperCAmelCase : Optional[Any] = self.all_tokens
self._create_trie(self.unique_no_split_tokens )
def __magic_name__ ( self : Tuple, __A : int ):
return self._id_to_token.get(__A, self.unk_token )
def __magic_name__ ( self : List[Any], __A : str ):
return self._token_to_id.get(__A, self._token_to_id.get(self.unk_token ) )
def __magic_name__ ( self : Any, __A : Optional[Any], **__A : Union[str, Any] ):
return text.split()
def __magic_name__ ( self : Optional[int], __A : Dict=False ):
return len(self._id_to_token )
def __magic_name__ ( self : int ):
return {token: i for i, token in enumerate(self.all_tokens )}
def __magic_name__ ( self : Tuple, __A : str ):
return self._token_to_id.get(__A, self._token_to_id.get(self.unk_token ) )
def __magic_name__ ( self : Any, __A : int ):
return self._id_to_token.get(__A, self.unk_token )
def __magic_name__ ( self : Union[str, Any], __A : List[int], __A : Optional[List[int]] = None ):
UpperCAmelCase : Optional[int] = [self.cls_token_id]
UpperCAmelCase : Optional[int] = [self.eos_token_id] # No sep token in ESM vocabulary
if token_ids_a is None:
if self.eos_token_id is None:
return cls + token_ids_a
else:
return cls + token_ids_a + sep
elif self.eos_token_id is None:
raise ValueError('''Cannot tokenize multiple sequences when EOS token is not set!''' )
return cls + token_ids_a + sep + token_ids_a + sep # Multiple inputs always have an EOS token
def __magic_name__ ( self : Any, __A : List, __A : Optional[List] = None, __A : bool = False ):
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'''You should not supply a second sequence if the provided sequence of '''
'''ids is already formatted with special tokens for the model.''' )
return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
UpperCAmelCase : Dict = [1] + ([0] * len(__A )) + [1]
if token_ids_a is not None:
mask += [0] * len(__A ) + [1]
return mask
def __magic_name__ ( self : Optional[int], __A : List[Any], __A : Dict ):
UpperCAmelCase : Union[str, Any] = os.path.join(__A, (filename_prefix + '''-''' if filename_prefix else '''''') + '''vocab.txt''' )
with open(__A, '''w''' ) as f:
f.write('''\n'''.join(self.all_tokens ) )
return (vocab_file,)
@property
def __magic_name__ ( self : Dict ):
return self.get_vocab_size(with_added_tokens=__A )
def __magic_name__ ( self : Optional[int], __A : Union[List[str], List[AddedToken]], __A : bool = False ):
return super()._add_tokens(__A, special_tokens=__A )
| 336 | 1 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed
def a__ ( UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : Any ) -> Any:
for param, grad_param in zip(model_a.parameters() , model_b.parameters() ):
if not param.requires_grad:
continue
if not did_step:
# Grads should not be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is False
), f'''Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'''
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is True
), f'''Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'''
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : Optional[int] , UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : str=True ) -> Dict:
model.train()
UpperCAmelCase : Optional[int] = model(UpperCAmelCase )
UpperCAmelCase : Dict = F.mse_loss(UpperCAmelCase , target.to(output.device ) )
if not do_backward:
loss /= accelerator.gradient_accumulation_steps
loss.backward()
else:
accelerator.backward(UpperCAmelCase )
def a__ ( UpperCAmelCase : int , UpperCAmelCase : List[str]=False ) -> Optional[int]:
set_seed(42 )
UpperCAmelCase : Optional[Any] = RegressionModel()
UpperCAmelCase : Any = deepcopy(UpperCAmelCase )
UpperCAmelCase : List[Any] = RegressionDataset(length=80 )
UpperCAmelCase : Union[str, Any] = DataLoader(UpperCAmelCase , batch_size=16 )
model.to(accelerator.device )
if sched:
UpperCAmelCase : Optional[int] = AdamW(params=model.parameters() , lr=1E-3 )
UpperCAmelCase : Optional[int] = AdamW(params=ddp_model.parameters() , lr=1E-3 )
UpperCAmelCase : str = LambdaLR(UpperCAmelCase , lr_lambda=lambda UpperCAmelCase : epoch**0.65 )
UpperCAmelCase : Any = LambdaLR(UpperCAmelCase , lr_lambda=lambda UpperCAmelCase : epoch**0.65 )
# Make a copy of `model`
if sched:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[str] = accelerator.prepare(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
else:
UpperCAmelCase , UpperCAmelCase : List[str] = accelerator.prepare(UpperCAmelCase , UpperCAmelCase )
if sched:
return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
return model, ddp_model, dataloader
def a__ ( UpperCAmelCase : Any ) -> Any:
# Test when on a single CPU or GPU that the context manager does nothing
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Tuple = get_training_setup(UpperCAmelCase )
# Use a single batch
UpperCAmelCase , UpperCAmelCase : Union[str, Any] = next(iter(UpperCAmelCase ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
UpperCAmelCase , UpperCAmelCase : Any = accelerator.gather((ddp_input, ddp_target) )
UpperCAmelCase , UpperCAmelCase : str = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(UpperCAmelCase ):
step_model(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
else:
# Sync grads
step_model(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
check_model_parameters(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
assert torch.allclose(
param.grad , ddp_param.grad ), f'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
# Shuffle ddp_input on each iteration
torch.manual_seed(1_337 + iteration )
UpperCAmelCase : List[Any] = ddp_input[torch.randperm(len(UpperCAmelCase ) )]
def a__ ( UpperCAmelCase : int ) -> str:
# Test on distributed setup that context manager behaves properly
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Any = get_training_setup(UpperCAmelCase )
# Use a single batch
UpperCAmelCase , UpperCAmelCase : Optional[int] = next(iter(UpperCAmelCase ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
UpperCAmelCase , UpperCAmelCase : Optional[int] = accelerator.gather((ddp_input, ddp_target) )
UpperCAmelCase , UpperCAmelCase : str = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(UpperCAmelCase ):
step_model(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
else:
# Sync grads
step_model(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if iteration % 2 == 0:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), f'''Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), f'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
# Shuffle ddp_input on each iteration
torch.manual_seed(1_337 + iteration )
UpperCAmelCase : int = ddp_input[torch.randperm(len(UpperCAmelCase ) )]
def a__ ( UpperCAmelCase : Optional[Any]=False , UpperCAmelCase : Tuple=False ) -> Optional[Any]:
UpperCAmelCase : Tuple = Accelerator(
split_batches=UpperCAmelCase , dispatch_batches=UpperCAmelCase , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Optional[int] = get_training_setup(UpperCAmelCase )
for iteration, batch in enumerate(UpperCAmelCase ):
UpperCAmelCase , UpperCAmelCase : Union[str, Any] = batch.values()
# Gather the distributed inputs and targs for the base model
UpperCAmelCase , UpperCAmelCase : Tuple = accelerator.gather((ddp_input, ddp_target) )
UpperCAmelCase , UpperCAmelCase : Optional[int] = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# Do "gradient accumulation" (noop)
with accelerator.accumulate(UpperCAmelCase ):
step_model(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if ((iteration + 1) % 2 == 0) or (iteration == len(UpperCAmelCase ) - 1):
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), f'''Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
else:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), f'''Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''
# Shuffle ddp_input on each iteration
torch.manual_seed(1_337 + iteration )
UpperCAmelCase : Any = ddp_input[torch.randperm(len(UpperCAmelCase ) )]
GradientState._reset_state()
def a__ ( UpperCAmelCase : List[str]=False , UpperCAmelCase : List[Any]=False ) -> str:
UpperCAmelCase : List[str] = Accelerator(
split_batches=UpperCAmelCase , dispatch_batches=UpperCAmelCase , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int = get_training_setup(UpperCAmelCase , UpperCAmelCase )
for iteration, batch in enumerate(UpperCAmelCase ):
UpperCAmelCase , UpperCAmelCase : Dict = batch.values()
# Gather the distributed inputs and targs for the base model
UpperCAmelCase , UpperCAmelCase : List[str] = accelerator.gather((ddp_input, ddp_target) )
UpperCAmelCase , UpperCAmelCase : Dict = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
model.train()
ddp_model.train()
step_model(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
opt.step()
if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(UpperCAmelCase )):
if split_batches:
sched.step()
else:
for _ in range(accelerator.num_processes ):
sched.step()
opt.zero_grad()
# Perform gradient accumulation under wrapper
with accelerator.accumulate(UpperCAmelCase ):
step_model(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
ddp_opt.step()
ddp_sched.step()
ddp_opt.zero_grad()
# Learning rates should be the same
assert (
opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
), f'''Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n'''
UpperCAmelCase : Any = (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(UpperCAmelCase ))
if accelerator.num_processes > 1:
check_model_parameters(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# Shuffle ddp_input on each iteration
torch.manual_seed(1_337 + iteration )
GradientState._reset_state()
def a__ ( ) -> Dict:
UpperCAmelCase : Union[str, Any] = Accelerator()
UpperCAmelCase : Optional[Any] = RegressionDataset(length=80 )
UpperCAmelCase : Any = DataLoader(UpperCAmelCase , batch_size=16 )
UpperCAmelCase : List[str] = RegressionDataset(length=96 )
UpperCAmelCase : List[Any] = DataLoader(UpperCAmelCase , batch_size=16 )
UpperCAmelCase , UpperCAmelCase : str = accelerator.prepare(UpperCAmelCase , UpperCAmelCase )
assert accelerator.gradient_state.active_dataloader is None
for iteration, _ in enumerate(UpperCAmelCase ):
assert id(accelerator.gradient_state.active_dataloader ) == id(UpperCAmelCase )
if iteration < len(UpperCAmelCase ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
if iteration == 1:
for batch_num, _ in enumerate(UpperCAmelCase ):
assert id(accelerator.gradient_state.active_dataloader ) == id(UpperCAmelCase )
if batch_num < len(UpperCAmelCase ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
assert accelerator.gradient_state.active_dataloader is None
def a__ ( ) -> List[str]:
UpperCAmelCase : Optional[Any] = Accelerator()
UpperCAmelCase : Optional[Any] = accelerator.state
if state.local_process_index == 0:
print('''**Test `accumulate` gradient accumulation with dataloader break**''' )
test_dataloader_break()
if state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print('''**Test NOOP `no_sync` context manager**''' )
test_noop_sync(UpperCAmelCase )
if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
if state.local_process_index == 0:
print('''**Test Distributed `no_sync` context manager**''' )
test_distributed_sync(UpperCAmelCase )
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if state.local_process_index == 0:
print(
'''**Test `accumulate` gradient accumulation, ''' , f'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**''' , )
test_gradient_accumulation(UpperCAmelCase , UpperCAmelCase )
# Currently will break on torch 2.0 +, need to investigate why
if is_torch_version('''<''' , '''2.0''' ) or state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print(
'''**Test `accumulate` gradient accumulation with optimizer and scheduler, ''' , '''`split_batches=False`, `dispatch_batches=False`**''' , )
test_gradient_accumulation_with_opt_and_scheduler()
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if not split_batch and not dispatch_batches:
continue
if state.local_process_index == 0:
print(
'''**Test `accumulate` gradient accumulation with optimizer and scheduler, ''' , f'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**''' , )
test_gradient_accumulation_with_opt_and_scheduler(UpperCAmelCase , UpperCAmelCase )
def a__ ( UpperCAmelCase : Any ) -> List[Any]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 336 |
import inspect
import unittest
from transformers import MobileNetVaConfig
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 transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class __UpperCAmelCase ( lowerCamelCase__ ):
def __magic_name__ ( self : Optional[Any] ):
UpperCAmelCase : str = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__A, '''tf_padding''' ) )
self.parent.assertTrue(hasattr(__A, '''depth_multiplier''' ) )
class __UpperCAmelCase :
def __init__( self : int, __A : List[Any], __A : str=1_3, __A : Dict=3, __A : int=3_2, __A : int=0.2_5, __A : List[str]=8, __A : int=8, __A : Dict=6, __A : str=3_2, __A : Any=True, __A : str=True, __A : int=True, __A : Union[str, Any]="relu6", __A : Any=1_2_8_0, __A : List[Any]=0.1, __A : Optional[Any]=0.0_2, __A : Tuple=True, __A : List[Any]=True, __A : str=1_0, __A : Optional[Any]=None, ):
UpperCAmelCase : Optional[int] = parent
UpperCAmelCase : List[str] = batch_size
UpperCAmelCase : List[str] = num_channels
UpperCAmelCase : str = image_size
UpperCAmelCase : Optional[int] = depth_multiplier
UpperCAmelCase : Union[str, Any] = depth_divisible_by
UpperCAmelCase : Optional[Any] = min_depth
UpperCAmelCase : List[str] = expand_ratio
UpperCAmelCase : Dict = tf_padding
UpperCAmelCase : str = output_stride
UpperCAmelCase : Union[str, Any] = first_layer_is_expansion
UpperCAmelCase : List[Any] = finegrained_output
UpperCAmelCase : Optional[Any] = hidden_act
UpperCAmelCase : str = last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier )
UpperCAmelCase : Optional[Any] = classifier_dropout_prob
UpperCAmelCase : Dict = use_labels
UpperCAmelCase : List[str] = is_training
UpperCAmelCase : Tuple = num_labels
UpperCAmelCase : Union[str, Any] = initializer_range
UpperCAmelCase : Any = scope
def __magic_name__ ( self : List[Any] ):
UpperCAmelCase : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : Dict = None
UpperCAmelCase : Any = None
if self.use_labels:
UpperCAmelCase : Dict = ids_tensor([self.batch_size], self.num_labels )
UpperCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels )
UpperCAmelCase : Optional[Any] = self.get_config()
return config, pixel_values, labels, pixel_labels
def __magic_name__ ( self : Any ):
return MobileNetVaConfig(
num_channels=self.num_channels, image_size=self.image_size, depth_multiplier=self.depth_multiplier, depth_divisible_by=self.depth_divisible_by, min_depth=self.min_depth, expand_ratio=self.expand_ratio, output_stride=self.output_stride, first_layer_is_expansion=self.first_layer_is_expansion, finegrained_output=self.finegrained_output, hidden_act=self.hidden_act, tf_padding=self.tf_padding, classifier_dropout_prob=self.classifier_dropout_prob, initializer_range=self.initializer_range, )
def __magic_name__ ( self : List[Any], __A : Dict, __A : Optional[Any], __A : Optional[int], __A : Union[str, Any] ):
UpperCAmelCase : Any = MobileNetVaModel(config=__A )
model.to(__A )
model.eval()
UpperCAmelCase : Optional[Any] = model(__A )
self.parent.assertEqual(
result.last_hidden_state.shape, (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
), )
self.parent.assertEqual(
result.pooler_output.shape, (self.batch_size, self.last_hidden_size), )
def __magic_name__ ( self : str, __A : Union[str, Any], __A : Dict, __A : Optional[Any], __A : str ):
UpperCAmelCase : Optional[int] = self.num_labels
UpperCAmelCase : Any = MobileNetVaForImageClassification(__A )
model.to(__A )
model.eval()
UpperCAmelCase : Optional[int] = model(__A, labels=__A )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def __magic_name__ ( self : List[Any], __A : Optional[Any], __A : List[str], __A : Dict, __A : Dict ):
UpperCAmelCase : Tuple = self.num_labels
UpperCAmelCase : Dict = MobileNetVaForSemanticSegmentation(__A )
model.to(__A )
model.eval()
UpperCAmelCase : Dict = model(__A )
self.parent.assertEqual(
result.logits.shape, (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
), )
UpperCAmelCase : Optional[Any] = model(__A, labels=__A )
self.parent.assertEqual(
result.logits.shape, (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
), )
def __magic_name__ ( self : Tuple ):
UpperCAmelCase : List[str] = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int = config_and_inputs
UpperCAmelCase : Optional[int] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = (
(MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
if is_torch_available()
else ()
)
UpperCamelCase = (
{
"""feature-extraction""": MobileNetVaModel,
"""image-classification""": MobileNetVaForImageClassification,
"""image-segmentation""": MobileNetVaForSemanticSegmentation,
}
if is_torch_available()
else {}
)
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : List[Any] = MobileNetVaModelTester(self )
UpperCAmelCase : List[Any] = MobileNetVaConfigTester(self, config_class=__A, has_text_modality=__A )
def __magic_name__ ( self : Tuple ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''MobileNetV2 does not use inputs_embeds''' )
def __magic_name__ ( self : Optional[int] ):
pass
@unittest.skip(reason='''MobileNetV2 does not support input and output embeddings''' )
def __magic_name__ ( self : Tuple ):
pass
@unittest.skip(reason='''MobileNetV2 does not output attentions''' )
def __magic_name__ ( self : Any ):
pass
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase , UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Optional[Any] = model_class(__A )
UpperCAmelCase : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase : Union[str, Any] = [*signature.parameters.keys()]
UpperCAmelCase : Any = ['''pixel_values''']
self.assertListEqual(arg_names[:1], __A )
def __magic_name__ ( self : List[Any] ):
UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __magic_name__ ( self : int ):
def check_hidden_states_output(__A : Any, __A : Optional[Any], __A : str ):
UpperCAmelCase : Union[str, Any] = model_class(__A )
model.to(__A )
model.eval()
with torch.no_grad():
UpperCAmelCase : Dict = model(**self._prepare_for_class(__A, __A ) )
UpperCAmelCase : Optional[Any] = outputs.hidden_states
UpperCAmelCase : List[Any] = 1_6
self.assertEqual(len(__A ), __A )
UpperCAmelCase , UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Tuple = True
check_hidden_states_output(__A, __A, __A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase : Tuple = True
check_hidden_states_output(__A, __A, __A )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__A )
def __magic_name__ ( self : int ):
UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__A )
@slow
def __magic_name__ ( self : Dict ):
for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase : Optional[Any] = MobileNetVaModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def a__ ( ) -> int:
UpperCAmelCase : Dict = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : List[Any] ):
return (
MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v2_1.0_224''' ) if is_vision_available() else None
)
@slow
def __magic_name__ ( self : Optional[Any] ):
UpperCAmelCase : List[Any] = MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v2_1.0_224''' ).to(__A )
UpperCAmelCase : Optional[int] = self.default_image_processor
UpperCAmelCase : Optional[Any] = prepare_img()
UpperCAmelCase : Dict = image_processor(images=__A, return_tensors='''pt''' ).to(__A )
# forward pass
with torch.no_grad():
UpperCAmelCase : str = model(**__A )
# verify the logits
UpperCAmelCase : int = torch.Size((1, 1_0_0_1) )
self.assertEqual(outputs.logits.shape, __A )
UpperCAmelCase : Tuple = torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5] ).to(__A )
self.assertTrue(torch.allclose(outputs.logits[0, :3], __A, atol=1E-4 ) )
@slow
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : Tuple = MobileNetVaForSemanticSegmentation.from_pretrained('''google/deeplabv3_mobilenet_v2_1.0_513''' )
UpperCAmelCase : List[Any] = model.to(__A )
UpperCAmelCase : Tuple = MobileNetVaImageProcessor.from_pretrained('''google/deeplabv3_mobilenet_v2_1.0_513''' )
UpperCAmelCase : List[Any] = prepare_img()
UpperCAmelCase : int = image_processor(images=__A, return_tensors='''pt''' ).to(__A )
# forward pass
with torch.no_grad():
UpperCAmelCase : Union[str, Any] = model(**__A )
UpperCAmelCase : Optional[Any] = outputs.logits
# verify the logits
UpperCAmelCase : Tuple = torch.Size((1, 2_1, 6_5, 6_5) )
self.assertEqual(logits.shape, __A )
UpperCAmelCase : Tuple = torch.tensor(
[
[[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
[[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
[[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
], device=__A, )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3], __A, atol=1E-4 ) )
| 336 | 1 |
from collections import defaultdict
def a__ ( UpperCAmelCase : int ) -> int:
UpperCAmelCase : Any = 1
UpperCAmelCase : Dict = True
for v in tree[start]:
if v not in visited:
ret += dfs(UpperCAmelCase )
if ret % 2 == 0:
cuts.append(UpperCAmelCase )
return ret
def a__ ( ) -> Tuple:
dfs(1 )
if __name__ == "__main__":
_lowerCamelCase , _lowerCamelCase : Dict = 1_0, 9
_lowerCamelCase : Union[str, Any] = defaultdict(list)
_lowerCamelCase : dict[int, bool] = {}
_lowerCamelCase : list[int] = []
_lowerCamelCase : Dict = 0
_lowerCamelCase : Dict = [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (1_0, 8)]
for u, v in edges:
tree[u].append(v)
tree[v].append(u)
even_tree()
print(len(cuts) - 1)
| 336 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
_lowerCamelCase : str = logging.get_logger(__name__)
_lowerCamelCase : Optional[int] = {
"Salesforce/codegen-350M-nl": "https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json",
"Salesforce/codegen-350M-multi": "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json",
"Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json",
"Salesforce/codegen-2B-nl": "https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json",
"Salesforce/codegen-2B-multi": "https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json",
"Salesforce/codegen-2B-mono": "https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json",
"Salesforce/codegen-6B-nl": "https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json",
"Salesforce/codegen-6B-multi": "https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json",
"Salesforce/codegen-6B-mono": "https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json",
"Salesforce/codegen-16B-nl": "https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json",
"Salesforce/codegen-16B-multi": "https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json",
"Salesforce/codegen-16B-mono": "https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json",
}
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """codegen"""
UpperCamelCase = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : Any, __A : Optional[int]=5_0_4_0_0, __A : Tuple=2_0_4_8, __A : Optional[int]=2_0_4_8, __A : List[str]=4_0_9_6, __A : List[str]=2_8, __A : Union[str, Any]=1_6, __A : Tuple=6_4, __A : Union[str, Any]=None, __A : Union[str, Any]="gelu_new", __A : Any=0.0, __A : Dict=0.0, __A : str=0.0, __A : Optional[int]=1E-5, __A : Any=0.0_2, __A : Any=True, __A : Union[str, Any]=5_0_2_5_6, __A : List[str]=5_0_2_5_6, __A : int=False, **__A : List[Any], ):
UpperCAmelCase : int = vocab_size
UpperCAmelCase : Tuple = n_ctx
UpperCAmelCase : Tuple = n_positions
UpperCAmelCase : Optional[int] = n_embd
UpperCAmelCase : Union[str, Any] = n_layer
UpperCAmelCase : List[str] = n_head
UpperCAmelCase : Tuple = n_inner
UpperCAmelCase : int = rotary_dim
UpperCAmelCase : List[Any] = activation_function
UpperCAmelCase : List[str] = resid_pdrop
UpperCAmelCase : Optional[Any] = embd_pdrop
UpperCAmelCase : str = attn_pdrop
UpperCAmelCase : Tuple = layer_norm_epsilon
UpperCAmelCase : Dict = initializer_range
UpperCAmelCase : Union[str, Any] = use_cache
UpperCAmelCase : Any = bos_token_id
UpperCAmelCase : List[str] = eos_token_id
super().__init__(
bos_token_id=__A, eos_token_id=__A, tie_word_embeddings=__A, **__A )
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : Any, __A : PretrainedConfig, __A : str = "default", __A : List[PatchingSpec] = None, __A : bool = False, ):
super().__init__(__A, task=__A, patching_specs=__A, use_past=__A )
if not getattr(self._config, '''pad_token_id''', __A ):
# TODO: how to do that better?
UpperCAmelCase : Union[str, Any] = 0
@property
def __magic_name__ ( self : str ):
UpperCAmelCase : Union[str, Any] = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
if self.use_past:
self.fill_with_past_key_values_(__A, direction='''inputs''' )
UpperCAmelCase : int = {0: '''batch''', 1: '''past_sequence + sequence'''}
else:
UpperCAmelCase : List[Any] = {0: '''batch''', 1: '''sequence'''}
return common_inputs
@property
def __magic_name__ ( self : Dict ):
return self._config.n_layer
@property
def __magic_name__ ( self : List[str] ):
return self._config.n_head
def __magic_name__ ( self : str, __A : PreTrainedTokenizer, __A : int = -1, __A : int = -1, __A : bool = False, __A : Optional[TensorType] = None, ):
UpperCAmelCase : Union[str, Any] = super(__A, self ).generate_dummy_inputs(
__A, batch_size=__A, seq_length=__A, is_pair=__A, framework=__A )
# We need to order the input in the way they appears in the forward()
UpperCAmelCase : Union[str, Any] = OrderedDict({'''input_ids''': common_inputs['''input_ids''']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
UpperCAmelCase , UpperCAmelCase : str = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
UpperCAmelCase : str = seqlen + 2
UpperCAmelCase : Optional[int] = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
UpperCAmelCase : Optional[int] = [
(torch.zeros(__A ), torch.zeros(__A )) for _ in range(self.num_layers )
]
UpperCAmelCase : Union[str, Any] = common_inputs['''attention_mask''']
if self.use_past:
UpperCAmelCase : Optional[Any] = ordered_inputs['''attention_mask'''].dtype
UpperCAmelCase : Dict = torch.cat(
[ordered_inputs['''attention_mask'''], torch.ones(__A, __A, dtype=__A )], dim=1 )
return ordered_inputs
@property
def __magic_name__ ( self : Tuple ):
return 1_3
| 336 | 1 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_tensorflow_text_available():
from transformers.models.bert import TFBertTokenizer
_lowerCamelCase : List[Any] = ["bert-base-uncased", "bert-base-cased"]
_lowerCamelCase : int = "hf-internal-testing/tiny-bert-tf-only"
if is_tf_available():
class __UpperCAmelCase ( tf.keras.Model ):
def __init__( self : List[Any], __A : Dict ):
super().__init__()
UpperCAmelCase : Optional[Any] = tokenizer
UpperCAmelCase : Any = AutoConfig.from_pretrained(__A )
UpperCAmelCase : str = TFAutoModel.from_config(__A )
def __magic_name__ ( self : Union[str, Any], __A : Tuple ):
UpperCAmelCase : List[Any] = self.tokenizer(__A )
UpperCAmelCase : Tuple = self.bert(**__A )
return out["pooler_output"]
@require_tf
@require_tensorflow_text
class __UpperCAmelCase ( unittest.TestCase ):
def __magic_name__ ( self : Optional[Any] ):
super().setUp()
UpperCAmelCase : List[Any] = [
BertTokenizer.from_pretrained(__A ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
] # repeat for when fast_bert_tokenizer=false
UpperCAmelCase : List[str] = [TFBertTokenizer.from_pretrained(__A ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
TFBertTokenizer.from_pretrained(__A, use_fast_bert_tokenizer=__A )
for checkpoint in TOKENIZER_CHECKPOINTS
]
assert len(self.tokenizers ) == len(self.tf_tokenizers )
UpperCAmelCase : Any = [
'''This is a straightforward English test sentence.''',
'''This one has some weird characters\rto\nsee\r\nif those\u00E9break things.''',
'''Now we\'re going to add some Chinese: 一 二 三 一二三''',
'''And some much more rare Chinese: 齉 堃 齉堃''',
'''Je vais aussi écrire en français pour tester les accents''',
'''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
]
UpperCAmelCase : Tuple = list(zip(self.test_sentences, self.test_sentences[::-1] ) )
def __magic_name__ ( self : List[str] ):
for tokenizer, tf_tokenizer in zip(self.tokenizers, self.tf_tokenizers ):
for test_inputs in (self.test_sentences, self.paired_sentences):
UpperCAmelCase : Optional[Any] = tokenizer(__A, return_tensors='''tf''', padding='''longest''' )
UpperCAmelCase : List[str] = tf_tokenizer(__A )
for key in python_outputs.keys():
self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape ) )
self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key], tf.intaa ) == tf_outputs[key] ) )
@slow
def __magic_name__ ( self : Optional[int] ):
for tf_tokenizer in self.tf_tokenizers:
UpperCAmelCase : Dict = tf_tokenizer(self.paired_sentences )
UpperCAmelCase : List[Any] = tf_tokenizer(
text=[sentence[0] for sentence in self.paired_sentences], text_pair=[sentence[1] for sentence in self.paired_sentences], )
for key in merged_outputs.keys():
self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key], tf.intaa ) == separated_outputs[key] ) )
@slow
def __magic_name__ ( self : Any ):
for tf_tokenizer in self.tf_tokenizers:
UpperCAmelCase : List[str] = tf.function(__A )
for test_inputs in (self.test_sentences, self.paired_sentences):
UpperCAmelCase : int = tf.constant(__A )
UpperCAmelCase : List[str] = compiled_tokenizer(__A )
UpperCAmelCase : Optional[int] = tf_tokenizer(__A )
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )
@slow
def __magic_name__ ( self : Any ):
for tf_tokenizer in self.tf_tokenizers:
UpperCAmelCase : List[str] = ModelToSave(tokenizer=__A )
UpperCAmelCase : Optional[Any] = tf.convert_to_tensor(self.test_sentences )
UpperCAmelCase : Any = model(__A ) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
UpperCAmelCase : Dict = Path(__A ) / '''saved.model'''
model.save(__A )
UpperCAmelCase : Tuple = tf.keras.models.load_model(__A )
UpperCAmelCase : Optional[Any] = loaded_model(__A )
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output ) ), 1E-5 )
| 336 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"pipelines_utils",
"0.22.0",
"Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.",
standard_warn=False,
stacklevel=3,
)
| 336 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : Tuple = {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : str = ["DeiTFeatureExtractor"]
_lowerCamelCase : List[Any] = ["DeiTImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[int] = [
"DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"DeiTForImageClassification",
"DeiTForImageClassificationWithTeacher",
"DeiTForMaskedImageModeling",
"DeiTModel",
"DeiTPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Any = [
"TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFDeiTForImageClassification",
"TFDeiTForImageClassificationWithTeacher",
"TFDeiTForMaskedImageModeling",
"TFDeiTModel",
"TFDeiTPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_deit import DeiTFeatureExtractor
from .image_processing_deit import DeiTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deit import (
DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
DeiTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deit import (
TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
TFDeiTPreTrainedModel,
)
else:
import sys
_lowerCamelCase : List[str] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 336 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
class __UpperCAmelCase :
# setable values
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None # sigma(t_i)
@classmethod
def __magic_name__ ( cls : Any ):
return cls()
@dataclass
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = 42
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@property
def __magic_name__ ( self : Optional[int] ):
return True
@register_to_config
def __init__( self : Optional[int], __A : float = 0.0_2, __A : float = 1_0_0, __A : float = 1.0_0_7, __A : float = 8_0, __A : float = 0.0_5, __A : float = 5_0, ):
pass
def __magic_name__ ( self : Optional[Any] ):
return KarrasVeSchedulerState.create()
def __magic_name__ ( self : int, __A : KarrasVeSchedulerState, __A : int, __A : Tuple = () ):
UpperCAmelCase : Optional[Any] = jnp.arange(0, __A )[::-1].copy()
UpperCAmelCase : Union[str, Any] = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in timesteps
]
return state.replace(
num_inference_steps=__A, schedule=jnp.array(__A, dtype=jnp.floataa ), timesteps=__A, )
def __magic_name__ ( self : List[Any], __A : KarrasVeSchedulerState, __A : jnp.ndarray, __A : float, __A : random.KeyArray, ):
if self.config.s_min <= sigma <= self.config.s_max:
UpperCAmelCase : int = min(self.config.s_churn / state.num_inference_steps, 2**0.5 - 1 )
else:
UpperCAmelCase : Optional[int] = 0
# sample eps ~ N(0, S_noise^2 * I)
UpperCAmelCase : Union[str, Any] = random.split(__A, num=1 )
UpperCAmelCase : List[str] = self.config.s_noise * random.normal(key=__A, shape=sample.shape )
UpperCAmelCase : Tuple = sigma + gamma * sigma
UpperCAmelCase : List[str] = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def __magic_name__ ( self : Tuple, __A : KarrasVeSchedulerState, __A : jnp.ndarray, __A : float, __A : float, __A : jnp.ndarray, __A : bool = True, ):
UpperCAmelCase : int = sample_hat + sigma_hat * model_output
UpperCAmelCase : Dict = (sample_hat - pred_original_sample) / sigma_hat
UpperCAmelCase : int = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=__A, derivative=__A, state=__A )
def __magic_name__ ( self : Tuple, __A : KarrasVeSchedulerState, __A : jnp.ndarray, __A : float, __A : float, __A : jnp.ndarray, __A : jnp.ndarray, __A : jnp.ndarray, __A : bool = True, ):
UpperCAmelCase : Tuple = sample_prev + sigma_prev * model_output
UpperCAmelCase : List[str] = (sample_prev - pred_original_sample) / sigma_prev
UpperCAmelCase : Union[str, Any] = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=__A, derivative=__A, state=__A )
def __magic_name__ ( self : Optional[Any], __A : KarrasVeSchedulerState, __A : Optional[int], __A : int, __A : Union[str, Any] ):
raise NotImplementedError()
| 336 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPImageProcessor, CLIPProcessor
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
def __magic_name__ ( self : Dict ):
UpperCAmelCase : Any = tempfile.mkdtemp()
# fmt: off
UpperCAmelCase : Tuple = ['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
# fmt: on
UpperCAmelCase : Optional[int] = dict(zip(__A, range(len(__A ) ) ) )
UpperCAmelCase : List[str] = ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
UpperCAmelCase : int = {'''unk_token''': '''<unk>'''}
UpperCAmelCase : List[Any] = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['''vocab_file'''] )
UpperCAmelCase : Union[str, Any] = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file, '''w''', encoding='''utf-8''' ) as fp:
fp.write(json.dumps(__A ) + '''\n''' )
with open(self.merges_file, '''w''', encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__A ) )
UpperCAmelCase : List[Any] = {
'''do_resize''': True,
'''size''': 2_0,
'''do_center_crop''': True,
'''crop_size''': 1_8,
'''do_normalize''': True,
'''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
'''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
}
UpperCAmelCase : str = os.path.join(self.tmpdirname, __A )
with open(self.image_processor_file, '''w''', encoding='''utf-8''' ) as fp:
json.dump(__A, __A )
def __magic_name__ ( self : Any, **__A : Any ):
return CLIPTokenizer.from_pretrained(self.tmpdirname, **__A )
def __magic_name__ ( self : List[Any], **__A : Union[str, Any] ):
return CLIPTokenizerFast.from_pretrained(self.tmpdirname, **__A )
def __magic_name__ ( self : Tuple, **__A : Dict ):
return CLIPImageProcessor.from_pretrained(self.tmpdirname, **__A )
def __magic_name__ ( self : Tuple ):
shutil.rmtree(self.tmpdirname )
def __magic_name__ ( self : Optional[Any] ):
UpperCAmelCase : Optional[Any] = [np.random.randint(2_5_5, size=(3, 3_0, 4_0_0), dtype=np.uinta )]
UpperCAmelCase : List[Any] = [Image.fromarray(np.moveaxis(__A, 0, -1 ) ) for x in image_inputs]
return image_inputs
def __magic_name__ ( self : Tuple ):
UpperCAmelCase : Tuple = self.get_tokenizer()
UpperCAmelCase : Any = self.get_rust_tokenizer()
UpperCAmelCase : Any = self.get_image_processor()
UpperCAmelCase : str = CLIPProcessor(tokenizer=__A, image_processor=__A )
processor_slow.save_pretrained(self.tmpdirname )
UpperCAmelCase : int = CLIPProcessor.from_pretrained(self.tmpdirname, use_fast=__A )
UpperCAmelCase : Union[str, Any] = CLIPProcessor(tokenizer=__A, image_processor=__A )
processor_fast.save_pretrained(self.tmpdirname )
UpperCAmelCase : Any = CLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab(), tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab(), tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab(), tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer, __A )
self.assertIsInstance(processor_fast.tokenizer, __A )
self.assertEqual(processor_slow.image_processor.to_json_string(), image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string(), image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor, __A )
self.assertIsInstance(processor_fast.image_processor, __A )
def __magic_name__ ( self : str ):
UpperCAmelCase : List[str] = CLIPProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
UpperCAmelCase : Union[str, Any] = self.get_tokenizer(bos_token='''(BOS)''', eos_token='''(EOS)''' )
UpperCAmelCase : Optional[int] = self.get_image_processor(do_normalize=__A, padding_value=1.0 )
UpperCAmelCase : List[str] = CLIPProcessor.from_pretrained(
self.tmpdirname, bos_token='''(BOS)''', eos_token='''(EOS)''', do_normalize=__A, padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer, __A )
self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor, __A )
def __magic_name__ ( self : Tuple ):
UpperCAmelCase : Any = self.get_image_processor()
UpperCAmelCase : Union[str, Any] = self.get_tokenizer()
UpperCAmelCase : Tuple = CLIPProcessor(tokenizer=__A, image_processor=__A )
UpperCAmelCase : List[str] = self.prepare_image_inputs()
UpperCAmelCase : List[str] = image_processor(__A, return_tensors='''np''' )
UpperCAmelCase : Dict = processor(images=__A, return_tensors='''np''' )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum(), input_processor[key].sum(), delta=1E-2 )
def __magic_name__ ( self : int ):
UpperCAmelCase : Tuple = self.get_image_processor()
UpperCAmelCase : Any = self.get_tokenizer()
UpperCAmelCase : str = CLIPProcessor(tokenizer=__A, image_processor=__A )
UpperCAmelCase : List[Any] = '''lower newer'''
UpperCAmelCase : Optional[int] = processor(text=__A )
UpperCAmelCase : Optional[int] = tokenizer(__A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key] )
def __magic_name__ ( self : List[Any] ):
UpperCAmelCase : str = self.get_image_processor()
UpperCAmelCase : Union[str, Any] = self.get_tokenizer()
UpperCAmelCase : List[str] = CLIPProcessor(tokenizer=__A, image_processor=__A )
UpperCAmelCase : Optional[int] = '''lower newer'''
UpperCAmelCase : List[str] = self.prepare_image_inputs()
UpperCAmelCase : str = processor(text=__A, images=__A )
self.assertListEqual(list(inputs.keys() ), ['''input_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(__A ):
processor()
def __magic_name__ ( self : Dict ):
UpperCAmelCase : List[Any] = self.get_image_processor()
UpperCAmelCase : Optional[Any] = self.get_tokenizer()
UpperCAmelCase : Union[str, Any] = CLIPProcessor(tokenizer=__A, image_processor=__A )
UpperCAmelCase : str = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
UpperCAmelCase : Optional[int] = processor.batch_decode(__A )
UpperCAmelCase : Dict = tokenizer.batch_decode(__A )
self.assertListEqual(__A, __A )
def __magic_name__ ( self : int ):
UpperCAmelCase : Optional[Any] = self.get_image_processor()
UpperCAmelCase : Dict = self.get_tokenizer()
UpperCAmelCase : List[str] = CLIPProcessor(tokenizer=__A, image_processor=__A )
UpperCAmelCase : List[Any] = '''lower newer'''
UpperCAmelCase : Union[str, Any] = self.prepare_image_inputs()
UpperCAmelCase : List[str] = processor(text=__A, images=__A )
self.assertListEqual(list(inputs.keys() ), processor.model_input_names )
| 336 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __UpperCAmelCase ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
UpperCamelCase = [("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]
def a__ ( ) -> Dict:
if os.name == "nt":
UpperCAmelCase : List[str] = CursorInfo()
UpperCAmelCase : List[Any] = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
UpperCAmelCase : Dict = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
elif os.name == "posix":
sys.stdout.write('''\033[?25l''' )
sys.stdout.flush()
def a__ ( ) -> Optional[int]:
if os.name == "nt":
UpperCAmelCase : int = CursorInfo()
UpperCAmelCase : int = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
UpperCAmelCase : Any = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
elif os.name == "posix":
sys.stdout.write('''\033[?25h''' )
sys.stdout.flush()
@contextmanager
def a__ ( ) -> Optional[Any]:
try:
hide_cursor()
yield
finally:
show_cursor()
| 336 | 1 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def a__ ( UpperCAmelCase : int , UpperCAmelCase : str , UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[Any] ) -> str:
UpperCAmelCase : Tuple = s.rsplit(UpperCAmelCase , UpperCAmelCase )
return new.join(UpperCAmelCase )
def a__ ( UpperCAmelCase : str ) -> int:
# encoder.embeddings are double copied in original FLAVA
return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items() )
def a__ ( UpperCAmelCase : List[str] ) -> List[Any]:
UpperCAmelCase : Dict = {}
UpperCAmelCase : int = ['''group_1''', '''group_2''', '''group_3''', '''group_4''']
for key, value in state_dict.items():
for group_key in group_keys:
if group_key in key:
UpperCAmelCase : Optional[Any] = key.replace(f'''{group_key}.''' , f'''{group_key}.group.''' )
if "res_path" in key:
UpperCAmelCase : Dict = key.replace('''res_path.''' , '''res_path.path.''' )
if key.endswith('''.w''' ):
UpperCAmelCase : List[Any] = rreplace(UpperCAmelCase , '''.w''' , '''.weight''' , 1 )
if key.endswith('''.b''' ):
UpperCAmelCase : int = rreplace(UpperCAmelCase , '''.b''' , '''.bias''' , 1 )
UpperCAmelCase : Any = value.float()
return upgrade
@torch.no_grad()
def a__ ( UpperCAmelCase : List[str] , UpperCAmelCase : Dict , UpperCAmelCase : Optional[int]=None , UpperCAmelCase : int=True ) -> Any:
from dall_e import Encoder
UpperCAmelCase : Optional[int] = Encoder()
if os.path.exists(UpperCAmelCase ):
UpperCAmelCase : int = torch.load(UpperCAmelCase )
else:
UpperCAmelCase : Optional[int] = torch.hub.load_state_dict_from_url(UpperCAmelCase )
if isinstance(UpperCAmelCase , UpperCAmelCase ):
UpperCAmelCase : Any = ckpt.state_dict()
encoder.load_state_dict(UpperCAmelCase )
if config_path is not None:
UpperCAmelCase : str = FlavaImageCodebookConfig.from_pretrained(UpperCAmelCase )
else:
UpperCAmelCase : str = FlavaImageCodebookConfig()
UpperCAmelCase : Dict = FlavaImageCodebook(UpperCAmelCase ).eval()
UpperCAmelCase : Tuple = encoder.state_dict()
UpperCAmelCase : Union[str, Any] = upgrade_state_dict(UpperCAmelCase )
hf_model.load_state_dict(UpperCAmelCase )
UpperCAmelCase : Optional[Any] = hf_model.state_dict()
UpperCAmelCase : Optional[int] = count_parameters(UpperCAmelCase )
UpperCAmelCase : Tuple = count_parameters(UpperCAmelCase )
assert torch.allclose(UpperCAmelCase , UpperCAmelCase , atol=1E-3 )
if save_checkpoint:
hf_model.save_pretrained(UpperCAmelCase )
else:
return hf_state_dict
if __name__ == "__main__":
_lowerCamelCase : int = 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 flava checkpoint")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
_lowerCamelCase : List[Any] = parser.parse_args()
convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 336 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowerCamelCase : Tuple = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"feature_extraction_encodec": ["EncodecFeatureExtractor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[Any] = [
"ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST",
"EncodecModel",
"EncodecPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
_lowerCamelCase : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 336 | 1 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
_lowerCamelCase : Dict = ""
_lowerCamelCase : Optional[int] = ""
_lowerCamelCase : Any = ""
_lowerCamelCase : Optional[int] = 1 # (0 is vertical, 1 is horizontal)
def a__ ( ) -> None:
UpperCAmelCase , UpperCAmelCase : str = get_dataset(UpperCAmelCase , UpperCAmelCase )
print('''Processing...''' )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[Any] = update_image_and_anno(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
for index, image in enumerate(UpperCAmelCase ):
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
UpperCAmelCase : int = random_chars(32 )
UpperCAmelCase : int = paths[index].split(os.sep )[-1].rsplit('''.''' , 1 )[0]
UpperCAmelCase : List[str] = f'''{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'''
cva.imwrite(f'''/{file_root}.jpg''' , UpperCAmelCase , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(f'''Success {index+1}/{len(UpperCAmelCase )} with {file_name}''' )
UpperCAmelCase : List[str] = []
for anno in new_annos[index]:
UpperCAmelCase : str = f'''{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'''
annos_list.append(UpperCAmelCase )
with open(f'''/{file_root}.txt''' , '''w''' ) as outfile:
outfile.write('''\n'''.join(line for line in annos_list ) )
def a__ ( UpperCAmelCase : str , UpperCAmelCase : str ) -> tuple[list, list]:
UpperCAmelCase : Tuple = []
UpperCAmelCase : Union[str, Any] = []
for label_file in glob.glob(os.path.join(UpperCAmelCase , '''*.txt''' ) ):
UpperCAmelCase : Optional[Any] = label_file.split(os.sep )[-1].rsplit('''.''' , 1 )[0]
with open(UpperCAmelCase ) as in_file:
UpperCAmelCase : Any = in_file.readlines()
UpperCAmelCase : int = os.path.join(UpperCAmelCase , f'''{label_name}.jpg''' )
UpperCAmelCase : str = []
for obj_list in obj_lists:
UpperCAmelCase : str = obj_list.rstrip('''\n''' ).split(''' ''' )
boxes.append(
[
int(obj[0] ),
float(obj[1] ),
float(obj[2] ),
float(obj[3] ),
float(obj[4] ),
] )
if not boxes:
continue
img_paths.append(UpperCAmelCase )
labels.append(UpperCAmelCase )
return img_paths, labels
def a__ ( UpperCAmelCase : list , UpperCAmelCase : list , UpperCAmelCase : int = 1 ) -> tuple[list, list, list]:
UpperCAmelCase : Union[str, Any] = []
UpperCAmelCase : List[str] = []
UpperCAmelCase : Optional[Any] = []
for idx in range(len(UpperCAmelCase ) ):
UpperCAmelCase : Dict = []
UpperCAmelCase : int = img_list[idx]
path_list.append(UpperCAmelCase )
UpperCAmelCase : int = anno_list[idx]
UpperCAmelCase : Any = cva.imread(UpperCAmelCase )
if flip_type == 1:
UpperCAmelCase : Any = cva.flip(UpperCAmelCase , UpperCAmelCase )
for bbox in img_annos:
UpperCAmelCase : Any = 1 - bbox[1]
new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]] )
elif flip_type == 0:
UpperCAmelCase : int = cva.flip(UpperCAmelCase , UpperCAmelCase )
for bbox in img_annos:
UpperCAmelCase : str = 1 - bbox[2]
new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]] )
new_annos_lists.append(UpperCAmelCase )
new_imgs_list.append(UpperCAmelCase )
return new_imgs_list, new_annos_lists, path_list
def a__ ( UpperCAmelCase : int = 32 ) -> str:
assert number_char > 1, "The number of character should greater than 1"
UpperCAmelCase : Dict = ascii_lowercase + digits
return "".join(random.choice(UpperCAmelCase ) for _ in range(UpperCAmelCase ) )
if __name__ == "__main__":
main()
print("DONE ✅")
| 336 |
from __future__ import annotations
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int ) -> list[str]:
if partitions <= 0:
raise ValueError('''partitions must be a positive number!''' )
if partitions > number_of_bytes:
raise ValueError('''partitions can not > number_of_bytes!''' )
UpperCAmelCase : str = number_of_bytes // partitions
UpperCAmelCase : Dict = []
for i in range(UpperCAmelCase ):
UpperCAmelCase : int = i * bytes_per_partition + 1
UpperCAmelCase : Optional[int] = (
number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
)
allocation_list.append(f'''{start_bytes}-{end_bytes}''' )
return allocation_list
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 | 1 |
import pprint
import requests
_lowerCamelCase : Union[str, Any] = "https://zenquotes.io/api"
def a__ ( ) -> list:
return requests.get(API_ENDPOINT_URL + '''/today''' ).json()
def a__ ( ) -> list:
return requests.get(API_ENDPOINT_URL + '''/random''' ).json()
if __name__ == "__main__":
_lowerCamelCase : List[Any] = random_quotes()
pprint.pprint(response)
| 336 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
_lowerCamelCase : Union[str, Any] = "Run commands across TPU VMs for initial setup before running `accelerate launch`."
def a__ ( UpperCAmelCase : Dict=None ) -> Optional[int]:
if subparsers is not None:
UpperCAmelCase : Tuple = subparsers.add_parser('''tpu-config''' , description=_description )
else:
UpperCAmelCase : Dict = argparse.ArgumentParser('''Accelerate tpu-config command''' , description=_description )
# Core arguments
UpperCAmelCase : Optional[int] = parser.add_argument_group(
'''Config Arguments''' , '''Arguments that can be configured through `accelerate config`.''' )
config_args.add_argument(
'''--config_file''' , type=UpperCAmelCase , default=UpperCAmelCase , help='''Path to the config file to use for accelerate.''' , )
config_args.add_argument(
'''--tpu_name''' , default=UpperCAmelCase , help='''The name of the TPU to use. If not specified, will use the TPU specified in the config file.''' , )
config_args.add_argument(
'''--tpu_zone''' , default=UpperCAmelCase , help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.''' , )
UpperCAmelCase : Union[str, Any] = parser.add_argument_group('''TPU Arguments''' , '''Arguments for options ran inside the TPU.''' )
pod_args.add_argument(
'''--use_alpha''' , action='''store_true''' , help='''Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.''' , )
pod_args.add_argument(
'''--command_file''' , default=UpperCAmelCase , help='''The path to the file containing the commands to run on the pod on startup.''' , )
pod_args.add_argument(
'''--command''' , action='''append''' , nargs='''+''' , help='''A command to run on the pod. Can be passed multiple times.''' , )
pod_args.add_argument(
'''--install_accelerate''' , action='''store_true''' , help='''Whether to install accelerate on the pod. Defaults to False.''' , )
pod_args.add_argument(
'''--accelerate_version''' , default='''latest''' , help='''The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.''' , )
pod_args.add_argument(
'''--debug''' , action='''store_true''' , help='''If set, will print the command that would be run instead of running it.''' )
if subparsers is not None:
parser.set_defaults(func=UpperCAmelCase )
return parser
def a__ ( UpperCAmelCase : Optional[int] ) -> Union[str, Any]:
UpperCAmelCase : Union[str, Any] = None
# Get the default from the config file if it exists.
if args.config_file is not None or os.path.isfile(UpperCAmelCase ):
UpperCAmelCase : Union[str, Any] = load_config_from_file(args.config_file )
if not args.command_file and defaults.command_file is not None and not args.command:
UpperCAmelCase : List[Any] = defaults.command_file
if not args.command and defaults.commands is not None:
UpperCAmelCase : List[str] = defaults.commands
if not args.tpu_name:
UpperCAmelCase : Tuple = defaults.tpu_name
if not args.tpu_zone:
UpperCAmelCase : int = defaults.tpu_zone
if args.accelerate_version == "dev":
UpperCAmelCase : Tuple = '''git+https://github.com/huggingface/accelerate.git'''
elif args.accelerate_version == "latest":
UpperCAmelCase : Dict = '''accelerate -U'''
elif isinstance(parse(args.accelerate_version ) , UpperCAmelCase ):
UpperCAmelCase : Optional[int] = f'''accelerate=={args.accelerate_version}'''
if not args.command_file and not args.command:
raise ValueError('''You must specify either a command file or a command to run on the pod.''' )
if args.command_file:
with open(args.command_file , '''r''' ) as f:
UpperCAmelCase : int = [f.read().splitlines()]
# To turn list of lists into list of strings
if isinstance(args.command[0] , UpperCAmelCase ):
UpperCAmelCase : int = [line for cmd in args.command for line in cmd]
# Default to the shared folder and install accelerate
UpperCAmelCase : Optional[int] = ['''cd /usr/share''']
if args.install_accelerate:
new_cmd += [f'''pip install {args.accelerate_version}''']
new_cmd += args.command
UpperCAmelCase : int = '''; '''.join(UpperCAmelCase )
# Then send it to gcloud
# Eventually try to use google-api-core to do this instead of subprocess
UpperCAmelCase : Any = ['''gcloud''']
if args.use_alpha:
cmd += ["alpha"]
cmd += [
"compute",
"tpus",
"tpu-vm",
"ssh",
args.tpu_name,
"--zone",
args.tpu_zone,
"--command",
args.command,
"--worker",
"all",
]
if args.debug:
print(f'''Running {" ".join(UpperCAmelCase )}''' )
return
subprocess.run(UpperCAmelCase )
print('''Successfully setup pod.''' )
def a__ ( ) -> Any:
UpperCAmelCase : Any = tpu_command_parser()
UpperCAmelCase : Tuple = parser.parse_args()
tpu_command_launcher(UpperCAmelCase )
| 336 | 1 |
def a__ ( ) -> int:
return [
a * b * (1_000 - a - b)
for a in range(1 , 999 )
for b in range(UpperCAmelCase , 999 )
if (a * a + b * b == (1_000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(f"""{solution() = }""")
| 336 |
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase : Optional[int] = logging.get_logger(__name__)
def a__ ( UpperCAmelCase : Union[str, Any] ) -> Optional[Any]:
print('''Loading config file...''' )
def flatten_yaml_as_dict(UpperCAmelCase : Tuple , UpperCAmelCase : Any="" , UpperCAmelCase : Dict="." ):
UpperCAmelCase : List[str] = []
for k, v in d.items():
UpperCAmelCase : List[Any] = parent_key + sep + k if parent_key else k
if isinstance(UpperCAmelCase , collections.abc.MutableMapping ):
items.extend(flatten_yaml_as_dict(UpperCAmelCase , UpperCAmelCase , sep=UpperCAmelCase ).items() )
else:
items.append((new_key, v) )
return dict(UpperCAmelCase )
UpperCAmelCase : List[str] = argparse.Namespace()
with open(UpperCAmelCase , '''r''' ) as yaml_file:
try:
UpperCAmelCase : List[str] = yaml.load(UpperCAmelCase , Loader=yaml.FullLoader )
UpperCAmelCase : Optional[int] = flatten_yaml_as_dict(UpperCAmelCase )
for k, v in flat_cfg.items():
setattr(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
except yaml.YAMLError as exc:
logger.error('''Error while loading config file: {}. Error message: {}'''.format(UpperCAmelCase , str(UpperCAmelCase ) ) )
return config
def a__ ( UpperCAmelCase : List[str] , UpperCAmelCase : int ) -> List[Any]:
UpperCAmelCase : int = MobileViTVaConfig()
UpperCAmelCase : str = False
# dataset
if task_name.startswith('''imagenet1k_''' ):
UpperCAmelCase : Any = 1_000
if int(task_name.strip().split('''_''' )[-1] ) == 384:
UpperCAmelCase : Any = 384
else:
UpperCAmelCase : Tuple = 256
UpperCAmelCase : int = '''imagenet-1k-id2label.json'''
elif task_name.startswith('''imagenet21k_to_1k_''' ):
UpperCAmelCase : Optional[Any] = 21_000
if int(task_name.strip().split('''_''' )[-1] ) == 384:
UpperCAmelCase : str = 384
else:
UpperCAmelCase : Dict = 256
UpperCAmelCase : List[Any] = '''imagenet-22k-id2label.json'''
elif task_name.startswith('''ade20k_''' ):
UpperCAmelCase : Optional[Any] = 151
UpperCAmelCase : Tuple = 512
UpperCAmelCase : Tuple = '''ade20k-id2label.json'''
UpperCAmelCase : Tuple = True
elif task_name.startswith('''voc_''' ):
UpperCAmelCase : Dict = 21
UpperCAmelCase : str = 512
UpperCAmelCase : Union[str, Any] = '''pascal-voc-id2label.json'''
UpperCAmelCase : Dict = True
# orig_config
UpperCAmelCase : List[Any] = load_orig_config_file(UpperCAmelCase )
assert getattr(UpperCAmelCase , '''model.classification.name''' , -1 ) == "mobilevit_v2", "Invalid model"
UpperCAmelCase : Tuple = getattr(UpperCAmelCase , '''model.classification.mitv2.width_multiplier''' , 1.0 )
assert (
getattr(UpperCAmelCase , '''model.classification.mitv2.attn_norm_layer''' , -1 ) == "layer_norm_2d"
), "Norm layers other than layer_norm_2d is not supported"
UpperCAmelCase : int = getattr(UpperCAmelCase , '''model.classification.activation.name''' , '''swish''' )
# config.image_size == getattr(orig_config, 'sampler.bs.crop_size_width', 256)
if is_segmentation_model:
UpperCAmelCase : str = getattr(UpperCAmelCase , '''model.segmentation.output_stride''' , 16 )
if "_deeplabv3" in task_name:
UpperCAmelCase : int = getattr(UpperCAmelCase , '''model.segmentation.deeplabv3.aspp_rates''' , [12, 24, 36] )
UpperCAmelCase : Any = getattr(UpperCAmelCase , '''model.segmentation.deeplabv3.aspp_out_channels''' , 512 )
UpperCAmelCase : Optional[Any] = getattr(UpperCAmelCase , '''model.segmentation.deeplabv3.aspp_dropout''' , 0.1 )
# id2label
UpperCAmelCase : Union[str, Any] = '''huggingface/label-files'''
UpperCAmelCase : List[Any] = json.load(open(hf_hub_download(UpperCAmelCase , UpperCAmelCase , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase : Any = {int(UpperCAmelCase ): v for k, v in idalabel.items()}
UpperCAmelCase : int = idalabel
UpperCAmelCase : Optional[int] = {v: k for k, v in idalabel.items()}
return config
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Optional[int] ) -> List[str]:
UpperCAmelCase : Union[str, Any] = dct.pop(UpperCAmelCase )
UpperCAmelCase : List[str] = val
def a__ ( UpperCAmelCase : Union[str, Any] , UpperCAmelCase : int=False ) -> Union[str, Any]:
if base_model:
UpperCAmelCase : Dict = ''''''
else:
UpperCAmelCase : Dict = '''mobilevitv2.'''
UpperCAmelCase : Optional[int] = []
for k in state_dict.keys():
if k[:8] == "encoder.":
UpperCAmelCase : List[str] = k[8:]
else:
UpperCAmelCase : Dict = k
if ".block." in k:
UpperCAmelCase : List[Any] = k_new.replace('''.block.''' , '''.''' )
if ".conv." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''.conv.''' , '''.convolution.''' )
if ".norm." in k:
UpperCAmelCase : List[str] = k_new.replace('''.norm.''' , '''.normalization.''' )
if "conv_1." in k:
UpperCAmelCase : Union[str, Any] = k_new.replace('''conv_1.''' , f'''{model_prefix}conv_stem.''' )
for i in [1, 2]:
if f'''layer_{i}.''' in k:
UpperCAmelCase : Union[str, Any] = k_new.replace(f'''layer_{i}.''' , f'''{model_prefix}encoder.layer.{i-1}.layer.''' )
if ".exp_1x1." in k:
UpperCAmelCase : Optional[Any] = k_new.replace('''.exp_1x1.''' , '''.expand_1x1.''' )
if ".red_1x1." in k:
UpperCAmelCase : int = k_new.replace('''.red_1x1.''' , '''.reduce_1x1.''' )
for i in [3, 4, 5]:
if f'''layer_{i}.0.''' in k:
UpperCAmelCase : Any = k_new.replace(f'''layer_{i}.0.''' , f'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.''' )
if f'''layer_{i}.1.local_rep.0.''' in k:
UpperCAmelCase : str = k_new.replace(f'''layer_{i}.1.local_rep.0.''' , f'''{model_prefix}encoder.layer.{i-1}.conv_kxk.''' )
if f'''layer_{i}.1.local_rep.1.''' in k:
UpperCAmelCase : int = k_new.replace(f'''layer_{i}.1.local_rep.1.''' , f'''{model_prefix}encoder.layer.{i-1}.conv_1x1.''' )
for i in [3, 4, 5]:
if i == 3:
UpperCAmelCase : Dict = [0, 1]
elif i == 4:
UpperCAmelCase : Dict = [0, 1, 2, 3]
elif i == 5:
UpperCAmelCase : int = [0, 1, 2]
for j in j_in:
if f'''layer_{i}.1.global_rep.{j}.''' in k:
UpperCAmelCase : Optional[Any] = k_new.replace(
f'''layer_{i}.1.global_rep.{j}.''' , f'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.''' )
if f'''layer_{i}.1.global_rep.{j+1}.''' in k:
UpperCAmelCase : Any = k_new.replace(
f'''layer_{i}.1.global_rep.{j+1}.''' , f'''{model_prefix}encoder.layer.{i-1}.layernorm.''' )
if f'''layer_{i}.1.conv_proj.''' in k:
UpperCAmelCase : Union[str, Any] = k_new.replace(f'''layer_{i}.1.conv_proj.''' , f'''{model_prefix}encoder.layer.{i-1}.conv_projection.''' )
if "pre_norm_attn.0." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''pre_norm_attn.0.''' , '''layernorm_before.''' )
if "pre_norm_attn.1." in k:
UpperCAmelCase : Optional[Any] = k_new.replace('''pre_norm_attn.1.''' , '''attention.''' )
if "pre_norm_ffn.0." in k:
UpperCAmelCase : List[Any] = k_new.replace('''pre_norm_ffn.0.''' , '''layernorm_after.''' )
if "pre_norm_ffn.1." in k:
UpperCAmelCase : List[Any] = k_new.replace('''pre_norm_ffn.1.''' , '''ffn.conv1.''' )
if "pre_norm_ffn.3." in k:
UpperCAmelCase : Any = k_new.replace('''pre_norm_ffn.3.''' , '''ffn.conv2.''' )
if "classifier.1." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''classifier.1.''' , '''classifier.''' )
if "seg_head." in k:
UpperCAmelCase : Union[str, Any] = k_new.replace('''seg_head.''' , '''segmentation_head.''' )
if ".aspp_layer." in k:
UpperCAmelCase : Tuple = k_new.replace('''.aspp_layer.''' , '''.''' )
if ".aspp_pool." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''.aspp_pool.''' , '''.''' )
rename_keys.append((k, k_new) )
return rename_keys
def a__ ( UpperCAmelCase : Union[str, Any] ) -> Any:
UpperCAmelCase : str = []
for k in state_dict.keys():
if k.startswith('''seg_head.aux_head.''' ):
keys_to_ignore.append(UpperCAmelCase )
for k in keys_to_ignore:
state_dict.pop(UpperCAmelCase , UpperCAmelCase )
def a__ ( ) -> Union[str, Any]:
UpperCAmelCase : int = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
UpperCAmelCase : List[str] = Image.open(requests.get(UpperCAmelCase , stream=UpperCAmelCase ).raw )
return im
@torch.no_grad()
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[Any] ) -> Union[str, Any]:
UpperCAmelCase : Union[str, Any] = get_mobilevitva_config(UpperCAmelCase , UpperCAmelCase )
# load original state_dict
UpperCAmelCase : List[str] = torch.load(UpperCAmelCase , map_location='''cpu''' )
# load huggingface model
if task_name.startswith('''ade20k_''' ) or task_name.startswith('''voc_''' ):
UpperCAmelCase : str = MobileViTVaForSemanticSegmentation(UpperCAmelCase ).eval()
UpperCAmelCase : str = False
else:
UpperCAmelCase : Union[str, Any] = MobileViTVaForImageClassification(UpperCAmelCase ).eval()
UpperCAmelCase : Any = False
# remove and rename some keys of load the original model
UpperCAmelCase : Optional[Any] = checkpoint
remove_unused_keys(UpperCAmelCase )
UpperCAmelCase : Optional[Any] = create_rename_keys(UpperCAmelCase , base_model=UpperCAmelCase )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# load modified state_dict
model.load_state_dict(UpperCAmelCase )
# Check outputs on an image, prepared by MobileViTImageProcessor
UpperCAmelCase : Dict = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
UpperCAmelCase : Any = image_processor(images=prepare_img() , return_tensors='''pt''' )
UpperCAmelCase : Union[str, Any] = model(**UpperCAmelCase )
# verify classification model
if task_name.startswith('''imagenet''' ):
UpperCAmelCase : Optional[Any] = outputs.logits
UpperCAmelCase : int = logits.argmax(-1 ).item()
print('''Predicted class:''' , model.config.idalabel[predicted_class_idx] )
if task_name.startswith('''imagenet1k_256''' ) and config.width_multiplier == 1.0:
# expected_logits for base variant
UpperCAmelCase : str = torch.tensor([-1.6_336E00, -7.3_204E-02, -5.1_883E-01] )
assert torch.allclose(logits[0, :3] , UpperCAmelCase , atol=1E-4 )
Path(UpperCAmelCase ).mkdir(exist_ok=UpperCAmelCase )
print(f'''Saving model {task_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(UpperCAmelCase )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(UpperCAmelCase )
if __name__ == "__main__":
_lowerCamelCase : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--task",
default="imagenet1k_256",
type=str,
help=(
"Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
"\n Classification (ImageNet-1k)\n - MobileViTV2 (256x256) : imagenet1k_256\n - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n imagenet21k_to_1k_256\n - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n ImageNet-1k 384x384) : imagenet21k_to_1k_384\n Segmentation\n - ADE20K Dataset : ade20k_deeplabv3\n - Pascal VOC 2012 Dataset: voc_deeplabv3\n "
),
choices=[
"imagenet1k_256",
"imagenet1k_384",
"imagenet21k_to_1k_256",
"imagenet21k_to_1k_384",
"ade20k_deeplabv3",
"voc_deeplabv3",
],
)
parser.add_argument(
"--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
)
parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
)
_lowerCamelCase : Optional[int] = parser.parse_args()
convert_mobilevitva_checkpoint(
args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
)
| 336 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
class __UpperCAmelCase :
# setable values
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None # sigma(t_i)
@classmethod
def __magic_name__ ( cls : Any ):
return cls()
@dataclass
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = 42
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@property
def __magic_name__ ( self : Optional[int] ):
return True
@register_to_config
def __init__( self : Optional[int], __A : float = 0.0_2, __A : float = 1_0_0, __A : float = 1.0_0_7, __A : float = 8_0, __A : float = 0.0_5, __A : float = 5_0, ):
pass
def __magic_name__ ( self : Optional[Any] ):
return KarrasVeSchedulerState.create()
def __magic_name__ ( self : int, __A : KarrasVeSchedulerState, __A : int, __A : Tuple = () ):
UpperCAmelCase : Optional[Any] = jnp.arange(0, __A )[::-1].copy()
UpperCAmelCase : Union[str, Any] = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in timesteps
]
return state.replace(
num_inference_steps=__A, schedule=jnp.array(__A, dtype=jnp.floataa ), timesteps=__A, )
def __magic_name__ ( self : List[Any], __A : KarrasVeSchedulerState, __A : jnp.ndarray, __A : float, __A : random.KeyArray, ):
if self.config.s_min <= sigma <= self.config.s_max:
UpperCAmelCase : int = min(self.config.s_churn / state.num_inference_steps, 2**0.5 - 1 )
else:
UpperCAmelCase : Optional[int] = 0
# sample eps ~ N(0, S_noise^2 * I)
UpperCAmelCase : Union[str, Any] = random.split(__A, num=1 )
UpperCAmelCase : List[str] = self.config.s_noise * random.normal(key=__A, shape=sample.shape )
UpperCAmelCase : Tuple = sigma + gamma * sigma
UpperCAmelCase : List[str] = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def __magic_name__ ( self : Tuple, __A : KarrasVeSchedulerState, __A : jnp.ndarray, __A : float, __A : float, __A : jnp.ndarray, __A : bool = True, ):
UpperCAmelCase : int = sample_hat + sigma_hat * model_output
UpperCAmelCase : Dict = (sample_hat - pred_original_sample) / sigma_hat
UpperCAmelCase : int = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=__A, derivative=__A, state=__A )
def __magic_name__ ( self : Tuple, __A : KarrasVeSchedulerState, __A : jnp.ndarray, __A : float, __A : float, __A : jnp.ndarray, __A : jnp.ndarray, __A : jnp.ndarray, __A : bool = True, ):
UpperCAmelCase : Tuple = sample_prev + sigma_prev * model_output
UpperCAmelCase : List[str] = (sample_prev - pred_original_sample) / sigma_prev
UpperCAmelCase : Union[str, Any] = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=__A, derivative=__A, state=__A )
def __magic_name__ ( self : Optional[Any], __A : KarrasVeSchedulerState, __A : Optional[int], __A : int, __A : Union[str, Any] ):
raise NotImplementedError()
| 336 |
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
if is_flax_available():
import jax.numpy as jnp
class __UpperCAmelCase ( lowerCamelCase__ ):
def __get__( self : Tuple, __A : Optional[Any], __A : Optional[int]=None ):
# See docs.python.org/3/howto/descriptor.html#properties
if obj is None:
return self
if self.fget is None:
raise AttributeError('''unreadable attribute''' )
UpperCAmelCase : str = '''__cached_''' + self.fget.__name__
UpperCAmelCase : int = getattr(__A, __A, __A )
if cached is None:
UpperCAmelCase : Any = self.fget(__A )
setattr(__A, __A, __A )
return cached
def a__ ( UpperCAmelCase : Optional[Any] ) -> Any:
UpperCAmelCase : Any = val.lower()
if val in {"y", "yes", "t", "true", "on", "1"}:
return 1
if val in {"n", "no", "f", "false", "off", "0"}:
return 0
raise ValueError(f'''invalid truth value {val!r}''' )
def a__ ( UpperCAmelCase : Dict ) -> List[str]:
if is_torch_fx_proxy(UpperCAmelCase ):
return True
if is_torch_available():
import torch
if isinstance(UpperCAmelCase , torch.Tensor ):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(UpperCAmelCase , tf.Tensor ):
return True
if is_flax_available():
import jax.numpy as jnp
from jax.core import Tracer
if isinstance(UpperCAmelCase , (jnp.ndarray, Tracer) ):
return True
return isinstance(UpperCAmelCase , np.ndarray )
def a__ ( UpperCAmelCase : List[Any] ) -> Union[str, Any]:
return isinstance(UpperCAmelCase , np.ndarray )
def a__ ( UpperCAmelCase : str ) -> Tuple:
return _is_numpy(UpperCAmelCase )
def a__ ( UpperCAmelCase : str ) -> List[Any]:
import torch
return isinstance(UpperCAmelCase , torch.Tensor )
def a__ ( UpperCAmelCase : str ) -> List[Any]:
return False if not is_torch_available() else _is_torch(UpperCAmelCase )
def a__ ( UpperCAmelCase : Tuple ) -> List[str]:
import torch
return isinstance(UpperCAmelCase , torch.device )
def a__ ( UpperCAmelCase : Any ) -> Any:
return False if not is_torch_available() else _is_torch_device(UpperCAmelCase )
def a__ ( UpperCAmelCase : Dict ) -> List[str]:
import torch
if isinstance(UpperCAmelCase , UpperCAmelCase ):
if hasattr(UpperCAmelCase , UpperCAmelCase ):
UpperCAmelCase : Union[str, Any] = getattr(UpperCAmelCase , UpperCAmelCase )
else:
return False
return isinstance(UpperCAmelCase , torch.dtype )
def a__ ( UpperCAmelCase : Optional[Any] ) -> Union[str, Any]:
return False if not is_torch_available() else _is_torch_dtype(UpperCAmelCase )
def a__ ( UpperCAmelCase : Any ) -> str:
import tensorflow as tf
return isinstance(UpperCAmelCase , tf.Tensor )
def a__ ( UpperCAmelCase : int ) -> Union[str, Any]:
return False if not is_tf_available() else _is_tensorflow(UpperCAmelCase )
def a__ ( UpperCAmelCase : List[str] ) -> Tuple:
import tensorflow as tf
# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
if hasattr(UpperCAmelCase , '''is_symbolic_tensor''' ):
return tf.is_symbolic_tensor(UpperCAmelCase )
return type(UpperCAmelCase ) == tf.Tensor
def a__ ( UpperCAmelCase : int ) -> List[Any]:
return False if not is_tf_available() else _is_tf_symbolic_tensor(UpperCAmelCase )
def a__ ( UpperCAmelCase : List[Any] ) -> Dict:
import jax.numpy as jnp # noqa: F811
return isinstance(UpperCAmelCase , jnp.ndarray )
def a__ ( UpperCAmelCase : List[Any] ) -> Optional[int]:
return False if not is_flax_available() else _is_jax(UpperCAmelCase )
def a__ ( UpperCAmelCase : int ) -> Tuple:
if isinstance(UpperCAmelCase , (dict, UserDict) ):
return {k: to_py_obj(UpperCAmelCase ) for k, v in obj.items()}
elif isinstance(UpperCAmelCase , (list, tuple) ):
return [to_py_obj(UpperCAmelCase ) for o in obj]
elif is_tf_tensor(UpperCAmelCase ):
return obj.numpy().tolist()
elif is_torch_tensor(UpperCAmelCase ):
return obj.detach().cpu().tolist()
elif is_jax_tensor(UpperCAmelCase ):
return np.asarray(UpperCAmelCase ).tolist()
elif isinstance(UpperCAmelCase , (np.ndarray, np.number) ): # tolist also works on 0d np arrays
return obj.tolist()
else:
return obj
def a__ ( UpperCAmelCase : Any ) -> List[str]:
if isinstance(UpperCAmelCase , (dict, UserDict) ):
return {k: to_numpy(UpperCAmelCase ) for k, v in obj.items()}
elif isinstance(UpperCAmelCase , (list, tuple) ):
return np.array(UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
return obj.numpy()
elif is_torch_tensor(UpperCAmelCase ):
return obj.detach().cpu().numpy()
elif is_jax_tensor(UpperCAmelCase ):
return np.asarray(UpperCAmelCase )
else:
return obj
class __UpperCAmelCase ( lowerCamelCase__ ):
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : Optional[Any] = fields(self )
# Safety and consistency checks
if not len(__A ):
raise ValueError(F'''{self.__class__.__name__} has no fields.''' )
if not all(field.default is None for field in class_fields[1:] ):
raise ValueError(F'''{self.__class__.__name__} should not have more than one required field.''' )
UpperCAmelCase : int = getattr(self, class_fields[0].name )
UpperCAmelCase : str = all(getattr(self, field.name ) is None for field in class_fields[1:] )
if other_fields_are_none and not is_tensor(__A ):
if isinstance(__A, __A ):
UpperCAmelCase : Tuple = first_field.items()
UpperCAmelCase : Any = True
else:
try:
UpperCAmelCase : Optional[Any] = iter(__A )
UpperCAmelCase : Optional[Any] = True
except TypeError:
UpperCAmelCase : Optional[int] = False
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for idx, element in enumerate(__A ):
if (
not isinstance(__A, (list, tuple) )
or not len(__A ) == 2
or not isinstance(element[0], __A )
):
if idx == 0:
# If we do not have an iterator of key/values, set it as attribute
UpperCAmelCase : Any = first_field
else:
# If we have a mixed iterator, raise an error
raise ValueError(
F'''Cannot set key/value for {element}. It needs to be a tuple (key, value).''' )
break
setattr(self, element[0], element[1] )
if element[1] is not None:
UpperCAmelCase : Union[str, Any] = element[1]
elif first_field is not None:
UpperCAmelCase : Union[str, Any] = first_field
else:
for field in class_fields:
UpperCAmelCase : Optional[Any] = getattr(self, field.name )
if v is not None:
UpperCAmelCase : Optional[int] = v
def __delitem__( self : Union[str, Any], *__A : str, **__A : Tuple ):
raise Exception(F'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.''' )
def __magic_name__ ( self : List[str], *__A : Union[str, Any], **__A : Optional[Any] ):
raise Exception(F'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.''' )
def __magic_name__ ( self : Any, *__A : Dict, **__A : str ):
raise Exception(F'''You cannot use ``pop`` on a {self.__class__.__name__} instance.''' )
def __magic_name__ ( self : Dict, *__A : int, **__A : Dict ):
raise Exception(F'''You cannot use ``update`` on a {self.__class__.__name__} instance.''' )
def __getitem__( self : List[str], __A : List[str] ):
if isinstance(__A, __A ):
UpperCAmelCase : int = dict(self.items() )
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__( self : Optional[Any], __A : Dict, __A : Union[str, Any] ):
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(__A, __A )
super().__setattr__(__A, __A )
def __setitem__( self : Dict, __A : List[Any], __A : Union[str, Any] ):
# Will raise a KeyException if needed
super().__setitem__(__A, __A )
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(__A, __A )
def __magic_name__ ( self : List[str] ):
return tuple(self[k] for k in self.keys() )
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@classmethod
def __magic_name__ ( cls : List[Any], __A : Tuple ):
raise ValueError(
F'''{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys() )}''' )
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """longest"""
UpperCamelCase = """max_length"""
UpperCamelCase = """do_not_pad"""
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """pt"""
UpperCamelCase = """tf"""
UpperCamelCase = """np"""
UpperCamelCase = """jax"""
class __UpperCAmelCase :
def __init__( self : Any, __A : List[ContextManager] ):
UpperCAmelCase : Tuple = context_managers
UpperCAmelCase : Tuple = ExitStack()
def __enter__( self : Any ):
for context_manager in self.context_managers:
self.stack.enter_context(__A )
def __exit__( self : List[Any], *__A : Union[str, Any], **__A : Dict ):
self.stack.__exit__(*__A, **__A )
def a__ ( UpperCAmelCase : Union[str, Any] ) -> str:
UpperCAmelCase : int = infer_framework(UpperCAmelCase )
if framework == "tf":
UpperCAmelCase : List[str] = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
UpperCAmelCase : List[Any] = inspect.signature(model_class.forward ) # PyTorch models
else:
UpperCAmelCase : Tuple = inspect.signature(model_class.__call__ ) # Flax models
for p in signature.parameters:
if p == "return_loss" and signature.parameters[p].default is True:
return True
return False
def a__ ( UpperCAmelCase : Dict ) -> Any:
UpperCAmelCase : List[Any] = model_class.__name__
UpperCAmelCase : Union[str, Any] = infer_framework(UpperCAmelCase )
if framework == "tf":
UpperCAmelCase : Tuple = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
UpperCAmelCase : Dict = inspect.signature(model_class.forward ) # PyTorch models
else:
UpperCAmelCase : Dict = inspect.signature(model_class.__call__ ) # Flax models
if "QuestionAnswering" in model_name:
return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
else:
return [p for p in signature.parameters if "label" in p]
def a__ ( UpperCAmelCase : MutableMapping , UpperCAmelCase : str = "" , UpperCAmelCase : str = "." ) -> Union[str, Any]:
def _flatten_dict(UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[str]="" , UpperCAmelCase : Any="." ):
for k, v in d.items():
UpperCAmelCase : List[str] = str(UpperCAmelCase ) + delimiter + str(UpperCAmelCase ) if parent_key else k
if v and isinstance(UpperCAmelCase , UpperCAmelCase ):
yield from flatten_dict(UpperCAmelCase , UpperCAmelCase , delimiter=UpperCAmelCase ).items()
else:
yield key, v
return dict(_flatten_dict(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) )
@contextmanager
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : bool = False ) -> Optional[Any]:
if use_temp_dir:
with tempfile.TemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield working_dir
def a__ ( UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[str]=None ) -> Optional[Any]:
if is_numpy_array(UpperCAmelCase ):
return np.transpose(UpperCAmelCase , axes=UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.T if axes is None else array.permute(*UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.transpose(UpperCAmelCase , perm=UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.transpose(UpperCAmelCase , axes=UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for transpose: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : str , UpperCAmelCase : Optional[int] ) -> List[str]:
if is_numpy_array(UpperCAmelCase ):
return np.reshape(UpperCAmelCase , UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.reshape(*UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.reshape(UpperCAmelCase , UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.reshape(UpperCAmelCase , UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for reshape: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : Tuple , UpperCAmelCase : Optional[int]=None ) -> Any:
if is_numpy_array(UpperCAmelCase ):
return np.squeeze(UpperCAmelCase , axis=UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.squeeze() if axis is None else array.squeeze(dim=UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.squeeze(UpperCAmelCase , axis=UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.squeeze(UpperCAmelCase , axis=UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for squeeze: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : str , UpperCAmelCase : int ) -> str:
if is_numpy_array(UpperCAmelCase ):
return np.expand_dims(UpperCAmelCase , UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.unsqueeze(dim=UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.expand_dims(UpperCAmelCase , axis=UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.expand_dims(UpperCAmelCase , axis=UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for expand_dims: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : Dict ) -> List[str]:
if is_numpy_array(UpperCAmelCase ):
return np.size(UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.numel()
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.size(UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return array.size
else:
raise ValueError(f'''Type not supported for expand_dims: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : List[str] , UpperCAmelCase : List[str] ) -> Dict:
for key, value in auto_map.items():
if isinstance(UpperCAmelCase , (tuple, list) ):
UpperCAmelCase : List[Any] = [f'''{repo_id}--{v}''' if (v is not None and '''--''' not in v) else v for v in value]
elif value is not None and "--" not in value:
UpperCAmelCase : List[Any] = f'''{repo_id}--{value}'''
return auto_map
def a__ ( UpperCAmelCase : Tuple ) -> Union[str, Any]:
for base_class in inspect.getmro(UpperCAmelCase ):
UpperCAmelCase : Any = base_class.__module__
UpperCAmelCase : Dict = base_class.__name__
if module.startswith('''tensorflow''' ) or module.startswith('''keras''' ) or name == "TFPreTrainedModel":
return "tf"
elif module.startswith('''torch''' ) or name == "PreTrainedModel":
return "pt"
elif module.startswith('''flax''' ) or module.startswith('''jax''' ) or name == "FlaxPreTrainedModel":
return "flax"
else:
raise TypeError(f'''Could not infer framework from class {model_class}.''' )
| 336 | 1 |
_lowerCamelCase : Union[str, Any] = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
_lowerCamelCase : Tuple = [{"type": "code", "content": INSTALL_CONTENT}]
_lowerCamelCase : List[Any] = {
"{processor_class}": "FakeProcessorClass",
"{model_class}": "FakeModelClass",
"{object_class}": "FakeObjectClass",
}
| 336 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __UpperCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = LayoutLMTokenizer
UpperCamelCase = LayoutLMTokenizerFast
UpperCamelCase = True
UpperCamelCase = True
def __magic_name__ ( self : Any ):
super().setUp()
UpperCAmelCase : Dict = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
UpperCAmelCase : 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] ) )
def __magic_name__ ( self : Union[str, Any], **__A : List[str] ):
return LayoutLMTokenizer.from_pretrained(self.tmpdirname, **__A )
def __magic_name__ ( self : Optional[int], __A : int ):
UpperCAmelCase : Optional[Any] = '''UNwant\u00E9d,running'''
UpperCAmelCase : Optional[int] = '''unwanted, running'''
return input_text, output_text
def __magic_name__ ( self : Any ):
UpperCAmelCase : Union[str, Any] = self.tokenizer_class(self.vocab_file )
UpperCAmelCase : Optional[Any] = tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(__A, ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ), [7, 4, 5, 1_0, 8, 9] )
def __magic_name__ ( self : Optional[int] ):
pass
| 336 | 1 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
_lowerCamelCase : Optional[Any] = ["small", "medium", "large"]
_lowerCamelCase : int = "lm_head.decoder.weight"
_lowerCamelCase : Tuple = "lm_head.weight"
def a__ ( UpperCAmelCase : str , UpperCAmelCase : str ) -> List[Any]:
UpperCAmelCase : Optional[Any] = torch.load(UpperCAmelCase )
UpperCAmelCase : int = d.pop(UpperCAmelCase )
os.makedirs(UpperCAmelCase , exist_ok=UpperCAmelCase )
torch.save(UpperCAmelCase , os.path.join(UpperCAmelCase , UpperCAmelCase ) )
if __name__ == "__main__":
_lowerCamelCase : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument("--dialogpt_path", default=".", type=str)
_lowerCamelCase : Union[str, Any] = parser.parse_args()
for MODEL in DIALOGPT_MODELS:
_lowerCamelCase : List[str] = os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
_lowerCamelCase : Union[str, Any] = f"""./DialoGPT-{MODEL}"""
convert_dialogpt_checkpoint(
checkpoint_path,
pytorch_dump_folder_path,
)
| 336 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTMAEForPreTraining, TFViTMAEModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __UpperCAmelCase :
def __init__( self : Any, __A : str, __A : Dict=1_3, __A : int=3_0, __A : Tuple=2, __A : Union[str, Any]=3, __A : Any=True, __A : str=True, __A : Dict=3_2, __A : List[Any]=2, __A : Optional[Any]=4, __A : Union[str, Any]=3_7, __A : int="gelu", __A : int=0.1, __A : List[Any]=0.1, __A : Tuple=1_0, __A : Tuple=0.0_2, __A : Any=3, __A : List[str]=0.6, __A : Any=None, ):
UpperCAmelCase : Union[str, Any] = parent
UpperCAmelCase : Dict = batch_size
UpperCAmelCase : List[str] = image_size
UpperCAmelCase : Dict = patch_size
UpperCAmelCase : int = num_channels
UpperCAmelCase : Union[str, Any] = is_training
UpperCAmelCase : Union[str, Any] = use_labels
UpperCAmelCase : Union[str, Any] = hidden_size
UpperCAmelCase : Optional[int] = num_hidden_layers
UpperCAmelCase : Union[str, Any] = num_attention_heads
UpperCAmelCase : List[str] = intermediate_size
UpperCAmelCase : Optional[int] = hidden_act
UpperCAmelCase : Tuple = hidden_dropout_prob
UpperCAmelCase : List[Any] = attention_probs_dropout_prob
UpperCAmelCase : Any = type_sequence_label_size
UpperCAmelCase : Tuple = initializer_range
UpperCAmelCase : Tuple = mask_ratio
UpperCAmelCase : Any = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
UpperCAmelCase : Tuple = (image_size // patch_size) ** 2
UpperCAmelCase : List[Any] = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : Any = None
if self.use_labels:
UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size], self.type_sequence_label_size )
UpperCAmelCase : str = self.get_config()
return config, pixel_values, labels
def __magic_name__ ( self : Optional[Any] ):
return ViTMAEConfig(
image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, decoder_hidden_size=self.hidden_size, decoder_num_hidden_layers=self.num_hidden_layers, decoder_num_attention_heads=self.num_attention_heads, decoder_intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, is_decoder=__A, initializer_range=self.initializer_range, mask_ratio=self.mask_ratio, )
def __magic_name__ ( self : str, __A : List[Any], __A : Any, __A : Any ):
UpperCAmelCase : Optional[Any] = TFViTMAEModel(config=__A )
UpperCAmelCase : Tuple = model(__A, training=__A )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self : Tuple, __A : str, __A : int, __A : str ):
UpperCAmelCase : Dict = TFViTMAEForPreTraining(__A )
UpperCAmelCase : int = model(__A, training=__A )
# expected sequence length = num_patches
UpperCAmelCase : int = (self.image_size // self.patch_size) ** 2
UpperCAmelCase : Optional[Any] = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
UpperCAmelCase : Tuple = 1
UpperCAmelCase : List[Any] = TFViTMAEForPreTraining(__A )
UpperCAmelCase : Union[str, Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCAmelCase : List[Any] = model(__A, training=__A )
UpperCAmelCase : Union[str, Any] = self.patch_size**2
self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels) )
def __magic_name__ ( self : List[Any] ):
UpperCAmelCase : Dict = self.prepare_config_and_inputs()
((UpperCAmelCase) , (UpperCAmelCase) , (UpperCAmelCase)) : Union[str, Any] = config_and_inputs
UpperCAmelCase : Optional[Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
UpperCamelCase = {"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : List[Any] = TFViTMAEModelTester(self )
UpperCAmelCase : int = ConfigTester(self, config_class=__A, has_text_modality=__A, hidden_size=3_7 )
def __magic_name__ ( self : List[str] ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMAE does not use inputs_embeds''' )
def __magic_name__ ( self : List[Any] ):
pass
def __magic_name__ ( self : List[str] ):
UpperCAmelCase , UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : List[str] = model_class(__A )
self.assertIsInstance(model.get_input_embeddings(), (tf.keras.layers.Layer) )
UpperCAmelCase : Union[str, Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__A, tf.keras.layers.Layer ) )
def __magic_name__ ( self : str ):
UpperCAmelCase , UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Any = model_class(__A )
UpperCAmelCase : Any = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase : int = [*signature.parameters.keys()]
UpperCAmelCase : Tuple = ['''pixel_values''']
self.assertListEqual(arg_names[:1], __A )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __magic_name__ ( self : str ):
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__A )
def __magic_name__ ( self : int ):
# make the mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Tuple = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : List[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCAmelCase : str = model_class(__A )
UpperCAmelCase : int = self._prepare_for_class(__A, __A )
UpperCAmelCase : Dict = model(__A, noise=__A )
UpperCAmelCase : Any = copy.deepcopy(self._prepare_for_class(__A, __A ) )
UpperCAmelCase : Union[str, Any] = model(**__A, noise=__A )
UpperCAmelCase : Dict = outputs_dict[0].numpy()
UpperCAmelCase : Tuple = outputs_keywords[0].numpy()
self.assertLess(np.sum(np.abs(output_dict - output_keywords ) ), 1E-6 )
def __magic_name__ ( self : Optional[Any] ):
# make the mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : str = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Union[str, Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
def prepare_numpy_arrays(__A : Union[str, Any] ):
UpperCAmelCase : str = {}
for k, v in inputs_dict.items():
if tf.is_tensor(__A ):
UpperCAmelCase : Tuple = v.numpy()
else:
UpperCAmelCase : str = np.array(__A )
return inputs_np_dict
for model_class in self.all_model_classes:
UpperCAmelCase : Dict = model_class(__A )
UpperCAmelCase : Any = self._prepare_for_class(__A, __A )
UpperCAmelCase : Optional[int] = prepare_numpy_arrays(__A )
UpperCAmelCase : str = model(__A, noise=__A )
UpperCAmelCase : str = model(**__A, noise=__A )
self.assert_outputs_same(__A, __A )
def __magic_name__ ( self : int, __A : str, __A : Union[str, Any], __A : Optional[Any] ):
# make masks reproducible
np.random.seed(2 )
UpperCAmelCase : Any = int((tf_model.config.image_size // tf_model.config.patch_size) ** 2 )
UpperCAmelCase : Tuple = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCAmelCase : int = tf.constant(__A )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
UpperCAmelCase : List[Any] = tf_noise
super().check_pt_tf_models(__A, __A, __A )
def __magic_name__ ( self : str ):
# make mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Union[str, Any] = {
module_member
for model_class in self.all_model_classes
for module in (import_module(model_class.__module__ ),)
for module_member_name in dir(__A )
if module_member_name.endswith('''MainLayer''' )
# This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
and module_member_name[: -len('''MainLayer''' )] == model_class.__name__[: -len('''Model''' )]
for module_member in (getattr(__A, __A ),)
if isinstance(__A, __A )
and tf.keras.layers.Layer in module_member.__bases__
and getattr(__A, '''_keras_serializable''', __A )
}
UpperCAmelCase : Union[str, Any] = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Optional[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCAmelCase : str = tf.convert_to_tensor(__A )
inputs_dict.update({'''noise''': noise} )
for main_layer_class in tf_main_layer_classes:
UpperCAmelCase : Tuple = main_layer_class(__A )
UpperCAmelCase : int = {
name: tf.keras.Input(tensor.shape[1:], dtype=tensor.dtype ) for name, tensor in inputs_dict.items()
}
UpperCAmelCase : List[Any] = tf.keras.Model(__A, outputs=main_layer(__A ) )
UpperCAmelCase : List[Any] = model(__A )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase : Any = os.path.join(__A, '''keras_model.h5''' )
model.save(__A )
UpperCAmelCase : List[str] = tf.keras.models.load_model(
__A, custom_objects={main_layer_class.__name__: main_layer_class} )
assert isinstance(__A, tf.keras.Model )
UpperCAmelCase : Tuple = model(__A )
self.assert_outputs_same(__A, __A )
@slow
def __magic_name__ ( self : Dict ):
# make mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Optional[Any] = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Optional[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCAmelCase : int = model_class(__A )
UpperCAmelCase : List[str] = self._prepare_for_class(__A, __A )
UpperCAmelCase : Union[str, Any] = model(__A, noise=__A )
if model_class.__name__ == "TFViTMAEModel":
UpperCAmelCase : Optional[int] = outputs.last_hidden_state.numpy()
UpperCAmelCase : Union[str, Any] = 0
else:
UpperCAmelCase : Optional[int] = outputs.logits.numpy()
UpperCAmelCase : int = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__A, saved_model=__A )
UpperCAmelCase : Dict = model_class.from_pretrained(__A )
UpperCAmelCase : str = model(__A, noise=__A )
if model_class.__name__ == "TFViTMAEModel":
UpperCAmelCase : int = after_outputs['''last_hidden_state'''].numpy()
UpperCAmelCase : Dict = 0
else:
UpperCAmelCase : Any = after_outputs['''logits'''].numpy()
UpperCAmelCase : Dict = 0
UpperCAmelCase : Union[str, Any] = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(__A, 1E-5 )
def __magic_name__ ( self : Optional[Any] ):
# make mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : List[Any] = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Tuple = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCAmelCase : Dict = model_class(__A )
UpperCAmelCase : int = self._prepare_for_class(__A, __A )
UpperCAmelCase : List[Any] = model(__A, noise=__A )
UpperCAmelCase : str = model.get_config()
# make sure that returned config is jsonifiable, which is required by keras
json.dumps(__A )
UpperCAmelCase : int = model_class.from_config(model.get_config() )
# make sure it also accepts a normal config
UpperCAmelCase : str = model_class.from_config(model.config )
UpperCAmelCase : List[str] = new_model(__A ) # Build model
new_model.set_weights(model.get_weights() )
UpperCAmelCase : Tuple = new_model(__A, noise=__A )
self.assert_outputs_same(__A, __A )
@unittest.skip(
reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.''' )
def __magic_name__ ( self : Optional[int] ):
pass
@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load''' )
def __magic_name__ ( self : Tuple ):
pass
@slow
def __magic_name__ ( self : str ):
UpperCAmelCase : Tuple = TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224''' )
self.assertIsNotNone(__A )
def a__ ( ) -> Dict:
UpperCAmelCase : int = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : List[str] ):
return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base''' ) if is_vision_available() else None
@slow
def __magic_name__ ( self : str ):
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
UpperCAmelCase : Tuple = TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base''' )
UpperCAmelCase : List[str] = self.default_image_processor
UpperCAmelCase : Any = prepare_img()
UpperCAmelCase : str = image_processor(images=__A, return_tensors='''tf''' )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
UpperCAmelCase : Optional[int] = ViTMAEConfig()
UpperCAmelCase : int = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
UpperCAmelCase : Tuple = np.random.uniform(size=(1, num_patches) )
# forward pass
UpperCAmelCase : Optional[int] = model(**__A, noise=__A )
# verify the logits
UpperCAmelCase : Union[str, Any] = tf.convert_to_tensor([1, 1_9_6, 7_6_8] )
self.assertEqual(outputs.logits.shape, __A )
UpperCAmelCase : List[str] = tf.convert_to_tensor(
[[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]] )
tf.debugging.assert_near(outputs.logits[0, :3, :3], __A, atol=1E-4 )
| 336 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCamelCase__ )
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = field(default="""automatic-speech-recognition""" , metadata={"""include_in_asdict_even_if_is_default""": True} )
UpperCamelCase = Features({"""audio""": Audio()} )
UpperCamelCase = Features({"""transcription""": Value("""string""" )} )
UpperCamelCase = "audio"
UpperCamelCase = "transcription"
def __magic_name__ ( self : Union[str, Any], __A : str ):
if self.audio_column not in features:
raise ValueError(F'''Column {self.audio_column} is not present in features.''' )
if not isinstance(features[self.audio_column], __A ):
raise ValueError(F'''Column {self.audio_column} is not an Audio type.''' )
UpperCAmelCase : List[Any] = copy.deepcopy(self )
UpperCAmelCase : str = self.input_schema.copy()
UpperCAmelCase : Tuple = features[self.audio_column]
UpperCAmelCase : List[str] = input_schema
return task_template
@property
def __magic_name__ ( self : List[Any] ):
return {self.audio_column: "audio", self.transcription_column: "transcription"}
| 336 |
def a__ ( UpperCAmelCase : int ) -> int:
UpperCAmelCase : list[list[int]] = [[0 for _ in range(UpperCAmelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
UpperCAmelCase : Optional[Any] = 1
for n in range(m + 1 ):
for k in range(1 , UpperCAmelCase ):
memo[n][k] += memo[n][k - 1]
if n - k > 0:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1]
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
try:
_lowerCamelCase : List[Any] = int(input("Enter a number: ").strip())
print(partition(n))
except ValueError:
print("Please enter a number.")
else:
try:
_lowerCamelCase : str = int(sys.argv[1])
print(partition(n))
except ValueError:
print("Please pass a number.")
| 336 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : List[str] = {"processing_layoutxlm": ["LayoutXLMProcessor"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[int] = ["LayoutXLMTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Any = ["LayoutXLMTokenizerFast"]
if TYPE_CHECKING:
from .processing_layoutxlm import LayoutXLMProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutxlm import LayoutXLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast
else:
import sys
_lowerCamelCase : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 336 |
from __future__ import annotations
def a__ ( UpperCAmelCase : list[list[int]] ) -> bool:
UpperCAmelCase : Union[str, Any] = len(UpperCAmelCase )
# We need to create solution object to save path.
UpperCAmelCase : int = [[0 for _ in range(UpperCAmelCase )] for _ in range(UpperCAmelCase )]
UpperCAmelCase : Union[str, Any] = run_maze(UpperCAmelCase , 0 , 0 , UpperCAmelCase )
if solved:
print('''\n'''.join(str(UpperCAmelCase ) for row in solutions ) )
else:
print('''No solution exists!''' )
return solved
def a__ ( UpperCAmelCase : list[list[int]] , UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : list[list[int]] ) -> bool:
UpperCAmelCase : Dict = len(UpperCAmelCase )
# Final check point.
if i == j == (size - 1):
UpperCAmelCase : Dict = 1
return True
UpperCAmelCase : Union[str, Any] = (not i < 0) and (not j < 0) # Check lower bounds
UpperCAmelCase : List[Any] = (i < size) and (j < size) # Check upper bounds
if lower_flag and upper_flag:
# check for already visited and block points.
UpperCAmelCase : Any = (not solutions[i][j]) and (not maze[i][j])
if block_flag:
# check visited
UpperCAmelCase : str = 1
# check for directions
if (
run_maze(UpperCAmelCase , i + 1 , UpperCAmelCase , UpperCAmelCase )
or run_maze(UpperCAmelCase , UpperCAmelCase , j + 1 , UpperCAmelCase )
or run_maze(UpperCAmelCase , i - 1 , UpperCAmelCase , UpperCAmelCase )
or run_maze(UpperCAmelCase , UpperCAmelCase , j - 1 , UpperCAmelCase )
):
return True
UpperCAmelCase : Any = 0
return False
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 | 1 |
from manim import *
class __UpperCAmelCase ( lowerCamelCase__ ):
def __magic_name__ ( self : int ):
UpperCAmelCase : List[Any] = Rectangle(height=0.5, width=0.5 )
UpperCAmelCase : Tuple = Rectangle(height=0.4_6, width=0.4_6 ).set_stroke(width=0 )
UpperCAmelCase : Optional[Any] = [mem.copy() for i in range(6 )]
UpperCAmelCase : Any = [mem.copy() for i in range(6 )]
UpperCAmelCase : Any = VGroup(*__A ).arrange(__A, buff=0 )
UpperCAmelCase : List[str] = VGroup(*__A ).arrange(__A, buff=0 )
UpperCAmelCase : Optional[int] = VGroup(__A, __A ).arrange(__A, buff=0 )
UpperCAmelCase : Union[str, Any] = Text('''CPU''', font_size=2_4 )
UpperCAmelCase : int = Group(__A, __A ).arrange(__A, buff=0.5, aligned_edge=__A )
cpu.move_to([-2.5, -0.5, 0] )
self.add(__A )
UpperCAmelCase : Dict = [mem.copy() for i in range(1 )]
UpperCAmelCase : Optional[int] = VGroup(*__A ).arrange(__A, buff=0 )
UpperCAmelCase : Dict = Text('''GPU''', font_size=2_4 )
UpperCAmelCase : List[str] = Group(__A, __A ).arrange(__A, buff=0.5, aligned_edge=__A )
gpu.align_to(__A, __A )
gpu.set_x(gpu.get_x() - 1 )
self.add(__A )
UpperCAmelCase : Optional[int] = [mem.copy() for i in range(6 )]
UpperCAmelCase : List[Any] = VGroup(*__A ).arrange(__A, buff=0 )
UpperCAmelCase : Any = Text('''Model''', font_size=2_4 )
UpperCAmelCase : Union[str, Any] = Group(__A, __A ).arrange(__A, buff=0.5, aligned_edge=__A )
model.move_to([3, -1.0, 0] )
self.play(
Create(__A, run_time=1 ), Create(__A, run_time=1 ), Create(__A, run_time=1 ), )
UpperCAmelCase : Optional[int] = MarkupText(
F'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.''', font_size=2_4, )
UpperCAmelCase : str = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
UpperCAmelCase : Optional[int] = MarkupText(
F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''', font_size=1_8, )
key_text.move_to([-5, 2.4, 0] )
step_a.move_to([2, 2, 0] )
self.play(Write(__A, run_time=2.5 ), Write(__A ), Write(__A ) )
self.add(__A )
UpperCAmelCase : Tuple = []
UpperCAmelCase : Optional[int] = []
UpperCAmelCase : str = []
for i, rect in enumerate(__A ):
UpperCAmelCase : Optional[int] = Rectangle(height=0.4_6, width=0.4_6 ).set_stroke(width=0.0 ).set_fill(__A, opacity=0.7 )
cpu_target.move_to(__A )
cpu_target.generate_target()
UpperCAmelCase : Optional[Any] = 0.4_6 / 4
UpperCAmelCase : Optional[int] = 0.4_6 / 3
if i == 0:
cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ), buff=0.0_2, direction=__A )
cpu_target.target.set_x(cpu_target.target.get_x() + 0.1 )
elif i == 3:
cpu_target.target.next_to(cpu_targs[0].target, direction=__A, buff=0.0 )
else:
cpu_target.target.next_to(cpu_targs[i - 1].target, direction=__A, buff=0.0 )
cpu_targs.append(__A )
first_animations.append(rect.animate(run_time=0.5 ).set_stroke(__A ) )
second_animations.append(MoveToTarget(__A, run_time=1.5 ) )
self.play(*__A )
self.play(*__A )
self.wait()
| 336 |
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class __UpperCAmelCase :
def __init__( self : List[Any], __A : List[str], __A : List[str]=1_3, __A : Any=6_4, __A : Optional[Any]=2, __A : str=3, __A : str=True, __A : str=True, __A : Optional[Any]=3_2, __A : List[str]=5, __A : int=4, __A : str=3_7, __A : str="gelu", __A : Dict=0.1, __A : List[Any]=0.1, __A : Dict=1_0, __A : int=0.0_2, __A : Any=[1, 1_6, 4, 4], __A : Optional[int]=None, ):
UpperCAmelCase : Union[str, Any] = parent
UpperCAmelCase : Any = batch_size
UpperCAmelCase : List[str] = image_size
UpperCAmelCase : List[str] = patch_size
UpperCAmelCase : Dict = num_channels
UpperCAmelCase : List[Any] = is_training
UpperCAmelCase : Dict = use_labels
UpperCAmelCase : Optional[int] = hidden_size
UpperCAmelCase : Union[str, Any] = num_hidden_layers
UpperCAmelCase : Optional[Any] = num_attention_heads
UpperCAmelCase : Any = intermediate_size
UpperCAmelCase : Any = hidden_act
UpperCAmelCase : Any = hidden_dropout_prob
UpperCAmelCase : Optional[int] = attention_probs_dropout_prob
UpperCAmelCase : str = type_sequence_label_size
UpperCAmelCase : Any = initializer_range
UpperCAmelCase : int = scope
UpperCAmelCase : List[str] = backbone_featmap_shape
# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
# the number of patches is based on the feature map of the backbone, which by default uses an output stride
# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
UpperCAmelCase : str = (self.image_size // 3_2) ** 2
UpperCAmelCase : List[str] = num_patches + 1
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : str = None
if self.use_labels:
UpperCAmelCase : Any = ids_tensor([self.batch_size], self.type_sequence_label_size )
UpperCAmelCase : Optional[int] = self.get_config()
return config, pixel_values, labels
def __magic_name__ ( self : Any ):
UpperCAmelCase : Dict = {
'''global_padding''': '''same''',
'''layer_type''': '''bottleneck''',
'''depths''': [3, 4, 9],
'''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
'''embedding_dynamic_padding''': True,
'''hidden_sizes''': [4, 8, 1_6, 3_2],
'''num_groups''': 2,
}
return ViTHybridConfig(
image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, is_decoder=__A, initializer_range=self.initializer_range, backbone_featmap_shape=self.backbone_featmap_shape, backbone_config=__A, )
def __magic_name__ ( self : Optional[int], __A : Optional[int], __A : int, __A : Tuple ):
UpperCAmelCase : int = ViTHybridModel(config=__A )
model.to(__A )
model.eval()
UpperCAmelCase : Tuple = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self : Tuple, __A : Dict, __A : str, __A : List[str] ):
UpperCAmelCase : str = self.type_sequence_label_size
UpperCAmelCase : List[Any] = ViTHybridForImageClassification(__A )
model.to(__A )
model.eval()
UpperCAmelCase : Dict = model(__A, labels=__A )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size) )
def __magic_name__ ( self : int ):
UpperCAmelCase : Union[str, Any] = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[str] = config_and_inputs
UpperCAmelCase : int = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
UpperCamelCase = (
{"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
if is_torch_available()
else {}
)
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : Any = ViTHybridModelTester(self )
UpperCAmelCase : List[Any] = ConfigTester(self, config_class=__A, has_text_modality=__A, hidden_size=3_7 )
def __magic_name__ ( self : int ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViT does not use inputs_embeds''' )
def __magic_name__ ( self : List[Any] ):
pass
def __magic_name__ ( self : int ):
UpperCAmelCase , UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Dict = model_class(__A )
self.assertIsInstance(model.get_input_embeddings(), (nn.Module) )
UpperCAmelCase : Optional[int] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__A, nn.Linear ) )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase , UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : List[Any] = model_class(__A )
UpperCAmelCase : Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase : str = [*signature.parameters.keys()]
UpperCAmelCase : Optional[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1], __A )
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__A )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase , UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Dict = _config_zero_init(__A )
for model_class in self.all_model_classes:
UpperCAmelCase : Optional[Any] = model_class(config=__A )
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
UpperCAmelCase : Union[str, Any] = [F'''{name}.{key}''' for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item(), [0.0, 1.0], msg=F'''Parameter {name} of model {model_class} seems not properly initialized''', )
@slow
def __magic_name__ ( self : List[str] ):
for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase : Union[str, Any] = ViTHybridModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def a__ ( ) -> Tuple:
UpperCAmelCase : Any = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : str ):
return (
ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : int = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
__A )
UpperCAmelCase : Tuple = self.default_image_processor
UpperCAmelCase : int = prepare_img()
UpperCAmelCase : Union[str, Any] = image_processor(images=__A, return_tensors='''pt''' ).to(__A )
# forward pass
with torch.no_grad():
UpperCAmelCase : Optional[Any] = model(**__A )
# verify the logits
UpperCAmelCase : str = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape, __A )
UpperCAmelCase : Optional[Any] = torch.tensor([-1.9_0_9_0, -0.4_9_9_3, -0.2_3_8_9] ).to(__A )
self.assertTrue(torch.allclose(outputs.logits[0, :3], __A, atol=1E-4 ) )
@slow
@require_accelerate
def __magic_name__ ( self : Dict ):
UpperCAmelCase : Union[str, Any] = ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384''' )
UpperCAmelCase : int = ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384''', device_map='''auto''' )
UpperCAmelCase : Tuple = prepare_img()
UpperCAmelCase : Optional[int] = image_processor(images=__A, return_tensors='''pt''' )
UpperCAmelCase : Dict = model(**__A )
UpperCAmelCase : Any = outputs.logits
# model predicts one of the 1000 ImageNet classes
UpperCAmelCase : Dict = logits.argmax(-1 ).item()
self.assertTrue(model.config.idalabel[predicted_class_idx], '''tabby, tabby cat''' )
| 336 | 1 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def a__ ( UpperCAmelCase : List[str] ) -> str:
UpperCAmelCase : str = []
for line in lines:
UpperCAmelCase : Dict = re.sub(r'''#.*''' , '''''' , UpperCAmelCase ) # remove comments
if line:
filtered_lines.append(UpperCAmelCase )
UpperCAmelCase : List[Any] = '''\n'''.join(UpperCAmelCase )
# Make a hash from all this code
UpperCAmelCase : Dict = full_str.encode('''utf-8''' )
return shaaaa(UpperCAmelCase ).hexdigest()
# get importable module names and hash for caching
_lowerCamelCase : str = {
"csv": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
"json": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
"pandas": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
"parquet": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
"arrow": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
"text": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
"imagefolder": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
"audiofolder": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
_lowerCamelCase : Optional[Any] = {
".csv": ("csv", {}),
".tsv": ("csv", {"sep": "\t"}),
".json": ("json", {}),
".jsonl": ("json", {}),
".parquet": ("parquet", {}),
".arrow": ("arrow", {}),
".txt": ("text", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("imagefolder", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("imagefolder", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("audiofolder", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("audiofolder", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_lowerCamelCase : Dict = {"imagefolder", "audiofolder"}
# Used to filter data files based on extensions given a module name
_lowerCamelCase : Dict[str, List[str]] = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append(".zip")
_MODULE_TO_EXTENSIONS["audiofolder"].append(".zip")
| 336 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def a__ ( ) -> tuple[list[int], int]:
UpperCAmelCase : str = [randint(-1_000 , 1_000 ) for i in range(10 )]
UpperCAmelCase : Any = randint(-5_000 , 5_000 )
return (arr, r)
_lowerCamelCase : Any = make_dataset()
def a__ ( UpperCAmelCase : list[int] , UpperCAmelCase : int ) -> tuple[int, ...]:
for triplet in permutations(UpperCAmelCase , 3 ):
if sum(UpperCAmelCase ) == target:
return tuple(sorted(UpperCAmelCase ) )
return (0, 0, 0)
def a__ ( UpperCAmelCase : list[int] , UpperCAmelCase : int ) -> tuple[int, int, int]:
arr.sort()
UpperCAmelCase : Tuple = len(UpperCAmelCase )
for i in range(n - 1 ):
UpperCAmelCase , UpperCAmelCase : int = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def a__ ( ) -> tuple[float, float]:
UpperCAmelCase : Union[str, Any] = '''
from __main__ import dataset, triplet_sum1, triplet_sum2
'''
UpperCAmelCase : Tuple = '''
triplet_sum1(*dataset)
'''
UpperCAmelCase : List[str] = '''
triplet_sum2(*dataset)
'''
UpperCAmelCase : Tuple = repeat(setup=UpperCAmelCase , stmt=UpperCAmelCase , repeat=5 , number=10_000 )
UpperCAmelCase : str = repeat(setup=UpperCAmelCase , stmt=UpperCAmelCase , repeat=5 , number=10_000 )
return (min(UpperCAmelCase ), min(UpperCAmelCase ))
if __name__ == "__main__":
from doctest import testmod
testmod()
_lowerCamelCase : int = solution_times()
print(f"""The time for naive implementation is {times[0]}.""")
print(f"""The time for optimized implementation is {times[1]}.""")
| 336 | 1 |
from __future__ import annotations
def a__ ( UpperCAmelCase : float , UpperCAmelCase : float , UpperCAmelCase : float , ) -> tuple[str, float]:
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif stress < 0:
raise ValueError('''Stress cannot be negative''' )
elif tangential_force < 0:
raise ValueError('''Tangential Force cannot be negative''' )
elif area < 0:
raise ValueError('''Area cannot be negative''' )
elif stress == 0:
return (
"stress",
tangential_force / area,
)
elif tangential_force == 0:
return (
"tangential_force",
stress * area,
)
else:
return (
"area",
tangential_force / stress,
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 |
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class __UpperCAmelCase :
def __magic_name__ ( self : int, __A : Dict ):
raise NotImplementedError()
def __magic_name__ ( self : int ):
raise NotImplementedError()
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : str, __A : "AutoTokenizer", __A : bool = False, **__A : str ):
UpperCAmelCase : List[str] = tokenizer
UpperCAmelCase : str = skip_prompt
UpperCAmelCase : List[str] = decode_kwargs
# variables used in the streaming process
UpperCAmelCase : Dict = []
UpperCAmelCase : List[str] = 0
UpperCAmelCase : Union[str, Any] = True
def __magic_name__ ( self : Dict, __A : Optional[int] ):
if len(value.shape ) > 1 and value.shape[0] > 1:
raise ValueError('''TextStreamer only supports batch size 1''' )
elif len(value.shape ) > 1:
UpperCAmelCase : Union[str, Any] = value[0]
if self.skip_prompt and self.next_tokens_are_prompt:
UpperCAmelCase : Optional[int] = False
return
# Add the new token to the cache and decodes the entire thing.
self.token_cache.extend(value.tolist() )
UpperCAmelCase : Any = self.tokenizer.decode(self.token_cache, **self.decode_kwargs )
# After the symbol for a new line, we flush the cache.
if text.endswith('''\n''' ):
UpperCAmelCase : Union[str, Any] = text[self.print_len :]
UpperCAmelCase : int = []
UpperCAmelCase : int = 0
# If the last token is a CJK character, we print the characters.
elif len(__A ) > 0 and self._is_chinese_char(ord(text[-1] ) ):
UpperCAmelCase : Union[str, Any] = text[self.print_len :]
self.print_len += len(__A )
# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
# which may change with the subsequent token -- there are probably smarter ways to do this!)
else:
UpperCAmelCase : Optional[Any] = text[self.print_len : text.rfind(''' ''' ) + 1]
self.print_len += len(__A )
self.on_finalized_text(__A )
def __magic_name__ ( self : str ):
# Flush the cache, if it exists
if len(self.token_cache ) > 0:
UpperCAmelCase : int = self.tokenizer.decode(self.token_cache, **self.decode_kwargs )
UpperCAmelCase : Dict = text[self.print_len :]
UpperCAmelCase : List[Any] = []
UpperCAmelCase : List[Any] = 0
else:
UpperCAmelCase : Dict = ''''''
UpperCAmelCase : str = True
self.on_finalized_text(__A, stream_end=__A )
def __magic_name__ ( self : List[str], __A : str, __A : bool = False ):
print(__A, flush=__A, end='''''' if not stream_end else None )
def __magic_name__ ( self : List[Any], __A : Optional[int] ):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0X4E00 and cp <= 0X9FFF)
or (cp >= 0X3400 and cp <= 0X4DBF) #
or (cp >= 0X20000 and cp <= 0X2A6DF) #
or (cp >= 0X2A700 and cp <= 0X2B73F) #
or (cp >= 0X2B740 and cp <= 0X2B81F) #
or (cp >= 0X2B820 and cp <= 0X2CEAF) #
or (cp >= 0XF900 and cp <= 0XFAFF)
or (cp >= 0X2F800 and cp <= 0X2FA1F) #
): #
return True
return False
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : Dict, __A : "AutoTokenizer", __A : bool = False, __A : Optional[float] = None, **__A : str ):
super().__init__(__A, __A, **__A )
UpperCAmelCase : Dict = Queue()
UpperCAmelCase : Any = None
UpperCAmelCase : Any = timeout
def __magic_name__ ( self : Dict, __A : str, __A : bool = False ):
self.text_queue.put(__A, timeout=self.timeout )
if stream_end:
self.text_queue.put(self.stop_signal, timeout=self.timeout )
def __iter__( self : int ):
return self
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : List[Any] = self.text_queue.get(timeout=self.timeout )
if value == self.stop_signal:
raise StopIteration()
else:
return value
| 336 | 1 |
_lowerCamelCase : Union[str, Any] = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def a__ ( UpperCAmelCase : dict , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Optional[int] ) -> list[str]:
UpperCAmelCase : Dict = set()
# keep track of all the paths to be checked
UpperCAmelCase : List[Any] = [[start]]
# return path if start is goal
if start == goal:
return [start]
# keeps looping until all possible paths have been checked
while queue:
# pop the first path from the queue
UpperCAmelCase : int = queue.pop(0 )
# get the last node from the path
UpperCAmelCase : Optional[Any] = path[-1]
if node not in explored:
UpperCAmelCase : Tuple = graph[node]
# go through all neighbour nodes, construct a new path and
# push it into the queue
for neighbour in neighbours:
UpperCAmelCase : int = list(UpperCAmelCase )
new_path.append(UpperCAmelCase )
queue.append(UpperCAmelCase )
# return path if neighbour is goal
if neighbour == goal:
return new_path
# mark node as explored
explored.add(UpperCAmelCase )
# in case there's no path between the 2 nodes
return []
def a__ ( UpperCAmelCase : dict , UpperCAmelCase : List[str] , UpperCAmelCase : List[Any] ) -> int:
if not graph or start not in graph or target not in graph:
return -1
if start == target:
return 0
UpperCAmelCase : List[Any] = [start]
UpperCAmelCase : str = set(UpperCAmelCase )
# Keep tab on distances from `start` node.
UpperCAmelCase : List[str] = {start: 0, target: -1}
while queue:
UpperCAmelCase : Tuple = queue.pop(0 )
if node == target:
UpperCAmelCase : List[str] = (
dist[node] if dist[target] == -1 else min(dist[target] , dist[node] )
)
for adjacent in graph[node]:
if adjacent not in visited:
visited.add(UpperCAmelCase )
queue.append(UpperCAmelCase )
UpperCAmelCase : Optional[Any] = dist[node] + 1
return dist[target]
if __name__ == "__main__":
print(bfs_shortest_path(demo_graph, "G", "D")) # returns ['G', 'C', 'A', 'B', 'D']
print(bfs_shortest_path_distance(demo_graph, "G", "D")) # returns 4
| 336 |
import numpy
# List of input, output pairs
_lowerCamelCase : Dict = (
((5, 2, 3), 1_5),
((6, 5, 9), 2_5),
((1_1, 1_2, 1_3), 4_1),
((1, 1, 1), 8),
((1_1, 1_2, 1_3), 4_1),
)
_lowerCamelCase : str = (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
_lowerCamelCase : Dict = [2, 4, 1, 5]
_lowerCamelCase : Dict = len(train_data)
_lowerCamelCase : int = 0.0_0_9
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : Optional[int]="train" ) -> Dict:
return calculate_hypothesis_value(UpperCAmelCase , UpperCAmelCase ) - output(
UpperCAmelCase , UpperCAmelCase )
def a__ ( UpperCAmelCase : int ) -> Any:
UpperCAmelCase : str = 0
for i in range(len(UpperCAmelCase ) - 1 ):
hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
hyp_val += parameter_vector[0]
return hyp_val
def a__ ( UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[Any] ) -> Optional[int]:
if data_set == "train":
return train_data[example_no][1]
elif data_set == "test":
return test_data[example_no][1]
return None
def a__ ( UpperCAmelCase : int , UpperCAmelCase : Optional[Any] ) -> List[str]:
if data_set == "train":
return _hypothesis_value(train_data[example_no][0] )
elif data_set == "test":
return _hypothesis_value(test_data[example_no][0] )
return None
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : str=m ) -> Dict:
UpperCAmelCase : Optional[int] = 0
for i in range(UpperCAmelCase ):
if index == -1:
summation_value += _error(UpperCAmelCase )
else:
summation_value += _error(UpperCAmelCase ) * train_data[i][0][index]
return summation_value
def a__ ( UpperCAmelCase : Dict ) -> Dict:
UpperCAmelCase : Dict = summation_of_cost_derivative(UpperCAmelCase , UpperCAmelCase ) / m
return cost_derivative_value
def a__ ( ) -> List[Any]:
global parameter_vector
# Tune these values to set a tolerance value for predicted output
UpperCAmelCase : List[str] = 0.000002
UpperCAmelCase : Any = 0
UpperCAmelCase : Dict = 0
while True:
j += 1
UpperCAmelCase : List[Any] = [0, 0, 0, 0]
for i in range(0 , len(UpperCAmelCase ) ):
UpperCAmelCase : List[str] = get_cost_derivative(i - 1 )
UpperCAmelCase : Tuple = (
parameter_vector[i] - LEARNING_RATE * cost_derivative
)
if numpy.allclose(
UpperCAmelCase , UpperCAmelCase , atol=UpperCAmelCase , rtol=UpperCAmelCase , ):
break
UpperCAmelCase : int = temp_parameter_vector
print(('''Number of iterations:''', j) )
def a__ ( ) -> List[Any]:
for i in range(len(UpperCAmelCase ) ):
print(('''Actual output value:''', output(UpperCAmelCase , '''test''' )) )
print(('''Hypothesis output:''', calculate_hypothesis_value(UpperCAmelCase , '''test''' )) )
if __name__ == "__main__":
run_gradient_descent()
print("\nTesting gradient descent for a linear hypothesis function.\n")
test_gradient_descent()
| 336 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
def __magic_name__ ( self : List[Any] ):
UpperCAmelCase : Optional[Any] = tempfile.mkdtemp()
UpperCAmelCase : List[Any] = BlipImageProcessor()
UpperCAmelCase : Union[str, Any] = BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-BertModel''' )
UpperCAmelCase : Optional[int] = BlipProcessor(__A, __A )
processor.save_pretrained(self.tmpdirname )
def __magic_name__ ( self : str, **__A : str ):
return AutoProcessor.from_pretrained(self.tmpdirname, **__A ).tokenizer
def __magic_name__ ( self : List[Any], **__A : str ):
return AutoProcessor.from_pretrained(self.tmpdirname, **__A ).image_processor
def __magic_name__ ( self : Tuple ):
shutil.rmtree(self.tmpdirname )
def __magic_name__ ( self : str ):
UpperCAmelCase : Union[str, Any] = [np.random.randint(2_5_5, size=(3, 3_0, 4_0_0), dtype=np.uinta )]
UpperCAmelCase : str = [Image.fromarray(np.moveaxis(__A, 0, -1 ) ) for x in image_inputs]
return image_inputs
def __magic_name__ ( self : str ):
UpperCAmelCase : List[str] = BlipProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
UpperCAmelCase : List[str] = self.get_tokenizer(bos_token='''(BOS)''', eos_token='''(EOS)''' )
UpperCAmelCase : Tuple = self.get_image_processor(do_normalize=__A, padding_value=1.0 )
UpperCAmelCase : Union[str, Any] = BlipProcessor.from_pretrained(
self.tmpdirname, bos_token='''(BOS)''', eos_token='''(EOS)''', do_normalize=__A, padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer, __A )
self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor, __A )
def __magic_name__ ( self : str ):
UpperCAmelCase : int = self.get_image_processor()
UpperCAmelCase : Dict = self.get_tokenizer()
UpperCAmelCase : Union[str, Any] = BlipProcessor(tokenizer=__A, image_processor=__A )
UpperCAmelCase : List[str] = self.prepare_image_inputs()
UpperCAmelCase : Optional[Any] = image_processor(__A, return_tensors='''np''' )
UpperCAmelCase : Tuple = processor(images=__A, return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1E-2 )
def __magic_name__ ( self : Tuple ):
UpperCAmelCase : Optional[int] = self.get_image_processor()
UpperCAmelCase : Any = self.get_tokenizer()
UpperCAmelCase : int = BlipProcessor(tokenizer=__A, image_processor=__A )
UpperCAmelCase : Optional[int] = '''lower newer'''
UpperCAmelCase : Any = processor(text=__A )
UpperCAmelCase : Optional[int] = tokenizer(__A, return_token_type_ids=__A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key] )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : List[Any] = self.get_image_processor()
UpperCAmelCase : Union[str, Any] = self.get_tokenizer()
UpperCAmelCase : int = BlipProcessor(tokenizer=__A, image_processor=__A )
UpperCAmelCase : Any = '''lower newer'''
UpperCAmelCase : Optional[int] = self.prepare_image_inputs()
UpperCAmelCase : Tuple = processor(text=__A, images=__A )
self.assertListEqual(list(inputs.keys() ), ['''pixel_values''', '''input_ids''', '''attention_mask'''] )
# test if it raises when no input is passed
with pytest.raises(__A ):
processor()
def __magic_name__ ( self : Tuple ):
UpperCAmelCase : Dict = self.get_image_processor()
UpperCAmelCase : Any = self.get_tokenizer()
UpperCAmelCase : Dict = BlipProcessor(tokenizer=__A, image_processor=__A )
UpperCAmelCase : Optional[Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
UpperCAmelCase : List[str] = processor.batch_decode(__A )
UpperCAmelCase : int = tokenizer.batch_decode(__A )
self.assertListEqual(__A, __A )
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : Any = self.get_image_processor()
UpperCAmelCase : Tuple = self.get_tokenizer()
UpperCAmelCase : str = BlipProcessor(tokenizer=__A, image_processor=__A )
UpperCAmelCase : Dict = '''lower newer'''
UpperCAmelCase : Optional[int] = self.prepare_image_inputs()
UpperCAmelCase : Optional[Any] = processor(text=__A, images=__A )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ), ['''pixel_values''', '''input_ids''', '''attention_mask'''] )
| 336 |
def a__ ( UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[int] ) -> Optional[Any]:
UpperCAmelCase : List[str] = 0
UpperCAmelCase : List[Any] = len(UpperCAmelCase ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
UpperCAmelCase : Optional[int] = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(UpperCAmelCase ):
return None
UpperCAmelCase : Optional[Any] = sorted_collection[point]
if current_item == item:
return point
else:
if point < left:
UpperCAmelCase : Any = left
UpperCAmelCase : List[str] = point
elif point > right:
UpperCAmelCase : Any = right
UpperCAmelCase : List[str] = point
else:
if item < current_item:
UpperCAmelCase : Optional[int] = point - 1
else:
UpperCAmelCase : str = point + 1
return None
def a__ ( UpperCAmelCase : Optional[Any] , UpperCAmelCase : int , UpperCAmelCase : str , UpperCAmelCase : Union[str, Any] ) -> Dict:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
UpperCAmelCase : List[str] = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(UpperCAmelCase ):
return None
if sorted_collection[point] == item:
return point
elif point < left:
return interpolation_search_by_recursion(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
elif point > right:
return interpolation_search_by_recursion(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
else:
if sorted_collection[point] > item:
return interpolation_search_by_recursion(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , point - 1 )
else:
return interpolation_search_by_recursion(
UpperCAmelCase , UpperCAmelCase , point + 1 , UpperCAmelCase )
def a__ ( UpperCAmelCase : Union[str, Any] ) -> int:
if collection != sorted(UpperCAmelCase ):
raise ValueError('''Collection must be ascending sorted''' )
return True
if __name__ == "__main__":
import sys
_lowerCamelCase : Optional[int] = 0
if debug == 1:
_lowerCamelCase : Dict = [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
try:
__assert_sorted(collection)
except ValueError:
sys.exit("Sequence must be ascending sorted to apply interpolation search")
_lowerCamelCase : List[Any] = 6_7
_lowerCamelCase : Optional[Any] = interpolation_search(collection, target)
if result is not None:
print(f"""{target} found at positions: {result}""")
else:
print("Not found")
| 336 | 1 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
_lowerCamelCase : List[Any] = importlib.util.find_spec("s3fs") is not None
if _has_safs:
from .safilesystem import SaFileSystem # noqa: F401
_lowerCamelCase : List[compression.BaseCompressedFileFileSystem] = [
compression.BzaFileSystem,
compression.GzipFileSystem,
compression.LzaFileSystem,
compression.XzFileSystem,
compression.ZstdFileSystem,
]
# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
warnings.warn(f"""A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.""")
fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
def a__ ( UpperCAmelCase : str ) -> str:
if "://" in dataset_path:
UpperCAmelCase : Optional[Any] = dataset_path.split('''://''' )[1]
return dataset_path
def a__ ( UpperCAmelCase : fsspec.AbstractFileSystem ) -> bool:
if fs is not None and fs.protocol != "file":
return True
else:
return False
def a__ ( UpperCAmelCase : fsspec.AbstractFileSystem , UpperCAmelCase : str , UpperCAmelCase : str ) -> List[str]:
UpperCAmelCase : Any = not is_remote_filesystem(UpperCAmelCase )
if is_local:
# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
shutil.move(fs._strip_protocol(UpperCAmelCase ) , fs._strip_protocol(UpperCAmelCase ) )
else:
fs.mv(UpperCAmelCase , UpperCAmelCase , recursive=UpperCAmelCase )
def a__ ( ) -> None:
if hasattr(fsspec.asyn , '''reset_lock''' ):
# for future fsspec>2022.05.0
fsspec.asyn.reset_lock()
else:
UpperCAmelCase : Tuple = None
UpperCAmelCase : str = None
UpperCAmelCase : Tuple = threading.Lock()
| 336 |
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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase : Any = logging.get_logger(__name__)
def a__ ( UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[Any]=False , UpperCAmelCase : List[str]=False ) -> Any:
UpperCAmelCase : Optional[int] = '''backbone.''' if is_semantic else ''''''
UpperCAmelCase : Dict = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f'''{prefix}blocks.{i}.norm1.weight''', f'''beit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((f'''{prefix}blocks.{i}.norm1.bias''', f'''beit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(f'''{prefix}blocks.{i}.attn.proj.weight''', f'''beit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append(
(f'''{prefix}blocks.{i}.attn.proj.bias''', f'''beit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((f'''{prefix}blocks.{i}.norm2.weight''', f'''beit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((f'''{prefix}blocks.{i}.norm2.bias''', f'''beit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.weight''', f'''beit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.bias''', f'''beit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.weight''', f'''beit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.bias''', f'''beit.encoder.layer.{i}.output.dense.bias''') )
# projection layer + position embeddings
rename_keys.extend(
[
(f'''{prefix}cls_token''', '''beit.embeddings.cls_token'''),
(f'''{prefix}patch_embed.proj.weight''', '''beit.embeddings.patch_embeddings.projection.weight'''),
(f'''{prefix}patch_embed.proj.bias''', '''beit.embeddings.patch_embeddings.projection.bias'''),
(f'''{prefix}pos_embed''', '''beit.embeddings.position_embeddings'''),
] )
if has_lm_head:
# mask token + layernorm
rename_keys.extend(
[
('''mask_token''', '''beit.embeddings.mask_token'''),
('''norm.weight''', '''layernorm.weight'''),
('''norm.bias''', '''layernorm.bias'''),
] )
else:
# layernorm + classification head
rename_keys.extend(
[
('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
('''head.weight''', '''classifier.weight'''),
('''head.bias''', '''classifier.bias'''),
] )
return rename_keys
def a__ ( UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str , UpperCAmelCase : str=False , UpperCAmelCase : Dict=False ) -> Any:
for i in range(config.num_hidden_layers ):
UpperCAmelCase : Tuple = '''backbone.''' if is_semantic else ''''''
# queries, keys and values
UpperCAmelCase : Optional[Any] = state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight''' )
UpperCAmelCase : Optional[Any] = state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias''' )
UpperCAmelCase : List[Any] = state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias''' )
UpperCAmelCase : Union[str, Any] = in_proj_weight[
: config.hidden_size, :
]
UpperCAmelCase : str = q_bias
UpperCAmelCase : List[str] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
UpperCAmelCase : List[str] = in_proj_weight[
-config.hidden_size :, :
]
UpperCAmelCase : int = v_bias
# gamma_1 and gamma_2
# we call them lambda because otherwise they are renamed when using .from_pretrained
UpperCAmelCase : int = state_dict.pop(f'''{prefix}blocks.{i}.gamma_1''' )
UpperCAmelCase : Optional[Any] = state_dict.pop(f'''{prefix}blocks.{i}.gamma_2''' )
UpperCAmelCase : str = gamma_a
UpperCAmelCase : Dict = gamma_a
def a__ ( UpperCAmelCase : Optional[int] , UpperCAmelCase : List[Any] , UpperCAmelCase : Tuple ) -> Optional[Any]:
UpperCAmelCase : Union[str, Any] = dct.pop(UpperCAmelCase )
UpperCAmelCase : str = val
def a__ ( ) -> Optional[int]:
UpperCAmelCase : List[Any] = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
UpperCAmelCase : Union[str, Any] = Image.open(requests.get(UpperCAmelCase , stream=UpperCAmelCase ).raw )
return im
@torch.no_grad()
def a__ ( UpperCAmelCase : Optional[int] , UpperCAmelCase : Tuple , UpperCAmelCase : List[Any]=False ) -> Union[str, Any]:
UpperCAmelCase : Dict = False if '''rvlcdip''' in checkpoint_url else True
UpperCAmelCase : Any = BeitConfig(use_absolute_position_embeddings=UpperCAmelCase , use_mask_token=UpperCAmelCase )
# size of the architecture
if "large" in checkpoint_url or "dit-l" in checkpoint_url:
UpperCAmelCase : List[Any] = 1_024
UpperCAmelCase : Optional[Any] = 4_096
UpperCAmelCase : Any = 24
UpperCAmelCase : Union[str, Any] = 16
# labels
if "rvlcdip" in checkpoint_url:
UpperCAmelCase : Optional[Any] = 16
UpperCAmelCase : List[Any] = '''huggingface/label-files'''
UpperCAmelCase : Any = '''rvlcdip-id2label.json'''
UpperCAmelCase : List[str] = json.load(open(hf_hub_download(UpperCAmelCase , UpperCAmelCase , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase : Dict = {int(UpperCAmelCase ): v for k, v in idalabel.items()}
UpperCAmelCase : Union[str, Any] = idalabel
UpperCAmelCase : Tuple = {v: k for k, v in idalabel.items()}
# load state_dict of original model, remove and rename some keys
UpperCAmelCase : Tuple = torch.hub.load_state_dict_from_url(UpperCAmelCase , map_location='''cpu''' )['''model''']
UpperCAmelCase : List[str] = create_rename_keys(UpperCAmelCase , has_lm_head=UpperCAmelCase )
for src, dest in rename_keys:
rename_key(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
read_in_q_k_v(UpperCAmelCase , UpperCAmelCase , has_lm_head=UpperCAmelCase )
# load HuggingFace model
UpperCAmelCase : Tuple = BeitForMaskedImageModeling(UpperCAmelCase ) if has_lm_head else BeitForImageClassification(UpperCAmelCase )
model.eval()
model.load_state_dict(UpperCAmelCase )
# Check outputs on an image
UpperCAmelCase : Dict = BeitImageProcessor(
size=config.image_size , resample=PILImageResampling.BILINEAR , do_center_crop=UpperCAmelCase )
UpperCAmelCase : List[str] = prepare_img()
UpperCAmelCase : Optional[Any] = image_processor(images=UpperCAmelCase , return_tensors='''pt''' )
UpperCAmelCase : str = encoding['''pixel_values''']
UpperCAmelCase : Any = model(UpperCAmelCase )
UpperCAmelCase : Optional[Any] = outputs.logits
# verify logits
UpperCAmelCase : List[Any] = [1, 16] if '''rvlcdip''' in checkpoint_url else [1, 196, 8_192]
assert logits.shape == torch.Size(UpperCAmelCase ), "Shape of logits not as expected"
Path(UpperCAmelCase ).mkdir(exist_ok=UpperCAmelCase )
print(f'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(UpperCAmelCase )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(UpperCAmelCase )
if push_to_hub:
if has_lm_head:
UpperCAmelCase : List[Any] = '''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
else:
UpperCAmelCase : Any = '''dit-base-finetuned-rvlcdip''' if '''dit-b''' in checkpoint_url else '''dit-large-finetuned-rvlcdip'''
image_processor.push_to_hub(
repo_path_or_name=Path(UpperCAmelCase , UpperCAmelCase ) , organization='''nielsr''' , commit_message='''Add image processor''' , use_temp_dir=UpperCAmelCase , )
model.push_to_hub(
repo_path_or_name=Path(UpperCAmelCase , UpperCAmelCase ) , organization='''nielsr''' , commit_message='''Add model''' , use_temp_dir=UpperCAmelCase , )
if __name__ == "__main__":
_lowerCamelCase : Tuple = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_url",
default="https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth",
type=str,
help="URL to the original PyTorch checkpoint (.pth file).",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
parser.add_argument(
"--push_to_hub",
action="store_true",
)
_lowerCamelCase : Optional[int] = parser.parse_args()
convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 336 | 1 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCamelCase : Dict = {"vocab_file": "vocab.txt"}
_lowerCamelCase : List[str] = {
"vocab_file": {
"facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
"facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
},
}
_lowerCamelCase : List[Any] = {
"facebook/esm2_t6_8M_UR50D": 1_0_2_4,
"facebook/esm2_t12_35M_UR50D": 1_0_2_4,
}
def a__ ( UpperCAmelCase : List[str] ) -> Any:
with open(UpperCAmelCase , '''r''' ) as f:
UpperCAmelCase : Dict = f.read().splitlines()
return [l.strip() for l in lines]
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = VOCAB_FILES_NAMES
UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase = ["""input_ids""", """attention_mask"""]
def __init__( self : Any, __A : Dict, __A : List[Any]="<unk>", __A : List[str]="<cls>", __A : Any="<pad>", __A : Union[str, Any]="<mask>", __A : int="<eos>", **__A : Tuple, ):
super().__init__(**__A )
UpperCAmelCase : Tuple = load_vocab_file(__A )
UpperCAmelCase : List[Any] = dict(enumerate(self.all_tokens ) )
UpperCAmelCase : str = {tok: ind for ind, tok in enumerate(self.all_tokens )}
UpperCAmelCase : Union[str, Any] = unk_token
UpperCAmelCase : Optional[Any] = cls_token
UpperCAmelCase : Optional[int] = pad_token
UpperCAmelCase : Optional[int] = mask_token
UpperCAmelCase : List[str] = eos_token
UpperCAmelCase : Optional[Any] = self.all_tokens
self._create_trie(self.unique_no_split_tokens )
def __magic_name__ ( self : Tuple, __A : int ):
return self._id_to_token.get(__A, self.unk_token )
def __magic_name__ ( self : List[Any], __A : str ):
return self._token_to_id.get(__A, self._token_to_id.get(self.unk_token ) )
def __magic_name__ ( self : Any, __A : Optional[Any], **__A : Union[str, Any] ):
return text.split()
def __magic_name__ ( self : Optional[int], __A : Dict=False ):
return len(self._id_to_token )
def __magic_name__ ( self : int ):
return {token: i for i, token in enumerate(self.all_tokens )}
def __magic_name__ ( self : Tuple, __A : str ):
return self._token_to_id.get(__A, self._token_to_id.get(self.unk_token ) )
def __magic_name__ ( self : Any, __A : int ):
return self._id_to_token.get(__A, self.unk_token )
def __magic_name__ ( self : Union[str, Any], __A : List[int], __A : Optional[List[int]] = None ):
UpperCAmelCase : Optional[int] = [self.cls_token_id]
UpperCAmelCase : Optional[int] = [self.eos_token_id] # No sep token in ESM vocabulary
if token_ids_a is None:
if self.eos_token_id is None:
return cls + token_ids_a
else:
return cls + token_ids_a + sep
elif self.eos_token_id is None:
raise ValueError('''Cannot tokenize multiple sequences when EOS token is not set!''' )
return cls + token_ids_a + sep + token_ids_a + sep # Multiple inputs always have an EOS token
def __magic_name__ ( self : Any, __A : List, __A : Optional[List] = None, __A : bool = False ):
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'''You should not supply a second sequence if the provided sequence of '''
'''ids is already formatted with special tokens for the model.''' )
return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
UpperCAmelCase : Dict = [1] + ([0] * len(__A )) + [1]
if token_ids_a is not None:
mask += [0] * len(__A ) + [1]
return mask
def __magic_name__ ( self : Optional[int], __A : List[Any], __A : Dict ):
UpperCAmelCase : Union[str, Any] = os.path.join(__A, (filename_prefix + '''-''' if filename_prefix else '''''') + '''vocab.txt''' )
with open(__A, '''w''' ) as f:
f.write('''\n'''.join(self.all_tokens ) )
return (vocab_file,)
@property
def __magic_name__ ( self : Dict ):
return self.get_vocab_size(with_added_tokens=__A )
def __magic_name__ ( self : Optional[int], __A : Union[List[str], List[AddedToken]], __A : bool = False ):
return super()._add_tokens(__A, special_tokens=__A )
| 336 |
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 : Optional[int], __A : Optional[int], __A : Any=1_3, __A : str=7, __A : Optional[int]=True, __A : Tuple=True, __A : Union[str, Any]=True, __A : Any=True, __A : Optional[int]=9_9, __A : Tuple=3_2, __A : str=5, __A : Union[str, Any]=4, __A : List[str]=3_7, __A : Tuple="gelu", __A : Optional[int]=0.1, __A : int=0.1, __A : Optional[Any]=5_1_2, __A : int=1_6, __A : Optional[Any]=2, __A : Union[str, Any]=0.0_2, __A : Optional[int]=4, ):
UpperCAmelCase : Any = parent
UpperCAmelCase : List[Any] = batch_size
UpperCAmelCase : Any = seq_length
UpperCAmelCase : Tuple = is_training
UpperCAmelCase : str = use_attention_mask
UpperCAmelCase : List[str] = use_token_type_ids
UpperCAmelCase : int = use_labels
UpperCAmelCase : List[Any] = vocab_size
UpperCAmelCase : Optional[int] = hidden_size
UpperCAmelCase : str = num_hidden_layers
UpperCAmelCase : Dict = num_attention_heads
UpperCAmelCase : Tuple = intermediate_size
UpperCAmelCase : List[str] = hidden_act
UpperCAmelCase : str = hidden_dropout_prob
UpperCAmelCase : int = attention_probs_dropout_prob
UpperCAmelCase : List[Any] = max_position_embeddings
UpperCAmelCase : Optional[Any] = type_vocab_size
UpperCAmelCase : Any = type_sequence_label_size
UpperCAmelCase : Optional[Any] = initializer_range
UpperCAmelCase : Any = num_choices
def __magic_name__ ( self : str ):
UpperCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
UpperCAmelCase : List[Any] = None
if self.use_attention_mask:
UpperCAmelCase : Any = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase : Any = None
if self.use_token_type_ids:
UpperCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size )
UpperCAmelCase : Union[str, Any] = 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=__A, initializer_range=self.initializer_range, )
return config, input_ids, token_type_ids, attention_mask
def __magic_name__ ( self : int ):
UpperCAmelCase : Any = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[Any] = config_and_inputs
UpperCAmelCase : Dict = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : List[Any] = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Dict = config_and_inputs
UpperCAmelCase : Any = True
UpperCAmelCase : Union[str, Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
UpperCAmelCase : List[Any] = 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 ( lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = True
UpperCamelCase = (
(
FlaxRobertaModel,
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __magic_name__ ( self : Optional[Any] ):
UpperCAmelCase : Dict = FlaxRobertaModelTester(self )
@slow
def __magic_name__ ( self : Any ):
for model_class_name in self.all_model_classes:
UpperCAmelCase : Dict = model_class_name.from_pretrained('''roberta-base''', from_pt=__A )
UpperCAmelCase : List[str] = model(np.ones((1, 1) ) )
self.assertIsNotNone(__A )
| 336 | 1 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
_lowerCamelCase : int = "\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Simplification},\n authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n journal = {Transactions of the Association for Computational Linguistics},\n volume = {4},\n year={2016},\n url = {https://www.aclweb.org/anthology/Q16-1029},\n pages = {401--415\n},\n@inproceedings{post-2018-call,\n title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n author = \"Post, Matt\",\n booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n month = oct,\n year = \"2018\",\n address = \"Belgium, Brussels\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/W18-6319\",\n pages = \"186--191\",\n}\n"
_lowerCamelCase : List[str] = "\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n"
_lowerCamelCase : Optional[Any] = "\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n sources: list of source sentences where each sentence should be a string.\n predictions: list of predicted sentences where each sentence should be a string.\n references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n sari: sari score\n sacrebleu: sacrebleu score\n exact: exact score\n\nExamples:\n >>> sources=[\"About 95 species are currently accepted .\"]\n >>> predictions=[\"About 95 you now get in .\"]\n >>> references=[[\"About 95 species are currently known .\"]]\n >>> wiki_split = datasets.load_metric(\"wiki_split\")\n >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n >>> print(results)\n {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}\n"
def a__ ( UpperCAmelCase : List[str] ) -> Tuple:
def remove_articles(UpperCAmelCase : Dict ):
UpperCAmelCase : List[str] = re.compile(r'''\b(a|an|the)\b''' , re.UNICODE )
return re.sub(UpperCAmelCase , ''' ''' , UpperCAmelCase )
def white_space_fix(UpperCAmelCase : List[str] ):
return " ".join(text.split() )
def remove_punc(UpperCAmelCase : str ):
UpperCAmelCase : Tuple = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(UpperCAmelCase : Optional[int] ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase ) ) ) )
def a__ ( UpperCAmelCase : str , UpperCAmelCase : str ) -> int:
return int(normalize_answer(UpperCAmelCase ) == normalize_answer(UpperCAmelCase ) )
def a__ ( UpperCAmelCase : List[str] , UpperCAmelCase : Union[str, Any] ) -> List[Any]:
UpperCAmelCase : Tuple = [any(compute_exact(UpperCAmelCase , UpperCAmelCase ) for ref in refs ) for pred, refs in zip(UpperCAmelCase , UpperCAmelCase )]
return (sum(UpperCAmelCase ) / len(UpperCAmelCase )) * 100
def a__ ( UpperCAmelCase : Optional[int] , UpperCAmelCase : Any , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Optional[int] ) -> List[str]:
UpperCAmelCase : Union[str, Any] = [rgram for rgrams in rgramslist for rgram in rgrams]
UpperCAmelCase : int = Counter(UpperCAmelCase )
UpperCAmelCase : List[str] = Counter(UpperCAmelCase )
UpperCAmelCase : str = Counter()
for sgram, scount in sgramcounter.items():
UpperCAmelCase : Union[str, Any] = scount * numref
UpperCAmelCase : Optional[int] = Counter(UpperCAmelCase )
UpperCAmelCase : Optional[Any] = Counter()
for cgram, ccount in cgramcounter.items():
UpperCAmelCase : int = ccount * numref
# KEEP
UpperCAmelCase : str = sgramcounter_rep & cgramcounter_rep
UpperCAmelCase : str = keepgramcounter_rep & rgramcounter
UpperCAmelCase : Tuple = sgramcounter_rep & rgramcounter
UpperCAmelCase : Optional[Any] = 0
UpperCAmelCase : Dict = 0
for keepgram in keepgramcountergood_rep:
keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
# Fix an alleged bug [2] in the keep score computation.
# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
keeptmpscorea += keepgramcountergood_rep[keepgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
UpperCAmelCase : Union[str, Any] = 1
UpperCAmelCase : str = 1
if len(UpperCAmelCase ) > 0:
UpperCAmelCase : int = keeptmpscorea / len(UpperCAmelCase )
if len(UpperCAmelCase ) > 0:
# Fix an alleged bug [2] in the keep score computation.
# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
UpperCAmelCase : Union[str, Any] = keeptmpscorea / sum(keepgramcounterall_rep.values() )
UpperCAmelCase : List[Any] = 0
if keepscore_precision > 0 or keepscore_recall > 0:
UpperCAmelCase : List[str] = 2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
# DELETION
UpperCAmelCase : int = sgramcounter_rep - cgramcounter_rep
UpperCAmelCase : str = delgramcounter_rep - rgramcounter
UpperCAmelCase : Tuple = sgramcounter_rep - rgramcounter
UpperCAmelCase : Any = 0
UpperCAmelCase : Optional[Any] = 0
for delgram in delgramcountergood_rep:
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
UpperCAmelCase : Any = 1
if len(UpperCAmelCase ) > 0:
UpperCAmelCase : List[str] = deltmpscorea / len(UpperCAmelCase )
# ADDITION
UpperCAmelCase : Tuple = set(UpperCAmelCase ) - set(UpperCAmelCase )
UpperCAmelCase : Any = set(UpperCAmelCase ) & set(UpperCAmelCase )
UpperCAmelCase : str = set(UpperCAmelCase ) - set(UpperCAmelCase )
UpperCAmelCase : Tuple = 0
for addgram in addgramcountergood:
addtmpscore += 1
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
UpperCAmelCase : Tuple = 1
UpperCAmelCase : Optional[Any] = 1
if len(UpperCAmelCase ) > 0:
UpperCAmelCase : Any = addtmpscore / len(UpperCAmelCase )
if len(UpperCAmelCase ) > 0:
UpperCAmelCase : List[str] = addtmpscore / len(UpperCAmelCase )
UpperCAmelCase : Optional[Any] = 0
if addscore_precision > 0 or addscore_recall > 0:
UpperCAmelCase : List[str] = 2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
return (keepscore, delscore_precision, addscore)
def a__ ( UpperCAmelCase : str , UpperCAmelCase : List[str] , UpperCAmelCase : Optional[Any] ) -> Dict:
UpperCAmelCase : Optional[Any] = len(UpperCAmelCase )
UpperCAmelCase : Union[str, Any] = ssent.split(''' ''' )
UpperCAmelCase : Optional[int] = csent.split(''' ''' )
UpperCAmelCase : Tuple = []
UpperCAmelCase : Optional[int] = []
UpperCAmelCase : Optional[Any] = []
UpperCAmelCase : Optional[int] = []
UpperCAmelCase : str = []
UpperCAmelCase : List[str] = []
UpperCAmelCase : str = []
UpperCAmelCase : Dict = []
UpperCAmelCase : Dict = []
UpperCAmelCase : str = []
for rsent in rsents:
UpperCAmelCase : List[str] = rsent.split(''' ''' )
UpperCAmelCase : List[str] = []
UpperCAmelCase : List[str] = []
UpperCAmelCase : Optional[Any] = []
ragramslist.append(UpperCAmelCase )
for i in range(0 , len(UpperCAmelCase ) - 1 ):
if i < len(UpperCAmelCase ) - 1:
UpperCAmelCase : Dict = ragrams[i] + ''' ''' + ragrams[i + 1]
ragrams.append(UpperCAmelCase )
if i < len(UpperCAmelCase ) - 2:
UpperCAmelCase : Optional[Any] = ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
ragrams.append(UpperCAmelCase )
if i < len(UpperCAmelCase ) - 3:
UpperCAmelCase : Union[str, Any] = ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
ragrams.append(UpperCAmelCase )
ragramslist.append(UpperCAmelCase )
ragramslist.append(UpperCAmelCase )
ragramslist.append(UpperCAmelCase )
for i in range(0 , len(UpperCAmelCase ) - 1 ):
if i < len(UpperCAmelCase ) - 1:
UpperCAmelCase : Optional[int] = sagrams[i] + ''' ''' + sagrams[i + 1]
sagrams.append(UpperCAmelCase )
if i < len(UpperCAmelCase ) - 2:
UpperCAmelCase : Union[str, Any] = sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
sagrams.append(UpperCAmelCase )
if i < len(UpperCAmelCase ) - 3:
UpperCAmelCase : int = sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
sagrams.append(UpperCAmelCase )
for i in range(0 , len(UpperCAmelCase ) - 1 ):
if i < len(UpperCAmelCase ) - 1:
UpperCAmelCase : Any = cagrams[i] + ''' ''' + cagrams[i + 1]
cagrams.append(UpperCAmelCase )
if i < len(UpperCAmelCase ) - 2:
UpperCAmelCase : Union[str, Any] = cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
cagrams.append(UpperCAmelCase )
if i < len(UpperCAmelCase ) - 3:
UpperCAmelCase : int = cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
cagrams.append(UpperCAmelCase )
((UpperCAmelCase) , (UpperCAmelCase) , (UpperCAmelCase)) : Dict = SARIngram(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
((UpperCAmelCase) , (UpperCAmelCase) , (UpperCAmelCase)) : Optional[int] = SARIngram(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
((UpperCAmelCase) , (UpperCAmelCase) , (UpperCAmelCase)) : Optional[Any] = SARIngram(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
((UpperCAmelCase) , (UpperCAmelCase) , (UpperCAmelCase)) : Optional[Any] = SARIngram(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
UpperCAmelCase : List[str] = sum([keepascore, keepascore, keepascore, keepascore] ) / 4
UpperCAmelCase : Union[str, Any] = sum([delascore, delascore, delascore, delascore] ) / 4
UpperCAmelCase : Optional[int] = sum([addascore, addascore, addascore, addascore] ) / 4
UpperCAmelCase : int = (avgkeepscore + avgdelscore + avgaddscore) / 3
return finalscore
def a__ ( UpperCAmelCase : Tuple , UpperCAmelCase : bool = True , UpperCAmelCase : str = "13a" , UpperCAmelCase : bool = True ) -> int:
# Normalization is requried for the ASSET dataset (one of the primary
# datasets in sentence simplification) to allow using space
# to split the sentence. Even though Wiki-Auto and TURK datasets,
# do not require normalization, we do it for consistency.
# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7
if lowercase:
UpperCAmelCase : Dict = sentence.lower()
if tokenizer in ["13a", "intl"]:
if version.parse(sacrebleu.__version__ ).major >= 2:
UpperCAmelCase : Optional[int] = sacrebleu.metrics.bleu._get_tokenizer(UpperCAmelCase )()(UpperCAmelCase )
else:
UpperCAmelCase : Union[str, Any] = sacrebleu.TOKENIZERS[tokenizer]()(UpperCAmelCase )
elif tokenizer == "moses":
UpperCAmelCase : Optional[Any] = sacremoses.MosesTokenizer().tokenize(UpperCAmelCase , return_str=UpperCAmelCase , escape=UpperCAmelCase )
elif tokenizer == "penn":
UpperCAmelCase : Any = sacremoses.MosesTokenizer().penn_tokenize(UpperCAmelCase , return_str=UpperCAmelCase )
else:
UpperCAmelCase : Any = sentence
if not return_str:
UpperCAmelCase : Dict = normalized_sent.split()
return normalized_sent
def a__ ( UpperCAmelCase : Optional[Any] , UpperCAmelCase : int , UpperCAmelCase : Any ) -> List[Any]:
if not (len(UpperCAmelCase ) == len(UpperCAmelCase ) == len(UpperCAmelCase )):
raise ValueError('''Sources length must match predictions and references lengths.''' )
UpperCAmelCase : str = 0
for src, pred, refs in zip(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
sari_score += SARIsent(normalize(UpperCAmelCase ) , normalize(UpperCAmelCase ) , [normalize(UpperCAmelCase ) for sent in refs] )
UpperCAmelCase : Dict = sari_score / len(UpperCAmelCase )
return 100 * sari_score
def a__ ( UpperCAmelCase : Tuple , UpperCAmelCase : Tuple , UpperCAmelCase : Union[str, Any]="exp" , UpperCAmelCase : Tuple=None , UpperCAmelCase : Union[str, Any]=False , UpperCAmelCase : str=False , UpperCAmelCase : List[Any]=False , ) -> Optional[Any]:
UpperCAmelCase : List[str] = len(references[0] )
if any(len(UpperCAmelCase ) != references_per_prediction for refs in references ):
raise ValueError('''Sacrebleu requires the same number of references for each prediction''' )
UpperCAmelCase : Tuple = [[refs[i] for refs in references] for i in range(UpperCAmelCase )]
UpperCAmelCase : Dict = sacrebleu.corpus_bleu(
UpperCAmelCase , UpperCAmelCase , smooth_method=UpperCAmelCase , smooth_value=UpperCAmelCase , force=UpperCAmelCase , lowercase=UpperCAmelCase , use_effective_order=UpperCAmelCase , )
return output.score
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __UpperCAmelCase ( datasets.Metric ):
def __magic_name__ ( self : List[Any] ):
return datasets.MetricInfo(
description=_DESCRIPTION, citation=_CITATION, 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/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py''',
'''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
'''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
'''https://github.com/mjpost/sacreBLEU''',
], reference_urls=[
'''https://www.aclweb.org/anthology/Q16-1029.pdf''',
'''https://github.com/mjpost/sacreBLEU''',
'''https://en.wikipedia.org/wiki/BLEU''',
'''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
], )
def __magic_name__ ( self : str, __A : Union[str, Any], __A : List[Any], __A : Optional[int] ):
UpperCAmelCase : Optional[Any] = {}
result.update({'''sari''': compute_sari(sources=__A, predictions=__A, references=__A )} )
result.update({'''sacrebleu''': compute_sacrebleu(predictions=__A, references=__A )} )
result.update({'''exact''': compute_em(predictions=__A, references=__A )} )
return result
| 336 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCamelCase : Dict = {"vocab_file": "vocab.txt"}
_lowerCamelCase : List[str] = {
"vocab_file": {
"facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
"facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
},
}
_lowerCamelCase : List[Any] = {
"facebook/esm2_t6_8M_UR50D": 1_0_2_4,
"facebook/esm2_t12_35M_UR50D": 1_0_2_4,
}
def a__ ( UpperCAmelCase : List[str] ) -> Any:
with open(UpperCAmelCase , '''r''' ) as f:
UpperCAmelCase : Dict = f.read().splitlines()
return [l.strip() for l in lines]
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = VOCAB_FILES_NAMES
UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase = ["""input_ids""", """attention_mask"""]
def __init__( self : Any, __A : Dict, __A : List[Any]="<unk>", __A : List[str]="<cls>", __A : Any="<pad>", __A : Union[str, Any]="<mask>", __A : int="<eos>", **__A : Tuple, ):
super().__init__(**__A )
UpperCAmelCase : Tuple = load_vocab_file(__A )
UpperCAmelCase : List[Any] = dict(enumerate(self.all_tokens ) )
UpperCAmelCase : str = {tok: ind for ind, tok in enumerate(self.all_tokens )}
UpperCAmelCase : Union[str, Any] = unk_token
UpperCAmelCase : Optional[Any] = cls_token
UpperCAmelCase : Optional[int] = pad_token
UpperCAmelCase : Optional[int] = mask_token
UpperCAmelCase : List[str] = eos_token
UpperCAmelCase : Optional[Any] = self.all_tokens
self._create_trie(self.unique_no_split_tokens )
def __magic_name__ ( self : Tuple, __A : int ):
return self._id_to_token.get(__A, self.unk_token )
def __magic_name__ ( self : List[Any], __A : str ):
return self._token_to_id.get(__A, self._token_to_id.get(self.unk_token ) )
def __magic_name__ ( self : Any, __A : Optional[Any], **__A : Union[str, Any] ):
return text.split()
def __magic_name__ ( self : Optional[int], __A : Dict=False ):
return len(self._id_to_token )
def __magic_name__ ( self : int ):
return {token: i for i, token in enumerate(self.all_tokens )}
def __magic_name__ ( self : Tuple, __A : str ):
return self._token_to_id.get(__A, self._token_to_id.get(self.unk_token ) )
def __magic_name__ ( self : Any, __A : int ):
return self._id_to_token.get(__A, self.unk_token )
def __magic_name__ ( self : Union[str, Any], __A : List[int], __A : Optional[List[int]] = None ):
UpperCAmelCase : Optional[int] = [self.cls_token_id]
UpperCAmelCase : Optional[int] = [self.eos_token_id] # No sep token in ESM vocabulary
if token_ids_a is None:
if self.eos_token_id is None:
return cls + token_ids_a
else:
return cls + token_ids_a + sep
elif self.eos_token_id is None:
raise ValueError('''Cannot tokenize multiple sequences when EOS token is not set!''' )
return cls + token_ids_a + sep + token_ids_a + sep # Multiple inputs always have an EOS token
def __magic_name__ ( self : Any, __A : List, __A : Optional[List] = None, __A : bool = False ):
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'''You should not supply a second sequence if the provided sequence of '''
'''ids is already formatted with special tokens for the model.''' )
return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
UpperCAmelCase : Dict = [1] + ([0] * len(__A )) + [1]
if token_ids_a is not None:
mask += [0] * len(__A ) + [1]
return mask
def __magic_name__ ( self : Optional[int], __A : List[Any], __A : Dict ):
UpperCAmelCase : Union[str, Any] = os.path.join(__A, (filename_prefix + '''-''' if filename_prefix else '''''') + '''vocab.txt''' )
with open(__A, '''w''' ) as f:
f.write('''\n'''.join(self.all_tokens ) )
return (vocab_file,)
@property
def __magic_name__ ( self : Dict ):
return self.get_vocab_size(with_added_tokens=__A )
def __magic_name__ ( self : Optional[int], __A : Union[List[str], List[AddedToken]], __A : bool = False ):
return super()._add_tokens(__A, special_tokens=__A )
| 336 | 1 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """Speech2TextFeatureExtractor"""
UpperCamelCase = """Speech2TextTokenizer"""
def __init__( self : Tuple, __A : Union[str, Any], __A : str ):
super().__init__(__A, __A )
UpperCAmelCase : str = self.feature_extractor
UpperCAmelCase : Any = False
def __call__( self : str, *__A : List[Any], **__A : Tuple ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*__A, **__A )
if "raw_speech" in kwargs:
warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.''' )
UpperCAmelCase : Optional[Any] = kwargs.pop('''raw_speech''' )
else:
UpperCAmelCase : List[Any] = kwargs.pop('''audio''', __A )
UpperCAmelCase : Optional[int] = kwargs.pop('''sampling_rate''', __A )
UpperCAmelCase : Tuple = kwargs.pop('''text''', __A )
if len(__A ) > 0:
UpperCAmelCase : List[str] = args[0]
UpperCAmelCase : Optional[Any] = args[1:]
if audio is None and text is None:
raise ValueError('''You need to specify either an `audio` or `text` input to process.''' )
if audio is not None:
UpperCAmelCase : int = self.feature_extractor(__A, *__A, sampling_rate=__A, **__A )
if text is not None:
UpperCAmelCase : Union[str, Any] = self.tokenizer(__A, **__A )
if text is None:
return inputs
elif audio is None:
return encodings
else:
UpperCAmelCase : Optional[Any] = encodings['''input_ids''']
return inputs
def __magic_name__ ( self : str, *__A : Union[str, Any], **__A : Union[str, Any] ):
return self.tokenizer.batch_decode(*__A, **__A )
def __magic_name__ ( self : Optional[int], *__A : Any, **__A : Dict ):
return self.tokenizer.decode(*__A, **__A )
@contextmanager
def __magic_name__ ( self : Any ):
warnings.warn(
'''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
'''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
'''your audio inputs, or in a separate call.''' )
UpperCAmelCase : List[str] = True
UpperCAmelCase : List[Any] = self.tokenizer
yield
UpperCAmelCase : Tuple = self.feature_extractor
UpperCAmelCase : Union[str, Any] = False
| 336 |
import inspect
import unittest
from transformers import MobileNetVaConfig
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 transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class __UpperCAmelCase ( lowerCamelCase__ ):
def __magic_name__ ( self : Optional[Any] ):
UpperCAmelCase : str = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__A, '''tf_padding''' ) )
self.parent.assertTrue(hasattr(__A, '''depth_multiplier''' ) )
class __UpperCAmelCase :
def __init__( self : int, __A : List[Any], __A : str=1_3, __A : Dict=3, __A : int=3_2, __A : int=0.2_5, __A : List[str]=8, __A : int=8, __A : Dict=6, __A : str=3_2, __A : Any=True, __A : str=True, __A : int=True, __A : Union[str, Any]="relu6", __A : Any=1_2_8_0, __A : List[Any]=0.1, __A : Optional[Any]=0.0_2, __A : Tuple=True, __A : List[Any]=True, __A : str=1_0, __A : Optional[Any]=None, ):
UpperCAmelCase : Optional[int] = parent
UpperCAmelCase : List[str] = batch_size
UpperCAmelCase : List[str] = num_channels
UpperCAmelCase : str = image_size
UpperCAmelCase : Optional[int] = depth_multiplier
UpperCAmelCase : Union[str, Any] = depth_divisible_by
UpperCAmelCase : Optional[Any] = min_depth
UpperCAmelCase : List[str] = expand_ratio
UpperCAmelCase : Dict = tf_padding
UpperCAmelCase : str = output_stride
UpperCAmelCase : Union[str, Any] = first_layer_is_expansion
UpperCAmelCase : List[Any] = finegrained_output
UpperCAmelCase : Optional[Any] = hidden_act
UpperCAmelCase : str = last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier )
UpperCAmelCase : Optional[Any] = classifier_dropout_prob
UpperCAmelCase : Dict = use_labels
UpperCAmelCase : List[str] = is_training
UpperCAmelCase : Tuple = num_labels
UpperCAmelCase : Union[str, Any] = initializer_range
UpperCAmelCase : Any = scope
def __magic_name__ ( self : List[Any] ):
UpperCAmelCase : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : Dict = None
UpperCAmelCase : Any = None
if self.use_labels:
UpperCAmelCase : Dict = ids_tensor([self.batch_size], self.num_labels )
UpperCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels )
UpperCAmelCase : Optional[Any] = self.get_config()
return config, pixel_values, labels, pixel_labels
def __magic_name__ ( self : Any ):
return MobileNetVaConfig(
num_channels=self.num_channels, image_size=self.image_size, depth_multiplier=self.depth_multiplier, depth_divisible_by=self.depth_divisible_by, min_depth=self.min_depth, expand_ratio=self.expand_ratio, output_stride=self.output_stride, first_layer_is_expansion=self.first_layer_is_expansion, finegrained_output=self.finegrained_output, hidden_act=self.hidden_act, tf_padding=self.tf_padding, classifier_dropout_prob=self.classifier_dropout_prob, initializer_range=self.initializer_range, )
def __magic_name__ ( self : List[Any], __A : Dict, __A : Optional[Any], __A : Optional[int], __A : Union[str, Any] ):
UpperCAmelCase : Any = MobileNetVaModel(config=__A )
model.to(__A )
model.eval()
UpperCAmelCase : Optional[Any] = model(__A )
self.parent.assertEqual(
result.last_hidden_state.shape, (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
), )
self.parent.assertEqual(
result.pooler_output.shape, (self.batch_size, self.last_hidden_size), )
def __magic_name__ ( self : str, __A : Union[str, Any], __A : Dict, __A : Optional[Any], __A : str ):
UpperCAmelCase : Optional[int] = self.num_labels
UpperCAmelCase : Any = MobileNetVaForImageClassification(__A )
model.to(__A )
model.eval()
UpperCAmelCase : Optional[int] = model(__A, labels=__A )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def __magic_name__ ( self : List[Any], __A : Optional[Any], __A : List[str], __A : Dict, __A : Dict ):
UpperCAmelCase : Tuple = self.num_labels
UpperCAmelCase : Dict = MobileNetVaForSemanticSegmentation(__A )
model.to(__A )
model.eval()
UpperCAmelCase : Dict = model(__A )
self.parent.assertEqual(
result.logits.shape, (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
), )
UpperCAmelCase : Optional[Any] = model(__A, labels=__A )
self.parent.assertEqual(
result.logits.shape, (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
), )
def __magic_name__ ( self : Tuple ):
UpperCAmelCase : List[str] = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int = config_and_inputs
UpperCAmelCase : Optional[int] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = (
(MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
if is_torch_available()
else ()
)
UpperCamelCase = (
{
"""feature-extraction""": MobileNetVaModel,
"""image-classification""": MobileNetVaForImageClassification,
"""image-segmentation""": MobileNetVaForSemanticSegmentation,
}
if is_torch_available()
else {}
)
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : List[Any] = MobileNetVaModelTester(self )
UpperCAmelCase : List[Any] = MobileNetVaConfigTester(self, config_class=__A, has_text_modality=__A )
def __magic_name__ ( self : Tuple ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''MobileNetV2 does not use inputs_embeds''' )
def __magic_name__ ( self : Optional[int] ):
pass
@unittest.skip(reason='''MobileNetV2 does not support input and output embeddings''' )
def __magic_name__ ( self : Tuple ):
pass
@unittest.skip(reason='''MobileNetV2 does not output attentions''' )
def __magic_name__ ( self : Any ):
pass
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase , UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Optional[Any] = model_class(__A )
UpperCAmelCase : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase : Union[str, Any] = [*signature.parameters.keys()]
UpperCAmelCase : Any = ['''pixel_values''']
self.assertListEqual(arg_names[:1], __A )
def __magic_name__ ( self : List[Any] ):
UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __magic_name__ ( self : int ):
def check_hidden_states_output(__A : Any, __A : Optional[Any], __A : str ):
UpperCAmelCase : Union[str, Any] = model_class(__A )
model.to(__A )
model.eval()
with torch.no_grad():
UpperCAmelCase : Dict = model(**self._prepare_for_class(__A, __A ) )
UpperCAmelCase : Optional[Any] = outputs.hidden_states
UpperCAmelCase : List[Any] = 1_6
self.assertEqual(len(__A ), __A )
UpperCAmelCase , UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Tuple = True
check_hidden_states_output(__A, __A, __A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase : Tuple = True
check_hidden_states_output(__A, __A, __A )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__A )
def __magic_name__ ( self : int ):
UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__A )
@slow
def __magic_name__ ( self : Dict ):
for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase : Optional[Any] = MobileNetVaModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def a__ ( ) -> int:
UpperCAmelCase : Dict = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : List[Any] ):
return (
MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v2_1.0_224''' ) if is_vision_available() else None
)
@slow
def __magic_name__ ( self : Optional[Any] ):
UpperCAmelCase : List[Any] = MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v2_1.0_224''' ).to(__A )
UpperCAmelCase : Optional[int] = self.default_image_processor
UpperCAmelCase : Optional[Any] = prepare_img()
UpperCAmelCase : Dict = image_processor(images=__A, return_tensors='''pt''' ).to(__A )
# forward pass
with torch.no_grad():
UpperCAmelCase : str = model(**__A )
# verify the logits
UpperCAmelCase : int = torch.Size((1, 1_0_0_1) )
self.assertEqual(outputs.logits.shape, __A )
UpperCAmelCase : Tuple = torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5] ).to(__A )
self.assertTrue(torch.allclose(outputs.logits[0, :3], __A, atol=1E-4 ) )
@slow
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : Tuple = MobileNetVaForSemanticSegmentation.from_pretrained('''google/deeplabv3_mobilenet_v2_1.0_513''' )
UpperCAmelCase : List[Any] = model.to(__A )
UpperCAmelCase : Tuple = MobileNetVaImageProcessor.from_pretrained('''google/deeplabv3_mobilenet_v2_1.0_513''' )
UpperCAmelCase : List[Any] = prepare_img()
UpperCAmelCase : int = image_processor(images=__A, return_tensors='''pt''' ).to(__A )
# forward pass
with torch.no_grad():
UpperCAmelCase : Union[str, Any] = model(**__A )
UpperCAmelCase : Optional[Any] = outputs.logits
# verify the logits
UpperCAmelCase : Tuple = torch.Size((1, 2_1, 6_5, 6_5) )
self.assertEqual(logits.shape, __A )
UpperCAmelCase : Tuple = torch.tensor(
[
[[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
[[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
[[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
], device=__A, )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3], __A, atol=1E-4 ) )
| 336 | 1 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
_lowerCamelCase : List[Any] = NewType("DataClass", Any)
_lowerCamelCase : List[str] = NewType("DataClassType", Any)
def a__ ( UpperCAmelCase : Any ) -> int:
if isinstance(UpperCAmelCase , UpperCAmelCase ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise ArgumentTypeError(
f'''Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).''' )
def a__ ( UpperCAmelCase : list ) -> Callable[[str], Any]:
UpperCAmelCase : Optional[int] = {str(UpperCAmelCase ): choice for choice in choices}
return lambda UpperCAmelCase : str_to_choice.get(UpperCAmelCase , UpperCAmelCase )
def a__ ( *,
UpperCAmelCase : Union[str, List[str]] = None , UpperCAmelCase : str = None , UpperCAmelCase : Any = dataclasses.MISSING , UpperCAmelCase : Callable[[], Any] = dataclasses.MISSING , UpperCAmelCase : dict = None , **UpperCAmelCase : Dict , ) -> dataclasses.Field:
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
UpperCAmelCase : Optional[Any] = {}
if aliases is not None:
UpperCAmelCase : List[str] = aliases
if help is not None:
UpperCAmelCase : int = help
return dataclasses.field(metadata=UpperCAmelCase , default=UpperCAmelCase , default_factory=UpperCAmelCase , **UpperCAmelCase )
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = 42
def __init__( self : Optional[int], __A : Union[DataClassType, Iterable[DataClassType]], **__A : Tuple ):
# To make the default appear when using --help
if "formatter_class" not in kwargs:
UpperCAmelCase : Union[str, Any] = ArgumentDefaultsHelpFormatter
super().__init__(**__A )
if dataclasses.is_dataclass(__A ):
UpperCAmelCase : List[Any] = [dataclass_types]
UpperCAmelCase : List[str] = list(__A )
for dtype in self.dataclass_types:
self._add_dataclass_arguments(__A )
@staticmethod
def __magic_name__ ( __A : ArgumentParser, __A : dataclasses.Field ):
UpperCAmelCase : Union[str, Any] = F'''--{field.name}'''
UpperCAmelCase : str = field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type, __A ):
raise RuntimeError(
'''Unresolved type detected, which should have been done with the help of '''
'''`typing.get_type_hints` method by default''' )
UpperCAmelCase : int = kwargs.pop('''aliases''', [] )
if isinstance(__A, __A ):
UpperCAmelCase : int = [aliases]
UpperCAmelCase : Optional[Any] = getattr(field.type, '''__origin__''', field.type )
if origin_type is Union or (hasattr(__A, '''UnionType''' ) and isinstance(__A, types.UnionType )):
if str not in field.type.__args__ and (
len(field.type.__args__ ) != 2 or type(__A ) not in field.type.__args__
):
raise ValueError(
'''Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'''
''' the argument parser only supports one type per argument.'''
F''' Problem encountered in field \'{field.name}\'.''' )
if type(__A ) not in field.type.__args__:
# filter `str` in Union
UpperCAmelCase : Any = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
UpperCAmelCase : str = getattr(field.type, '''__origin__''', field.type )
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
UpperCAmelCase : List[Any] = (
field.type.__args__[0] if isinstance(__A, field.type.__args__[1] ) else field.type.__args__[1]
)
UpperCAmelCase : Tuple = getattr(field.type, '''__origin__''', field.type )
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
UpperCAmelCase : List[Any] = {}
if origin_type is Literal or (isinstance(field.type, __A ) and issubclass(field.type, __A )):
if origin_type is Literal:
UpperCAmelCase : Tuple = field.type.__args__
else:
UpperCAmelCase : Optional[Any] = [x.value for x in field.type]
UpperCAmelCase : Dict = make_choice_type_function(kwargs['''choices'''] )
if field.default is not dataclasses.MISSING:
UpperCAmelCase : Union[str, Any] = field.default
else:
UpperCAmelCase : List[str] = True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
UpperCAmelCase : List[str] = copy(__A )
# Hack because type=bool in argparse does not behave as we want.
UpperCAmelCase : List[Any] = string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
UpperCAmelCase : str = False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
UpperCAmelCase : List[Any] = default
# This tells argparse we accept 0 or 1 value after --field_name
UpperCAmelCase : List[Any] = '''?'''
# This is the value that will get picked if we do --field_name (without value)
UpperCAmelCase : List[Any] = True
elif isclass(__A ) and issubclass(__A, __A ):
UpperCAmelCase : Any = field.type.__args__[0]
UpperCAmelCase : Dict = '''+'''
if field.default_factory is not dataclasses.MISSING:
UpperCAmelCase : Dict = field.default_factory()
elif field.default is dataclasses.MISSING:
UpperCAmelCase : Optional[int] = True
else:
UpperCAmelCase : Any = field.type
if field.default is not dataclasses.MISSING:
UpperCAmelCase : List[str] = field.default
elif field.default_factory is not dataclasses.MISSING:
UpperCAmelCase : Optional[Any] = field.default_factory()
else:
UpperCAmelCase : Tuple = True
parser.add_argument(__A, *__A, **__A )
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
UpperCAmelCase : Tuple = False
parser.add_argument(F'''--no_{field.name}''', action='''store_false''', dest=field.name, **__A )
def __magic_name__ ( self : int, __A : DataClassType ):
if hasattr(__A, '''_argument_group_name''' ):
UpperCAmelCase : Optional[Any] = self.add_argument_group(dtype._argument_group_name )
else:
UpperCAmelCase : str = self
try:
UpperCAmelCase : Dict[str, type] = get_type_hints(__A )
except NameError:
raise RuntimeError(
F'''Type resolution failed for {dtype}. Try declaring the class in global scope or '''
'''removing line of `from __future__ import annotations` which opts in Postponed '''
'''Evaluation of Annotations (PEP 563)''' )
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 1_0) and "unsupported operand type(s) for |" in str(__A ):
UpperCAmelCase : List[str] = '''.'''.join(map(__A, sys.version_info[:3] ) )
raise RuntimeError(
F'''Type resolution failed for {dtype} on Python {python_version}. Try removing '''
'''line of `from __future__ import annotations` which opts in union types as '''
'''`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '''
'''support Python versions that lower than 3.10, you need to use '''
'''`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '''
'''`X | None`.''' ) from ex
raise
for field in dataclasses.fields(__A ):
if not field.init:
continue
UpperCAmelCase : int = type_hints[field.name]
self._parse_dataclass_field(__A, __A )
def __magic_name__ ( self : Optional[int], __A : str=None, __A : Any=False, __A : Optional[int]=True, __A : int=None, __A : List[Any]=None, ):
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )):
UpperCAmelCase : Any = []
if args_filename:
args_files.append(Path(__A ) )
elif look_for_args_file and len(sys.argv ):
args_files.append(Path(sys.argv[0] ).with_suffix('''.args''' ) )
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
UpperCAmelCase : Tuple = ArgumentParser()
args_file_parser.add_argument(__A, type=__A, action='''append''' )
# Use only remaining args for further parsing (remove the args_file_flag)
UpperCAmelCase , UpperCAmelCase : List[str] = args_file_parser.parse_known_args(args=__A )
UpperCAmelCase : Union[str, Any] = vars(__A ).get(args_file_flag.lstrip('''-''' ), __A )
if cmd_args_file_paths:
args_files.extend([Path(__A ) for p in cmd_args_file_paths] )
UpperCAmelCase : Union[str, Any] = []
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
UpperCAmelCase : Optional[int] = file_args + args if args is not None else file_args + sys.argv[1:]
UpperCAmelCase , UpperCAmelCase : List[str] = self.parse_known_args(args=__A )
UpperCAmelCase : Tuple = []
for dtype in self.dataclass_types:
UpperCAmelCase : Optional[Any] = {f.name for f in dataclasses.fields(__A ) if f.init}
UpperCAmelCase : Tuple = {k: v for k, v in vars(__A ).items() if k in keys}
for k in keys:
delattr(__A, __A )
UpperCAmelCase : Dict = dtype(**__A )
outputs.append(__A )
if len(namespace.__dict__ ) > 0:
# additional namespace.
outputs.append(__A )
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(F'''Some specified arguments are not used by the HfArgumentParser: {remaining_args}''' )
return (*outputs,)
def __magic_name__ ( self : Optional[int], __A : Dict[str, Any], __A : bool = False ):
UpperCAmelCase : Tuple = set(args.keys() )
UpperCAmelCase : Dict = []
for dtype in self.dataclass_types:
UpperCAmelCase : Any = {f.name for f in dataclasses.fields(__A ) if f.init}
UpperCAmelCase : List[Any] = {k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys() )
UpperCAmelCase : Tuple = dtype(**__A )
outputs.append(__A )
if not allow_extra_keys and unused_keys:
raise ValueError(F'''Some keys are not used by the HfArgumentParser: {sorted(__A )}''' )
return tuple(__A )
def __magic_name__ ( self : Tuple, __A : str, __A : bool = False ):
with open(Path(__A ), encoding='''utf-8''' ) as open_json_file:
UpperCAmelCase : List[str] = json.loads(open_json_file.read() )
UpperCAmelCase : Dict = self.parse_dict(__A, allow_extra_keys=__A )
return tuple(__A )
def __magic_name__ ( self : Union[str, Any], __A : str, __A : bool = False ):
UpperCAmelCase : List[Any] = self.parse_dict(yaml.safe_load(Path(__A ).read_text() ), allow_extra_keys=__A )
return tuple(__A )
| 336 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
_lowerCamelCase : str = logging.get_logger(__name__)
_lowerCamelCase : Optional[int] = {
"Salesforce/codegen-350M-nl": "https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json",
"Salesforce/codegen-350M-multi": "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json",
"Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json",
"Salesforce/codegen-2B-nl": "https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json",
"Salesforce/codegen-2B-multi": "https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json",
"Salesforce/codegen-2B-mono": "https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json",
"Salesforce/codegen-6B-nl": "https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json",
"Salesforce/codegen-6B-multi": "https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json",
"Salesforce/codegen-6B-mono": "https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json",
"Salesforce/codegen-16B-nl": "https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json",
"Salesforce/codegen-16B-multi": "https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json",
"Salesforce/codegen-16B-mono": "https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json",
}
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """codegen"""
UpperCamelCase = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : Any, __A : Optional[int]=5_0_4_0_0, __A : Tuple=2_0_4_8, __A : Optional[int]=2_0_4_8, __A : List[str]=4_0_9_6, __A : List[str]=2_8, __A : Union[str, Any]=1_6, __A : Tuple=6_4, __A : Union[str, Any]=None, __A : Union[str, Any]="gelu_new", __A : Any=0.0, __A : Dict=0.0, __A : str=0.0, __A : Optional[int]=1E-5, __A : Any=0.0_2, __A : Any=True, __A : Union[str, Any]=5_0_2_5_6, __A : List[str]=5_0_2_5_6, __A : int=False, **__A : List[Any], ):
UpperCAmelCase : int = vocab_size
UpperCAmelCase : Tuple = n_ctx
UpperCAmelCase : Tuple = n_positions
UpperCAmelCase : Optional[int] = n_embd
UpperCAmelCase : Union[str, Any] = n_layer
UpperCAmelCase : List[str] = n_head
UpperCAmelCase : Tuple = n_inner
UpperCAmelCase : int = rotary_dim
UpperCAmelCase : List[Any] = activation_function
UpperCAmelCase : List[str] = resid_pdrop
UpperCAmelCase : Optional[Any] = embd_pdrop
UpperCAmelCase : str = attn_pdrop
UpperCAmelCase : Tuple = layer_norm_epsilon
UpperCAmelCase : Dict = initializer_range
UpperCAmelCase : Union[str, Any] = use_cache
UpperCAmelCase : Any = bos_token_id
UpperCAmelCase : List[str] = eos_token_id
super().__init__(
bos_token_id=__A, eos_token_id=__A, tie_word_embeddings=__A, **__A )
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : Any, __A : PretrainedConfig, __A : str = "default", __A : List[PatchingSpec] = None, __A : bool = False, ):
super().__init__(__A, task=__A, patching_specs=__A, use_past=__A )
if not getattr(self._config, '''pad_token_id''', __A ):
# TODO: how to do that better?
UpperCAmelCase : Union[str, Any] = 0
@property
def __magic_name__ ( self : str ):
UpperCAmelCase : Union[str, Any] = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
if self.use_past:
self.fill_with_past_key_values_(__A, direction='''inputs''' )
UpperCAmelCase : int = {0: '''batch''', 1: '''past_sequence + sequence'''}
else:
UpperCAmelCase : List[Any] = {0: '''batch''', 1: '''sequence'''}
return common_inputs
@property
def __magic_name__ ( self : Dict ):
return self._config.n_layer
@property
def __magic_name__ ( self : List[str] ):
return self._config.n_head
def __magic_name__ ( self : str, __A : PreTrainedTokenizer, __A : int = -1, __A : int = -1, __A : bool = False, __A : Optional[TensorType] = None, ):
UpperCAmelCase : Union[str, Any] = super(__A, self ).generate_dummy_inputs(
__A, batch_size=__A, seq_length=__A, is_pair=__A, framework=__A )
# We need to order the input in the way they appears in the forward()
UpperCAmelCase : Union[str, Any] = OrderedDict({'''input_ids''': common_inputs['''input_ids''']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
UpperCAmelCase , UpperCAmelCase : str = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
UpperCAmelCase : str = seqlen + 2
UpperCAmelCase : Optional[int] = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
UpperCAmelCase : Optional[int] = [
(torch.zeros(__A ), torch.zeros(__A )) for _ in range(self.num_layers )
]
UpperCAmelCase : Union[str, Any] = common_inputs['''attention_mask''']
if self.use_past:
UpperCAmelCase : Optional[Any] = ordered_inputs['''attention_mask'''].dtype
UpperCAmelCase : Dict = torch.cat(
[ordered_inputs['''attention_mask'''], torch.ones(__A, __A, dtype=__A )], dim=1 )
return ordered_inputs
@property
def __magic_name__ ( self : Tuple ):
return 1_3
| 336 | 1 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
_lowerCamelCase : List[str] = logging.get_logger(__name__)
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = ["""input_values""", """attention_mask"""]
def __init__( self : Union[str, Any], __A : int = 1, __A : int = 1_6_0_0_0, __A : float = 0.0, __A : bool = False, __A : int = 8_0, __A : int = 1_6, __A : int = 6_4, __A : str = "hann_window", __A : float = 1.0, __A : float = 8_0, __A : float = 7_6_0_0, __A : float = 1E-10, __A : int = 2, __A : bool = True, **__A : Dict, ):
super().__init__(feature_size=__A, sampling_rate=__A, padding_value=__A, **__A )
UpperCAmelCase : Optional[Any] = do_normalize
UpperCAmelCase : Dict = return_attention_mask
UpperCAmelCase : Union[str, Any] = num_mel_bins
UpperCAmelCase : str = hop_length
UpperCAmelCase : List[str] = win_length
UpperCAmelCase : Optional[Any] = win_function
UpperCAmelCase : Dict = frame_signal_scale
UpperCAmelCase : List[Any] = fmin
UpperCAmelCase : Any = fmax
UpperCAmelCase : str = mel_floor
UpperCAmelCase : str = reduction_factor
UpperCAmelCase : Union[str, Any] = win_length * sampling_rate // 1_0_0_0
UpperCAmelCase : int = hop_length * sampling_rate // 1_0_0_0
UpperCAmelCase : int = optimal_fft_length(self.sample_size )
UpperCAmelCase : int = (self.n_fft // 2) + 1
UpperCAmelCase : Tuple = window_function(window_length=self.sample_size, name=self.win_function, periodic=__A )
UpperCAmelCase : Tuple = mel_filter_bank(
num_frequency_bins=self.n_freqs, num_mel_filters=self.num_mel_bins, min_frequency=self.fmin, max_frequency=self.fmax, sampling_rate=self.sampling_rate, norm='''slaney''', mel_scale='''slaney''', )
if frame_signal_scale != 1.0:
warnings.warn(
'''The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers''', __A, )
if reduction_factor != 2.0:
warnings.warn(
'''The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers''', __A, )
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def __magic_name__ ( __A : List[np.ndarray], __A : List[np.ndarray], __A : float = 0.0 ):
if attention_mask is not None:
UpperCAmelCase : List[str] = np.array(__A, np.intaa )
UpperCAmelCase : List[Any] = []
for vector, length in zip(__A, attention_mask.sum(-1 ) ):
UpperCAmelCase : Optional[Any] = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
if length < normed_slice.shape[0]:
UpperCAmelCase : Tuple = padding_value
normed_input_values.append(__A )
else:
UpperCAmelCase : Any = [(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]
return normed_input_values
def __magic_name__ ( self : List[str], __A : np.ndarray, ):
UpperCAmelCase : List[str] = spectrogram(
__A, window=self.window, frame_length=self.sample_size, hop_length=self.sample_stride, fft_length=self.n_fft, mel_filters=self.mel_filters, mel_floor=self.mel_floor, log_mel='''log10''', )
return log_mel_spec.T
def __call__( self : List[str], __A : Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None, __A : Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None, __A : Union[bool, str, PaddingStrategy] = False, __A : Optional[int] = None, __A : bool = False, __A : Optional[int] = None, __A : Optional[bool] = None, __A : Optional[Union[str, TensorType]] = None, __A : Optional[int] = None, **__A : int, ):
if audio is None and audio_target is None:
raise ValueError('''You must provide either `audio` or `audio_target` values.''' )
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
F''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
F''' {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
'''It is strongly recommended to pass the ``sampling_rate`` argument to this function. '''
'''Failing to do so can result in silent errors that might be hard to debug.''' )
if audio is not None:
UpperCAmelCase : Union[str, Any] = self._process_audio(
__A, __A, __A, __A, __A, __A, __A, __A, **__A, )
else:
UpperCAmelCase : Optional[int] = None
if audio_target is not None:
UpperCAmelCase : Any = self._process_audio(
__A, __A, __A, __A, __A, __A, __A, __A, **__A, )
if inputs is None:
return inputs_target
else:
UpperCAmelCase : Optional[int] = inputs_target['''input_values''']
UpperCAmelCase : Dict = inputs_target.get('''attention_mask''' )
if decoder_attention_mask is not None:
UpperCAmelCase : str = decoder_attention_mask
return inputs
def __magic_name__ ( self : Optional[Any], __A : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]], __A : bool = False, __A : Union[bool, str, PaddingStrategy] = False, __A : Optional[int] = None, __A : bool = False, __A : Optional[int] = None, __A : Optional[bool] = None, __A : Optional[Union[str, TensorType]] = None, **__A : Tuple, ):
UpperCAmelCase : Union[str, Any] = isinstance(__A, np.ndarray ) and len(speech.shape ) > 1
if is_batched_numpy and len(speech.shape ) > 2:
raise ValueError(F'''Only mono-channel audio is supported for input to {self}''' )
UpperCAmelCase : Union[str, Any] = is_batched_numpy or (
isinstance(__A, (list, tuple) ) and (isinstance(speech[0], (np.ndarray, tuple, list) ))
)
if is_batched:
UpperCAmelCase : Tuple = [np.asarray(__A, dtype=np.floataa ) for speech in speech]
elif not is_batched and not isinstance(__A, np.ndarray ):
UpperCAmelCase : Tuple = np.asarray(__A, dtype=np.floataa )
elif isinstance(__A, np.ndarray ) and speech.dtype is np.dtype(np.floataa ):
UpperCAmelCase : List[Any] = speech.astype(np.floataa )
# always return batch
if not is_batched:
UpperCAmelCase : int = [speech]
# needed to make pad() work on spectrogram inputs
UpperCAmelCase : Tuple = self.feature_size
# convert into correct format for padding
if is_target:
UpperCAmelCase : Dict = [self._extract_mel_features(__A ) for waveform in speech]
UpperCAmelCase : Optional[Any] = BatchFeature({'''input_values''': features} )
UpperCAmelCase : Tuple = self.num_mel_bins
else:
UpperCAmelCase : Optional[Any] = BatchFeature({'''input_values''': speech} )
UpperCAmelCase : List[str] = self.pad(
__A, padding=__A, max_length=__A, truncation=__A, pad_to_multiple_of=__A, return_attention_mask=__A, **__A, )
UpperCAmelCase : List[Any] = feature_size_hack
# convert input values to correct format
UpperCAmelCase : Optional[int] = padded_inputs['''input_values''']
if not isinstance(input_values[0], np.ndarray ):
UpperCAmelCase : List[Any] = [np.asarray(__A, dtype=np.floataa ) for array in input_values]
elif (
not isinstance(__A, np.ndarray )
and isinstance(input_values[0], np.ndarray )
and input_values[0].dtype is np.dtype(np.floataa )
):
UpperCAmelCase : str = [array.astype(np.floataa ) for array in input_values]
elif isinstance(__A, np.ndarray ) and input_values.dtype is np.dtype(np.floataa ):
UpperCAmelCase : Dict = input_values.astype(np.floataa )
# convert attention_mask to correct format
UpperCAmelCase : List[str] = padded_inputs.get('''attention_mask''' )
if attention_mask is not None:
UpperCAmelCase : str = [np.asarray(__A, dtype=np.intaa ) for array in attention_mask]
# zero-mean and unit-variance normalization
if not is_target and self.do_normalize:
UpperCAmelCase : int = (
attention_mask
if self._get_padding_strategies(__A, max_length=__A ) is not PaddingStrategy.DO_NOT_PAD
else None
)
UpperCAmelCase : Any = self.zero_mean_unit_var_norm(
padded_inputs['''input_values'''], attention_mask=__A, padding_value=self.padding_value )
if return_tensors is not None:
UpperCAmelCase : Optional[Any] = padded_inputs.convert_to_tensors(__A )
return padded_inputs
def __magic_name__ ( self : Optional[Any] ):
UpperCAmelCase : Any = super().to_dict()
# Don't serialize these as they are derived from the other properties.
UpperCAmelCase : int = ['''window''', '''mel_filters''', '''sample_size''', '''sample_stride''', '''n_fft''', '''n_freqs''']
for name in names:
if name in output:
del output[name]
return output
| 336 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"pipelines_utils",
"0.22.0",
"Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.",
standard_warn=False,
stacklevel=3,
)
| 336 | 1 |
from math import factorial, radians
def a__ ( UpperCAmelCase : float , UpperCAmelCase : int = 18 , UpperCAmelCase : int = 10 ) -> float:
UpperCAmelCase : Any = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Converting from degrees to radians
UpperCAmelCase : Tuple = radians(UpperCAmelCase )
UpperCAmelCase : Any = angle_in_radians
UpperCAmelCase : Tuple = 3
UpperCAmelCase : Tuple = -1
for _ in range(UpperCAmelCase ):
result += (b * (angle_in_radians**a)) / factorial(UpperCAmelCase )
UpperCAmelCase : Union[str, Any] = -b # One positive term and the next will be negative and so on...
a += 2 # Increased by 2 for every term.
return round(UpperCAmelCase , UpperCAmelCase )
if __name__ == "__main__":
__import__("doctest").testmod()
| 336 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
class __UpperCAmelCase :
# setable values
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None # sigma(t_i)
@classmethod
def __magic_name__ ( cls : Any ):
return cls()
@dataclass
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = 42
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@property
def __magic_name__ ( self : Optional[int] ):
return True
@register_to_config
def __init__( self : Optional[int], __A : float = 0.0_2, __A : float = 1_0_0, __A : float = 1.0_0_7, __A : float = 8_0, __A : float = 0.0_5, __A : float = 5_0, ):
pass
def __magic_name__ ( self : Optional[Any] ):
return KarrasVeSchedulerState.create()
def __magic_name__ ( self : int, __A : KarrasVeSchedulerState, __A : int, __A : Tuple = () ):
UpperCAmelCase : Optional[Any] = jnp.arange(0, __A )[::-1].copy()
UpperCAmelCase : Union[str, Any] = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in timesteps
]
return state.replace(
num_inference_steps=__A, schedule=jnp.array(__A, dtype=jnp.floataa ), timesteps=__A, )
def __magic_name__ ( self : List[Any], __A : KarrasVeSchedulerState, __A : jnp.ndarray, __A : float, __A : random.KeyArray, ):
if self.config.s_min <= sigma <= self.config.s_max:
UpperCAmelCase : int = min(self.config.s_churn / state.num_inference_steps, 2**0.5 - 1 )
else:
UpperCAmelCase : Optional[int] = 0
# sample eps ~ N(0, S_noise^2 * I)
UpperCAmelCase : Union[str, Any] = random.split(__A, num=1 )
UpperCAmelCase : List[str] = self.config.s_noise * random.normal(key=__A, shape=sample.shape )
UpperCAmelCase : Tuple = sigma + gamma * sigma
UpperCAmelCase : List[str] = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def __magic_name__ ( self : Tuple, __A : KarrasVeSchedulerState, __A : jnp.ndarray, __A : float, __A : float, __A : jnp.ndarray, __A : bool = True, ):
UpperCAmelCase : int = sample_hat + sigma_hat * model_output
UpperCAmelCase : Dict = (sample_hat - pred_original_sample) / sigma_hat
UpperCAmelCase : int = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=__A, derivative=__A, state=__A )
def __magic_name__ ( self : Tuple, __A : KarrasVeSchedulerState, __A : jnp.ndarray, __A : float, __A : float, __A : jnp.ndarray, __A : jnp.ndarray, __A : jnp.ndarray, __A : bool = True, ):
UpperCAmelCase : Tuple = sample_prev + sigma_prev * model_output
UpperCAmelCase : List[str] = (sample_prev - pred_original_sample) / sigma_prev
UpperCAmelCase : Union[str, Any] = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=__A, derivative=__A, state=__A )
def __magic_name__ ( self : Optional[Any], __A : KarrasVeSchedulerState, __A : Optional[int], __A : int, __A : Union[str, Any] ):
raise NotImplementedError()
| 336 | 1 |
import heapq as hq
import math
from collections.abc import Iterator
class __UpperCAmelCase :
def __init__( self : int, __A : Union[str, Any] ):
UpperCAmelCase : Dict = str(id_ )
UpperCAmelCase : int = None
UpperCAmelCase : Any = None
UpperCAmelCase : str = []
UpperCAmelCase : Any = {} # {vertex:distance}
def __lt__( self : List[str], __A : List[Any] ):
return self.key < other.key
def __repr__( self : Optional[Any] ):
return self.id
def __magic_name__ ( self : List[Any], __A : Union[str, Any] ):
self.neighbors.append(__A )
def __magic_name__ ( self : Optional[int], __A : List[Any], __A : Tuple ):
UpperCAmelCase : Any = weight
def a__ ( UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[int] , UpperCAmelCase : List[Any] , UpperCAmelCase : Union[str, Any] ) -> Dict:
# add the neighbors:
graph[a - 1].add_neighbor(graph[b - 1] )
graph[b - 1].add_neighbor(graph[a - 1] )
# add the edges:
graph[a - 1].add_edge(graph[b - 1] , UpperCAmelCase )
graph[b - 1].add_edge(graph[a - 1] , UpperCAmelCase )
def a__ ( UpperCAmelCase : list , UpperCAmelCase : Vertex ) -> list:
UpperCAmelCase : Optional[Any] = []
for u in graph:
UpperCAmelCase : int = math.inf
UpperCAmelCase : Dict = None
UpperCAmelCase : List[Any] = 0
UpperCAmelCase : List[Any] = graph[:]
while q:
UpperCAmelCase : int = min(UpperCAmelCase )
q.remove(UpperCAmelCase )
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
UpperCAmelCase : Tuple = u
UpperCAmelCase : List[str] = u.edges[v.id]
for i in range(1 , len(UpperCAmelCase ) ):
a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
return a
def a__ ( UpperCAmelCase : list , UpperCAmelCase : Vertex ) -> Iterator[tuple]:
for u in graph:
UpperCAmelCase : Optional[Any] = math.inf
UpperCAmelCase : List[Any] = None
UpperCAmelCase : List[Any] = 0
UpperCAmelCase : Tuple = list(UpperCAmelCase )
hq.heapify(UpperCAmelCase )
while h:
UpperCAmelCase : int = hq.heappop(UpperCAmelCase )
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
UpperCAmelCase : int = u
UpperCAmelCase : Any = u.edges[v.id]
hq.heapify(UpperCAmelCase )
for i in range(1 , len(UpperCAmelCase ) ):
yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)
def a__ ( ) -> None:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __UpperCAmelCase ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
UpperCamelCase = [("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]
def a__ ( ) -> Dict:
if os.name == "nt":
UpperCAmelCase : List[str] = CursorInfo()
UpperCAmelCase : List[Any] = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
UpperCAmelCase : Dict = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
elif os.name == "posix":
sys.stdout.write('''\033[?25l''' )
sys.stdout.flush()
def a__ ( ) -> Optional[int]:
if os.name == "nt":
UpperCAmelCase : int = CursorInfo()
UpperCAmelCase : int = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
UpperCAmelCase : Any = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
elif os.name == "posix":
sys.stdout.write('''\033[?25h''' )
sys.stdout.flush()
@contextmanager
def a__ ( ) -> Optional[Any]:
try:
hide_cursor()
yield
finally:
show_cursor()
| 336 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Tuple = logging.get_logger(__name__)
_lowerCamelCase : Union[str, Any] = {
"google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/config.json",
# See all CANINE models at https://huggingface.co/models?filter=canine
}
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """canine"""
def __init__( self : Dict, __A : str=7_6_8, __A : Tuple=1_2, __A : str=1_2, __A : List[str]=3_0_7_2, __A : Union[str, Any]="gelu", __A : Any=0.1, __A : Union[str, Any]=0.1, __A : Dict=1_6_3_8_4, __A : List[str]=1_6, __A : str=0.0_2, __A : List[str]=1E-12, __A : Optional[int]=0, __A : str=0XE000, __A : int=0XE001, __A : Any=4, __A : Tuple=4, __A : str=8, __A : Optional[int]=1_6_3_8_4, __A : Optional[Any]=1_2_8, **__A : List[Any], ):
super().__init__(pad_token_id=__A, bos_token_id=__A, eos_token_id=__A, **__A )
UpperCAmelCase : List[str] = max_position_embeddings
UpperCAmelCase : Any = hidden_size
UpperCAmelCase : Dict = num_hidden_layers
UpperCAmelCase : int = num_attention_heads
UpperCAmelCase : str = intermediate_size
UpperCAmelCase : Dict = hidden_act
UpperCAmelCase : Optional[Any] = hidden_dropout_prob
UpperCAmelCase : Optional[Any] = attention_probs_dropout_prob
UpperCAmelCase : Optional[Any] = initializer_range
UpperCAmelCase : Optional[int] = type_vocab_size
UpperCAmelCase : Union[str, Any] = layer_norm_eps
# Character config:
UpperCAmelCase : Dict = downsampling_rate
UpperCAmelCase : Tuple = upsampling_kernel_size
UpperCAmelCase : str = num_hash_functions
UpperCAmelCase : Tuple = num_hash_buckets
UpperCAmelCase : Dict = local_transformer_stride
| 336 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowerCamelCase : Tuple = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"feature_extraction_encodec": ["EncodecFeatureExtractor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[Any] = [
"ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST",
"EncodecModel",
"EncodecPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
_lowerCamelCase : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 336 | 1 |
def a__ ( UpperCAmelCase : float ) -> float:
return 10 - x * x
def a__ ( UpperCAmelCase : float , UpperCAmelCase : float ) -> float:
# Bolzano theory in order to find if there is a root between a and b
if equation(UpperCAmelCase ) * equation(UpperCAmelCase ) >= 0:
raise ValueError('''Wrong space!''' )
UpperCAmelCase : Optional[Any] = a
while (b - a) >= 0.01:
# Find middle point
UpperCAmelCase : Optional[Any] = (a + b) / 2
# Check if middle point is root
if equation(UpperCAmelCase ) == 0.0:
break
# Decide the side to repeat the steps
if equation(UpperCAmelCase ) * equation(UpperCAmelCase ) < 0:
UpperCAmelCase : Union[str, Any] = c
else:
UpperCAmelCase : str = c
return c
if __name__ == "__main__":
import doctest
doctest.testmod()
print(bisection(-2, 5))
print(bisection(0, 6))
| 336 |
from __future__ import annotations
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int ) -> list[str]:
if partitions <= 0:
raise ValueError('''partitions must be a positive number!''' )
if partitions > number_of_bytes:
raise ValueError('''partitions can not > number_of_bytes!''' )
UpperCAmelCase : str = number_of_bytes // partitions
UpperCAmelCase : Dict = []
for i in range(UpperCAmelCase ):
UpperCAmelCase : int = i * bytes_per_partition + 1
UpperCAmelCase : Optional[int] = (
number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
)
allocation_list.append(f'''{start_bytes}-{end_bytes}''' )
return allocation_list
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 | 1 |
from ....utils import logging
_lowerCamelCase : Optional[int] = logging.get_logger(__name__)
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : Tuple, __A : List[str], __A : Any=None, __A : int=2_0_4_8 ):
UpperCAmelCase : Union[str, Any] = config.__dict__
UpperCAmelCase : int = modal_hidden_size
if num_labels:
UpperCAmelCase : Tuple = num_labels
| 336 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
_lowerCamelCase : Union[str, Any] = "Run commands across TPU VMs for initial setup before running `accelerate launch`."
def a__ ( UpperCAmelCase : Dict=None ) -> Optional[int]:
if subparsers is not None:
UpperCAmelCase : Tuple = subparsers.add_parser('''tpu-config''' , description=_description )
else:
UpperCAmelCase : Dict = argparse.ArgumentParser('''Accelerate tpu-config command''' , description=_description )
# Core arguments
UpperCAmelCase : Optional[int] = parser.add_argument_group(
'''Config Arguments''' , '''Arguments that can be configured through `accelerate config`.''' )
config_args.add_argument(
'''--config_file''' , type=UpperCAmelCase , default=UpperCAmelCase , help='''Path to the config file to use for accelerate.''' , )
config_args.add_argument(
'''--tpu_name''' , default=UpperCAmelCase , help='''The name of the TPU to use. If not specified, will use the TPU specified in the config file.''' , )
config_args.add_argument(
'''--tpu_zone''' , default=UpperCAmelCase , help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.''' , )
UpperCAmelCase : Union[str, Any] = parser.add_argument_group('''TPU Arguments''' , '''Arguments for options ran inside the TPU.''' )
pod_args.add_argument(
'''--use_alpha''' , action='''store_true''' , help='''Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.''' , )
pod_args.add_argument(
'''--command_file''' , default=UpperCAmelCase , help='''The path to the file containing the commands to run on the pod on startup.''' , )
pod_args.add_argument(
'''--command''' , action='''append''' , nargs='''+''' , help='''A command to run on the pod. Can be passed multiple times.''' , )
pod_args.add_argument(
'''--install_accelerate''' , action='''store_true''' , help='''Whether to install accelerate on the pod. Defaults to False.''' , )
pod_args.add_argument(
'''--accelerate_version''' , default='''latest''' , help='''The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.''' , )
pod_args.add_argument(
'''--debug''' , action='''store_true''' , help='''If set, will print the command that would be run instead of running it.''' )
if subparsers is not None:
parser.set_defaults(func=UpperCAmelCase )
return parser
def a__ ( UpperCAmelCase : Optional[int] ) -> Union[str, Any]:
UpperCAmelCase : Union[str, Any] = None
# Get the default from the config file if it exists.
if args.config_file is not None or os.path.isfile(UpperCAmelCase ):
UpperCAmelCase : Union[str, Any] = load_config_from_file(args.config_file )
if not args.command_file and defaults.command_file is not None and not args.command:
UpperCAmelCase : List[Any] = defaults.command_file
if not args.command and defaults.commands is not None:
UpperCAmelCase : List[str] = defaults.commands
if not args.tpu_name:
UpperCAmelCase : Tuple = defaults.tpu_name
if not args.tpu_zone:
UpperCAmelCase : int = defaults.tpu_zone
if args.accelerate_version == "dev":
UpperCAmelCase : Tuple = '''git+https://github.com/huggingface/accelerate.git'''
elif args.accelerate_version == "latest":
UpperCAmelCase : Dict = '''accelerate -U'''
elif isinstance(parse(args.accelerate_version ) , UpperCAmelCase ):
UpperCAmelCase : Optional[int] = f'''accelerate=={args.accelerate_version}'''
if not args.command_file and not args.command:
raise ValueError('''You must specify either a command file or a command to run on the pod.''' )
if args.command_file:
with open(args.command_file , '''r''' ) as f:
UpperCAmelCase : int = [f.read().splitlines()]
# To turn list of lists into list of strings
if isinstance(args.command[0] , UpperCAmelCase ):
UpperCAmelCase : int = [line for cmd in args.command for line in cmd]
# Default to the shared folder and install accelerate
UpperCAmelCase : Optional[int] = ['''cd /usr/share''']
if args.install_accelerate:
new_cmd += [f'''pip install {args.accelerate_version}''']
new_cmd += args.command
UpperCAmelCase : int = '''; '''.join(UpperCAmelCase )
# Then send it to gcloud
# Eventually try to use google-api-core to do this instead of subprocess
UpperCAmelCase : Any = ['''gcloud''']
if args.use_alpha:
cmd += ["alpha"]
cmd += [
"compute",
"tpus",
"tpu-vm",
"ssh",
args.tpu_name,
"--zone",
args.tpu_zone,
"--command",
args.command,
"--worker",
"all",
]
if args.debug:
print(f'''Running {" ".join(UpperCAmelCase )}''' )
return
subprocess.run(UpperCAmelCase )
print('''Successfully setup pod.''' )
def a__ ( ) -> Any:
UpperCAmelCase : Any = tpu_command_parser()
UpperCAmelCase : Tuple = parser.parse_args()
tpu_command_launcher(UpperCAmelCase )
| 336 | 1 |
from __future__ import annotations
def a__ ( UpperCAmelCase : dict , UpperCAmelCase : str ) -> set[str]:
UpperCAmelCase , UpperCAmelCase : Optional[int] = set(UpperCAmelCase ), [start]
while stack:
UpperCAmelCase : Any = stack.pop()
explored.add(UpperCAmelCase )
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add adjacent elements to stack without exploring them
for adj in reversed(graph[v] ):
if adj not in explored:
stack.append(UpperCAmelCase )
return explored
_lowerCamelCase : Optional[Any] = {
"A": ["B", "C", "D"],
"B": ["A", "D", "E"],
"C": ["A", "F"],
"D": ["B", "D"],
"E": ["B", "F"],
"F": ["C", "E", "G"],
"G": ["F"],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
print(depth_first_search(G, "A"))
| 336 |
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase : Optional[int] = logging.get_logger(__name__)
def a__ ( UpperCAmelCase : Union[str, Any] ) -> Optional[Any]:
print('''Loading config file...''' )
def flatten_yaml_as_dict(UpperCAmelCase : Tuple , UpperCAmelCase : Any="" , UpperCAmelCase : Dict="." ):
UpperCAmelCase : List[str] = []
for k, v in d.items():
UpperCAmelCase : List[Any] = parent_key + sep + k if parent_key else k
if isinstance(UpperCAmelCase , collections.abc.MutableMapping ):
items.extend(flatten_yaml_as_dict(UpperCAmelCase , UpperCAmelCase , sep=UpperCAmelCase ).items() )
else:
items.append((new_key, v) )
return dict(UpperCAmelCase )
UpperCAmelCase : List[str] = argparse.Namespace()
with open(UpperCAmelCase , '''r''' ) as yaml_file:
try:
UpperCAmelCase : List[str] = yaml.load(UpperCAmelCase , Loader=yaml.FullLoader )
UpperCAmelCase : Optional[int] = flatten_yaml_as_dict(UpperCAmelCase )
for k, v in flat_cfg.items():
setattr(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
except yaml.YAMLError as exc:
logger.error('''Error while loading config file: {}. Error message: {}'''.format(UpperCAmelCase , str(UpperCAmelCase ) ) )
return config
def a__ ( UpperCAmelCase : List[str] , UpperCAmelCase : int ) -> List[Any]:
UpperCAmelCase : int = MobileViTVaConfig()
UpperCAmelCase : str = False
# dataset
if task_name.startswith('''imagenet1k_''' ):
UpperCAmelCase : Any = 1_000
if int(task_name.strip().split('''_''' )[-1] ) == 384:
UpperCAmelCase : Any = 384
else:
UpperCAmelCase : Tuple = 256
UpperCAmelCase : int = '''imagenet-1k-id2label.json'''
elif task_name.startswith('''imagenet21k_to_1k_''' ):
UpperCAmelCase : Optional[Any] = 21_000
if int(task_name.strip().split('''_''' )[-1] ) == 384:
UpperCAmelCase : str = 384
else:
UpperCAmelCase : Dict = 256
UpperCAmelCase : List[Any] = '''imagenet-22k-id2label.json'''
elif task_name.startswith('''ade20k_''' ):
UpperCAmelCase : Optional[Any] = 151
UpperCAmelCase : Tuple = 512
UpperCAmelCase : Tuple = '''ade20k-id2label.json'''
UpperCAmelCase : Tuple = True
elif task_name.startswith('''voc_''' ):
UpperCAmelCase : Dict = 21
UpperCAmelCase : str = 512
UpperCAmelCase : Union[str, Any] = '''pascal-voc-id2label.json'''
UpperCAmelCase : Dict = True
# orig_config
UpperCAmelCase : List[Any] = load_orig_config_file(UpperCAmelCase )
assert getattr(UpperCAmelCase , '''model.classification.name''' , -1 ) == "mobilevit_v2", "Invalid model"
UpperCAmelCase : Tuple = getattr(UpperCAmelCase , '''model.classification.mitv2.width_multiplier''' , 1.0 )
assert (
getattr(UpperCAmelCase , '''model.classification.mitv2.attn_norm_layer''' , -1 ) == "layer_norm_2d"
), "Norm layers other than layer_norm_2d is not supported"
UpperCAmelCase : int = getattr(UpperCAmelCase , '''model.classification.activation.name''' , '''swish''' )
# config.image_size == getattr(orig_config, 'sampler.bs.crop_size_width', 256)
if is_segmentation_model:
UpperCAmelCase : str = getattr(UpperCAmelCase , '''model.segmentation.output_stride''' , 16 )
if "_deeplabv3" in task_name:
UpperCAmelCase : int = getattr(UpperCAmelCase , '''model.segmentation.deeplabv3.aspp_rates''' , [12, 24, 36] )
UpperCAmelCase : Any = getattr(UpperCAmelCase , '''model.segmentation.deeplabv3.aspp_out_channels''' , 512 )
UpperCAmelCase : Optional[Any] = getattr(UpperCAmelCase , '''model.segmentation.deeplabv3.aspp_dropout''' , 0.1 )
# id2label
UpperCAmelCase : Union[str, Any] = '''huggingface/label-files'''
UpperCAmelCase : List[Any] = json.load(open(hf_hub_download(UpperCAmelCase , UpperCAmelCase , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase : Any = {int(UpperCAmelCase ): v for k, v in idalabel.items()}
UpperCAmelCase : int = idalabel
UpperCAmelCase : Optional[int] = {v: k for k, v in idalabel.items()}
return config
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Optional[int] ) -> List[str]:
UpperCAmelCase : Union[str, Any] = dct.pop(UpperCAmelCase )
UpperCAmelCase : List[str] = val
def a__ ( UpperCAmelCase : Union[str, Any] , UpperCAmelCase : int=False ) -> Union[str, Any]:
if base_model:
UpperCAmelCase : Dict = ''''''
else:
UpperCAmelCase : Dict = '''mobilevitv2.'''
UpperCAmelCase : Optional[int] = []
for k in state_dict.keys():
if k[:8] == "encoder.":
UpperCAmelCase : List[str] = k[8:]
else:
UpperCAmelCase : Dict = k
if ".block." in k:
UpperCAmelCase : List[Any] = k_new.replace('''.block.''' , '''.''' )
if ".conv." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''.conv.''' , '''.convolution.''' )
if ".norm." in k:
UpperCAmelCase : List[str] = k_new.replace('''.norm.''' , '''.normalization.''' )
if "conv_1." in k:
UpperCAmelCase : Union[str, Any] = k_new.replace('''conv_1.''' , f'''{model_prefix}conv_stem.''' )
for i in [1, 2]:
if f'''layer_{i}.''' in k:
UpperCAmelCase : Union[str, Any] = k_new.replace(f'''layer_{i}.''' , f'''{model_prefix}encoder.layer.{i-1}.layer.''' )
if ".exp_1x1." in k:
UpperCAmelCase : Optional[Any] = k_new.replace('''.exp_1x1.''' , '''.expand_1x1.''' )
if ".red_1x1." in k:
UpperCAmelCase : int = k_new.replace('''.red_1x1.''' , '''.reduce_1x1.''' )
for i in [3, 4, 5]:
if f'''layer_{i}.0.''' in k:
UpperCAmelCase : Any = k_new.replace(f'''layer_{i}.0.''' , f'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.''' )
if f'''layer_{i}.1.local_rep.0.''' in k:
UpperCAmelCase : str = k_new.replace(f'''layer_{i}.1.local_rep.0.''' , f'''{model_prefix}encoder.layer.{i-1}.conv_kxk.''' )
if f'''layer_{i}.1.local_rep.1.''' in k:
UpperCAmelCase : int = k_new.replace(f'''layer_{i}.1.local_rep.1.''' , f'''{model_prefix}encoder.layer.{i-1}.conv_1x1.''' )
for i in [3, 4, 5]:
if i == 3:
UpperCAmelCase : Dict = [0, 1]
elif i == 4:
UpperCAmelCase : Dict = [0, 1, 2, 3]
elif i == 5:
UpperCAmelCase : int = [0, 1, 2]
for j in j_in:
if f'''layer_{i}.1.global_rep.{j}.''' in k:
UpperCAmelCase : Optional[Any] = k_new.replace(
f'''layer_{i}.1.global_rep.{j}.''' , f'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.''' )
if f'''layer_{i}.1.global_rep.{j+1}.''' in k:
UpperCAmelCase : Any = k_new.replace(
f'''layer_{i}.1.global_rep.{j+1}.''' , f'''{model_prefix}encoder.layer.{i-1}.layernorm.''' )
if f'''layer_{i}.1.conv_proj.''' in k:
UpperCAmelCase : Union[str, Any] = k_new.replace(f'''layer_{i}.1.conv_proj.''' , f'''{model_prefix}encoder.layer.{i-1}.conv_projection.''' )
if "pre_norm_attn.0." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''pre_norm_attn.0.''' , '''layernorm_before.''' )
if "pre_norm_attn.1." in k:
UpperCAmelCase : Optional[Any] = k_new.replace('''pre_norm_attn.1.''' , '''attention.''' )
if "pre_norm_ffn.0." in k:
UpperCAmelCase : List[Any] = k_new.replace('''pre_norm_ffn.0.''' , '''layernorm_after.''' )
if "pre_norm_ffn.1." in k:
UpperCAmelCase : List[Any] = k_new.replace('''pre_norm_ffn.1.''' , '''ffn.conv1.''' )
if "pre_norm_ffn.3." in k:
UpperCAmelCase : Any = k_new.replace('''pre_norm_ffn.3.''' , '''ffn.conv2.''' )
if "classifier.1." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''classifier.1.''' , '''classifier.''' )
if "seg_head." in k:
UpperCAmelCase : Union[str, Any] = k_new.replace('''seg_head.''' , '''segmentation_head.''' )
if ".aspp_layer." in k:
UpperCAmelCase : Tuple = k_new.replace('''.aspp_layer.''' , '''.''' )
if ".aspp_pool." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''.aspp_pool.''' , '''.''' )
rename_keys.append((k, k_new) )
return rename_keys
def a__ ( UpperCAmelCase : Union[str, Any] ) -> Any:
UpperCAmelCase : str = []
for k in state_dict.keys():
if k.startswith('''seg_head.aux_head.''' ):
keys_to_ignore.append(UpperCAmelCase )
for k in keys_to_ignore:
state_dict.pop(UpperCAmelCase , UpperCAmelCase )
def a__ ( ) -> Union[str, Any]:
UpperCAmelCase : int = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
UpperCAmelCase : List[str] = Image.open(requests.get(UpperCAmelCase , stream=UpperCAmelCase ).raw )
return im
@torch.no_grad()
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[Any] ) -> Union[str, Any]:
UpperCAmelCase : Union[str, Any] = get_mobilevitva_config(UpperCAmelCase , UpperCAmelCase )
# load original state_dict
UpperCAmelCase : List[str] = torch.load(UpperCAmelCase , map_location='''cpu''' )
# load huggingface model
if task_name.startswith('''ade20k_''' ) or task_name.startswith('''voc_''' ):
UpperCAmelCase : str = MobileViTVaForSemanticSegmentation(UpperCAmelCase ).eval()
UpperCAmelCase : str = False
else:
UpperCAmelCase : Union[str, Any] = MobileViTVaForImageClassification(UpperCAmelCase ).eval()
UpperCAmelCase : Any = False
# remove and rename some keys of load the original model
UpperCAmelCase : Optional[Any] = checkpoint
remove_unused_keys(UpperCAmelCase )
UpperCAmelCase : Optional[Any] = create_rename_keys(UpperCAmelCase , base_model=UpperCAmelCase )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# load modified state_dict
model.load_state_dict(UpperCAmelCase )
# Check outputs on an image, prepared by MobileViTImageProcessor
UpperCAmelCase : Dict = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
UpperCAmelCase : Any = image_processor(images=prepare_img() , return_tensors='''pt''' )
UpperCAmelCase : Union[str, Any] = model(**UpperCAmelCase )
# verify classification model
if task_name.startswith('''imagenet''' ):
UpperCAmelCase : Optional[Any] = outputs.logits
UpperCAmelCase : int = logits.argmax(-1 ).item()
print('''Predicted class:''' , model.config.idalabel[predicted_class_idx] )
if task_name.startswith('''imagenet1k_256''' ) and config.width_multiplier == 1.0:
# expected_logits for base variant
UpperCAmelCase : str = torch.tensor([-1.6_336E00, -7.3_204E-02, -5.1_883E-01] )
assert torch.allclose(logits[0, :3] , UpperCAmelCase , atol=1E-4 )
Path(UpperCAmelCase ).mkdir(exist_ok=UpperCAmelCase )
print(f'''Saving model {task_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(UpperCAmelCase )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(UpperCAmelCase )
if __name__ == "__main__":
_lowerCamelCase : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--task",
default="imagenet1k_256",
type=str,
help=(
"Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
"\n Classification (ImageNet-1k)\n - MobileViTV2 (256x256) : imagenet1k_256\n - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n imagenet21k_to_1k_256\n - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n ImageNet-1k 384x384) : imagenet21k_to_1k_384\n Segmentation\n - ADE20K Dataset : ade20k_deeplabv3\n - Pascal VOC 2012 Dataset: voc_deeplabv3\n "
),
choices=[
"imagenet1k_256",
"imagenet1k_384",
"imagenet21k_to_1k_256",
"imagenet21k_to_1k_384",
"ade20k_deeplabv3",
"voc_deeplabv3",
],
)
parser.add_argument(
"--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
)
parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
)
_lowerCamelCase : Optional[int] = parser.parse_args()
convert_mobilevitva_checkpoint(
args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
)
| 336 | 1 |
from math import ceil
def a__ ( UpperCAmelCase : int = 1_001 ) -> int:
UpperCAmelCase : Optional[int] = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
UpperCAmelCase : Dict = 2 * i + 1
UpperCAmelCase : Union[str, Any] = 2 * i
UpperCAmelCase : Tuple = total + 4 * odd**2 - 6 * even
return total
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution())
else:
try:
_lowerCamelCase : Tuple = int(sys.argv[1])
print(solution(n))
except ValueError:
print("Invalid entry - please enter a number")
| 336 |
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
if is_flax_available():
import jax.numpy as jnp
class __UpperCAmelCase ( lowerCamelCase__ ):
def __get__( self : Tuple, __A : Optional[Any], __A : Optional[int]=None ):
# See docs.python.org/3/howto/descriptor.html#properties
if obj is None:
return self
if self.fget is None:
raise AttributeError('''unreadable attribute''' )
UpperCAmelCase : str = '''__cached_''' + self.fget.__name__
UpperCAmelCase : int = getattr(__A, __A, __A )
if cached is None:
UpperCAmelCase : Any = self.fget(__A )
setattr(__A, __A, __A )
return cached
def a__ ( UpperCAmelCase : Optional[Any] ) -> Any:
UpperCAmelCase : Any = val.lower()
if val in {"y", "yes", "t", "true", "on", "1"}:
return 1
if val in {"n", "no", "f", "false", "off", "0"}:
return 0
raise ValueError(f'''invalid truth value {val!r}''' )
def a__ ( UpperCAmelCase : Dict ) -> List[str]:
if is_torch_fx_proxy(UpperCAmelCase ):
return True
if is_torch_available():
import torch
if isinstance(UpperCAmelCase , torch.Tensor ):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(UpperCAmelCase , tf.Tensor ):
return True
if is_flax_available():
import jax.numpy as jnp
from jax.core import Tracer
if isinstance(UpperCAmelCase , (jnp.ndarray, Tracer) ):
return True
return isinstance(UpperCAmelCase , np.ndarray )
def a__ ( UpperCAmelCase : List[Any] ) -> Union[str, Any]:
return isinstance(UpperCAmelCase , np.ndarray )
def a__ ( UpperCAmelCase : str ) -> Tuple:
return _is_numpy(UpperCAmelCase )
def a__ ( UpperCAmelCase : str ) -> List[Any]:
import torch
return isinstance(UpperCAmelCase , torch.Tensor )
def a__ ( UpperCAmelCase : str ) -> List[Any]:
return False if not is_torch_available() else _is_torch(UpperCAmelCase )
def a__ ( UpperCAmelCase : Tuple ) -> List[str]:
import torch
return isinstance(UpperCAmelCase , torch.device )
def a__ ( UpperCAmelCase : Any ) -> Any:
return False if not is_torch_available() else _is_torch_device(UpperCAmelCase )
def a__ ( UpperCAmelCase : Dict ) -> List[str]:
import torch
if isinstance(UpperCAmelCase , UpperCAmelCase ):
if hasattr(UpperCAmelCase , UpperCAmelCase ):
UpperCAmelCase : Union[str, Any] = getattr(UpperCAmelCase , UpperCAmelCase )
else:
return False
return isinstance(UpperCAmelCase , torch.dtype )
def a__ ( UpperCAmelCase : Optional[Any] ) -> Union[str, Any]:
return False if not is_torch_available() else _is_torch_dtype(UpperCAmelCase )
def a__ ( UpperCAmelCase : Any ) -> str:
import tensorflow as tf
return isinstance(UpperCAmelCase , tf.Tensor )
def a__ ( UpperCAmelCase : int ) -> Union[str, Any]:
return False if not is_tf_available() else _is_tensorflow(UpperCAmelCase )
def a__ ( UpperCAmelCase : List[str] ) -> Tuple:
import tensorflow as tf
# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
if hasattr(UpperCAmelCase , '''is_symbolic_tensor''' ):
return tf.is_symbolic_tensor(UpperCAmelCase )
return type(UpperCAmelCase ) == tf.Tensor
def a__ ( UpperCAmelCase : int ) -> List[Any]:
return False if not is_tf_available() else _is_tf_symbolic_tensor(UpperCAmelCase )
def a__ ( UpperCAmelCase : List[Any] ) -> Dict:
import jax.numpy as jnp # noqa: F811
return isinstance(UpperCAmelCase , jnp.ndarray )
def a__ ( UpperCAmelCase : List[Any] ) -> Optional[int]:
return False if not is_flax_available() else _is_jax(UpperCAmelCase )
def a__ ( UpperCAmelCase : int ) -> Tuple:
if isinstance(UpperCAmelCase , (dict, UserDict) ):
return {k: to_py_obj(UpperCAmelCase ) for k, v in obj.items()}
elif isinstance(UpperCAmelCase , (list, tuple) ):
return [to_py_obj(UpperCAmelCase ) for o in obj]
elif is_tf_tensor(UpperCAmelCase ):
return obj.numpy().tolist()
elif is_torch_tensor(UpperCAmelCase ):
return obj.detach().cpu().tolist()
elif is_jax_tensor(UpperCAmelCase ):
return np.asarray(UpperCAmelCase ).tolist()
elif isinstance(UpperCAmelCase , (np.ndarray, np.number) ): # tolist also works on 0d np arrays
return obj.tolist()
else:
return obj
def a__ ( UpperCAmelCase : Any ) -> List[str]:
if isinstance(UpperCAmelCase , (dict, UserDict) ):
return {k: to_numpy(UpperCAmelCase ) for k, v in obj.items()}
elif isinstance(UpperCAmelCase , (list, tuple) ):
return np.array(UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
return obj.numpy()
elif is_torch_tensor(UpperCAmelCase ):
return obj.detach().cpu().numpy()
elif is_jax_tensor(UpperCAmelCase ):
return np.asarray(UpperCAmelCase )
else:
return obj
class __UpperCAmelCase ( lowerCamelCase__ ):
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : Optional[Any] = fields(self )
# Safety and consistency checks
if not len(__A ):
raise ValueError(F'''{self.__class__.__name__} has no fields.''' )
if not all(field.default is None for field in class_fields[1:] ):
raise ValueError(F'''{self.__class__.__name__} should not have more than one required field.''' )
UpperCAmelCase : int = getattr(self, class_fields[0].name )
UpperCAmelCase : str = all(getattr(self, field.name ) is None for field in class_fields[1:] )
if other_fields_are_none and not is_tensor(__A ):
if isinstance(__A, __A ):
UpperCAmelCase : Tuple = first_field.items()
UpperCAmelCase : Any = True
else:
try:
UpperCAmelCase : Optional[Any] = iter(__A )
UpperCAmelCase : Optional[Any] = True
except TypeError:
UpperCAmelCase : Optional[int] = False
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for idx, element in enumerate(__A ):
if (
not isinstance(__A, (list, tuple) )
or not len(__A ) == 2
or not isinstance(element[0], __A )
):
if idx == 0:
# If we do not have an iterator of key/values, set it as attribute
UpperCAmelCase : Any = first_field
else:
# If we have a mixed iterator, raise an error
raise ValueError(
F'''Cannot set key/value for {element}. It needs to be a tuple (key, value).''' )
break
setattr(self, element[0], element[1] )
if element[1] is not None:
UpperCAmelCase : Union[str, Any] = element[1]
elif first_field is not None:
UpperCAmelCase : Union[str, Any] = first_field
else:
for field in class_fields:
UpperCAmelCase : Optional[Any] = getattr(self, field.name )
if v is not None:
UpperCAmelCase : Optional[int] = v
def __delitem__( self : Union[str, Any], *__A : str, **__A : Tuple ):
raise Exception(F'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.''' )
def __magic_name__ ( self : List[str], *__A : Union[str, Any], **__A : Optional[Any] ):
raise Exception(F'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.''' )
def __magic_name__ ( self : Any, *__A : Dict, **__A : str ):
raise Exception(F'''You cannot use ``pop`` on a {self.__class__.__name__} instance.''' )
def __magic_name__ ( self : Dict, *__A : int, **__A : Dict ):
raise Exception(F'''You cannot use ``update`` on a {self.__class__.__name__} instance.''' )
def __getitem__( self : List[str], __A : List[str] ):
if isinstance(__A, __A ):
UpperCAmelCase : int = dict(self.items() )
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__( self : Optional[Any], __A : Dict, __A : Union[str, Any] ):
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(__A, __A )
super().__setattr__(__A, __A )
def __setitem__( self : Dict, __A : List[Any], __A : Union[str, Any] ):
# Will raise a KeyException if needed
super().__setitem__(__A, __A )
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(__A, __A )
def __magic_name__ ( self : List[str] ):
return tuple(self[k] for k in self.keys() )
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@classmethod
def __magic_name__ ( cls : List[Any], __A : Tuple ):
raise ValueError(
F'''{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys() )}''' )
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """longest"""
UpperCamelCase = """max_length"""
UpperCamelCase = """do_not_pad"""
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """pt"""
UpperCamelCase = """tf"""
UpperCamelCase = """np"""
UpperCamelCase = """jax"""
class __UpperCAmelCase :
def __init__( self : Any, __A : List[ContextManager] ):
UpperCAmelCase : Tuple = context_managers
UpperCAmelCase : Tuple = ExitStack()
def __enter__( self : Any ):
for context_manager in self.context_managers:
self.stack.enter_context(__A )
def __exit__( self : List[Any], *__A : Union[str, Any], **__A : Dict ):
self.stack.__exit__(*__A, **__A )
def a__ ( UpperCAmelCase : Union[str, Any] ) -> str:
UpperCAmelCase : int = infer_framework(UpperCAmelCase )
if framework == "tf":
UpperCAmelCase : List[str] = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
UpperCAmelCase : List[Any] = inspect.signature(model_class.forward ) # PyTorch models
else:
UpperCAmelCase : Tuple = inspect.signature(model_class.__call__ ) # Flax models
for p in signature.parameters:
if p == "return_loss" and signature.parameters[p].default is True:
return True
return False
def a__ ( UpperCAmelCase : Dict ) -> Any:
UpperCAmelCase : List[Any] = model_class.__name__
UpperCAmelCase : Union[str, Any] = infer_framework(UpperCAmelCase )
if framework == "tf":
UpperCAmelCase : Tuple = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
UpperCAmelCase : Dict = inspect.signature(model_class.forward ) # PyTorch models
else:
UpperCAmelCase : Dict = inspect.signature(model_class.__call__ ) # Flax models
if "QuestionAnswering" in model_name:
return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
else:
return [p for p in signature.parameters if "label" in p]
def a__ ( UpperCAmelCase : MutableMapping , UpperCAmelCase : str = "" , UpperCAmelCase : str = "." ) -> Union[str, Any]:
def _flatten_dict(UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[str]="" , UpperCAmelCase : Any="." ):
for k, v in d.items():
UpperCAmelCase : List[str] = str(UpperCAmelCase ) + delimiter + str(UpperCAmelCase ) if parent_key else k
if v and isinstance(UpperCAmelCase , UpperCAmelCase ):
yield from flatten_dict(UpperCAmelCase , UpperCAmelCase , delimiter=UpperCAmelCase ).items()
else:
yield key, v
return dict(_flatten_dict(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) )
@contextmanager
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : bool = False ) -> Optional[Any]:
if use_temp_dir:
with tempfile.TemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield working_dir
def a__ ( UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[str]=None ) -> Optional[Any]:
if is_numpy_array(UpperCAmelCase ):
return np.transpose(UpperCAmelCase , axes=UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.T if axes is None else array.permute(*UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.transpose(UpperCAmelCase , perm=UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.transpose(UpperCAmelCase , axes=UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for transpose: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : str , UpperCAmelCase : Optional[int] ) -> List[str]:
if is_numpy_array(UpperCAmelCase ):
return np.reshape(UpperCAmelCase , UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.reshape(*UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.reshape(UpperCAmelCase , UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.reshape(UpperCAmelCase , UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for reshape: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : Tuple , UpperCAmelCase : Optional[int]=None ) -> Any:
if is_numpy_array(UpperCAmelCase ):
return np.squeeze(UpperCAmelCase , axis=UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.squeeze() if axis is None else array.squeeze(dim=UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.squeeze(UpperCAmelCase , axis=UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.squeeze(UpperCAmelCase , axis=UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for squeeze: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : str , UpperCAmelCase : int ) -> str:
if is_numpy_array(UpperCAmelCase ):
return np.expand_dims(UpperCAmelCase , UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.unsqueeze(dim=UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.expand_dims(UpperCAmelCase , axis=UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.expand_dims(UpperCAmelCase , axis=UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for expand_dims: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : Dict ) -> List[str]:
if is_numpy_array(UpperCAmelCase ):
return np.size(UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.numel()
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.size(UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return array.size
else:
raise ValueError(f'''Type not supported for expand_dims: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : List[str] , UpperCAmelCase : List[str] ) -> Dict:
for key, value in auto_map.items():
if isinstance(UpperCAmelCase , (tuple, list) ):
UpperCAmelCase : List[Any] = [f'''{repo_id}--{v}''' if (v is not None and '''--''' not in v) else v for v in value]
elif value is not None and "--" not in value:
UpperCAmelCase : List[Any] = f'''{repo_id}--{value}'''
return auto_map
def a__ ( UpperCAmelCase : Tuple ) -> Union[str, Any]:
for base_class in inspect.getmro(UpperCAmelCase ):
UpperCAmelCase : Any = base_class.__module__
UpperCAmelCase : Dict = base_class.__name__
if module.startswith('''tensorflow''' ) or module.startswith('''keras''' ) or name == "TFPreTrainedModel":
return "tf"
elif module.startswith('''torch''' ) or name == "PreTrainedModel":
return "pt"
elif module.startswith('''flax''' ) or module.startswith('''jax''' ) or name == "FlaxPreTrainedModel":
return "flax"
else:
raise TypeError(f'''Could not infer framework from class {model_class}.''' )
| 336 | 1 |
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
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCamelCase : Optional[int] = {"vocab_file": "spiece.model"}
_lowerCamelCase : Dict = {
"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
_lowerCamelCase : Any = {
"t5-small": 5_1_2,
"t5-base": 5_1_2,
"t5-large": 5_1_2,
"t5-3b": 5_1_2,
"t5-11b": 5_1_2,
}
_lowerCamelCase : List[Any] = "▁"
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = VOCAB_FILES_NAMES
UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase = ["""input_ids""", """attention_mask"""]
def __init__( self : int, __A : Any, __A : Any="</s>", __A : List[str]="<unk>", __A : Dict="<pad>", __A : List[Any]=1_0_0, __A : Any=None, __A : Optional[Dict[str, Any]] = None, __A : List[str]=True, **__A : Tuple, ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
UpperCAmelCase : Any = [F'''<extra_id_{i}>''' for i in range(__A )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
UpperCAmelCase : Union[str, Any] = len(set(filter(lambda __A : bool('''extra_id''' in str(__A ) ), __A ) ) )
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''' )
UpperCAmelCase : List[Any] = legacy
UpperCAmelCase : int = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=__A, unk_token=__A, pad_token=__A, extra_ids=__A, additional_special_tokens=__A, sp_model_kwargs=self.sp_model_kwargs, legacy=__A, **__A, )
UpperCAmelCase : Union[str, Any] = vocab_file
UpperCAmelCase : str = extra_ids
UpperCAmelCase : Union[str, Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__A )
@staticmethod
def __magic_name__ ( __A : str, __A : Union[str, Any], __A : List[Any] ):
if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
UpperCAmelCase : int = 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.''', __A, )
return max_model_length
@property
def __magic_name__ ( self : List[str] ):
return self.sp_model.get_piece_size() + self._extra_ids
def __magic_name__ ( self : Dict ):
UpperCAmelCase : int = {self.convert_ids_to_tokens(__A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __magic_name__ ( self : str, __A : List[int], __A : Optional[List[int]] = None, __A : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__A, token_ids_a=__A, already_has_special_tokens=__A )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(__A )) + [1]
return ([0] * len(__A )) + [1] + ([0] * len(__A )) + [1]
def __magic_name__ ( self : str ):
return list(
set(filter(lambda __A : bool(re.search(R'''<extra_id_\d+>''', __A ) ) is not None, self.additional_special_tokens ) ) )
def __magic_name__ ( self : Union[str, Any] ):
return [self._convert_token_to_id(__A ) for token in self.get_sentinel_tokens()]
def __magic_name__ ( self : Tuple, __A : List[int] ):
if len(__A ) > 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 __magic_name__ ( self : List[Any], __A : List[int], __A : Optional[List[int]] = None ):
UpperCAmelCase : Tuple = [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 __magic_name__ ( self : List[Any], __A : List[int], __A : Optional[List[int]] = None ):
UpperCAmelCase : List[Any] = self._add_eos_if_not_present(__A )
if token_ids_a is None:
return token_ids_a
else:
UpperCAmelCase : Union[str, Any] = self._add_eos_if_not_present(__A )
return token_ids_a + token_ids_a
def __getstate__( self : Dict ):
UpperCAmelCase : List[Any] = self.__dict__.copy()
UpperCAmelCase : List[Any] = None
return state
def __setstate__( self : List[str], __A : List[str] ):
UpperCAmelCase : List[Any] = d
# for backward compatibility
if not hasattr(self, '''sp_model_kwargs''' ):
UpperCAmelCase : List[str] = {}
UpperCAmelCase : List[str] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __magic_name__ ( self : Union[str, Any], __A : "TextInput", **__A : List[str] ):
# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
# the beginning of the text
if not self.legacy:
UpperCAmelCase : int = SPIECE_UNDERLINE + text.replace(__A, ''' ''' )
return super().tokenize(__A, **__A )
def __magic_name__ ( self : List[Any], __A : Tuple, **__A : Any ):
if not self.legacy:
UpperCAmelCase : int = text.startswith(__A )
if is_first:
UpperCAmelCase : str = text[1:]
UpperCAmelCase : str = self.sp_model.encode(__A, out_type=__A )
if not self.legacy and not is_first and not text.startswith(''' ''' ) and tokens[0].startswith(__A ):
UpperCAmelCase : Optional[Any] = ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def __magic_name__ ( self : Union[str, Any], __A : Dict ):
if token.startswith('''<extra_id_''' ):
UpperCAmelCase : Union[str, Any] = re.match(R'''<extra_id_(\d+)>''', __A )
UpperCAmelCase : Optional[Any] = int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(__A )
def __magic_name__ ( self : Union[str, Any], __A : Optional[int] ):
if index < self.sp_model.get_piece_size():
UpperCAmelCase : Union[str, Any] = self.sp_model.IdToPiece(__A )
else:
UpperCAmelCase : Tuple = F'''<extra_id_{self.vocab_size - 1 - index}>'''
return token
def __magic_name__ ( self : Tuple, __A : Union[str, Any] ):
UpperCAmelCase : str = []
UpperCAmelCase : Any = ''''''
UpperCAmelCase : Union[str, Any] = 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(__A ) + token
UpperCAmelCase : Union[str, Any] = True
UpperCAmelCase : int = []
else:
current_sub_tokens.append(__A )
UpperCAmelCase : Any = False
out_string += self.sp_model.decode(__A )
return out_string.strip()
def __magic_name__ ( self : Optional[Any], __A : str, __A : Optional[str] = None ):
if not os.path.isdir(__A ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCAmelCase : Optional[Any] = os.path.join(
__A, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file, __A )
elif not os.path.isfile(self.vocab_file ):
with open(__A, '''wb''' ) as fi:
UpperCAmelCase : Any = self.sp_model.serialized_model_proto()
fi.write(__A )
return (out_vocab_file,)
| 336 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __UpperCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = LayoutLMTokenizer
UpperCamelCase = LayoutLMTokenizerFast
UpperCamelCase = True
UpperCamelCase = True
def __magic_name__ ( self : Any ):
super().setUp()
UpperCAmelCase : Dict = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
UpperCAmelCase : 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] ) )
def __magic_name__ ( self : Union[str, Any], **__A : List[str] ):
return LayoutLMTokenizer.from_pretrained(self.tmpdirname, **__A )
def __magic_name__ ( self : Optional[int], __A : int ):
UpperCAmelCase : Optional[Any] = '''UNwant\u00E9d,running'''
UpperCAmelCase : Optional[int] = '''unwanted, running'''
return input_text, output_text
def __magic_name__ ( self : Any ):
UpperCAmelCase : Union[str, Any] = self.tokenizer_class(self.vocab_file )
UpperCAmelCase : Optional[Any] = tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(__A, ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ), [7, 4, 5, 1_0, 8, 9] )
def __magic_name__ ( self : Optional[int] ):
pass
| 336 | 1 |
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
_lowerCamelCase : str = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
" (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"
}
def a__ ( UpperCAmelCase : str = "dhaka" , UpperCAmelCase : int = 5 ) -> int:
UpperCAmelCase : List[Any] = min(UpperCAmelCase , 50 ) # Prevent abuse!
UpperCAmelCase : Tuple = {
'''q''': query,
'''tbm''': '''isch''',
'''hl''': '''en''',
'''ijn''': '''0''',
}
UpperCAmelCase : Optional[Any] = requests.get('''https://www.google.com/search''' , params=UpperCAmelCase , headers=UpperCAmelCase )
UpperCAmelCase : Optional[Any] = BeautifulSoup(html.text , '''html.parser''' )
UpperCAmelCase : Optional[Any] = ''''''.join(
re.findall(r'''AF_initDataCallback\(([^<]+)\);''' , str(soup.select('''script''' ) ) ) )
UpperCAmelCase : Tuple = json.dumps(UpperCAmelCase )
UpperCAmelCase : Dict = json.loads(UpperCAmelCase )
UpperCAmelCase : Any = re.findall(
r'''\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",''' , UpperCAmelCase , )
if not matched_google_image_data:
return 0
UpperCAmelCase : Optional[Any] = re.sub(
r'''\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]''' , '''''' , str(UpperCAmelCase ) , )
UpperCAmelCase : List[Any] = re.findall(
r'''(?:\'|,),\[\"(https:|http.*?)\",\d+,\d+\]''' , UpperCAmelCase , )
for index, fixed_full_res_image in enumerate(UpperCAmelCase ):
if index >= max_images:
return index
UpperCAmelCase : Tuple = bytes(UpperCAmelCase , '''ascii''' ).decode(
'''unicode-escape''' )
UpperCAmelCase : int = bytes(UpperCAmelCase , '''ascii''' ).decode(
'''unicode-escape''' )
UpperCAmelCase : str = urllib.request.build_opener()
UpperCAmelCase : Union[str, Any] = [
(
'''User-Agent''',
'''Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'''
''' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582''',
)
]
urllib.request.install_opener(UpperCAmelCase )
UpperCAmelCase : int = f'''query_{query.replace(" " , "_" )}'''
if not os.path.exists(UpperCAmelCase ):
os.makedirs(UpperCAmelCase )
urllib.request.urlretrieve( # noqa: S310
UpperCAmelCase , f'''{path_name}/original_size_img_{index}.jpg''' )
return index
if __name__ == "__main__":
try:
_lowerCamelCase : Dict = download_images_from_google_query(sys.argv[1])
print(f"""{image_count} images were downloaded to disk.""")
except IndexError:
print("Please provide a search term.")
raise
| 336 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTMAEForPreTraining, TFViTMAEModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __UpperCAmelCase :
def __init__( self : Any, __A : str, __A : Dict=1_3, __A : int=3_0, __A : Tuple=2, __A : Union[str, Any]=3, __A : Any=True, __A : str=True, __A : Dict=3_2, __A : List[Any]=2, __A : Optional[Any]=4, __A : Union[str, Any]=3_7, __A : int="gelu", __A : int=0.1, __A : List[Any]=0.1, __A : Tuple=1_0, __A : Tuple=0.0_2, __A : Any=3, __A : List[str]=0.6, __A : Any=None, ):
UpperCAmelCase : Union[str, Any] = parent
UpperCAmelCase : Dict = batch_size
UpperCAmelCase : List[str] = image_size
UpperCAmelCase : Dict = patch_size
UpperCAmelCase : int = num_channels
UpperCAmelCase : Union[str, Any] = is_training
UpperCAmelCase : Union[str, Any] = use_labels
UpperCAmelCase : Union[str, Any] = hidden_size
UpperCAmelCase : Optional[int] = num_hidden_layers
UpperCAmelCase : Union[str, Any] = num_attention_heads
UpperCAmelCase : List[str] = intermediate_size
UpperCAmelCase : Optional[int] = hidden_act
UpperCAmelCase : Tuple = hidden_dropout_prob
UpperCAmelCase : List[Any] = attention_probs_dropout_prob
UpperCAmelCase : Any = type_sequence_label_size
UpperCAmelCase : Tuple = initializer_range
UpperCAmelCase : Tuple = mask_ratio
UpperCAmelCase : Any = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
UpperCAmelCase : Tuple = (image_size // patch_size) ** 2
UpperCAmelCase : List[Any] = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : Any = None
if self.use_labels:
UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size], self.type_sequence_label_size )
UpperCAmelCase : str = self.get_config()
return config, pixel_values, labels
def __magic_name__ ( self : Optional[Any] ):
return ViTMAEConfig(
image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, decoder_hidden_size=self.hidden_size, decoder_num_hidden_layers=self.num_hidden_layers, decoder_num_attention_heads=self.num_attention_heads, decoder_intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, is_decoder=__A, initializer_range=self.initializer_range, mask_ratio=self.mask_ratio, )
def __magic_name__ ( self : str, __A : List[Any], __A : Any, __A : Any ):
UpperCAmelCase : Optional[Any] = TFViTMAEModel(config=__A )
UpperCAmelCase : Tuple = model(__A, training=__A )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self : Tuple, __A : str, __A : int, __A : str ):
UpperCAmelCase : Dict = TFViTMAEForPreTraining(__A )
UpperCAmelCase : int = model(__A, training=__A )
# expected sequence length = num_patches
UpperCAmelCase : int = (self.image_size // self.patch_size) ** 2
UpperCAmelCase : Optional[Any] = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
UpperCAmelCase : Tuple = 1
UpperCAmelCase : List[Any] = TFViTMAEForPreTraining(__A )
UpperCAmelCase : Union[str, Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCAmelCase : List[Any] = model(__A, training=__A )
UpperCAmelCase : Union[str, Any] = self.patch_size**2
self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels) )
def __magic_name__ ( self : List[Any] ):
UpperCAmelCase : Dict = self.prepare_config_and_inputs()
((UpperCAmelCase) , (UpperCAmelCase) , (UpperCAmelCase)) : Union[str, Any] = config_and_inputs
UpperCAmelCase : Optional[Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
UpperCamelCase = {"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : List[Any] = TFViTMAEModelTester(self )
UpperCAmelCase : int = ConfigTester(self, config_class=__A, has_text_modality=__A, hidden_size=3_7 )
def __magic_name__ ( self : List[str] ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMAE does not use inputs_embeds''' )
def __magic_name__ ( self : List[Any] ):
pass
def __magic_name__ ( self : List[str] ):
UpperCAmelCase , UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : List[str] = model_class(__A )
self.assertIsInstance(model.get_input_embeddings(), (tf.keras.layers.Layer) )
UpperCAmelCase : Union[str, Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__A, tf.keras.layers.Layer ) )
def __magic_name__ ( self : str ):
UpperCAmelCase , UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Any = model_class(__A )
UpperCAmelCase : Any = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase : int = [*signature.parameters.keys()]
UpperCAmelCase : Tuple = ['''pixel_values''']
self.assertListEqual(arg_names[:1], __A )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __magic_name__ ( self : str ):
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__A )
def __magic_name__ ( self : int ):
# make the mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Tuple = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : List[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCAmelCase : str = model_class(__A )
UpperCAmelCase : int = self._prepare_for_class(__A, __A )
UpperCAmelCase : Dict = model(__A, noise=__A )
UpperCAmelCase : Any = copy.deepcopy(self._prepare_for_class(__A, __A ) )
UpperCAmelCase : Union[str, Any] = model(**__A, noise=__A )
UpperCAmelCase : Dict = outputs_dict[0].numpy()
UpperCAmelCase : Tuple = outputs_keywords[0].numpy()
self.assertLess(np.sum(np.abs(output_dict - output_keywords ) ), 1E-6 )
def __magic_name__ ( self : Optional[Any] ):
# make the mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : str = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Union[str, Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
def prepare_numpy_arrays(__A : Union[str, Any] ):
UpperCAmelCase : str = {}
for k, v in inputs_dict.items():
if tf.is_tensor(__A ):
UpperCAmelCase : Tuple = v.numpy()
else:
UpperCAmelCase : str = np.array(__A )
return inputs_np_dict
for model_class in self.all_model_classes:
UpperCAmelCase : Dict = model_class(__A )
UpperCAmelCase : Any = self._prepare_for_class(__A, __A )
UpperCAmelCase : Optional[int] = prepare_numpy_arrays(__A )
UpperCAmelCase : str = model(__A, noise=__A )
UpperCAmelCase : str = model(**__A, noise=__A )
self.assert_outputs_same(__A, __A )
def __magic_name__ ( self : int, __A : str, __A : Union[str, Any], __A : Optional[Any] ):
# make masks reproducible
np.random.seed(2 )
UpperCAmelCase : Any = int((tf_model.config.image_size // tf_model.config.patch_size) ** 2 )
UpperCAmelCase : Tuple = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCAmelCase : int = tf.constant(__A )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
UpperCAmelCase : List[Any] = tf_noise
super().check_pt_tf_models(__A, __A, __A )
def __magic_name__ ( self : str ):
# make mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Union[str, Any] = {
module_member
for model_class in self.all_model_classes
for module in (import_module(model_class.__module__ ),)
for module_member_name in dir(__A )
if module_member_name.endswith('''MainLayer''' )
# This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
and module_member_name[: -len('''MainLayer''' )] == model_class.__name__[: -len('''Model''' )]
for module_member in (getattr(__A, __A ),)
if isinstance(__A, __A )
and tf.keras.layers.Layer in module_member.__bases__
and getattr(__A, '''_keras_serializable''', __A )
}
UpperCAmelCase : Union[str, Any] = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Optional[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCAmelCase : str = tf.convert_to_tensor(__A )
inputs_dict.update({'''noise''': noise} )
for main_layer_class in tf_main_layer_classes:
UpperCAmelCase : Tuple = main_layer_class(__A )
UpperCAmelCase : int = {
name: tf.keras.Input(tensor.shape[1:], dtype=tensor.dtype ) for name, tensor in inputs_dict.items()
}
UpperCAmelCase : List[Any] = tf.keras.Model(__A, outputs=main_layer(__A ) )
UpperCAmelCase : List[Any] = model(__A )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase : Any = os.path.join(__A, '''keras_model.h5''' )
model.save(__A )
UpperCAmelCase : List[str] = tf.keras.models.load_model(
__A, custom_objects={main_layer_class.__name__: main_layer_class} )
assert isinstance(__A, tf.keras.Model )
UpperCAmelCase : Tuple = model(__A )
self.assert_outputs_same(__A, __A )
@slow
def __magic_name__ ( self : Dict ):
# make mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Optional[Any] = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Optional[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCAmelCase : int = model_class(__A )
UpperCAmelCase : List[str] = self._prepare_for_class(__A, __A )
UpperCAmelCase : Union[str, Any] = model(__A, noise=__A )
if model_class.__name__ == "TFViTMAEModel":
UpperCAmelCase : Optional[int] = outputs.last_hidden_state.numpy()
UpperCAmelCase : Union[str, Any] = 0
else:
UpperCAmelCase : Optional[int] = outputs.logits.numpy()
UpperCAmelCase : int = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__A, saved_model=__A )
UpperCAmelCase : Dict = model_class.from_pretrained(__A )
UpperCAmelCase : str = model(__A, noise=__A )
if model_class.__name__ == "TFViTMAEModel":
UpperCAmelCase : int = after_outputs['''last_hidden_state'''].numpy()
UpperCAmelCase : Dict = 0
else:
UpperCAmelCase : Any = after_outputs['''logits'''].numpy()
UpperCAmelCase : Dict = 0
UpperCAmelCase : Union[str, Any] = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(__A, 1E-5 )
def __magic_name__ ( self : Optional[Any] ):
# make mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : List[Any] = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Tuple = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCAmelCase : Dict = model_class(__A )
UpperCAmelCase : int = self._prepare_for_class(__A, __A )
UpperCAmelCase : List[Any] = model(__A, noise=__A )
UpperCAmelCase : str = model.get_config()
# make sure that returned config is jsonifiable, which is required by keras
json.dumps(__A )
UpperCAmelCase : int = model_class.from_config(model.get_config() )
# make sure it also accepts a normal config
UpperCAmelCase : str = model_class.from_config(model.config )
UpperCAmelCase : List[str] = new_model(__A ) # Build model
new_model.set_weights(model.get_weights() )
UpperCAmelCase : Tuple = new_model(__A, noise=__A )
self.assert_outputs_same(__A, __A )
@unittest.skip(
reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.''' )
def __magic_name__ ( self : Optional[int] ):
pass
@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load''' )
def __magic_name__ ( self : Tuple ):
pass
@slow
def __magic_name__ ( self : str ):
UpperCAmelCase : Tuple = TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224''' )
self.assertIsNotNone(__A )
def a__ ( ) -> Dict:
UpperCAmelCase : int = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : List[str] ):
return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base''' ) if is_vision_available() else None
@slow
def __magic_name__ ( self : str ):
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
UpperCAmelCase : Tuple = TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base''' )
UpperCAmelCase : List[str] = self.default_image_processor
UpperCAmelCase : Any = prepare_img()
UpperCAmelCase : str = image_processor(images=__A, return_tensors='''tf''' )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
UpperCAmelCase : Optional[int] = ViTMAEConfig()
UpperCAmelCase : int = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
UpperCAmelCase : Tuple = np.random.uniform(size=(1, num_patches) )
# forward pass
UpperCAmelCase : Optional[int] = model(**__A, noise=__A )
# verify the logits
UpperCAmelCase : Union[str, Any] = tf.convert_to_tensor([1, 1_9_6, 7_6_8] )
self.assertEqual(outputs.logits.shape, __A )
UpperCAmelCase : List[str] = tf.convert_to_tensor(
[[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]] )
tf.debugging.assert_near(outputs.logits[0, :3, :3], __A, atol=1E-4 )
| 336 | 1 |
# Copyright 2023 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 typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase : Optional[Any] = {"configuration_mra": ["MRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MraConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[Any] = [
"MRA_PRETRAINED_MODEL_ARCHIVE_LIST",
"MraForMaskedLM",
"MraForMultipleChoice",
"MraForQuestionAnswering",
"MraForSequenceClassification",
"MraForTokenClassification",
"MraLayer",
"MraModel",
"MraPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mra import (
MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraLayer,
MraModel,
MraPreTrainedModel,
)
else:
import sys
_lowerCamelCase : int = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 336 |
def a__ ( UpperCAmelCase : int ) -> int:
UpperCAmelCase : list[list[int]] = [[0 for _ in range(UpperCAmelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
UpperCAmelCase : Optional[Any] = 1
for n in range(m + 1 ):
for k in range(1 , UpperCAmelCase ):
memo[n][k] += memo[n][k - 1]
if n - k > 0:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1]
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
try:
_lowerCamelCase : List[Any] = int(input("Enter a number: ").strip())
print(partition(n))
except ValueError:
print("Please enter a number.")
else:
try:
_lowerCamelCase : str = int(sys.argv[1])
print(partition(n))
except ValueError:
print("Please pass a number.")
| 336 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : Optional[int] = {
"configuration_electra": ["ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "ElectraConfig", "ElectraOnnxConfig"],
"tokenization_electra": ["ElectraTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[Any] = ["ElectraTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : int = [
"ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST",
"ElectraForCausalLM",
"ElectraForMaskedLM",
"ElectraForMultipleChoice",
"ElectraForPreTraining",
"ElectraForQuestionAnswering",
"ElectraForSequenceClassification",
"ElectraForTokenClassification",
"ElectraModel",
"ElectraPreTrainedModel",
"load_tf_weights_in_electra",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : int = [
"TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFElectraForMaskedLM",
"TFElectraForMultipleChoice",
"TFElectraForPreTraining",
"TFElectraForQuestionAnswering",
"TFElectraForSequenceClassification",
"TFElectraForTokenClassification",
"TFElectraModel",
"TFElectraPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Tuple = [
"FlaxElectraForCausalLM",
"FlaxElectraForMaskedLM",
"FlaxElectraForMultipleChoice",
"FlaxElectraForPreTraining",
"FlaxElectraForQuestionAnswering",
"FlaxElectraForSequenceClassification",
"FlaxElectraForTokenClassification",
"FlaxElectraModel",
"FlaxElectraPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
from .tokenization_electra import ElectraTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_electra_fast import ElectraTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_electra import (
ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
ElectraForCausalLM,
ElectraForMaskedLM,
ElectraForMultipleChoice,
ElectraForPreTraining,
ElectraForQuestionAnswering,
ElectraForSequenceClassification,
ElectraForTokenClassification,
ElectraModel,
ElectraPreTrainedModel,
load_tf_weights_in_electra,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_electra import (
TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFElectraForMaskedLM,
TFElectraForMultipleChoice,
TFElectraForPreTraining,
TFElectraForQuestionAnswering,
TFElectraForSequenceClassification,
TFElectraForTokenClassification,
TFElectraModel,
TFElectraPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_electra import (
FlaxElectraForCausalLM,
FlaxElectraForMaskedLM,
FlaxElectraForMultipleChoice,
FlaxElectraForPreTraining,
FlaxElectraForQuestionAnswering,
FlaxElectraForSequenceClassification,
FlaxElectraForTokenClassification,
FlaxElectraModel,
FlaxElectraPreTrainedModel,
)
else:
import sys
_lowerCamelCase : Any = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 336 |
from __future__ import annotations
def a__ ( UpperCAmelCase : list[list[int]] ) -> bool:
UpperCAmelCase : Union[str, Any] = len(UpperCAmelCase )
# We need to create solution object to save path.
UpperCAmelCase : int = [[0 for _ in range(UpperCAmelCase )] for _ in range(UpperCAmelCase )]
UpperCAmelCase : Union[str, Any] = run_maze(UpperCAmelCase , 0 , 0 , UpperCAmelCase )
if solved:
print('''\n'''.join(str(UpperCAmelCase ) for row in solutions ) )
else:
print('''No solution exists!''' )
return solved
def a__ ( UpperCAmelCase : list[list[int]] , UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : list[list[int]] ) -> bool:
UpperCAmelCase : Dict = len(UpperCAmelCase )
# Final check point.
if i == j == (size - 1):
UpperCAmelCase : Dict = 1
return True
UpperCAmelCase : Union[str, Any] = (not i < 0) and (not j < 0) # Check lower bounds
UpperCAmelCase : List[Any] = (i < size) and (j < size) # Check upper bounds
if lower_flag and upper_flag:
# check for already visited and block points.
UpperCAmelCase : Any = (not solutions[i][j]) and (not maze[i][j])
if block_flag:
# check visited
UpperCAmelCase : str = 1
# check for directions
if (
run_maze(UpperCAmelCase , i + 1 , UpperCAmelCase , UpperCAmelCase )
or run_maze(UpperCAmelCase , UpperCAmelCase , j + 1 , UpperCAmelCase )
or run_maze(UpperCAmelCase , i - 1 , UpperCAmelCase , UpperCAmelCase )
or run_maze(UpperCAmelCase , UpperCAmelCase , j - 1 , UpperCAmelCase )
):
return True
UpperCAmelCase : Any = 0
return False
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowerCamelCase : Tuple = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : int = [
"GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
"GPTBigCodeForSequenceClassification",
"GPTBigCodeForTokenClassification",
"GPTBigCodeForCausalLM",
"GPTBigCodeModel",
"GPTBigCodePreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_bigcode import (
GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
GPTBigCodeModel,
GPTBigCodePreTrainedModel,
)
else:
import sys
_lowerCamelCase : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 336 |
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class __UpperCAmelCase :
def __init__( self : List[Any], __A : List[str], __A : List[str]=1_3, __A : Any=6_4, __A : Optional[Any]=2, __A : str=3, __A : str=True, __A : str=True, __A : Optional[Any]=3_2, __A : List[str]=5, __A : int=4, __A : str=3_7, __A : str="gelu", __A : Dict=0.1, __A : List[Any]=0.1, __A : Dict=1_0, __A : int=0.0_2, __A : Any=[1, 1_6, 4, 4], __A : Optional[int]=None, ):
UpperCAmelCase : Union[str, Any] = parent
UpperCAmelCase : Any = batch_size
UpperCAmelCase : List[str] = image_size
UpperCAmelCase : List[str] = patch_size
UpperCAmelCase : Dict = num_channels
UpperCAmelCase : List[Any] = is_training
UpperCAmelCase : Dict = use_labels
UpperCAmelCase : Optional[int] = hidden_size
UpperCAmelCase : Union[str, Any] = num_hidden_layers
UpperCAmelCase : Optional[Any] = num_attention_heads
UpperCAmelCase : Any = intermediate_size
UpperCAmelCase : Any = hidden_act
UpperCAmelCase : Any = hidden_dropout_prob
UpperCAmelCase : Optional[int] = attention_probs_dropout_prob
UpperCAmelCase : str = type_sequence_label_size
UpperCAmelCase : Any = initializer_range
UpperCAmelCase : int = scope
UpperCAmelCase : List[str] = backbone_featmap_shape
# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
# the number of patches is based on the feature map of the backbone, which by default uses an output stride
# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
UpperCAmelCase : str = (self.image_size // 3_2) ** 2
UpperCAmelCase : List[str] = num_patches + 1
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : str = None
if self.use_labels:
UpperCAmelCase : Any = ids_tensor([self.batch_size], self.type_sequence_label_size )
UpperCAmelCase : Optional[int] = self.get_config()
return config, pixel_values, labels
def __magic_name__ ( self : Any ):
UpperCAmelCase : Dict = {
'''global_padding''': '''same''',
'''layer_type''': '''bottleneck''',
'''depths''': [3, 4, 9],
'''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
'''embedding_dynamic_padding''': True,
'''hidden_sizes''': [4, 8, 1_6, 3_2],
'''num_groups''': 2,
}
return ViTHybridConfig(
image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, is_decoder=__A, initializer_range=self.initializer_range, backbone_featmap_shape=self.backbone_featmap_shape, backbone_config=__A, )
def __magic_name__ ( self : Optional[int], __A : Optional[int], __A : int, __A : Tuple ):
UpperCAmelCase : int = ViTHybridModel(config=__A )
model.to(__A )
model.eval()
UpperCAmelCase : Tuple = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self : Tuple, __A : Dict, __A : str, __A : List[str] ):
UpperCAmelCase : str = self.type_sequence_label_size
UpperCAmelCase : List[Any] = ViTHybridForImageClassification(__A )
model.to(__A )
model.eval()
UpperCAmelCase : Dict = model(__A, labels=__A )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size) )
def __magic_name__ ( self : int ):
UpperCAmelCase : Union[str, Any] = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[str] = config_and_inputs
UpperCAmelCase : int = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
UpperCamelCase = (
{"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
if is_torch_available()
else {}
)
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : Any = ViTHybridModelTester(self )
UpperCAmelCase : List[Any] = ConfigTester(self, config_class=__A, has_text_modality=__A, hidden_size=3_7 )
def __magic_name__ ( self : int ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViT does not use inputs_embeds''' )
def __magic_name__ ( self : List[Any] ):
pass
def __magic_name__ ( self : int ):
UpperCAmelCase , UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Dict = model_class(__A )
self.assertIsInstance(model.get_input_embeddings(), (nn.Module) )
UpperCAmelCase : Optional[int] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__A, nn.Linear ) )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase , UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : List[Any] = model_class(__A )
UpperCAmelCase : Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase : str = [*signature.parameters.keys()]
UpperCAmelCase : Optional[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1], __A )
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__A )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase , UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Dict = _config_zero_init(__A )
for model_class in self.all_model_classes:
UpperCAmelCase : Optional[Any] = model_class(config=__A )
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
UpperCAmelCase : Union[str, Any] = [F'''{name}.{key}''' for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item(), [0.0, 1.0], msg=F'''Parameter {name} of model {model_class} seems not properly initialized''', )
@slow
def __magic_name__ ( self : List[str] ):
for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase : Union[str, Any] = ViTHybridModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def a__ ( ) -> Tuple:
UpperCAmelCase : Any = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : str ):
return (
ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : int = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
__A )
UpperCAmelCase : Tuple = self.default_image_processor
UpperCAmelCase : int = prepare_img()
UpperCAmelCase : Union[str, Any] = image_processor(images=__A, return_tensors='''pt''' ).to(__A )
# forward pass
with torch.no_grad():
UpperCAmelCase : Optional[Any] = model(**__A )
# verify the logits
UpperCAmelCase : str = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape, __A )
UpperCAmelCase : Optional[Any] = torch.tensor([-1.9_0_9_0, -0.4_9_9_3, -0.2_3_8_9] ).to(__A )
self.assertTrue(torch.allclose(outputs.logits[0, :3], __A, atol=1E-4 ) )
@slow
@require_accelerate
def __magic_name__ ( self : Dict ):
UpperCAmelCase : Union[str, Any] = ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384''' )
UpperCAmelCase : int = ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384''', device_map='''auto''' )
UpperCAmelCase : Tuple = prepare_img()
UpperCAmelCase : Optional[int] = image_processor(images=__A, return_tensors='''pt''' )
UpperCAmelCase : Dict = model(**__A )
UpperCAmelCase : Any = outputs.logits
# model predicts one of the 1000 ImageNet classes
UpperCAmelCase : Dict = logits.argmax(-1 ).item()
self.assertTrue(model.config.idalabel[predicted_class_idx], '''tabby, tabby cat''' )
| 336 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Dict = logging.get_logger(__name__)
_lowerCamelCase : str = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"
),
}
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """audio-spectrogram-transformer"""
def __init__( self : Dict, __A : Optional[int]=7_6_8, __A : Optional[int]=1_2, __A : Optional[int]=1_2, __A : Tuple=3_0_7_2, __A : str="gelu", __A : Optional[Any]=0.0, __A : int=0.0, __A : List[str]=0.0_2, __A : Optional[Any]=1E-12, __A : Tuple=1_6, __A : str=True, __A : List[str]=1_0, __A : str=1_0, __A : List[Any]=1_0_2_4, __A : List[Any]=1_2_8, **__A : Dict, ):
super().__init__(**__A )
UpperCAmelCase : str = hidden_size
UpperCAmelCase : Any = num_hidden_layers
UpperCAmelCase : str = num_attention_heads
UpperCAmelCase : Dict = intermediate_size
UpperCAmelCase : List[str] = hidden_act
UpperCAmelCase : str = hidden_dropout_prob
UpperCAmelCase : Union[str, Any] = attention_probs_dropout_prob
UpperCAmelCase : Tuple = initializer_range
UpperCAmelCase : Union[str, Any] = layer_norm_eps
UpperCAmelCase : List[str] = patch_size
UpperCAmelCase : List[str] = qkv_bias
UpperCAmelCase : int = frequency_stride
UpperCAmelCase : Optional[int] = time_stride
UpperCAmelCase : Any = max_length
UpperCAmelCase : Optional[Any] = num_mel_bins
| 336 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def a__ ( ) -> tuple[list[int], int]:
UpperCAmelCase : str = [randint(-1_000 , 1_000 ) for i in range(10 )]
UpperCAmelCase : Any = randint(-5_000 , 5_000 )
return (arr, r)
_lowerCamelCase : Any = make_dataset()
def a__ ( UpperCAmelCase : list[int] , UpperCAmelCase : int ) -> tuple[int, ...]:
for triplet in permutations(UpperCAmelCase , 3 ):
if sum(UpperCAmelCase ) == target:
return tuple(sorted(UpperCAmelCase ) )
return (0, 0, 0)
def a__ ( UpperCAmelCase : list[int] , UpperCAmelCase : int ) -> tuple[int, int, int]:
arr.sort()
UpperCAmelCase : Tuple = len(UpperCAmelCase )
for i in range(n - 1 ):
UpperCAmelCase , UpperCAmelCase : int = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def a__ ( ) -> tuple[float, float]:
UpperCAmelCase : Union[str, Any] = '''
from __main__ import dataset, triplet_sum1, triplet_sum2
'''
UpperCAmelCase : Tuple = '''
triplet_sum1(*dataset)
'''
UpperCAmelCase : List[str] = '''
triplet_sum2(*dataset)
'''
UpperCAmelCase : Tuple = repeat(setup=UpperCAmelCase , stmt=UpperCAmelCase , repeat=5 , number=10_000 )
UpperCAmelCase : str = repeat(setup=UpperCAmelCase , stmt=UpperCAmelCase , repeat=5 , number=10_000 )
return (min(UpperCAmelCase ), min(UpperCAmelCase ))
if __name__ == "__main__":
from doctest import testmod
testmod()
_lowerCamelCase : int = solution_times()
print(f"""The time for naive implementation is {times[0]}.""")
print(f"""The time for optimized implementation is {times[1]}.""")
| 336 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_lowerCamelCase : Dict = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[int] = [
"GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
"GPTNeoForCausalLM",
"GPTNeoForQuestionAnswering",
"GPTNeoForSequenceClassification",
"GPTNeoForTokenClassification",
"GPTNeoModel",
"GPTNeoPreTrainedModel",
"load_tf_weights_in_gpt_neo",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Dict = [
"FlaxGPTNeoForCausalLM",
"FlaxGPTNeoModel",
"FlaxGPTNeoPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neo import (
GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoForCausalLM,
GPTNeoForQuestionAnswering,
GPTNeoForSequenceClassification,
GPTNeoForTokenClassification,
GPTNeoModel,
GPTNeoPreTrainedModel,
load_tf_weights_in_gpt_neo,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel
else:
import sys
_lowerCamelCase : Any = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 336 |
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class __UpperCAmelCase :
def __magic_name__ ( self : int, __A : Dict ):
raise NotImplementedError()
def __magic_name__ ( self : int ):
raise NotImplementedError()
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : str, __A : "AutoTokenizer", __A : bool = False, **__A : str ):
UpperCAmelCase : List[str] = tokenizer
UpperCAmelCase : str = skip_prompt
UpperCAmelCase : List[str] = decode_kwargs
# variables used in the streaming process
UpperCAmelCase : Dict = []
UpperCAmelCase : List[str] = 0
UpperCAmelCase : Union[str, Any] = True
def __magic_name__ ( self : Dict, __A : Optional[int] ):
if len(value.shape ) > 1 and value.shape[0] > 1:
raise ValueError('''TextStreamer only supports batch size 1''' )
elif len(value.shape ) > 1:
UpperCAmelCase : Union[str, Any] = value[0]
if self.skip_prompt and self.next_tokens_are_prompt:
UpperCAmelCase : Optional[int] = False
return
# Add the new token to the cache and decodes the entire thing.
self.token_cache.extend(value.tolist() )
UpperCAmelCase : Any = self.tokenizer.decode(self.token_cache, **self.decode_kwargs )
# After the symbol for a new line, we flush the cache.
if text.endswith('''\n''' ):
UpperCAmelCase : Union[str, Any] = text[self.print_len :]
UpperCAmelCase : int = []
UpperCAmelCase : int = 0
# If the last token is a CJK character, we print the characters.
elif len(__A ) > 0 and self._is_chinese_char(ord(text[-1] ) ):
UpperCAmelCase : Union[str, Any] = text[self.print_len :]
self.print_len += len(__A )
# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
# which may change with the subsequent token -- there are probably smarter ways to do this!)
else:
UpperCAmelCase : Optional[Any] = text[self.print_len : text.rfind(''' ''' ) + 1]
self.print_len += len(__A )
self.on_finalized_text(__A )
def __magic_name__ ( self : str ):
# Flush the cache, if it exists
if len(self.token_cache ) > 0:
UpperCAmelCase : int = self.tokenizer.decode(self.token_cache, **self.decode_kwargs )
UpperCAmelCase : Dict = text[self.print_len :]
UpperCAmelCase : List[Any] = []
UpperCAmelCase : List[Any] = 0
else:
UpperCAmelCase : Dict = ''''''
UpperCAmelCase : str = True
self.on_finalized_text(__A, stream_end=__A )
def __magic_name__ ( self : List[str], __A : str, __A : bool = False ):
print(__A, flush=__A, end='''''' if not stream_end else None )
def __magic_name__ ( self : List[Any], __A : Optional[int] ):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0X4E00 and cp <= 0X9FFF)
or (cp >= 0X3400 and cp <= 0X4DBF) #
or (cp >= 0X20000 and cp <= 0X2A6DF) #
or (cp >= 0X2A700 and cp <= 0X2B73F) #
or (cp >= 0X2B740 and cp <= 0X2B81F) #
or (cp >= 0X2B820 and cp <= 0X2CEAF) #
or (cp >= 0XF900 and cp <= 0XFAFF)
or (cp >= 0X2F800 and cp <= 0X2FA1F) #
): #
return True
return False
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : Dict, __A : "AutoTokenizer", __A : bool = False, __A : Optional[float] = None, **__A : str ):
super().__init__(__A, __A, **__A )
UpperCAmelCase : Dict = Queue()
UpperCAmelCase : Any = None
UpperCAmelCase : Any = timeout
def __magic_name__ ( self : Dict, __A : str, __A : bool = False ):
self.text_queue.put(__A, timeout=self.timeout )
if stream_end:
self.text_queue.put(self.stop_signal, timeout=self.timeout )
def __iter__( self : int ):
return self
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : List[Any] = self.text_queue.get(timeout=self.timeout )
if value == self.stop_signal:
raise StopIteration()
else:
return value
| 336 | 1 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : Dict, __A : Union[str, Any], __A : List[str] ):
UpperCAmelCase : int = params
UpperCAmelCase : Dict = np.array(__A )
UpperCAmelCase : Optional[Any] = np.array([len(__A ) for t in data] )
self.check()
self.remove_long_sequences()
self.remove_empty_sequences()
self.remove_unknown_sequences()
self.check()
self.print_statistics()
def __getitem__( self : int, __A : Dict ):
return (self.token_ids[index], self.lengths[index])
def __len__( self : Union[str, Any] ):
return len(self.lengths )
def __magic_name__ ( self : int ):
assert len(self.token_ids ) == len(self.lengths )
assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )
def __magic_name__ ( self : Dict ):
UpperCAmelCase : List[Any] = self.params.max_model_input_size
UpperCAmelCase : Dict = self.lengths > max_len
logger.info(F'''Splitting {sum(__A )} too long sequences.''' )
def divide_chunks(__A : Union[str, Any], __A : int ):
return [l[i : i + n] for i in range(0, len(__A ), __A )]
UpperCAmelCase : Optional[int] = []
UpperCAmelCase : Optional[int] = []
if self.params.mlm:
UpperCAmelCase , UpperCAmelCase : Optional[Any] = self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
else:
UpperCAmelCase , UpperCAmelCase : Optional[int] = self.params.special_tok_ids['''bos_token'''], self.params.special_tok_ids['''eos_token''']
for seq_, len_ in zip(self.token_ids, self.lengths ):
assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
if len_ <= max_len:
new_tok_ids.append(seq_ )
new_lengths.append(len_ )
else:
UpperCAmelCase : Dict = []
for sub_s in divide_chunks(seq_, max_len - 2 ):
if sub_s[0] != cls_id:
UpperCAmelCase : int = np.insert(__A, 0, __A )
if sub_s[-1] != sep_id:
UpperCAmelCase : Union[str, Any] = np.insert(__A, len(__A ), __A )
assert len(__A ) <= max_len
assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
sub_seqs.append(__A )
new_tok_ids.extend(__A )
new_lengths.extend([len(__A ) for l in sub_seqs] )
UpperCAmelCase : str = np.array(__A )
UpperCAmelCase : Optional[int] = np.array(__A )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : Dict = len(self )
UpperCAmelCase : Optional[int] = self.lengths > 1_1
UpperCAmelCase : int = self.token_ids[indices]
UpperCAmelCase : Optional[Any] = self.lengths[indices]
UpperCAmelCase : str = len(self )
logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.''' )
def __magic_name__ ( self : Optional[Any] ):
if "unk_token" not in self.params.special_tok_ids:
return
else:
UpperCAmelCase : Dict = self.params.special_tok_ids['''unk_token''']
UpperCAmelCase : Union[str, Any] = len(self )
UpperCAmelCase : List[Any] = np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
UpperCAmelCase : Tuple = (unk_occs / self.lengths) < 0.5
UpperCAmelCase : Union[str, Any] = self.token_ids[indices]
UpperCAmelCase : str = self.lengths[indices]
UpperCAmelCase : Union[str, Any] = len(self )
logger.info(F'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).''' )
def __magic_name__ ( self : int ):
if not self.params.is_master:
return
logger.info(F'''{len(self )} sequences''' )
# data_len = sum(self.lengths)
# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
# unk_idx = self.params.special_tok_ids['unk_token']
# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
def __magic_name__ ( self : str, __A : Union[str, Any] ):
UpperCAmelCase : Dict = [t[0] for t in batch]
UpperCAmelCase : Any = [t[1] for t in batch]
assert len(__A ) == len(__A )
# Max for paddings
UpperCAmelCase : List[str] = max(__A )
# Pad token ids
if self.params.mlm:
UpperCAmelCase : Tuple = self.params.special_tok_ids['''pad_token''']
else:
UpperCAmelCase : Optional[int] = self.params.special_tok_ids['''unk_token''']
UpperCAmelCase : Any = [list(t.astype(__A ) ) + [pad_idx] * (max_seq_len_ - len(__A )) for t in token_ids]
assert len(tk_ ) == len(__A )
assert all(len(__A ) == max_seq_len_ for t in tk_ )
UpperCAmelCase : Optional[Any] = torch.tensor(tk_ ) # (bs, max_seq_len_)
UpperCAmelCase : int = torch.tensor(__A ) # (bs)
return tk_t, lg_t
| 336 |
import numpy
# List of input, output pairs
_lowerCamelCase : Dict = (
((5, 2, 3), 1_5),
((6, 5, 9), 2_5),
((1_1, 1_2, 1_3), 4_1),
((1, 1, 1), 8),
((1_1, 1_2, 1_3), 4_1),
)
_lowerCamelCase : str = (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
_lowerCamelCase : Dict = [2, 4, 1, 5]
_lowerCamelCase : Dict = len(train_data)
_lowerCamelCase : int = 0.0_0_9
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : Optional[int]="train" ) -> Dict:
return calculate_hypothesis_value(UpperCAmelCase , UpperCAmelCase ) - output(
UpperCAmelCase , UpperCAmelCase )
def a__ ( UpperCAmelCase : int ) -> Any:
UpperCAmelCase : str = 0
for i in range(len(UpperCAmelCase ) - 1 ):
hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
hyp_val += parameter_vector[0]
return hyp_val
def a__ ( UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[Any] ) -> Optional[int]:
if data_set == "train":
return train_data[example_no][1]
elif data_set == "test":
return test_data[example_no][1]
return None
def a__ ( UpperCAmelCase : int , UpperCAmelCase : Optional[Any] ) -> List[str]:
if data_set == "train":
return _hypothesis_value(train_data[example_no][0] )
elif data_set == "test":
return _hypothesis_value(test_data[example_no][0] )
return None
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : str=m ) -> Dict:
UpperCAmelCase : Optional[int] = 0
for i in range(UpperCAmelCase ):
if index == -1:
summation_value += _error(UpperCAmelCase )
else:
summation_value += _error(UpperCAmelCase ) * train_data[i][0][index]
return summation_value
def a__ ( UpperCAmelCase : Dict ) -> Dict:
UpperCAmelCase : Dict = summation_of_cost_derivative(UpperCAmelCase , UpperCAmelCase ) / m
return cost_derivative_value
def a__ ( ) -> List[Any]:
global parameter_vector
# Tune these values to set a tolerance value for predicted output
UpperCAmelCase : List[str] = 0.000002
UpperCAmelCase : Any = 0
UpperCAmelCase : Dict = 0
while True:
j += 1
UpperCAmelCase : List[Any] = [0, 0, 0, 0]
for i in range(0 , len(UpperCAmelCase ) ):
UpperCAmelCase : List[str] = get_cost_derivative(i - 1 )
UpperCAmelCase : Tuple = (
parameter_vector[i] - LEARNING_RATE * cost_derivative
)
if numpy.allclose(
UpperCAmelCase , UpperCAmelCase , atol=UpperCAmelCase , rtol=UpperCAmelCase , ):
break
UpperCAmelCase : int = temp_parameter_vector
print(('''Number of iterations:''', j) )
def a__ ( ) -> List[Any]:
for i in range(len(UpperCAmelCase ) ):
print(('''Actual output value:''', output(UpperCAmelCase , '''test''' )) )
print(('''Hypothesis output:''', calculate_hypothesis_value(UpperCAmelCase , '''test''' )) )
if __name__ == "__main__":
run_gradient_descent()
print("\nTesting gradient descent for a linear hypothesis function.\n")
test_gradient_descent()
| 336 | 1 |
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
def a__ ( UpperCAmelCase : str , UpperCAmelCase : Tuple ) -> Dict:
UpperCAmelCase : Union[str, Any] = b.T
UpperCAmelCase : int = np.sum(np.square(UpperCAmelCase ) , axis=1 )
UpperCAmelCase : str = np.sum(np.square(UpperCAmelCase ) , axis=0 )
UpperCAmelCase : List[str] = np.matmul(UpperCAmelCase , UpperCAmelCase )
UpperCAmelCase : Tuple = aa[:, None] - 2 * ab + ba[None, :]
return d
def a__ ( UpperCAmelCase : int , UpperCAmelCase : Any ) -> List[Any]:
UpperCAmelCase : str = x.reshape(-1 , 3 )
UpperCAmelCase : Any = squared_euclidean_distance(UpperCAmelCase , UpperCAmelCase )
return np.argmin(UpperCAmelCase , axis=1 )
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = ["""pixel_values"""]
def __init__( self : List[str], __A : Optional[Union[List[List[int]], np.ndarray]] = None, __A : bool = True, __A : Dict[str, int] = None, __A : PILImageResampling = PILImageResampling.BILINEAR, __A : bool = True, __A : bool = True, **__A : Dict, ):
super().__init__(**__A )
UpperCAmelCase : Any = size if size is not None else {'''height''': 2_5_6, '''width''': 2_5_6}
UpperCAmelCase : Optional[int] = get_size_dict(__A )
UpperCAmelCase : int = np.array(__A ) if clusters is not None else None
UpperCAmelCase : str = do_resize
UpperCAmelCase : Optional[int] = size
UpperCAmelCase : Optional[Any] = resample
UpperCAmelCase : Dict = do_normalize
UpperCAmelCase : str = do_color_quantize
def __magic_name__ ( self : str, __A : np.ndarray, __A : Dict[str, int], __A : PILImageResampling = PILImageResampling.BILINEAR, __A : Optional[Union[str, ChannelDimension]] = None, **__A : Optional[Any], ):
UpperCAmelCase : Any = get_size_dict(__A )
if "height" not in size or "width" not in size:
raise ValueError(F'''Size dictionary must contain both height and width keys. Got {size.keys()}''' )
return resize(
__A, size=(size['''height'''], size['''width''']), resample=__A, data_format=__A, **__A )
def __magic_name__ ( self : Dict, __A : np.ndarray, __A : Optional[Union[str, ChannelDimension]] = None, ):
UpperCAmelCase : Tuple = rescale(image=__A, scale=1 / 1_2_7.5, data_format=__A )
UpperCAmelCase : Optional[int] = image - 1
return image
def __magic_name__ ( self : List[Any], __A : ImageInput, __A : bool = None, __A : Dict[str, int] = None, __A : PILImageResampling = None, __A : bool = None, __A : Optional[bool] = None, __A : Optional[Union[List[List[int]], np.ndarray]] = None, __A : Optional[Union[str, TensorType]] = None, __A : Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST, **__A : Dict, ):
UpperCAmelCase : Dict = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase : Any = size if size is not None else self.size
UpperCAmelCase : List[str] = get_size_dict(__A )
UpperCAmelCase : Tuple = resample if resample is not None else self.resample
UpperCAmelCase : Union[str, Any] = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase : Tuple = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
UpperCAmelCase : Optional[int] = clusters if clusters is not None else self.clusters
UpperCAmelCase : Any = np.array(__A )
UpperCAmelCase : Optional[Any] = make_list_of_images(__A )
if not valid_images(__A ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None or resample is None:
raise ValueError('''Size and resample must be specified if do_resize is True.''' )
if do_color_quantize and clusters is None:
raise ValueError('''Clusters must be specified if do_color_quantize is True.''' )
# All transformations expect numpy arrays.
UpperCAmelCase : int = [to_numpy_array(__A ) for image in images]
if do_resize:
UpperCAmelCase : Tuple = [self.resize(image=__A, size=__A, resample=__A ) for image in images]
if do_normalize:
UpperCAmelCase : int = [self.normalize(image=__A ) for image in images]
if do_color_quantize:
UpperCAmelCase : str = [to_channel_dimension_format(__A, ChannelDimension.LAST ) for image in images]
# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
UpperCAmelCase : List[str] = np.array(__A )
UpperCAmelCase : Optional[Any] = color_quantize(__A, __A ).reshape(images.shape[:-1] )
# flatten to (batch_size, height*width)
UpperCAmelCase : List[Any] = images.shape[0]
UpperCAmelCase : Tuple = images.reshape(__A, -1 )
# We need to convert back to a list of images to keep consistent behaviour across processors.
UpperCAmelCase : Dict = list(__A )
else:
UpperCAmelCase : Optional[int] = [to_channel_dimension_format(__A, __A ) for image in images]
UpperCAmelCase : Tuple = {'''input_ids''': images}
return BatchFeature(data=__A, tensor_type=__A )
| 336 |
def a__ ( UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[int] ) -> Optional[Any]:
UpperCAmelCase : List[str] = 0
UpperCAmelCase : List[Any] = len(UpperCAmelCase ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
UpperCAmelCase : Optional[int] = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(UpperCAmelCase ):
return None
UpperCAmelCase : Optional[Any] = sorted_collection[point]
if current_item == item:
return point
else:
if point < left:
UpperCAmelCase : Any = left
UpperCAmelCase : List[str] = point
elif point > right:
UpperCAmelCase : Any = right
UpperCAmelCase : List[str] = point
else:
if item < current_item:
UpperCAmelCase : Optional[int] = point - 1
else:
UpperCAmelCase : str = point + 1
return None
def a__ ( UpperCAmelCase : Optional[Any] , UpperCAmelCase : int , UpperCAmelCase : str , UpperCAmelCase : Union[str, Any] ) -> Dict:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
UpperCAmelCase : List[str] = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(UpperCAmelCase ):
return None
if sorted_collection[point] == item:
return point
elif point < left:
return interpolation_search_by_recursion(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
elif point > right:
return interpolation_search_by_recursion(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
else:
if sorted_collection[point] > item:
return interpolation_search_by_recursion(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , point - 1 )
else:
return interpolation_search_by_recursion(
UpperCAmelCase , UpperCAmelCase , point + 1 , UpperCAmelCase )
def a__ ( UpperCAmelCase : Union[str, Any] ) -> int:
if collection != sorted(UpperCAmelCase ):
raise ValueError('''Collection must be ascending sorted''' )
return True
if __name__ == "__main__":
import sys
_lowerCamelCase : Optional[int] = 0
if debug == 1:
_lowerCamelCase : Dict = [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
try:
__assert_sorted(collection)
except ValueError:
sys.exit("Sequence must be ascending sorted to apply interpolation search")
_lowerCamelCase : List[Any] = 6_7
_lowerCamelCase : Optional[Any] = interpolation_search(collection, target)
if result is not None:
print(f"""{target} found at positions: {result}""")
else:
print("Not found")
| 336 | 1 |
import os
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 logging
_lowerCamelCase : str = logging.get_logger(__name__)
_lowerCamelCase : int = {"vocab_file": "sentencepiece.bpe.model"}
_lowerCamelCase : List[Any] = {
"vocab_file": {
"camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
}
}
_lowerCamelCase : Optional[Any] = {
"camembert-base": 5_1_2,
}
_lowerCamelCase : Any = "▁"
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = VOCAB_FILES_NAMES
UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase = ["""input_ids""", """attention_mask"""]
def __init__( self : int, __A : List[Any], __A : str="<s>", __A : str="</s>", __A : List[Any]="</s>", __A : Any="<s>", __A : Optional[int]="<unk>", __A : str="<pad>", __A : str="<mask>", __A : Union[str, Any]=["<s>NOTUSED", "</s>NOTUSED"], __A : Optional[Dict[str, Any]] = None, **__A : Union[str, Any], ):
# Mask token behave like a normal word, i.e. include the space before it
UpperCAmelCase : Optional[int] = AddedToken(__A, lstrip=__A, rstrip=__A ) if isinstance(__A, __A ) else mask_token
UpperCAmelCase : Any = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=__A, eos_token=__A, unk_token=__A, sep_token=__A, cls_token=__A, pad_token=__A, mask_token=__A, additional_special_tokens=__A, sp_model_kwargs=self.sp_model_kwargs, **__A, )
UpperCAmelCase : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(__A ) )
UpperCAmelCase : str = vocab_file
# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
# sentencepiece vocabulary (this is the case for <s> and </s>
UpperCAmelCase : Optional[int] = {'''<s>NOTUSED''': 0, '''<pad>''': 1, '''</s>NOTUSED''': 2, '''<unk>''': 3}
UpperCAmelCase : Any = len(self.fairseq_tokens_to_ids )
UpperCAmelCase : str = len(self.sp_model ) + len(self.fairseq_tokens_to_ids )
UpperCAmelCase : Tuple = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __magic_name__ ( self : List[Any], __A : List[int], __A : Optional[List[int]] = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
UpperCAmelCase : str = [self.cls_token_id]
UpperCAmelCase : Optional[Any] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def __magic_name__ ( self : Dict, __A : List[int], __A : Optional[List[int]] = None, __A : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__A, token_ids_a=__A, already_has_special_tokens=__A )
if token_ids_a is None:
return [1] + ([0] * len(__A )) + [1]
return [1] + ([0] * len(__A )) + [1, 1] + ([0] * len(__A )) + [1]
def __magic_name__ ( self : Tuple, __A : List[int], __A : Optional[List[int]] = None ):
UpperCAmelCase : Tuple = [self.sep_token_id]
UpperCAmelCase : Optional[int] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def __magic_name__ ( self : List[str] ):
return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : Optional[Any] = {self.convert_ids_to_tokens(__A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __magic_name__ ( self : Optional[int], __A : str ):
return self.sp_model.encode(__A, out_type=__A )
def __magic_name__ ( self : Dict, __A : List[Any] ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
elif self.sp_model.PieceToId(__A ) == 0:
# Convert sentence piece unk token to fairseq unk token index
return self.unk_token_id
return self.fairseq_offset + self.sp_model.PieceToId(__A )
def __magic_name__ ( self : Dict, __A : List[str] ):
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def __magic_name__ ( self : int, __A : List[Any] ):
UpperCAmelCase : str = []
UpperCAmelCase : Tuple = ''''''
UpperCAmelCase : int = 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(__A ) + token
UpperCAmelCase : Optional[Any] = True
UpperCAmelCase : Optional[Any] = []
else:
current_sub_tokens.append(__A )
UpperCAmelCase : Any = False
out_string += self.sp_model.decode(__A )
return out_string.strip()
def __getstate__( self : int ):
UpperCAmelCase : List[str] = self.__dict__.copy()
UpperCAmelCase : Tuple = None
return state
def __setstate__( self : Tuple, __A : Optional[int] ):
UpperCAmelCase : Dict = d
# for backward compatibility
if not hasattr(self, '''sp_model_kwargs''' ):
UpperCAmelCase : int = {}
UpperCAmelCase : List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __magic_name__ ( self : Any, __A : str, __A : Optional[str] = None ):
if not os.path.isdir(__A ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCAmelCase : List[str] = os.path.join(
__A, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file, __A )
elif not os.path.isfile(self.vocab_file ):
with open(__A, '''wb''' ) as fi:
UpperCAmelCase : Optional[int] = self.sp_model.serialized_model_proto()
fi.write(__A )
return (out_vocab_file,)
| 336 |
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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase : Any = logging.get_logger(__name__)
def a__ ( UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[Any]=False , UpperCAmelCase : List[str]=False ) -> Any:
UpperCAmelCase : Optional[int] = '''backbone.''' if is_semantic else ''''''
UpperCAmelCase : Dict = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f'''{prefix}blocks.{i}.norm1.weight''', f'''beit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((f'''{prefix}blocks.{i}.norm1.bias''', f'''beit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(f'''{prefix}blocks.{i}.attn.proj.weight''', f'''beit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append(
(f'''{prefix}blocks.{i}.attn.proj.bias''', f'''beit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((f'''{prefix}blocks.{i}.norm2.weight''', f'''beit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((f'''{prefix}blocks.{i}.norm2.bias''', f'''beit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.weight''', f'''beit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.bias''', f'''beit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.weight''', f'''beit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.bias''', f'''beit.encoder.layer.{i}.output.dense.bias''') )
# projection layer + position embeddings
rename_keys.extend(
[
(f'''{prefix}cls_token''', '''beit.embeddings.cls_token'''),
(f'''{prefix}patch_embed.proj.weight''', '''beit.embeddings.patch_embeddings.projection.weight'''),
(f'''{prefix}patch_embed.proj.bias''', '''beit.embeddings.patch_embeddings.projection.bias'''),
(f'''{prefix}pos_embed''', '''beit.embeddings.position_embeddings'''),
] )
if has_lm_head:
# mask token + layernorm
rename_keys.extend(
[
('''mask_token''', '''beit.embeddings.mask_token'''),
('''norm.weight''', '''layernorm.weight'''),
('''norm.bias''', '''layernorm.bias'''),
] )
else:
# layernorm + classification head
rename_keys.extend(
[
('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
('''head.weight''', '''classifier.weight'''),
('''head.bias''', '''classifier.bias'''),
] )
return rename_keys
def a__ ( UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str , UpperCAmelCase : str=False , UpperCAmelCase : Dict=False ) -> Any:
for i in range(config.num_hidden_layers ):
UpperCAmelCase : Tuple = '''backbone.''' if is_semantic else ''''''
# queries, keys and values
UpperCAmelCase : Optional[Any] = state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight''' )
UpperCAmelCase : Optional[Any] = state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias''' )
UpperCAmelCase : List[Any] = state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias''' )
UpperCAmelCase : Union[str, Any] = in_proj_weight[
: config.hidden_size, :
]
UpperCAmelCase : str = q_bias
UpperCAmelCase : List[str] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
UpperCAmelCase : List[str] = in_proj_weight[
-config.hidden_size :, :
]
UpperCAmelCase : int = v_bias
# gamma_1 and gamma_2
# we call them lambda because otherwise they are renamed when using .from_pretrained
UpperCAmelCase : int = state_dict.pop(f'''{prefix}blocks.{i}.gamma_1''' )
UpperCAmelCase : Optional[Any] = state_dict.pop(f'''{prefix}blocks.{i}.gamma_2''' )
UpperCAmelCase : str = gamma_a
UpperCAmelCase : Dict = gamma_a
def a__ ( UpperCAmelCase : Optional[int] , UpperCAmelCase : List[Any] , UpperCAmelCase : Tuple ) -> Optional[Any]:
UpperCAmelCase : Union[str, Any] = dct.pop(UpperCAmelCase )
UpperCAmelCase : str = val
def a__ ( ) -> Optional[int]:
UpperCAmelCase : List[Any] = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
UpperCAmelCase : Union[str, Any] = Image.open(requests.get(UpperCAmelCase , stream=UpperCAmelCase ).raw )
return im
@torch.no_grad()
def a__ ( UpperCAmelCase : Optional[int] , UpperCAmelCase : Tuple , UpperCAmelCase : List[Any]=False ) -> Union[str, Any]:
UpperCAmelCase : Dict = False if '''rvlcdip''' in checkpoint_url else True
UpperCAmelCase : Any = BeitConfig(use_absolute_position_embeddings=UpperCAmelCase , use_mask_token=UpperCAmelCase )
# size of the architecture
if "large" in checkpoint_url or "dit-l" in checkpoint_url:
UpperCAmelCase : List[Any] = 1_024
UpperCAmelCase : Optional[Any] = 4_096
UpperCAmelCase : Any = 24
UpperCAmelCase : Union[str, Any] = 16
# labels
if "rvlcdip" in checkpoint_url:
UpperCAmelCase : Optional[Any] = 16
UpperCAmelCase : List[Any] = '''huggingface/label-files'''
UpperCAmelCase : Any = '''rvlcdip-id2label.json'''
UpperCAmelCase : List[str] = json.load(open(hf_hub_download(UpperCAmelCase , UpperCAmelCase , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase : Dict = {int(UpperCAmelCase ): v for k, v in idalabel.items()}
UpperCAmelCase : Union[str, Any] = idalabel
UpperCAmelCase : Tuple = {v: k for k, v in idalabel.items()}
# load state_dict of original model, remove and rename some keys
UpperCAmelCase : Tuple = torch.hub.load_state_dict_from_url(UpperCAmelCase , map_location='''cpu''' )['''model''']
UpperCAmelCase : List[str] = create_rename_keys(UpperCAmelCase , has_lm_head=UpperCAmelCase )
for src, dest in rename_keys:
rename_key(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
read_in_q_k_v(UpperCAmelCase , UpperCAmelCase , has_lm_head=UpperCAmelCase )
# load HuggingFace model
UpperCAmelCase : Tuple = BeitForMaskedImageModeling(UpperCAmelCase ) if has_lm_head else BeitForImageClassification(UpperCAmelCase )
model.eval()
model.load_state_dict(UpperCAmelCase )
# Check outputs on an image
UpperCAmelCase : Dict = BeitImageProcessor(
size=config.image_size , resample=PILImageResampling.BILINEAR , do_center_crop=UpperCAmelCase )
UpperCAmelCase : List[str] = prepare_img()
UpperCAmelCase : Optional[Any] = image_processor(images=UpperCAmelCase , return_tensors='''pt''' )
UpperCAmelCase : str = encoding['''pixel_values''']
UpperCAmelCase : Any = model(UpperCAmelCase )
UpperCAmelCase : Optional[Any] = outputs.logits
# verify logits
UpperCAmelCase : List[Any] = [1, 16] if '''rvlcdip''' in checkpoint_url else [1, 196, 8_192]
assert logits.shape == torch.Size(UpperCAmelCase ), "Shape of logits not as expected"
Path(UpperCAmelCase ).mkdir(exist_ok=UpperCAmelCase )
print(f'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(UpperCAmelCase )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(UpperCAmelCase )
if push_to_hub:
if has_lm_head:
UpperCAmelCase : List[Any] = '''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
else:
UpperCAmelCase : Any = '''dit-base-finetuned-rvlcdip''' if '''dit-b''' in checkpoint_url else '''dit-large-finetuned-rvlcdip'''
image_processor.push_to_hub(
repo_path_or_name=Path(UpperCAmelCase , UpperCAmelCase ) , organization='''nielsr''' , commit_message='''Add image processor''' , use_temp_dir=UpperCAmelCase , )
model.push_to_hub(
repo_path_or_name=Path(UpperCAmelCase , UpperCAmelCase ) , organization='''nielsr''' , commit_message='''Add model''' , use_temp_dir=UpperCAmelCase , )
if __name__ == "__main__":
_lowerCamelCase : Tuple = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_url",
default="https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth",
type=str,
help="URL to the original PyTorch checkpoint (.pth file).",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
parser.add_argument(
"--push_to_hub",
action="store_true",
)
_lowerCamelCase : Optional[int] = parser.parse_args()
convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 336 | 1 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def a__ ( UpperCAmelCase : Optional[Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : Union[str, Any] ) -> List[str]:
# Initialise PyTorch model
UpperCAmelCase : Union[str, Any] = BigBirdConfig.from_json_file(UpperCAmelCase )
print(f'''Building PyTorch model from configuration: {config}''' )
if is_trivia_qa:
UpperCAmelCase : str = BigBirdForQuestionAnswering(UpperCAmelCase )
else:
UpperCAmelCase : str = BigBirdForPreTraining(UpperCAmelCase )
# Load weights from tf checkpoint
load_tf_weights_in_big_bird(UpperCAmelCase , UpperCAmelCase , is_trivia_qa=UpperCAmelCase )
# Save pytorch-model
print(f'''Save PyTorch model to {pytorch_dump_path}''' )
model.save_pretrained(UpperCAmelCase )
if __name__ == "__main__":
_lowerCamelCase : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--big_bird_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained BERT model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--is_trivia_qa", action="store_true", help="Whether to convert a model with a trivia_qa head."
)
_lowerCamelCase : int = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
)
| 336 |
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 : Optional[int], __A : Optional[int], __A : Any=1_3, __A : str=7, __A : Optional[int]=True, __A : Tuple=True, __A : Union[str, Any]=True, __A : Any=True, __A : Optional[int]=9_9, __A : Tuple=3_2, __A : str=5, __A : Union[str, Any]=4, __A : List[str]=3_7, __A : Tuple="gelu", __A : Optional[int]=0.1, __A : int=0.1, __A : Optional[Any]=5_1_2, __A : int=1_6, __A : Optional[Any]=2, __A : Union[str, Any]=0.0_2, __A : Optional[int]=4, ):
UpperCAmelCase : Any = parent
UpperCAmelCase : List[Any] = batch_size
UpperCAmelCase : Any = seq_length
UpperCAmelCase : Tuple = is_training
UpperCAmelCase : str = use_attention_mask
UpperCAmelCase : List[str] = use_token_type_ids
UpperCAmelCase : int = use_labels
UpperCAmelCase : List[Any] = vocab_size
UpperCAmelCase : Optional[int] = hidden_size
UpperCAmelCase : str = num_hidden_layers
UpperCAmelCase : Dict = num_attention_heads
UpperCAmelCase : Tuple = intermediate_size
UpperCAmelCase : List[str] = hidden_act
UpperCAmelCase : str = hidden_dropout_prob
UpperCAmelCase : int = attention_probs_dropout_prob
UpperCAmelCase : List[Any] = max_position_embeddings
UpperCAmelCase : Optional[Any] = type_vocab_size
UpperCAmelCase : Any = type_sequence_label_size
UpperCAmelCase : Optional[Any] = initializer_range
UpperCAmelCase : Any = num_choices
def __magic_name__ ( self : str ):
UpperCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
UpperCAmelCase : List[Any] = None
if self.use_attention_mask:
UpperCAmelCase : Any = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase : Any = None
if self.use_token_type_ids:
UpperCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size )
UpperCAmelCase : Union[str, Any] = 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=__A, initializer_range=self.initializer_range, )
return config, input_ids, token_type_ids, attention_mask
def __magic_name__ ( self : int ):
UpperCAmelCase : Any = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[Any] = config_and_inputs
UpperCAmelCase : Dict = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : List[Any] = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Dict = config_and_inputs
UpperCAmelCase : Any = True
UpperCAmelCase : Union[str, Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
UpperCAmelCase : List[Any] = 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 ( lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = True
UpperCamelCase = (
(
FlaxRobertaModel,
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __magic_name__ ( self : Optional[Any] ):
UpperCAmelCase : Dict = FlaxRobertaModelTester(self )
@slow
def __magic_name__ ( self : Any ):
for model_class_name in self.all_model_classes:
UpperCAmelCase : Dict = model_class_name.from_pretrained('''roberta-base''', from_pt=__A )
UpperCAmelCase : List[str] = model(np.ones((1, 1) ) )
self.assertIsNotNone(__A )
| 336 | 1 |
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