code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'configuration_time_series_transformer': [
'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TimeSeriesTransformerConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TimeSeriesTransformerForPrediction',
'TimeSeriesTransformerModel',
'TimeSeriesTransformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TimeSeriesTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimeSeriesTransformerForPrediction,
TimeSeriesTransformerModel,
TimeSeriesTransformerPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 29 |
import logging
from transformers import PretrainedConfig
a__ = logging.getLogger(__name__)
a__ = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Any = "bertabs"
def __init__( self , _a=3_0_5_2_2 , _a=5_1_2 , _a=6 , _a=5_1_2 , _a=8 , _a=5_1_2 , _a=0.2 , _a=6 , _a=7_6_8 , _a=8 , _a=2_0_4_8 , _a=0.2 , **_a , ) -> Any:
super().__init__(**_a )
_a : int = vocab_size
_a : List[str] = max_pos
_a : Tuple = enc_layers
_a : Optional[Any] = enc_hidden_size
_a : int = enc_heads
_a : Optional[Any] = enc_ff_size
_a : List[str] = enc_dropout
_a : Tuple = dec_layers
_a : Optional[Any] = dec_hidden_size
_a : Optional[Any] = dec_heads
_a : Optional[Any] = dec_ff_size
_a : List[Any] = dec_dropout
| 235 | 0 |
'''simple docstring'''
def __lowerCamelCase ( A__ , A__ ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(0 ) != 0 )
def __lowerCamelCase ( ) -> None:
"""simple docstring"""
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ) == 1
assert nand_gate(1 , 0 ) == 1
assert nand_gate(1 , 1 ) == 0
if __name__ == "__main__":
print(nand_gate(0, 0))
print(nand_gate(0, 1))
print(nand_gate(1, 0))
print(nand_gate(1, 1))
| 357 |
'''simple docstring'''
def __lowerCamelCase ( A__ , A__ ) -> float:
"""simple docstring"""
if density <= 0:
raise ValueError('Impossible fluid density' )
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus' )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import doctest
doctest.testmod()
| 249 | 0 |
"""simple docstring"""
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class _UpperCamelCase :
'''simple docstring'''
def __init__( self , __a , __a=2 , __a=8 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=16 , __a=5 , __a=2 , __a=36 , __a="gelu" , __a=0.0 , __a=0.0 , __a=5_12 , __a=16 , __a=2 , __a=0.0_2 , __a=3 , __a=4 , __a=None , ):
__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 = 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 snake_case ( self ):
__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 snake_case ( self ):
return MraConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__a , initializer_range=self.initializer_range , )
def snake_case ( self ):
__lowerCAmelCase = self.get_config()
__lowerCAmelCase = 3_00
return config
def snake_case ( self ):
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) = self.prepare_config_and_inputs()
__lowerCAmelCase = True
__lowerCAmelCase = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def snake_case ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCAmelCase = MraModel(config=__a )
model.to(__a )
model.eval()
__lowerCAmelCase = model(__a , attention_mask=__a , token_type_ids=__a )
__lowerCAmelCase = model(__a , token_type_ids=__a )
__lowerCAmelCase = model(__a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case ( self , __a , __a , __a , __a , __a , __a , __a , __a , __a , ):
__lowerCAmelCase = True
__lowerCAmelCase = MraModel(__a )
model.to(__a )
model.eval()
__lowerCAmelCase = model(
__a , attention_mask=__a , token_type_ids=__a , encoder_hidden_states=__a , encoder_attention_mask=__a , )
__lowerCAmelCase = model(
__a , attention_mask=__a , token_type_ids=__a , encoder_hidden_states=__a , )
__lowerCAmelCase = model(__a , attention_mask=__a , token_type_ids=__a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCAmelCase = MraForMaskedLM(config=__a )
model.to(__a )
model.eval()
__lowerCAmelCase = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCAmelCase = MraForQuestionAnswering(config=__a )
model.to(__a )
model.eval()
__lowerCAmelCase = model(
__a , attention_mask=__a , token_type_ids=__a , start_positions=__a , end_positions=__a , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def snake_case ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCAmelCase = self.num_labels
__lowerCAmelCase = MraForSequenceClassification(__a )
model.to(__a )
model.eval()
__lowerCAmelCase = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def snake_case ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCAmelCase = self.num_labels
__lowerCAmelCase = MraForTokenClassification(config=__a )
model.to(__a )
model.eval()
__lowerCAmelCase = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def snake_case ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCAmelCase = self.num_choices
__lowerCAmelCase = MraForMultipleChoice(config=__a )
model.to(__a )
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(
__a , attention_mask=__a , token_type_ids=__a , labels=__a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def snake_case ( self ):
__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 _UpperCamelCase ( lowerCAmelCase__ ,unittest.TestCase ):
'''simple docstring'''
__UpperCAmelCase : List[Any] =(
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
__UpperCAmelCase : str =False
__UpperCAmelCase : int =False
__UpperCAmelCase : Optional[Any] =False
__UpperCAmelCase : Tuple =False
__UpperCAmelCase : List[Any] =()
def snake_case ( self ):
__lowerCAmelCase = MraModelTester(self )
__lowerCAmelCase = ConfigTester(self , config_class=__a , hidden_size=37 )
def snake_case ( self ):
self.config_tester.run_common_tests()
def snake_case ( self ):
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a )
def snake_case ( self ):
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__lowerCAmelCase = type
self.model_tester.create_and_check_model(*__a )
def snake_case ( self ):
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__a )
def snake_case ( self ):
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__a )
def snake_case ( self ):
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__a )
def snake_case ( self ):
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__a )
def snake_case ( self ):
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__a )
@slow
def snake_case ( self ):
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase = MraModel.from_pretrained(__a )
self.assertIsNotNone(__a )
@unittest.skip(reason="MRA does not output attentions" )
def snake_case ( self ):
return
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def snake_case ( self ):
__lowerCAmelCase = MraModel.from_pretrained("uw-madison/mra-base-512-4" )
__lowerCAmelCase = torch.arange(2_56 ).unsqueeze(0 )
with torch.no_grad():
__lowerCAmelCase = model(__a )[0]
__lowerCAmelCase = torch.Size((1, 2_56, 7_68) )
self.assertEqual(output.shape , __a )
__lowerCAmelCase = torch.tensor(
[[[-0.0_1_4_0, 0.0_8_3_0, -0.0_3_8_1], [0.1_5_4_6, 0.1_4_0_2, 0.0_2_2_0], [0.1_1_6_2, 0.0_8_5_1, 0.0_1_6_5]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __a , atol=1e-4 ) )
@slow
def snake_case ( self ):
__lowerCAmelCase = MraForMaskedLM.from_pretrained("uw-madison/mra-base-512-4" )
__lowerCAmelCase = torch.arange(2_56 ).unsqueeze(0 )
with torch.no_grad():
__lowerCAmelCase = model(__a )[0]
__lowerCAmelCase = 5_02_65
__lowerCAmelCase = torch.Size((1, 2_56, vocab_size) )
self.assertEqual(output.shape , __a )
__lowerCAmelCase = torch.tensor(
[[[9.2_5_9_5, -3.6_0_3_8, 1_1.8_8_1_9], [9.3_8_6_9, -3.2_6_9_3, 1_1.0_9_5_6], [1_1.8_5_2_4, -3.4_9_3_8, 1_3.1_2_1_0]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __a , atol=1e-4 ) )
@slow
def snake_case ( self ):
__lowerCAmelCase = MraForMaskedLM.from_pretrained("uw-madison/mra-base-4096-8-d3" )
__lowerCAmelCase = torch.arange(40_96 ).unsqueeze(0 )
with torch.no_grad():
__lowerCAmelCase = model(__a )[0]
__lowerCAmelCase = 5_02_65
__lowerCAmelCase = torch.Size((1, 40_96, vocab_size) )
self.assertEqual(output.shape , __a )
__lowerCAmelCase = torch.tensor(
[[[5.4_7_8_9, -2.3_5_6_4, 7.5_0_6_4], [7.9_0_6_7, -1.3_3_6_9, 9.9_6_6_8], [9.0_7_1_2, -1.8_1_0_6, 7.0_3_8_0]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __a , atol=1e-4 ) )
| 57 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
A : Any = "examples/"
A : Optional[Any] = {
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(R"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
"setup": (re.compile(R"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), R"\1version=\"VERSION\","),
"doc": (re.compile(R"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
A : Optional[int] = {
"init": "src/transformers/__init__.py",
"setup": "setup.py",
}
A : List[Any] = "README.md"
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
with open(_UpperCamelCase , "r" , encoding="utf-8" , newline="\n" ) as f:
__lowerCAmelCase = f.read()
__lowerCAmelCase , __lowerCAmelCase = REPLACE_PATTERNS[pattern]
__lowerCAmelCase = replace.replace("VERSION" , _UpperCamelCase )
__lowerCAmelCase = re_pattern.sub(_UpperCamelCase , _UpperCamelCase )
with open(_UpperCamelCase , "w" , encoding="utf-8" , newline="\n" ) as f:
f.write(_UpperCamelCase )
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
for folder, directories, fnames in os.walk(_UpperCamelCase ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove("research_projects" )
if "legacy" in directories:
directories.remove("legacy" )
for fname in fnames:
if fname.endswith(".py" ):
update_version_in_file(os.path.join(_UpperCamelCase , _UpperCamelCase ) , _UpperCamelCase , pattern="examples" )
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase=False ):
'''simple docstring'''
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
if not patch:
update_version_in_examples(_UpperCamelCase )
def _lowerCamelCase ( ):
'''simple docstring'''
__lowerCAmelCase = "🤗 Transformers currently provides the following architectures"
__lowerCAmelCase = "1. Want to contribute a new model?"
with open(_UpperCamelCase , "r" , encoding="utf-8" , newline="\n" ) as f:
__lowerCAmelCase = f.readlines()
# Find the start of the list.
__lowerCAmelCase = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
__lowerCAmelCase = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith("1." ):
__lowerCAmelCase = lines[index].replace(
"https://huggingface.co/docs/transformers/main/model_doc" , "https://huggingface.co/docs/transformers/model_doc" , )
index += 1
with open(_UpperCamelCase , "w" , encoding="utf-8" , newline="\n" ) as f:
f.writelines(_UpperCamelCase )
def _lowerCamelCase ( ):
'''simple docstring'''
with open(REPLACE_FILES["init"] , "r" ) as f:
__lowerCAmelCase = f.read()
__lowerCAmelCase = REPLACE_PATTERNS["init"][0].search(_UpperCamelCase ).groups()[0]
return packaging.version.parse(_UpperCamelCase )
def _lowerCamelCase ( _UpperCamelCase=False ):
'''simple docstring'''
__lowerCAmelCase = get_version()
if patch and default_version.is_devrelease:
raise ValueError("Can't create a patch version from the dev branch, checkout a released version!" )
if default_version.is_devrelease:
__lowerCAmelCase = default_version.base_version
elif patch:
__lowerCAmelCase = f"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
else:
__lowerCAmelCase = f"{default_version.major}.{default_version.minor + 1}.0"
# Now let's ask nicely if that's the right one.
__lowerCAmelCase = input(f"Which version are you releasing? [{default_version}]" )
if len(_UpperCamelCase ) == 0:
__lowerCAmelCase = default_version
print(f"Updating version to {version}." )
global_version_update(_UpperCamelCase , patch=_UpperCamelCase )
if not patch:
print("Cleaning main README, don't forget to run `make fix-copies`." )
clean_main_ref_in_model_list()
def _lowerCamelCase ( ):
'''simple docstring'''
__lowerCAmelCase = get_version()
__lowerCAmelCase = f"{current_version.major}.{current_version.minor + 1}.0.dev0"
__lowerCAmelCase = current_version.base_version
# Check with the user we got that right.
__lowerCAmelCase = input(f"Which version are we developing now? [{dev_version}]" )
if len(_UpperCamelCase ) == 0:
__lowerCAmelCase = dev_version
print(f"Updating version to {version}." )
global_version_update(_UpperCamelCase )
print("Cleaning main README, don't forget to run `make fix-copies`." )
clean_main_ref_in_model_list()
if __name__ == "__main__":
A : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
A : Dict = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print("Nothing to do after a patch :-)")
else:
post_release_work()
| 57 | 1 |
'''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
UpperCAmelCase = '\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n and Jeffrey Dean},\n year={2016},\n eprint={1609.08144},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n'
UpperCAmelCase = '\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe \'GLEU score\'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore\'s range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n'
UpperCAmelCase = '\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n predictions (list of str): list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references (list of list of str): list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n \'google_bleu\': google_bleu score\n\nExamples:\n Example 1:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results["google_bleu"], 2))\n 0.44\n\n Example 2:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results["google_bleu"], 2))\n 0.61\n\n Example 3:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n >>> print(round(results["google_bleu"], 2))\n 0.53\n\n Example 4:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n >>> print(round(results["google_bleu"], 2))\n 0.4\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __snake_case( datasets.Metric ):
'''simple docstring'''
def __snake_case ( self ) -> MetricInfo:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Sequence(datasets.Value("""string""" , id="""token""" ) , id="""sequence""" ),
"""references""": datasets.Sequence(
datasets.Sequence(datasets.Value("""string""" , id="""token""" ) , id="""sequence""" ) , id="""references""" ),
} ) , )
def __snake_case ( self , A_ , A_ , A_ = 1 , A_ = 4 , ) -> Dict[str, float]:
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=A_ , hypotheses=A_ , min_len=A_ , max_len=A_ )
} | 351 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = '▁'
UpperCAmelCase = {
'vocab_file': 'vocab.json',
'spm_file': 'sentencepiece.bpe.model',
'tokenizer_config_file': 'tokenizer_config.json',
}
UpperCAmelCase = {
'vocab_file': {
'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json',
'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json',
},
'spm_file': {
'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model',
'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model',
},
'tokenizer_config_file': {
'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json',
'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json',
},
}
UpperCAmelCase = {
'facebook/m2m100_418M': 1024,
}
# fmt: off
UpperCAmelCase = {
'm2m100': ['af', 'am', 'ar', 'ast', 'az', 'ba', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'ceb', 'cs', 'cy', 'da', 'de', 'el', 'en', 'es', 'et', 'fa', 'ff', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'ht', 'hu', 'hy', 'id', 'ig', 'ilo', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'lb', 'lg', 'ln', 'lo', 'lt', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'ne', 'nl', 'no', 'ns', 'oc', 'or', 'pa', 'pl', 'ps', 'pt', 'ro', 'ru', 'sd', 'si', 'sk', 'sl', 'so', 'sq', 'sr', 'ss', 'su', 'sv', 'sw', 'ta', 'th', 'tl', 'tn', 'tr', 'uk', 'ur', 'uz', 'vi', 'wo', 'xh', 'yi', 'yo', 'zh', 'zu'],
'wmt21': ['en', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de']
}
class __snake_case( _lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Optional[int] = VOCAB_FILES_NAMES
UpperCAmelCase : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase : int = ["input_ids", "attention_mask"]
UpperCAmelCase : List[int] = []
UpperCAmelCase : List[int] = []
def __init__( self , A_ , A_ , A_=None , A_=None , A_="<s>" , A_="</s>" , A_="</s>" , A_="<pad>" , A_="<unk>" , A_="m2m100" , A_ = None , A_=8 , **A_ , ) -> None:
lowerCAmelCase = {} if sp_model_kwargs is None else sp_model_kwargs
lowerCAmelCase = language_codes
lowerCAmelCase = FAIRSEQ_LANGUAGE_CODES[language_codes]
lowerCAmelCase = {lang_code: f'__{lang_code}__' for lang_code in fairseq_language_code}
lowerCAmelCase = kwargs.get("""additional_special_tokens""" , [] )
kwargs["additional_special_tokens"] += [
self.get_lang_token(A_ )
for lang_code in fairseq_language_code
if self.get_lang_token(A_ ) not in kwargs["additional_special_tokens"]
]
super().__init__(
src_lang=A_ , tgt_lang=A_ , bos_token=A_ , eos_token=A_ , sep_token=A_ , unk_token=A_ , pad_token=A_ , language_codes=A_ , sp_model_kwargs=self.sp_model_kwargs , num_madeup_words=A_ , **A_ , )
lowerCAmelCase = vocab_file
lowerCAmelCase = load_json(A_ )
lowerCAmelCase = {v: k for k, v in self.encoder.items()}
lowerCAmelCase = spm_file
lowerCAmelCase = load_spm(A_ , self.sp_model_kwargs )
lowerCAmelCase = len(self.encoder )
lowerCAmelCase = {
self.get_lang_token(A_ ): self.encoder_size + i for i, lang_code in enumerate(A_ )
}
lowerCAmelCase = {lang_code: self.encoder_size + i for i, lang_code in enumerate(A_ )}
lowerCAmelCase = {v: k for k, v in self.lang_token_to_id.items()}
lowerCAmelCase = src_lang if src_lang is not None else """en"""
lowerCAmelCase = tgt_lang
lowerCAmelCase = self.get_lang_id(self._src_lang )
self.set_src_lang_special_tokens(self._src_lang )
lowerCAmelCase = num_madeup_words
@property
def __snake_case ( self ) -> int:
return len(self.encoder ) + len(self.lang_token_to_id )
@property
def __snake_case ( self ) -> str:
return self._src_lang
@src_lang.setter
def __snake_case ( self , A_ ) -> None:
lowerCAmelCase = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def __snake_case ( self , A_ ) -> List[str]:
return self.sp_model.encode(A_ , out_type=A_ )
def __snake_case ( self , A_ ) -> Any:
if token in self.lang_token_to_id:
return self.lang_token_to_id[token]
return self.encoder.get(A_ , self.encoder[self.unk_token] )
def __snake_case ( self , A_ ) -> str:
if index in self.id_to_lang_token:
return self.id_to_lang_token[index]
return self.decoder.get(A_ , self.unk_token )
def __snake_case ( self , A_ ) -> List[str]:
lowerCAmelCase = []
lowerCAmelCase = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(A_ ) + token
lowerCAmelCase = []
else:
current_sub_tokens.append(A_ )
out_string += self.sp_model.decode(A_ )
return out_string.strip()
def __snake_case ( self , A_ , A_ = None , A_ = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A_ , token_ids_a=A_ , already_has_special_tokens=A_ )
lowerCAmelCase = [1] * len(self.prefix_tokens )
lowerCAmelCase = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(A_ )) + suffix_ones
return prefix_ones + ([0] * len(A_ )) + ([0] * len(A_ )) + suffix_ones
def __snake_case ( self , A_ , A_ = None ) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def __snake_case ( self ) -> Dict:
lowerCAmelCase = {self.convert_ids_to_tokens(A_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> Dict:
lowerCAmelCase = self.__dict__.copy()
lowerCAmelCase = None
return state
def __setstate__( self , A_ ) -> None:
lowerCAmelCase = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
lowerCAmelCase = {}
lowerCAmelCase = load_spm(self.spm_file , self.sp_model_kwargs )
def __snake_case ( self , A_ , A_ = None ) -> Tuple[str]:
lowerCAmelCase = Path(A_ )
if not save_dir.is_dir():
raise OSError(f'{save_directory} should be a directory' )
lowerCAmelCase = save_dir / (
(filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
)
lowerCAmelCase = save_dir / (
(filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
)
save_json(self.encoder , A_ )
if os.path.abspath(self.spm_file ) != os.path.abspath(A_ ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file , A_ )
elif not os.path.isfile(self.spm_file ):
with open(A_ , """wb""" ) as fi:
lowerCAmelCase = self.sp_model.serialized_model_proto()
fi.write(A_ )
return (str(A_ ), str(A_ ))
def __snake_case ( self , A_ , A_ = "en" , A_ = None , A_ = "ro" , **A_ , ) -> BatchEncoding:
lowerCAmelCase = src_lang
lowerCAmelCase = tgt_lang
self.set_src_lang_special_tokens(self.src_lang )
return super().prepare_seqaseq_batch(A_ , A_ , **A_ )
def __snake_case ( self , A_ , A_ , A_ , **A_ ) -> str:
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
lowerCAmelCase = src_lang
lowerCAmelCase = self(A_ , add_special_tokens=A_ , **A_ )
lowerCAmelCase = self.get_lang_id(A_ )
lowerCAmelCase = tgt_lang_id
return inputs
def __snake_case ( self ) -> Any:
self.set_src_lang_special_tokens(self.src_lang )
def __snake_case ( self ) -> Optional[int]:
self.set_tgt_lang_special_tokens(self.tgt_lang )
def __snake_case ( self , A_ ) -> None:
lowerCAmelCase = self.get_lang_token(A_ )
lowerCAmelCase = self.lang_token_to_id[lang_token]
lowerCAmelCase = [self.cur_lang_id]
lowerCAmelCase = [self.eos_token_id]
def __snake_case ( self , A_ ) -> None:
lowerCAmelCase = self.get_lang_token(A_ )
lowerCAmelCase = self.lang_token_to_id[lang_token]
lowerCAmelCase = [self.cur_lang_id]
lowerCAmelCase = [self.eos_token_id]
def __snake_case ( self , A_ ) -> str:
return self.lang_code_to_token[lang]
def __snake_case ( self , A_ ) -> int:
lowerCAmelCase = self.get_lang_token(A_ )
return self.lang_token_to_id[lang_token]
def _snake_case ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Dict[str, Any] ) -> sentencepiece.SentencePieceProcessor:
"""simple docstring"""
lowerCAmelCase = sentencepiece.SentencePieceProcessor(**_SCREAMING_SNAKE_CASE )
spm.Load(str(_SCREAMING_SNAKE_CASE ) )
return spm
def _snake_case ( _SCREAMING_SNAKE_CASE : str ) -> Union[Dict, List]:
"""simple docstring"""
with open(_SCREAMING_SNAKE_CASE , """r""" ) as f:
return json.load(_SCREAMING_SNAKE_CASE )
def _snake_case ( _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : str ) -> None:
"""simple docstring"""
with open(_SCREAMING_SNAKE_CASE , """w""" ) as f:
json.dump(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , indent=2 ) | 187 | 0 |
'''simple docstring'''
def __lowercase ( __lowercase ) -> int:
'''simple docstring'''
assert isinstance(__lowercase , __lowercase ), F'''The input value of [n={number}] is not an integer'''
if number == 1:
return 2
elif number < 1:
_A = F'''The input value of [n={number}] has to be > 0'''
raise ValueError(__lowercase )
else:
_A = sylvester(number - 1 )
_A = num - 1
_A = num
return lower * upper + 1
if __name__ == "__main__":
print(F"""The 8th number in Sylvester's sequence: {sylvester(8)}""")
| 79 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCamelCase_ = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = ["SpeechEncoderDecoderModel"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = ["FlaxSpeechEncoderDecoderModel"]
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:
import sys
UpperCamelCase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 251 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
lowerCamelCase : Dict =logging.get_logger(__name__)
lowerCamelCase : Optional[Any] ={
'''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json''',
# See all Marian models at https://huggingface.co/models?filter=marian
}
class __a ( __SCREAMING_SNAKE_CASE ):
_lowerCAmelCase : List[str] = 'marian'
_lowerCAmelCase : Any = ['past_key_values']
_lowerCAmelCase : int = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE : Dict=5_81_01 , SCREAMING_SNAKE_CASE : Optional[Any]=None , SCREAMING_SNAKE_CASE : Optional[int]=10_24 , SCREAMING_SNAKE_CASE : Optional[int]=12 , SCREAMING_SNAKE_CASE : str=40_96 , SCREAMING_SNAKE_CASE : Dict=16 , SCREAMING_SNAKE_CASE : Tuple=12 , SCREAMING_SNAKE_CASE : List[Any]=40_96 , SCREAMING_SNAKE_CASE : Union[str, Any]=16 , SCREAMING_SNAKE_CASE : Optional[int]=0.0 , SCREAMING_SNAKE_CASE : int=0.0 , SCREAMING_SNAKE_CASE : Optional[int]=True , SCREAMING_SNAKE_CASE : int=True , SCREAMING_SNAKE_CASE : str="gelu" , SCREAMING_SNAKE_CASE : Optional[int]=10_24 , SCREAMING_SNAKE_CASE : Union[str, Any]=0.1 , SCREAMING_SNAKE_CASE : int=0.0 , SCREAMING_SNAKE_CASE : Dict=0.0 , SCREAMING_SNAKE_CASE : Dict=0.0_2 , SCREAMING_SNAKE_CASE : Any=5_81_00 , SCREAMING_SNAKE_CASE : Optional[Any]=False , SCREAMING_SNAKE_CASE : Union[str, Any]=5_81_00 , SCREAMING_SNAKE_CASE : Optional[int]=0 , SCREAMING_SNAKE_CASE : List[str]=0 , SCREAMING_SNAKE_CASE : List[Any]=True , **SCREAMING_SNAKE_CASE : Optional[Any] , ):
'''simple docstring'''
UpperCamelCase__ : Optional[int] = vocab_size
UpperCamelCase__ : int = decoder_vocab_size or vocab_size
UpperCamelCase__ : Optional[Any] = max_position_embeddings
UpperCamelCase__ : Dict = d_model
UpperCamelCase__ : Optional[int] = encoder_ffn_dim
UpperCamelCase__ : Tuple = encoder_layers
UpperCamelCase__ : Union[str, Any] = encoder_attention_heads
UpperCamelCase__ : Tuple = decoder_ffn_dim
UpperCamelCase__ : Optional[Any] = decoder_layers
UpperCamelCase__ : Optional[Any] = decoder_attention_heads
UpperCamelCase__ : Any = dropout
UpperCamelCase__ : Optional[int] = attention_dropout
UpperCamelCase__ : Optional[Any] = activation_dropout
UpperCamelCase__ : Dict = activation_function
UpperCamelCase__ : str = init_std
UpperCamelCase__ : Any = encoder_layerdrop
UpperCamelCase__ : Tuple = decoder_layerdrop
UpperCamelCase__ : Union[str, Any] = use_cache
UpperCamelCase__ : List[str] = encoder_layers
UpperCamelCase__ : List[str] = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCamelCase__ : Dict = share_encoder_decoder_embeddings
super().__init__(
pad_token_id=_a , eos_token_id=_a , is_encoder_decoder=_a , decoder_start_token_id=_a , forced_eos_token_id=_a , **_a , )
class __a ( __SCREAMING_SNAKE_CASE ):
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def __lowercase ( self : List[Any] ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
UpperCamelCase__ : int = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
UpperCamelCase__ : Dict = {0: "batch"}
UpperCamelCase__ : str = {0: "batch", 1: "past_decoder_sequence + sequence"}
else:
UpperCamelCase__ : int = {0: "batch", 1: "decoder_sequence"}
UpperCamelCase__ : Tuple = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(_a , direction="inputs" )
elif self.task == "causal-lm":
# TODO: figure this case out.
UpperCamelCase__ : Any = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
UpperCamelCase__ , UpperCamelCase__ : str = self.num_layers
for i in range(_a ):
UpperCamelCase__ : Any = {0: "batch", 2: "past_sequence + sequence"}
UpperCamelCase__ : int = {0: "batch", 2: "past_sequence + sequence"}
else:
UpperCamelCase__ : List[str] = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
] )
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
UpperCamelCase__ : Optional[int] = super().outputs
else:
UpperCamelCase__ : Union[str, Any] = super(_a , self ).outputs
if self.use_past:
UpperCamelCase__ , UpperCamelCase__ : Optional[Any] = self.num_layers
for i in range(_a ):
UpperCamelCase__ : List[Any] = {0: "batch", 2: "past_sequence + sequence"}
UpperCamelCase__ : Optional[int] = {0: "batch", 2: "past_sequence + sequence"}
return common_outputs
def __lowercase ( self : Union[str, Any] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Tuple = -1 , SCREAMING_SNAKE_CASE : Optional[Any] = -1 , SCREAMING_SNAKE_CASE : Optional[int] = False , SCREAMING_SNAKE_CASE : List[str] = None , ):
'''simple docstring'''
UpperCamelCase__ : Dict = self._generate_dummy_inputs_for_encoder_and_decoder(
_a , _a , _a , _a , _a )
# Generate decoder inputs
UpperCamelCase__ : Tuple = seq_length if not self.use_past else 1
UpperCamelCase__ : str = self._generate_dummy_inputs_for_encoder_and_decoder(
_a , _a , _a , _a , _a )
UpperCamelCase__ : Union[str, Any] = {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
UpperCamelCase__ : str = dict(**_a , **_a )
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__ : Dict = common_inputs["input_ids"].shape
UpperCamelCase__ : Optional[int] = common_inputs["decoder_input_ids"].shape[1]
UpperCamelCase__ , UpperCamelCase__ : Union[str, Any] = self.num_attention_heads
UpperCamelCase__ : Dict = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
UpperCamelCase__ : Union[str, Any] = decoder_seq_length + 3
UpperCamelCase__ : Tuple = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
UpperCamelCase__ : Dict = torch.cat(
[common_inputs["decoder_attention_mask"], torch.ones(_a , _a )] , dim=1 )
UpperCamelCase__ : Any = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
UpperCamelCase__ , UpperCamelCase__ : Tuple = self.num_layers
UpperCamelCase__ : Any = min(_a , _a )
UpperCamelCase__ : Optional[int] = max(_a , _a ) - min_num_layers
UpperCamelCase__ : List[str] = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"
for _ in range(_a ):
common_inputs["past_key_values"].append(
(
torch.zeros(_a ),
torch.zeros(_a ),
torch.zeros(_a ),
torch.zeros(_a ),
) )
# TODO: test this.
UpperCamelCase__ : Union[str, Any] = encoder_shape if remaining_side_name == "encoder" else decoder_shape
for _ in range(_a , _a ):
common_inputs["past_key_values"].append((torch.zeros(_a ), torch.zeros(_a )) )
return common_inputs
def __lowercase ( self : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Optional[Any] = -1 , SCREAMING_SNAKE_CASE : List[str] = -1 , SCREAMING_SNAKE_CASE : List[Any] = False , SCREAMING_SNAKE_CASE : Union[str, Any] = None , ):
'''simple docstring'''
UpperCamelCase__ : Optional[int] = self._generate_dummy_inputs_for_encoder_and_decoder(
_a , _a , _a , _a , _a )
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__ : int = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
UpperCamelCase__ : Any = seqlen + 2
UpperCamelCase__ , UpperCamelCase__ : str = self.num_layers
UpperCamelCase__ , UpperCamelCase__ : Dict = self.num_attention_heads
UpperCamelCase__ : List[str] = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
UpperCamelCase__ : Optional[Any] = common_inputs["attention_mask"].dtype
UpperCamelCase__ : str = torch.cat(
[common_inputs["attention_mask"], torch.ones(_a , _a , dtype=_a )] , dim=1 )
UpperCamelCase__ : Optional[Any] = [
(torch.zeros(_a ), torch.zeros(_a )) for _ in range(_a )
]
return common_inputs
def __lowercase ( self : List[str] , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : int = -1 , SCREAMING_SNAKE_CASE : Union[str, Any] = -1 , SCREAMING_SNAKE_CASE : int = False , SCREAMING_SNAKE_CASE : Tuple = None , ):
'''simple docstring'''
UpperCamelCase__ : str = compute_effective_axis_dimension(
_a , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
UpperCamelCase__ : List[Any] = tokenizer.num_special_tokens_to_add(_a )
UpperCamelCase__ : int = compute_effective_axis_dimension(
_a , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=_a )
# Generate dummy inputs according to compute batch and sequence
UpperCamelCase__ : Union[str, Any] = [" ".join([tokenizer.unk_token] ) * seq_length] * batch_size
UpperCamelCase__ : List[Any] = dict(tokenizer(_a , return_tensors=_a ) )
return common_inputs
def __lowercase ( self : int , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Optional[int] = -1 , SCREAMING_SNAKE_CASE : Optional[Any] = -1 , SCREAMING_SNAKE_CASE : List[str] = False , SCREAMING_SNAKE_CASE : str = None , ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
UpperCamelCase__ : Any = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
_a , batch_size=_a , seq_length=_a , is_pair=_a , framework=_a )
else:
UpperCamelCase__ : Optional[Any] = self._generate_dummy_inputs_for_causal_lm(
_a , batch_size=_a , seq_length=_a , is_pair=_a , framework=_a )
return common_inputs
def __lowercase ( self : Any , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : Dict ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
UpperCamelCase__ : str = super()._flatten_past_key_values_(_a , _a , _a , _a )
else:
UpperCamelCase__ : Any = super(_a , self )._flatten_past_key_values_(
_a , _a , _a , _a )
@property
def __lowercase ( self : Tuple ):
'''simple docstring'''
return 1e-4 | 361 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Dict =logging.get_logger(__name__)
class __a ( A__ ):
_lowerCAmelCase : Optional[int] = '''timm_backbone'''
def __init__( self : Dict , SCREAMING_SNAKE_CASE : Dict=None , SCREAMING_SNAKE_CASE : List[Any]=3 , SCREAMING_SNAKE_CASE : List[Any]=True , SCREAMING_SNAKE_CASE : Dict=True , SCREAMING_SNAKE_CASE : Union[str, Any]=None , **SCREAMING_SNAKE_CASE : int , ):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Union[str, Any] = backbone
UpperCamelCase__ : Dict = num_channels
UpperCamelCase__ : str = features_only
UpperCamelCase__ : Dict = use_pretrained_backbone
UpperCamelCase__ : Tuple = True
UpperCamelCase__ : List[Any] = out_indices if out_indices is not None else (-1,) | 196 | 0 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
_SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : List[str] = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
_SCREAMING_SNAKE_CASE : Tuple = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class _snake_case :
lowerCAmelCase_ : str = field(
default=lowercase_ , metadata={"help": "Model type selected in the list: " + ", ".join(lowercase_ )} )
lowerCAmelCase_ : str = field(
default=lowercase_ , metadata={"help": "The input data dir. Should contain the .json files for the SQuAD task."} )
lowerCAmelCase_ : int = field(
default=128 , metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
lowerCAmelCase_ : int = field(
default=128 , metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."} , )
lowerCAmelCase_ : int = field(
default=64 , metadata={
"help": (
"The maximum number of tokens for the question. Questions longer than this will "
"be truncated to this length."
)
} , )
lowerCAmelCase_ : int = field(
default=30 , metadata={
"help": (
"The maximum length of an answer that can be generated. This is needed because the start "
"and end predictions are not conditioned on one another."
)
} , )
lowerCAmelCase_ : bool = field(
default=lowercase_ , metadata={"help": "Overwrite the cached training and evaluation sets"} )
lowerCAmelCase_ : bool = field(
default=lowercase_ , metadata={"help": "If true, the SQuAD examples contain some that do not have an answer."} )
lowerCAmelCase_ : float = field(
default=0.0 , metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."} )
lowerCAmelCase_ : int = field(
default=20 , metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."} )
lowerCAmelCase_ : int = field(
default=0 , metadata={
"help": (
"language id of input for language-specific xlm models (see"
" tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"
)
} , )
lowerCAmelCase_ : int = field(default=1 , metadata={"help": "multiple threads for converting example to features"} )
class _snake_case ( lowercase_ ):
lowerCAmelCase_ : int = "train"
lowerCAmelCase_ : Tuple = "dev"
class _snake_case ( lowercase_ ):
lowerCAmelCase_ : SquadDataTrainingArguments
lowerCAmelCase_ : List[SquadFeatures]
lowerCAmelCase_ : Split
lowerCAmelCase_ : bool
def __init__( self , a__ , a__ , a__ = None , a__ = Split.train , a__ = False , a__ = None , a__ = "pt" , ) -> Any:
'''simple docstring'''
snake_case_ = args
snake_case_ = is_language_sensitive
snake_case_ = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(a__ , a__ ):
try:
snake_case_ = Split[mode]
except KeyError:
raise KeyError("mode is not a valid split name" )
snake_case_ = mode
# Load data features from cache or dataset file
snake_case_ = "v2" if args.version_2_with_negative else "v1"
snake_case_ = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}' , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
snake_case_ = cached_features_file + ".lock"
with FileLock(a__ ):
if os.path.exists(a__ ) and not args.overwrite_cache:
snake_case_ = time.time()
snake_case_ = torch.load(a__ )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
snake_case_ = self.old_features["features"]
snake_case_ = self.old_features.get("dataset" , a__ )
snake_case_ = self.old_features.get("examples" , a__ )
logger.info(
F'Loading features from cached file {cached_features_file} [took %.3f s]' , time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
F'Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'
" future run" )
else:
if mode == Split.dev:
snake_case_ = self.processor.get_dev_examples(args.data_dir )
else:
snake_case_ = self.processor.get_train_examples(args.data_dir )
snake_case_ , snake_case_ = squad_convert_examples_to_features(
examples=self.examples , tokenizer=a__ , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=a__ , )
snake_case_ = time.time()
torch.save(
{"features": self.features, "dataset": self.dataset, "examples": self.examples} , a__ , )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]' )
def __len__( self ) -> str:
'''simple docstring'''
return len(self.features )
def __getitem__( self , a__ ) -> Dict[str, torch.Tensor]:
'''simple docstring'''
snake_case_ = self.features[i]
snake_case_ = torch.tensor(feature.input_ids , dtype=torch.long )
snake_case_ = torch.tensor(feature.attention_mask , dtype=torch.long )
snake_case_ = torch.tensor(feature.token_type_ids , dtype=torch.long )
snake_case_ = torch.tensor(feature.cls_index , dtype=torch.long )
snake_case_ = torch.tensor(feature.p_mask , dtype=torch.float )
snake_case_ = torch.tensor(feature.is_impossible , dtype=torch.float )
snake_case_ = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({"cls_index": cls_index, "p_mask": p_mask} )
if self.args.version_2_with_negative:
inputs.update({"is_impossible": is_impossible} )
if self.is_language_sensitive:
inputs.update({"langs": (torch.ones(input_ids.shape , dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
snake_case_ = torch.tensor(feature.start_position , dtype=torch.long )
snake_case_ = torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({"start_positions": start_positions, "end_positions": end_positions} )
return inputs
| 85 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ : list ) -> bool:
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(lowerCAmelCase__ ) == 0:
raise ValueError('''Input list must be a non empty list''' )
if len(lowerCAmelCase__ ) == 1:
return True
__a = series[1] - series[0]
for index in range(len(lowerCAmelCase__ ) - 1 ):
if series[index + 1] - series[index] != common_diff:
return False
return True
def lowercase ( lowerCAmelCase__ : list ) -> float:
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(lowerCAmelCase__ ) == 0:
raise ValueError('''Input list must be a non empty list''' )
__a = 0
for val in series:
answer += val
return answer / len(lowerCAmelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 45 | 0 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : list[int] ) -> bool:
return len(set(__UpperCamelCase ) ) == len(__UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 177 |
from __future__ import annotations
_lowerCamelCase = list[list[int]]
# assigning initial values to the grid
_lowerCamelCase = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
_lowerCamelCase = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Matrix , __UpperCamelCase : int , __UpperCamelCase : int , __UpperCamelCase : int ) -> bool:
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Matrix ) -> tuple[int, int] | None:
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Matrix ) -> Matrix | None:
if location := find_empty_location(__UpperCamelCase ):
UpperCAmelCase_ , UpperCAmelCase_ = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 , 10 ):
if is_safe(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase_ = digit
if sudoku(__UpperCamelCase ) is not None:
return grid
UpperCAmelCase_ = 0
return None
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Matrix ) -> None:
for row in grid:
for cell in row:
print(__UpperCamelCase , end=''' ''' )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print('\nExample grid:\n' + '=' * 20)
print_solution(example_grid)
print('\nExample grid solution:')
_lowerCamelCase = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print('Cannot find a solution.')
| 177 | 1 |
'''simple docstring'''
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class __A ( unittest.TestCase , UpperCamelCase__ ):
def _lowercase (self : Any ):
UpperCAmelCase_ = load_tool("text-classification" )
self.tool.setup()
UpperCAmelCase_ = load_tool("text-classification" , remote=__a )
def _lowercase (self : Optional[Any] ):
UpperCAmelCase_ = self.tool("That's quite cool" , ["positive", "negative"] )
self.assertEqual(__a , "positive" )
def _lowercase (self : List[Any] ):
UpperCAmelCase_ = self.remote_tool("That's quite cool" , ["positive", "negative"] )
self.assertEqual(__a , "positive" )
def _lowercase (self : Union[str, Any] ):
UpperCAmelCase_ = self.tool(text="That's quite cool" , labels=["positive", "negative"] )
self.assertEqual(__a , "positive" )
def _lowercase (self : List[Any] ):
UpperCAmelCase_ = self.remote_tool(text="That's quite cool" , labels=["positive", "negative"] )
self.assertEqual(__a , "positive" )
| 1 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCAmelCase_ ( snake_case ):
@staticmethod
@abstractmethod
def _lowerCamelCase ( UpperCamelCase_ ) -> Union[str, Any]:
raise NotImplementedError()
@abstractmethod
def _lowerCamelCase ( self ) -> str:
raise NotImplementedError()
| 249 | 0 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
__lowerCAmelCase = re.compile(r'\b(a|an|the)\b', re.UNICODE)
__lowerCAmelCase = None
def __SCREAMING_SNAKE_CASE ( ):
_snake_case = argparse.ArgumentParser("""Official evaluation script for SQuAD version 2.0.""" )
parser.add_argument("""data_file""" , metavar="""data.json""" , help="""Input data JSON file.""" )
parser.add_argument("""pred_file""" , metavar="""pred.json""" , help="""Model predictions.""" )
parser.add_argument(
"""--out-file""" , """-o""" , metavar="""eval.json""" , help="""Write accuracy metrics to file (default is stdout).""" )
parser.add_argument(
"""--na-prob-file""" , """-n""" , metavar="""na_prob.json""" , help="""Model estimates of probability of no answer.""" )
parser.add_argument(
"""--na-prob-thresh""" , """-t""" , type=_SCREAMING_SNAKE_CASE , default=1.0 , help="""Predict \"\" if no-answer probability exceeds this (default = 1.0).""" , )
parser.add_argument(
"""--out-image-dir""" , """-p""" , metavar="""out_images""" , default=_SCREAMING_SNAKE_CASE , help="""Save precision-recall curves to directory.""" )
parser.add_argument("""--verbose""" , """-v""" , action="""store_true""" )
if len(sys.argv ) == 1:
parser.print_help()
sys.exit(1 )
return parser.parse_args()
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
_snake_case = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
_snake_case = bool(qa["""answers"""]["""text"""] )
return qid_to_has_ans
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
def remove_articles(_SCREAMING_SNAKE_CASE ):
return ARTICLES_REGEX.sub(""" """ , _SCREAMING_SNAKE_CASE )
def white_space_fix(_SCREAMING_SNAKE_CASE ):
return " ".join(text.split() )
def remove_punc(_SCREAMING_SNAKE_CASE ):
_snake_case = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(_SCREAMING_SNAKE_CASE ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(_SCREAMING_SNAKE_CASE ) ) ) )
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
if not s:
return []
return normalize_answer(_SCREAMING_SNAKE_CASE ).split()
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return int(normalize_answer(_SCREAMING_SNAKE_CASE ) == normalize_answer(_SCREAMING_SNAKE_CASE ) )
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_snake_case = get_tokens(_SCREAMING_SNAKE_CASE )
_snake_case = get_tokens(_SCREAMING_SNAKE_CASE )
_snake_case = collections.Counter(_SCREAMING_SNAKE_CASE ) & collections.Counter(_SCREAMING_SNAKE_CASE )
_snake_case = sum(common.values() )
if len(_SCREAMING_SNAKE_CASE ) == 0 or len(_SCREAMING_SNAKE_CASE ) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks )
if num_same == 0:
return 0
_snake_case = 1.0 * num_same / len(_SCREAMING_SNAKE_CASE )
_snake_case = 1.0 * num_same / len(_SCREAMING_SNAKE_CASE )
_snake_case = (2 * precision * recall) / (precision + recall)
return fa
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_snake_case = {}
_snake_case = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
_snake_case = qa["""id"""]
_snake_case = [t for t in qa["""answers"""]["""text"""] if normalize_answer(_SCREAMING_SNAKE_CASE )]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
_snake_case = [""""""]
if qid not in preds:
print(f"""Missing prediction for {qid}""" )
continue
_snake_case = preds[qid]
# Take max over all gold answers
_snake_case = max(compute_exact(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for a in gold_answers )
_snake_case = max(compute_fa(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for a in gold_answers )
return exact_scores, fa_scores
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_snake_case = {}
for qid, s in scores.items():
_snake_case = na_probs[qid] > na_prob_thresh
if pred_na:
_snake_case = float(not qid_to_has_ans[qid] )
else:
_snake_case = s
return new_scores
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ):
if not qid_list:
_snake_case = len(_SCREAMING_SNAKE_CASE )
return collections.OrderedDict(
[
("""exact""", 100.0 * sum(exact_scores.values() ) / total),
("""f1""", 100.0 * sum(fa_scores.values() ) / total),
("""total""", total),
] )
else:
_snake_case = len(_SCREAMING_SNAKE_CASE )
return collections.OrderedDict(
[
("""exact""", 100.0 * sum(exact_scores[k] for k in qid_list ) / total),
("""f1""", 100.0 * sum(fa_scores[k] for k in qid_list ) / total),
("""total""", total),
] )
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
for k in new_eval:
_snake_case = new_eval[k]
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
plt.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , color="""b""" , alpha=0.2 , where="""post""" )
plt.fill_between(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , step="""post""" , alpha=0.2 , color="""b""" )
plt.xlabel("""Recall""" )
plt.ylabel("""Precision""" )
plt.xlim([0.0, 1.05] )
plt.ylim([0.0, 1.05] )
plt.title(_SCREAMING_SNAKE_CASE )
plt.savefig(_SCREAMING_SNAKE_CASE )
plt.clf()
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ):
_snake_case = sorted(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : na_probs[k] )
_snake_case = 0.0
_snake_case = 1.0
_snake_case = 0.0
_snake_case = [1.0]
_snake_case = [0.0]
_snake_case = 0.0
for i, qid in enumerate(_SCREAMING_SNAKE_CASE ):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
_snake_case = true_pos / float(i + 1 )
_snake_case = true_pos / float(_SCREAMING_SNAKE_CASE )
if i == len(_SCREAMING_SNAKE_CASE ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(_SCREAMING_SNAKE_CASE )
recalls.append(_SCREAMING_SNAKE_CASE )
if out_image:
plot_pr_curve(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return {"ap": 100.0 * avg_prec}
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if out_image_dir and not os.path.exists(_SCREAMING_SNAKE_CASE ):
os.makedirs(_SCREAMING_SNAKE_CASE )
_snake_case = sum(1 for v in qid_to_has_ans.values() if v )
if num_true_pos == 0:
return
_snake_case = make_precision_recall_eval(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , out_image=os.path.join(_SCREAMING_SNAKE_CASE , """pr_exact.png""" ) , title="""Precision-Recall curve for Exact Match score""" , )
_snake_case = make_precision_recall_eval(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , out_image=os.path.join(_SCREAMING_SNAKE_CASE , """pr_f1.png""" ) , title="""Precision-Recall curve for F1 score""" , )
_snake_case = {k: float(_SCREAMING_SNAKE_CASE ) for k, v in qid_to_has_ans.items()}
_snake_case = make_precision_recall_eval(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , out_image=os.path.join(_SCREAMING_SNAKE_CASE , """pr_oracle.png""" ) , title="""Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)""" , )
merge_eval(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , """pr_exact""" )
merge_eval(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , """pr_f1""" )
merge_eval(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , """pr_oracle""" )
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if not qid_list:
return
_snake_case = [na_probs[k] for k in qid_list]
_snake_case = np.ones_like(_SCREAMING_SNAKE_CASE ) / float(len(_SCREAMING_SNAKE_CASE ) )
plt.hist(_SCREAMING_SNAKE_CASE , weights=_SCREAMING_SNAKE_CASE , bins=20 , range=(0.0, 1.0) )
plt.xlabel("""Model probability of no-answer""" )
plt.ylabel("""Proportion of dataset""" )
plt.title(f"""Histogram of no-answer probability: {name}""" )
plt.savefig(os.path.join(_SCREAMING_SNAKE_CASE , f"""na_prob_hist_{name}.png""" ) )
plt.clf()
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_snake_case = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
_snake_case = num_no_ans
_snake_case = cur_score
_snake_case = 0.0
_snake_case = sorted(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : na_probs[k] )
for i, qid in enumerate(_SCREAMING_SNAKE_CASE ):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
_snake_case = scores[qid]
else:
if preds[qid]:
_snake_case = -1
else:
_snake_case = 0
cur_score += diff
if cur_score > best_score:
_snake_case = cur_score
_snake_case = na_probs[qid]
return 100.0 * best_score / len(_SCREAMING_SNAKE_CASE ), best_thresh
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_snake_case, _snake_case = find_best_thresh(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_snake_case, _snake_case = find_best_thresh(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_snake_case = best_exact
_snake_case = exact_thresh
_snake_case = best_fa
_snake_case = fa_thresh
def __SCREAMING_SNAKE_CASE ( ):
with open(OPTS.data_file ) as f:
_snake_case = json.load(_SCREAMING_SNAKE_CASE )
_snake_case = dataset_json["""data"""]
with open(OPTS.pred_file ) as f:
_snake_case = json.load(_SCREAMING_SNAKE_CASE )
if OPTS.na_prob_file:
with open(OPTS.na_prob_file ) as f:
_snake_case = json.load(_SCREAMING_SNAKE_CASE )
else:
_snake_case = {k: 0.0 for k in preds}
_snake_case = make_qid_to_has_ans(_SCREAMING_SNAKE_CASE ) # maps qid to True/False
_snake_case = [k for k, v in qid_to_has_ans.items() if v]
_snake_case = [k for k, v in qid_to_has_ans.items() if not v]
_snake_case, _snake_case = get_raw_scores(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_snake_case = apply_no_ans_threshold(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , OPTS.na_prob_thresh )
_snake_case = apply_no_ans_threshold(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , OPTS.na_prob_thresh )
_snake_case = make_eval_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if has_ans_qids:
_snake_case = make_eval_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , qid_list=_SCREAMING_SNAKE_CASE )
merge_eval(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , """HasAns""" )
if no_ans_qids:
_snake_case = make_eval_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , qid_list=_SCREAMING_SNAKE_CASE )
merge_eval(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , """NoAns""" )
if OPTS.na_prob_file:
find_all_best_thresh(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , OPTS.out_image_dir )
histogram_na_prob(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , OPTS.out_image_dir , """hasAns""" )
histogram_na_prob(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , OPTS.out_image_dir , """noAns""" )
if OPTS.out_file:
with open(OPTS.out_file , """w""" ) as f:
json.dump(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
print(json.dumps(_SCREAMING_SNAKE_CASE , indent=2 ) )
if __name__ == "__main__":
__lowerCAmelCase = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
main() | 370 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class _lowerCAmelCase ( __snake_case , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ = DiTPipeline
lowerCAmelCase_ = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
lowerCAmelCase_ = PipelineTesterMixin.required_optional_params - {
"latents",
"num_images_per_prompt",
"callback",
"callback_steps",
}
lowerCAmelCase_ = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
lowerCAmelCase_ = False
def lowercase (self ) -> Union[str, Any]:
torch.manual_seed(0 )
_snake_case = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=UpperCAmelCase , activation_fn="""gelu-approximate""" , num_embeds_ada_norm=1000 , norm_type="""ada_norm_zero""" , norm_elementwise_affine=UpperCAmelCase , )
_snake_case = AutoencoderKL()
_snake_case = DDIMScheduler()
_snake_case = {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
return components
def lowercase (self , UpperCAmelCase , UpperCAmelCase=0 ) -> List[str]:
if str(UpperCAmelCase ).startswith("""mps""" ):
_snake_case = torch.manual_seed(UpperCAmelCase )
else:
_snake_case = torch.Generator(device=UpperCAmelCase ).manual_seed(UpperCAmelCase )
_snake_case = {
"""class_labels""": [1],
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def lowercase (self ) -> Union[str, Any]:
_snake_case = """cpu"""
_snake_case = self.get_dummy_components()
_snake_case = self.pipeline_class(**UpperCAmelCase )
pipe.to(UpperCAmelCase )
pipe.set_progress_bar_config(disable=UpperCAmelCase )
_snake_case = self.get_dummy_inputs(UpperCAmelCase )
_snake_case = pipe(**UpperCAmelCase ).images
_snake_case = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
_snake_case = np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457] )
_snake_case = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(UpperCAmelCase , 1e-3 )
def lowercase (self ) -> List[str]:
self._test_inference_batch_single_identical(relax_max_difference=UpperCAmelCase , expected_max_diff=1e-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def lowercase (self ) -> Union[str, Any]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@require_torch_gpu
@slow
class _lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def lowercase (self ) -> Tuple:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase (self ) -> Any:
_snake_case = torch.manual_seed(0 )
_snake_case = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256""" )
pipe.to("""cuda""" )
_snake_case = ["""vase""", """umbrella""", """white shark""", """white wolf"""]
_snake_case = pipe.get_label_ids(UpperCAmelCase )
_snake_case = pipe(UpperCAmelCase , generator=UpperCAmelCase , num_inference_steps=40 , output_type="""np""" ).images
for word, image in zip(UpperCAmelCase , UpperCAmelCase ):
_snake_case = load_numpy(
f"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy""" )
assert np.abs((expected_image - image).max() ) < 1e-2
def lowercase (self ) -> Union[str, Any]:
_snake_case = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512""" )
_snake_case = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("""cuda""" )
_snake_case = ["""vase""", """umbrella"""]
_snake_case = pipe.get_label_ids(UpperCAmelCase )
_snake_case = torch.manual_seed(0 )
_snake_case = pipe(UpperCAmelCase , generator=UpperCAmelCase , num_inference_steps=25 , output_type="""np""" ).images
for word, image in zip(UpperCAmelCase , UpperCAmelCase ):
_snake_case = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
f"""/dit/{word}_512.npy""" )
assert np.abs((expected_image - image).max() ) < 1e-1 | 270 | 0 |
'''simple docstring'''
import string
def lowerCamelCase__ ( _A ):
for key in range(len(string.ascii_uppercase ) ):
a : Dict = ''
for symbol in message:
if symbol in string.ascii_uppercase:
a : Union[str, Any] = string.ascii_uppercase.find(_A )
a : List[str] = num - key
if num < 0:
a : Union[str, Any] = num + len(string.ascii_uppercase )
a : str = translated + string.ascii_uppercase[num]
else:
a : List[str] = translated + symbol
print(f"""Decryption using Key #{key}: {translated}""" )
def lowerCamelCase__ ( ):
a : Tuple = input('Encrypted message: ' )
a : int = message.upper()
decrypt(_A )
if __name__ == "__main__":
import doctest
doctest.testmod()
main() | 297 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ = CodeGenTokenizer
lowerCAmelCase_ = CodeGenTokenizerFast
lowerCAmelCase_ = True
lowerCAmelCase_ = {'''add_prefix_space''': True}
lowerCAmelCase_ = False
def snake_case__ ( self : List[Any] ):
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
snake_case_ = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
"<|endoftext|>",
]
snake_case_ = dict(zip(__lowercase , range(len(__lowercase ) ) ) )
snake_case_ = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
snake_case_ = {"unk_token": "<unk>"}
snake_case_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
snake_case_ = 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(__lowercase ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(__lowercase ) )
def snake_case__ ( self : Union[str, Any] , **__lowercase : List[str] ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname , **__lowercase )
def snake_case__ ( self : Optional[Any] , **__lowercase : Union[str, Any] ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname , **__lowercase )
def snake_case__ ( self : Optional[int] , __lowercase : List[str] ):
"""simple docstring"""
snake_case_ = "lower newer"
snake_case_ = "lower newer"
return input_text, output_text
def snake_case__ ( self : Optional[Any] ):
"""simple docstring"""
snake_case_ = CodeGenTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
snake_case_ = "lower newer"
snake_case_ = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
snake_case_ = tokenizer.tokenize(__lowercase , add_prefix_space=__lowercase )
self.assertListEqual(__lowercase , __lowercase )
snake_case_ = tokens + [tokenizer.unk_token]
snake_case_ = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase ) , __lowercase )
def snake_case__ ( self : Optional[int] ):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
snake_case_ = self.get_tokenizer()
snake_case_ = self.get_rust_tokenizer(add_prefix_space=__lowercase )
snake_case_ = "lower newer"
# Testing tokenization
snake_case_ = tokenizer.tokenize(__lowercase , add_prefix_space=__lowercase )
snake_case_ = rust_tokenizer.tokenize(__lowercase )
self.assertListEqual(__lowercase , __lowercase )
# Testing conversion to ids without special tokens
snake_case_ = tokenizer.encode(__lowercase , add_special_tokens=__lowercase , add_prefix_space=__lowercase )
snake_case_ = rust_tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
self.assertListEqual(__lowercase , __lowercase )
# Testing conversion to ids with special tokens
snake_case_ = self.get_rust_tokenizer(add_prefix_space=__lowercase )
snake_case_ = tokenizer.encode(__lowercase , add_prefix_space=__lowercase )
snake_case_ = rust_tokenizer.encode(__lowercase )
self.assertListEqual(__lowercase , __lowercase )
# Testing the unknown token
snake_case_ = tokens + [rust_tokenizer.unk_token]
snake_case_ = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__lowercase ) , __lowercase )
def snake_case__ ( self : Any , *__lowercase : Union[str, Any] , **__lowercase : Tuple ):
"""simple docstring"""
pass
def snake_case__ ( self : int , __lowercase : str=15 ):
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
snake_case_ = self.rust_tokenizer_class.from_pretrained(__lowercase , **__lowercase )
# Simple input
snake_case_ = "This is a simple input"
snake_case_ = ["This is a simple input 1", "This is a simple input 2"]
snake_case_ = ("This is a simple input", "This is a pair")
snake_case_ = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(__lowercase , tokenizer_r.encode , __lowercase , max_length=__lowercase , padding="max_length" )
# Simple input
self.assertRaises(__lowercase , tokenizer_r.encode_plus , __lowercase , max_length=__lowercase , padding="max_length" )
# Simple input
self.assertRaises(
__lowercase , tokenizer_r.batch_encode_plus , __lowercase , max_length=__lowercase , padding="max_length" , )
# Pair input
self.assertRaises(__lowercase , tokenizer_r.encode , __lowercase , max_length=__lowercase , padding="max_length" )
# Pair input
self.assertRaises(__lowercase , tokenizer_r.encode_plus , __lowercase , max_length=__lowercase , padding="max_length" )
# Pair input
self.assertRaises(
__lowercase , tokenizer_r.batch_encode_plus , __lowercase , max_length=__lowercase , padding="max_length" , )
def snake_case__ ( self : str ):
"""simple docstring"""
snake_case_ = CodeGenTokenizer.from_pretrained(self.tmpdirname , pad_token="<pad>" )
# Simple input
snake_case_ = "This is a simple input"
snake_case_ = ["This is a simple input looooooooong", "This is a simple input"]
snake_case_ = ("This is a simple input", "This is a pair")
snake_case_ = [
("This is a simple input loooooong", "This is a simple input"),
("This is a simple pair loooooong", "This is a simple pair"),
]
snake_case_ = tokenizer.pad_token_id
snake_case_ = tokenizer(__lowercase , padding="max_length" , max_length=30 , return_tensors="np" )
snake_case_ = tokenizer(__lowercase , padding=__lowercase , truncate=__lowercase , return_tensors="np" )
snake_case_ = tokenizer(*__lowercase , padding="max_length" , max_length=60 , return_tensors="np" )
snake_case_ = tokenizer(__lowercase , padding=__lowercase , truncate=__lowercase , return_tensors="np" )
# s
# test single string max_length padding
self.assertEqual(out_s["input_ids"].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s["input_ids"] )
self.assertTrue(0 in out_s["attention_mask"] )
# s2
# test automatic padding
self.assertEqual(out_sa["input_ids"].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa["input_ids"][0] )
self.assertFalse(0 in out_sa["attention_mask"][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa["input_ids"][1] )
self.assertTrue(0 in out_sa["attention_mask"][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p["input_ids"].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p["input_ids"] )
self.assertTrue(0 in out_p["attention_mask"] )
# p2
# test automatic padding pair
self.assertEqual(out_pa["input_ids"].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa["input_ids"][0] )
self.assertFalse(0 in out_pa["attention_mask"][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa["input_ids"][1] )
self.assertTrue(0 in out_pa["attention_mask"][1] )
def snake_case__ ( self : Tuple ):
"""simple docstring"""
snake_case_ = "$$$"
snake_case_ = CodeGenTokenizer.from_pretrained(self.tmpdirname , bos_token=__lowercase , add_bos_token=__lowercase )
snake_case_ = "This is a simple input"
snake_case_ = ["This is a simple input 1", "This is a simple input 2"]
snake_case_ = tokenizer.bos_token_id
snake_case_ = tokenizer(__lowercase )
snake_case_ = tokenizer(__lowercase )
self.assertEqual(out_s.input_ids[0] , __lowercase )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
snake_case_ = tokenizer.decode(out_s.input_ids )
snake_case_ = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , __lowercase )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def snake_case__ ( self : Tuple ):
"""simple docstring"""
snake_case_ = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono" )
snake_case_ = "\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#"
snake_case_ = "\nif len_a > len_b: result = a\nelse: result = b"
snake_case_ = tokenizer.encode(__lowercase )
snake_case_ = ["^#", re.escape("<|endoftext|>" ), "^'''", "^\"\"\"", "\n\n\n"]
snake_case_ = tokenizer.decode(__lowercase , truncate_before_pattern=__lowercase )
self.assertEqual(__lowercase , __lowercase )
def snake_case__ ( self : Dict ):
"""simple docstring"""
pass
| 187 | 0 |
"""simple docstring"""
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def lowerCamelCase_( _lowerCamelCase ) -> int:
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = [False] * len(__A )
_lowerCamelCase : Optional[Any] = [-1] * len(__A )
def dfs(_lowerCamelCase , _lowerCamelCase ):
_lowerCamelCase : Dict = True
_lowerCamelCase : str = c
for u in graph[v]:
if not visited[u]:
dfs(__A , 1 - c )
for i in range(len(__A ) ):
if not visited[i]:
dfs(__A , 0 )
for i in range(len(__A ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
_lowerCAmelCase : Tuple = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph)) | 357 |
"""simple docstring"""
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
_lowerCAmelCase : List[Any] = get_logger(__name__)
class A_ :
lowerCAmelCase__ = 'dummy_data'
lowerCAmelCase__ = 'datasets'
lowerCAmelCase__ = False
def __init__( self: List[str] ,__lowerCAmelCase: str ,__lowerCAmelCase: str ,__lowerCAmelCase: Union[Version, str] ,__lowerCAmelCase: Optional[str] = None ,__lowerCAmelCase: bool = False ,__lowerCAmelCase: bool = True ,__lowerCAmelCase: Optional[List[Callable]] = None ,):
'''simple docstring'''
_lowerCamelCase : str = 0
_lowerCamelCase : List[str] = dataset_name
_lowerCamelCase : Optional[int] = cache_dir
_lowerCamelCase : Optional[int] = use_local_dummy_data
_lowerCamelCase : int = config
# download_callbacks take a single url as input
_lowerCamelCase : List[Callable] = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
_lowerCamelCase : int = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
_lowerCamelCase : Tuple = str(__lowerCAmelCase )
# to be downloaded
_lowerCamelCase : Optional[Any] = None
_lowerCamelCase : Dict = None
@property
def _lowercase ( self: str ):
'''simple docstring'''
if self._dummy_file is None:
_lowerCamelCase : List[str] = self.download_dummy_data()
return self._dummy_file
@property
def _lowercase ( self: str ):
'''simple docstring'''
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join("dummy" ,self.config.name ,self.version_name )
# structure is dummy / version_name
return os.path.join("dummy" ,self.version_name )
@property
def _lowercase ( self: Optional[Any] ):
'''simple docstring'''
return os.path.join(self.dummy_data_folder ,"dummy_data.zip" )
def _lowercase ( self: Optional[Any] ):
'''simple docstring'''
_lowerCamelCase : Dict = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
_lowerCamelCase : Optional[int] = cached_path(
__lowerCAmelCase ,cache_dir=self.cache_dir ,extract_compressed_file=__lowerCAmelCase ,force_extract=__lowerCAmelCase )
return os.path.join(__lowerCAmelCase ,self.dummy_file_name )
@property
def _lowercase ( self: Tuple ):
'''simple docstring'''
return os.path.join(self.datasets_scripts_dir ,self.dataset_name ,self.dummy_zip_file )
@property
def _lowercase ( self: List[str] ):
'''simple docstring'''
if self._bucket_url is None:
_lowerCamelCase : List[str] = hf_github_url(self.dataset_name ,self.dummy_zip_file.replace(os.sep ,"/" ) )
return self._bucket_url
@property
def _lowercase ( self: Union[str, Any] ):
'''simple docstring'''
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep ,"/" ).split("/" )[:-1] )
def _lowercase ( self: Union[str, Any] ,__lowerCAmelCase: str ,*__lowerCAmelCase: List[Any] ):
'''simple docstring'''
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
_lowerCamelCase : Tuple = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
_lowerCamelCase : Optional[Any] = self.dummy_file_name
# special case when data_url is a dict
if isinstance(__lowerCAmelCase ,__lowerCAmelCase ):
return self.create_dummy_data_dict(__lowerCAmelCase ,__lowerCAmelCase )
elif isinstance(__lowerCAmelCase ,(list, tuple) ):
return self.create_dummy_data_list(__lowerCAmelCase ,__lowerCAmelCase )
else:
return self.create_dummy_data_single(__lowerCAmelCase ,__lowerCAmelCase )
def _lowercase ( self: str ,__lowerCAmelCase: Optional[int] ,*__lowerCAmelCase: Optional[int] ):
'''simple docstring'''
return self.download_and_extract(__lowerCAmelCase )
def _lowercase ( self: List[Any] ,__lowerCAmelCase: Dict ,__lowerCAmelCase: int ):
'''simple docstring'''
return self.download_and_extract(__lowerCAmelCase )
def _lowercase ( self: Optional[int] ,__lowerCAmelCase: Optional[int] ,*__lowerCAmelCase: List[str] ,**__lowerCAmelCase: Optional[int] ):
'''simple docstring'''
return path
def _lowercase ( self: Optional[int] ):
'''simple docstring'''
return {}
def _lowercase ( self: Optional[Any] ,__lowerCAmelCase: Dict ,__lowerCAmelCase: str ):
'''simple docstring'''
_lowerCamelCase : str = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(__lowerCAmelCase ,__lowerCAmelCase ):
for single_url in single_urls:
download_callback(__lowerCAmelCase )
else:
_lowerCamelCase : Union[str, Any] = single_urls
download_callback(__lowerCAmelCase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(__lowerCAmelCase ,__lowerCAmelCase ):
_lowerCamelCase : Dict = [os.path.join(__lowerCAmelCase ,urllib.parse.quote_plus(Path(__lowerCAmelCase ).name ) ) for x in single_urls]
else:
_lowerCamelCase : Union[str, Any] = single_urls
_lowerCamelCase : List[str] = os.path.join(__lowerCAmelCase ,urllib.parse.quote_plus(Path(__lowerCAmelCase ).name ) )
_lowerCamelCase : List[Any] = value
# make sure that values are unique
if all(isinstance(__lowerCAmelCase ,__lowerCAmelCase ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
_lowerCamelCase : List[Any] = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def _lowercase ( self: int ,__lowerCAmelCase: List[str] ,__lowerCAmelCase: Tuple ):
'''simple docstring'''
_lowerCamelCase : Dict = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
_lowerCamelCase : List[str] = all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}" ,__lowerCAmelCase ) ) for url in data_url )
_lowerCamelCase : Optional[Any] = all(
url.startswith("https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed" ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
_lowerCamelCase : Tuple = [data_url[0]] * len(__lowerCAmelCase )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(__lowerCAmelCase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
_lowerCamelCase : List[Any] = os.path.join(__lowerCAmelCase ,urllib.parse.quote_plus(single_url.split("/" )[-1] ) )
dummy_data_list.append(__lowerCAmelCase )
return dummy_data_list
def _lowercase ( self: Union[str, Any] ,__lowerCAmelCase: Optional[Any] ,__lowerCAmelCase: List[Any] ):
'''simple docstring'''
for download_callback in self.download_callbacks:
download_callback(__lowerCAmelCase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
_lowerCamelCase : Optional[int] = os.path.join(__lowerCAmelCase ,urllib.parse.quote_plus(data_url.split("/" )[-1] ) )
if os.path.exists(__lowerCAmelCase ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def _lowercase ( self: Optional[Any] ):
'''simple docstring'''
pass
def _lowercase ( self: Optional[int] ):
'''simple docstring'''
pass
def _lowercase ( self: List[Any] ,__lowerCAmelCase: Optional[int] ):
'''simple docstring'''
def _iter_archive_members(__lowerCAmelCase: Any ):
# this preserves the order of the members inside the ZIP archive
_lowerCamelCase : Tuple = Path(self.dummy_file ).parent
_lowerCamelCase : str = path.relative_to(__lowerCAmelCase )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
_lowerCamelCase : Optional[int] = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(__lowerCAmelCase )
_lowerCamelCase : Optional[Any] = Path(__lowerCAmelCase )
_lowerCamelCase : int = _iter_archive_members(__lowerCAmelCase ) if self.use_local_dummy_data else path.rglob("*" )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith((".", "__") ):
yield file_path.relative_to(__lowerCAmelCase ).as_posix(), file_path.open("rb" )
def _lowercase ( self: str ,__lowerCAmelCase: Optional[int] ):
'''simple docstring'''
if not isinstance(__lowerCAmelCase ,__lowerCAmelCase ):
_lowerCamelCase : List[Any] = [paths]
for path in paths:
if os.path.isfile(__lowerCAmelCase ):
if os.path.basename(__lowerCAmelCase ).startswith((".", "__") ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(__lowerCAmelCase ):
if os.path.basename(__lowerCAmelCase ).startswith((".", "__") ):
continue
dirnames.sort()
for filename in sorted(__lowerCAmelCase ):
if filename.startswith((".", "__") ):
continue
yield os.path.join(__lowerCAmelCase ,__lowerCAmelCase ) | 340 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ : int ) -> int:
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) or number < 0:
raise ValueError('Input must be a non-negative integer' )
A__ = 0
while number:
# This way we arrive at next set bit (next 1) instead of looping
# through each bit and checking for 1s hence the
# loop won't run 32 times it will only run the number of `1` times
number &= number - 1
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 7 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class __a :
@staticmethod
def SCREAMING_SNAKE_CASE__ ( *lowerCAmelCase__ , **lowerCAmelCase__ ) -> List[str]:
'''simple docstring'''
pass
def snake_case_ ( snake_case ) -> Optional[Any]:
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
__lowerCAmelCase = (
'''https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'''
)
@is_pipeline_test
@require_torch
@require_vision
class __a ( unittest.TestCase ):
__lowercase : Dict = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
'''simple docstring'''
lowercase__: Optional[Any] = pipeline(
'document-question-answering' , model=lowerCAmelCase__ , tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
lowercase__: int = INVOICE_URL
lowercase__: Dict = list(zip(*apply_tesseract(load_image(lowerCAmelCase__ ) , lowerCAmelCase__ , '' ) ) )
lowercase__: str = 'What is the placebo?'
lowercase__: Any = [
{
'image': load_image(lowerCAmelCase__ ),
'question': question,
},
{
'image': image,
'question': question,
},
{
'image': image,
'question': question,
'word_boxes': word_boxes,
},
]
return dqa_pipeline, examples
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
'''simple docstring'''
lowercase__: str = dqa_pipeline(lowerCAmelCase__ , top_k=2 )
self.assertEqual(
lowerCAmelCase__ , [
[
{'score': ANY(lowerCAmelCase__ ), 'answer': ANY(lowerCAmelCase__ ), 'start': ANY(lowerCAmelCase__ ), 'end': ANY(lowerCAmelCase__ )},
{'score': ANY(lowerCAmelCase__ ), 'answer': ANY(lowerCAmelCase__ ), 'start': ANY(lowerCAmelCase__ ), 'end': ANY(lowerCAmelCase__ )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def SCREAMING_SNAKE_CASE__ ( self ) -> Dict:
'''simple docstring'''
lowercase__: Union[str, Any] = pipeline('document-question-answering' , model='hf-internal-testing/tiny-random-layoutlmv2' )
lowercase__: Optional[Any] = INVOICE_URL
lowercase__: int = 'How many cats are there?'
lowercase__: List[str] = [
{'score': 0.0_0_0_1, 'answer': 'oy 2312/2019', 'start': 38, 'end': 39},
{'score': 0.0_0_0_1, 'answer': 'oy 2312/2019 DUE', 'start': 38, 'end': 40},
]
lowercase__: Dict = dqa_pipeline(image=lowerCAmelCase__ , question=lowerCAmelCase__ , top_k=2 )
self.assertEqual(nested_simplify(lowerCAmelCase__ , decimals=4 ) , lowerCAmelCase__ )
lowercase__: Tuple = dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(nested_simplify(lowerCAmelCase__ , decimals=4 ) , lowerCAmelCase__ )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
lowercase__: str = './tests/fixtures/tests_samples/COCO/000000039769.png'
lowercase__: Tuple = dqa_pipeline(image=lowerCAmelCase__ , question=lowerCAmelCase__ , top_k=2 )
self.assertEqual(lowerCAmelCase__ , [] )
# We can optionnally pass directly the words and bounding boxes
lowercase__: int = './tests/fixtures/tests_samples/COCO/000000039769.png'
lowercase__: List[Any] = []
lowercase__: Optional[int] = []
lowercase__: Any = dqa_pipeline(image=lowerCAmelCase__ , question=lowerCAmelCase__ , words=lowerCAmelCase__ , boxes=lowerCAmelCase__ , top_k=2 )
self.assertEqual(lowerCAmelCase__ , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def SCREAMING_SNAKE_CASE__ ( self ) -> Any:
'''simple docstring'''
lowercase__: List[str] = pipeline(
'document-question-answering' , model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa' , revision='9977165' , )
lowercase__: int = INVOICE_URL
lowercase__: str = 'What is the invoice number?'
lowercase__: Union[str, Any] = dqa_pipeline(image=lowerCAmelCase__ , question=lowerCAmelCase__ , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
{'score': 0.9_9_4_4, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.0_0_0_9, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
lowercase__: Any = dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
{'score': 0.9_9_4_4, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.0_0_0_9, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
lowercase__: Optional[int] = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
[
{'score': 0.9_9_4_4, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.0_0_0_9, 'answer': 'us-001', 'start': 16, 'end': 16},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def SCREAMING_SNAKE_CASE__ ( self ) -> str:
'''simple docstring'''
lowercase__: Any = pipeline(
'document-question-answering' , model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa' , revision='9977165' , max_seq_len=50 , )
lowercase__: Optional[int] = INVOICE_URL
lowercase__: Union[str, Any] = 'What is the invoice number?'
lowercase__: Optional[Any] = dqa_pipeline(image=lowerCAmelCase__ , question=lowerCAmelCase__ , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
{'score': 0.9_9_7_4, 'answer': '1110212019', 'start': 23, 'end': 23},
{'score': 0.9_9_4_8, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
lowercase__: Tuple = dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
{'score': 0.9_9_7_4, 'answer': '1110212019', 'start': 23, 'end': 23},
{'score': 0.9_9_4_8, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
lowercase__: Dict = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
[
{'score': 0.9_9_7_4, 'answer': '1110212019', 'start': 23, 'end': 23},
{'score': 0.9_9_4_8, 'answer': 'us-001', 'start': 16, 'end': 16},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def SCREAMING_SNAKE_CASE__ ( self ) -> str:
'''simple docstring'''
lowercase__: Optional[Any] = AutoTokenizer.from_pretrained(
'impira/layoutlm-document-qa' , revision='3dc6de3' , add_prefix_space=lowerCAmelCase__ )
lowercase__: Optional[Any] = pipeline(
'document-question-answering' , model='impira/layoutlm-document-qa' , tokenizer=lowerCAmelCase__ , revision='3dc6de3' , )
lowercase__: List[str] = INVOICE_URL
lowercase__: Union[str, Any] = 'What is the invoice number?'
lowercase__: Dict = dqa_pipeline(image=lowerCAmelCase__ , question=lowerCAmelCase__ , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
{'score': 0.4_2_5_1, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.0_8_1_9, 'answer': '1110212019', 'start': 23, 'end': 23},
] , )
lowercase__: List[str] = dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
{'score': 0.4_2_5_1, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.0_8_1_9, 'answer': '1110212019', 'start': 23, 'end': 23},
] , )
lowercase__: int = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
[
{'score': 0.4_2_5_1, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.0_8_1_9, 'answer': '1110212019', 'start': 23, 'end': 23},
]
]
* 2 , )
lowercase__: Any = list(zip(*apply_tesseract(load_image(lowerCAmelCase__ ) , lowerCAmelCase__ , '' ) ) )
# This model should also work if `image` is set to None
lowercase__: List[Any] = dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
{'score': 0.4_2_5_1, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.0_8_1_9, 'answer': '1110212019', 'start': 23, 'end': 23},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def SCREAMING_SNAKE_CASE__ ( self ) -> Dict:
'''simple docstring'''
lowercase__: Any = AutoTokenizer.from_pretrained(
'impira/layoutlm-document-qa' , revision='3dc6de3' , add_prefix_space=lowerCAmelCase__ )
lowercase__: str = pipeline(
'document-question-answering' , model='impira/layoutlm-document-qa' , tokenizer=lowerCAmelCase__ , revision='3dc6de3' , max_seq_len=50 , )
lowercase__: Optional[Any] = INVOICE_URL
lowercase__: Optional[Any] = 'What is the invoice number?'
lowercase__: Optional[int] = dqa_pipeline(image=lowerCAmelCase__ , question=lowerCAmelCase__ , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
{'score': 0.9_9_9_9, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.9_9_9_8, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
lowercase__: Any = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
[
{'score': 0.9_9_9_9, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.9_9_9_8, 'answer': 'us-001', 'start': 16, 'end': 16},
]
]
* 2 , )
lowercase__: Optional[int] = list(zip(*apply_tesseract(load_image(lowerCAmelCase__ ) , lowerCAmelCase__ , '' ) ) )
# This model should also work if `image` is set to None
lowercase__: Tuple = dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
{'score': 0.9_9_9_9, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.9_9_9_8, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
@slow
@require_torch
def SCREAMING_SNAKE_CASE__ ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase__: List[Any] = pipeline(
'document-question-answering' , model='naver-clova-ix/donut-base-finetuned-docvqa' , tokenizer=AutoTokenizer.from_pretrained('naver-clova-ix/donut-base-finetuned-docvqa' ) , feature_extractor='naver-clova-ix/donut-base-finetuned-docvqa' , )
lowercase__: int = INVOICE_URL
lowercase__: int = 'What is the invoice number?'
lowercase__: Union[str, Any] = dqa_pipeline(image=lowerCAmelCase__ , question=lowerCAmelCase__ , top_k=2 )
self.assertEqual(nested_simplify(lowerCAmelCase__ , decimals=4 ) , [{'answer': 'us-001'}] )
@require_tf
@unittest.skip('Document question answering not implemented in TF' )
def SCREAMING_SNAKE_CASE__ ( self ) -> List[str]:
'''simple docstring'''
pass
| 196 | 0 |
from __future__ import annotations
from collections.abc import Generator
def A__ ( ) -> Generator[int, None, None]:
UpperCamelCase_: dict[int, int] = {}
UpperCamelCase_: int = 2
while True:
UpperCamelCase_: int = factor_map.pop(lowerCamelCase , lowerCamelCase )
if factor:
UpperCamelCase_: Optional[Any] = factor + prime
while x in factor_map:
x += factor
UpperCamelCase_: List[str] = factor
else:
UpperCamelCase_: Dict = prime
yield prime
prime += 1
def A__ ( lowerCamelCase = 1E1_0 ) -> int:
UpperCamelCase_: Union[str, Any] = sieve()
UpperCamelCase_: List[Any] = 1
while True:
UpperCamelCase_: Tuple = next(lowerCamelCase )
if (2 * prime * n) > limit:
return n
# Ignore the next prime as the reminder will be 2.
next(lowerCamelCase )
n += 2
if __name__ == "__main__":
print(solution())
| 223 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCamelCase ( _A ):
'''simple docstring'''
__UpperCamelCase : Union[str, Any] = ["""image_processor""", """tokenizer"""]
__UpperCamelCase : List[Any] = """AutoImageProcessor"""
__UpperCamelCase : Tuple = """AutoTokenizer"""
def __init__( self : Any , snake_case_ : Optional[Any] , snake_case_ : Any ):
super().__init__(snake_case_ , snake_case_ )
UpperCamelCase_: int = self.image_processor
def __call__( self : str , snake_case_ : Optional[int]=None , snake_case_ : int=None , snake_case_ : Dict=None , **snake_case_ : Optional[int] ):
if text is None and images is None:
raise ValueError("""You have to specify either text or images. Both cannot be none.""" )
if text is not None:
UpperCamelCase_: List[Any] = self.tokenizer(snake_case_ , return_tensors=snake_case_ , **snake_case_ )
if images is not None:
UpperCamelCase_: Any = self.image_processor(snake_case_ , return_tensors=snake_case_ , **snake_case_ )
if text is not None and images is not None:
UpperCamelCase_: Tuple = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**snake_case_ ) , tensor_type=snake_case_ )
def lowerCAmelCase__ ( self : List[str] , *snake_case_ : int , **snake_case_ : Optional[Any] ):
return self.tokenizer.batch_decode(*snake_case_ , **snake_case_ )
def lowerCAmelCase__ ( self : int , *snake_case_ : Optional[Any] , **snake_case_ : str ):
return self.tokenizer.decode(*snake_case_ , **snake_case_ )
@property
def lowerCAmelCase__ ( self : Union[str, Any] ):
return ["input_ids", "attention_mask", "pixel_values"]
| 223 | 1 |
"""simple docstring"""
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
__A = "python tqdm regex requests packaging filelock numpy tokenizers".split()
if sys.version_info < (3, 7):
pkgs_to_check_at_runtime.append("dataclasses")
if sys.version_info < (3, 8):
pkgs_to_check_at_runtime.append("importlib_metadata")
for pkg in pkgs_to_check_at_runtime:
if pkg in deps:
if pkg == "tokenizers":
# must be loaded here, or else tqdm check may fail
from .utils import is_tokenizers_available
if not is_tokenizers_available():
continue # not required, check version only if installed
require_version_core(deps[pkg])
else:
raise ValueError(f'''can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py''')
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase=None ) -> Dict:
require_version(deps[pkg] , __UpperCAmelCase )
| 177 | """simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class UpperCAmelCase (unittest.TestCase ):
"""simple docstring"""
def __init__( self , _UpperCAmelCase , _UpperCAmelCase=13 , _UpperCAmelCase=7 , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=99 , _UpperCAmelCase=32 , _UpperCAmelCase=5 , _UpperCAmelCase=4 , _UpperCAmelCase=37 , _UpperCAmelCase="gelu" , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.1 , _UpperCAmelCase=512 , _UpperCAmelCase=16 , _UpperCAmelCase=2 , _UpperCAmelCase=0.02 , _UpperCAmelCase=4 , ):
lowercase__: Dict = parent
lowercase__: List[str] = batch_size
lowercase__: Optional[Any] = seq_length
lowercase__: List[Any] = is_training
lowercase__: int = use_attention_mask
lowercase__: Tuple = use_token_type_ids
lowercase__: Union[str, Any] = use_labels
lowercase__: str = vocab_size
lowercase__: str = hidden_size
lowercase__: str = num_hidden_layers
lowercase__: Optional[int] = num_attention_heads
lowercase__: List[str] = intermediate_size
lowercase__: List[str] = hidden_act
lowercase__: Tuple = hidden_dropout_prob
lowercase__: int = attention_probs_dropout_prob
lowercase__: int = max_position_embeddings
lowercase__: Union[str, Any] = type_vocab_size
lowercase__: List[Any] = type_sequence_label_size
lowercase__: Any = initializer_range
lowercase__: str = num_choices
def _snake_case ( self ):
lowercase__: int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase__: List[Any] = None
if self.use_attention_mask:
lowercase__: Tuple = random_attention_mask([self.batch_size, self.seq_length] )
lowercase__: List[Any] = None
if self.use_token_type_ids:
lowercase__: str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase__: Optional[int] = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_UpperCAmelCase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _snake_case ( self ):
lowercase__: str = self.prepare_config_and_inputs()
lowercase__, lowercase__, lowercase__, lowercase__: Optional[Any] = config_and_inputs
lowercase__: Dict = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
@require_flax
class UpperCAmelCase (_UpperCAmelCase ,unittest.TestCase ):
"""simple docstring"""
_UpperCAmelCase :List[str] = True
_UpperCAmelCase :Union[str, Any] = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _snake_case ( self ):
lowercase__: str = FlaxRoFormerModelTester(self )
@slow
def _snake_case ( self ):
for model_class_name in self.all_model_classes:
lowercase__: Dict = model_class_name.from_pretrained('''junnyu/roformer_chinese_small''' , from_pt=_UpperCAmelCase )
lowercase__: int = model(np.ones((1, 1) ) )
self.assertIsNotNone(_UpperCAmelCase )
@require_flax
class UpperCAmelCase (unittest.TestCase ):
"""simple docstring"""
@slow
def _snake_case ( self ):
lowercase__: Any = FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base''' )
lowercase__: Optional[int] = jnp.array([[0, 1, 2, 3, 4, 5]] )
lowercase__: List[Any] = model(_UpperCAmelCase )[0]
lowercase__: str = 50000
lowercase__: Tuple = (1, 6, vocab_size)
self.assertEqual(output.shape , _UpperCAmelCase )
lowercase__: List[Any] = jnp.array(
[[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , _UpperCAmelCase , atol=1e-4 ) )
| 177 | 1 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
A_ :int = logging.getLogger(__name__)
def A ( a_ ,a_ ) -> Tuple:
# save results
if os.path.exists(a_ ):
if os.path.exists(os.path.join(a_ ,'config.json' ) ) and os.path.isfile(
os.path.join(a_ ,'config.json' ) ):
os.remove(os.path.join(a_ ,'config.json' ) )
if os.path.exists(os.path.join(a_ ,'pytorch_model.bin' ) ) and os.path.isfile(
os.path.join(a_ ,'pytorch_model.bin' ) ):
os.remove(os.path.join(a_ ,'pytorch_model.bin' ) )
else:
os.makedirs(a_ )
model.save_pretrained(a_ )
def A ( a_ ,a_=False ) -> Optional[Any]:
__UpperCamelCase : Optional[Any] =2
if unlogit:
__UpperCamelCase : List[str] =torch.pow(a_ ,a_ )
__UpperCamelCase : str =p * torch.log(a_ )
__UpperCamelCase : str =0
return -plogp.sum(dim=-1 )
def A ( a_ ) -> str:
logger.info('lv, h >\t' + '\t'.join(F'{x + 1}' for x in range(len(a_ ) ) ) )
for row in range(len(a_ ) ):
if tensor.dtype != torch.long:
logger.info(F'layer {row + 1}:\t' + '\t'.join(F'{x:.5f}' for x in tensor[row].cpu().data ) )
else:
logger.info(F'layer {row + 1}:\t' + '\t'.join(F'{x:d}' for x in tensor[row].cpu().data ) )
def A ( a_ ,a_ ,a_ ,a_=True ,a_=True ,a_=None ,a_=False ) -> List[str]:
__UpperCamelCase : Any =model.config.num_hidden_layers, model.config.num_attention_heads
__UpperCamelCase : Union[str, Any] =torch.zeros(a_ ,a_ ).to(args.device )
__UpperCamelCase : List[Any] =torch.zeros(a_ ,a_ ).to(args.device )
if head_mask is None:
__UpperCamelCase : str =torch.ones(a_ ,a_ ).to(args.device )
head_mask.requires_grad_(requires_grad=a_ )
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
__UpperCamelCase : List[str] =None
__UpperCamelCase : str =0.0
__UpperCamelCase : str =0.0
for step, inputs in enumerate(tqdm(a_ ,desc='Iteration' ,disable=args.local_rank not in [-1, 0] ) ):
__UpperCamelCase : List[str] =tuple(t.to(args.device ) for t in inputs )
(__UpperCamelCase ) : Optional[int] =inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
__UpperCamelCase : List[str] =model(a_ ,labels=a_ ,head_mask=a_ )
# (loss), lm_logits, presents, (all hidden_states), (attentions)
__UpperCamelCase : Dict =(
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(a_ ):
__UpperCamelCase : Any =entropy(attn.detach() ,a_ )
attn_entropy[layer] += masked_entropy.sum(-1 ).sum(0 ).sum(0 ).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(a_ ).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
__UpperCamelCase : List[Any] =2
__UpperCamelCase : Any =torch.pow(torch.pow(a_ ,a_ ).sum(-1 ) ,1 / exponent )
head_importance /= norm_by_layer.unsqueeze(-1 ) + 1e-20
if not args.dont_normalize_global_importance:
__UpperCamelCase : Tuple =(head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info('Attention entropies' )
print_ad_tensor(a_ )
if compute_importance:
logger.info('Head importance scores' )
print_ad_tensor(a_ )
logger.info('Head ranked by importance scores' )
__UpperCamelCase : List[Any] =torch.zeros(head_importance.numel() ,dtype=torch.long ,device=args.device )
__UpperCamelCase : Union[str, Any] =torch.arange(
head_importance.numel() ,device=args.device )
__UpperCamelCase : List[str] =head_ranks.view_as(a_ )
print_ad_tensor(a_ )
return attn_entropy, head_importance, total_loss
def A ( a_ ,a_ ,a_ ) -> str:
__UpperCamelCase : str =compute_heads_importance(a_ ,a_ ,a_ ,compute_entropy=a_ )
__UpperCamelCase : Any =1 / loss # instead of downsteam score use the LM loss
logger.info('Pruning: original score: %f, threshold: %f' ,a_ ,original_score * args.masking_threshold )
__UpperCamelCase : str =torch.ones_like(a_ )
__UpperCamelCase : Tuple =max(1 ,int(new_head_mask.numel() * args.masking_amount ) )
__UpperCamelCase : List[Any] =original_score
while current_score >= original_score * args.masking_threshold:
__UpperCamelCase : int =new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
__UpperCamelCase : Optional[Any] =float('Inf' )
__UpperCamelCase : List[Any] =head_importance.view(-1 ).sort()[1]
if len(a_ ) <= num_to_mask:
print('BREAK BY num_to_mask' )
break
# mask heads
__UpperCamelCase : Tuple =current_heads_to_mask[:num_to_mask]
logger.info('Heads to mask: %s' ,str(current_heads_to_mask.tolist() ) )
__UpperCamelCase : Tuple =new_head_mask.view(-1 )
__UpperCamelCase : List[str] =0.0
__UpperCamelCase : Optional[Any] =new_head_mask.view_as(a_ )
__UpperCamelCase : Any =new_head_mask.clone().detach()
print_ad_tensor(a_ )
# Compute metric and head importance again
__UpperCamelCase : Any =compute_heads_importance(
a_ ,a_ ,a_ ,compute_entropy=a_ ,head_mask=a_ )
__UpperCamelCase : Optional[Any] =1 / loss
logger.info(
'Masking: current score: %f, remaining heads %d (%.1f percents)' ,a_ ,new_head_mask.sum() ,new_head_mask.sum() / new_head_mask.numel() * 100 ,)
logger.info('Final head mask' )
print_ad_tensor(a_ )
np.save(os.path.join(args.output_dir ,'head_mask.npy' ) ,head_mask.detach().cpu().numpy() )
return head_mask
def A ( a_ ,a_ ,a_ ,a_ ) -> List[str]:
__UpperCamelCase : int =datetime.now()
__UpperCamelCase : List[str] =compute_heads_importance(
a_ ,a_ ,a_ ,compute_entropy=a_ ,compute_importance=a_ ,head_mask=a_ )
__UpperCamelCase : Optional[Any] =1 / loss
__UpperCamelCase : Dict =datetime.now() - before_time
__UpperCamelCase : Union[str, Any] =sum(p.numel() for p in model.parameters() )
__UpperCamelCase : int ={
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(a_ ) )
}
for k, v in heads_to_prune.items():
if isinstance(a_ ,a_ ):
__UpperCamelCase : Dict =[
v,
]
assert sum(len(a_ ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
model.prune_heads(a_ )
__UpperCamelCase : List[Any] =sum(p.numel() for p in model.parameters() )
__UpperCamelCase : str =datetime.now()
__UpperCamelCase : Tuple =compute_heads_importance(
a_ ,a_ ,a_ ,compute_entropy=a_ ,compute_importance=a_ ,head_mask=a_ ,actually_pruned=a_ ,)
__UpperCamelCase : Union[str, Any] =1 / loss
__UpperCamelCase : Optional[Any] =datetime.now() - before_time
logger.info(
'Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)' ,a_ ,a_ ,pruned_num_params / original_num_params * 100 ,)
logger.info('Pruning: score with masking: %f score with pruning: %f' ,a_ ,a_ )
logger.info('Pruning: speed ratio (original timing / new timing): %f percents' ,original_time / new_time * 100 )
save_model(a_ ,args.output_dir )
def A ( ) -> Union[str, Any]:
__UpperCamelCase : Optional[Any] =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--data_dir' ,default=a_ ,type=a_ ,required=a_ ,help='The input data dir. Should contain the .tsv files (or other data files) for the task.' ,)
parser.add_argument(
'--model_name_or_path' ,default=a_ ,type=a_ ,required=a_ ,help='Path to pretrained model or model identifier from huggingface.co/models' ,)
parser.add_argument(
'--output_dir' ,default=a_ ,type=a_ ,required=a_ ,help='The output directory where the model predictions and checkpoints will be written.' ,)
# Other parameters
parser.add_argument(
'--config_name' ,default='' ,type=a_ ,help='Pretrained config name or path if not the same as model_name_or_path' ,)
parser.add_argument(
'--tokenizer_name' ,default='' ,type=a_ ,help='Pretrained tokenizer name or path if not the same as model_name_or_path' ,)
parser.add_argument(
'--cache_dir' ,default=a_ ,type=a_ ,help='Where do you want to store the pre-trained models downloaded from s3' ,)
parser.add_argument(
'--data_subset' ,type=a_ ,default=-1 ,help='If > 0: limit the data to a subset of data_subset instances.' )
parser.add_argument(
'--overwrite_output_dir' ,action='store_true' ,help='Whether to overwrite data in output directory' )
parser.add_argument(
'--overwrite_cache' ,action='store_true' ,help='Overwrite the cached training and evaluation sets' )
parser.add_argument(
'--dont_normalize_importance_by_layer' ,action='store_true' ,help='Don\'t normalize importance score by layers' )
parser.add_argument(
'--dont_normalize_global_importance' ,action='store_true' ,help='Don\'t normalize all importance scores between 0 and 1' ,)
parser.add_argument(
'--try_masking' ,action='store_true' ,help='Whether to try to mask head until a threshold of accuracy.' )
parser.add_argument(
'--masking_threshold' ,default=0.9 ,type=a_ ,help='masking threshold in term of metrics (stop masking when metric < threshold * original metric value).' ,)
parser.add_argument(
'--masking_amount' ,default=0.1 ,type=a_ ,help='Amount to heads to masking at each masking step.' )
parser.add_argument('--metric_name' ,default='acc' ,type=a_ ,help='Metric to use for head masking.' )
parser.add_argument(
'--max_seq_length' ,default=128 ,type=a_ ,help=(
'The maximum total input sequence length after WordPiece tokenization. \n'
'Sequences longer than this will be truncated, sequences shorter padded.'
) ,)
parser.add_argument('--batch_size' ,default=1 ,type=a_ ,help='Batch size.' )
parser.add_argument('--seed' ,type=a_ ,default=42 )
parser.add_argument('--local_rank' ,type=a_ ,default=-1 ,help='local_rank for distributed training on gpus' )
parser.add_argument('--no_cuda' ,action='store_true' ,help='Whether not to use CUDA when available' )
parser.add_argument('--server_ip' ,type=a_ ,default='' ,help='Can be used for distant debugging.' )
parser.add_argument('--server_port' ,type=a_ ,default='' ,help='Can be used for distant debugging.' )
__UpperCamelCase : int =parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('Waiting for debugger attach' )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) ,redirect_output=a_ )
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
__UpperCamelCase : Optional[int] =torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu' )
__UpperCamelCase : Optional[Any] =0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank )
__UpperCamelCase : Union[str, Any] =torch.device('cuda' ,args.local_rank )
__UpperCamelCase : List[str] =1
torch.distributed.init_process_group(backend='nccl' ) # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN )
logger.info('device: {} n_gpu: {}, distributed: {}'.format(args.device ,args.n_gpu ,bool(args.local_rank != -1 ) ) )
__UpperCamelCase : Any =GPTaLMHeadModel.from_pretrained(args.model_name_or_path )
# Distributed and parallel training
model.to(args.device )
if args.local_rank != -1:
__UpperCamelCase : str =nn.parallel.DistributedDataParallel(
a_ ,device_ids=[args.local_rank] ,output_device=args.local_rank ,find_unused_parameters=a_ )
elif args.n_gpu > 1:
__UpperCamelCase : Union[str, Any] =nn.DataParallel(a_ )
# Print/save training arguments
os.makedirs(args.output_dir ,exist_ok=a_ )
torch.save(a_ ,os.path.join(args.output_dir ,'run_args.bin' ) )
logger.info('Training/evaluation parameters %s' ,a_ )
# Prepare dataset
__UpperCamelCase : Optional[Any] =np.concatenate(
[
np.loadtxt(args.data_dir ,dtype=np.intaa ),
] )
__UpperCamelCase : Optional[int] =(torch.from_numpy(a_ ),)
__UpperCamelCase : str =TensorDataset(*a_ )
__UpperCamelCase : Optional[int] =RandomSampler(a_ )
__UpperCamelCase : List[str] =DataLoader(a_ ,sampler=a_ ,batch_size=args.batch_size )
# Compute head entropy and importance score
compute_heads_importance(a_ ,a_ ,a_ )
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
__UpperCamelCase : int =mask_heads(a_ ,a_ ,a_ )
prune_heads(a_ ,a_ ,a_ ,a_ )
if __name__ == "__main__":
main()
| 366 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class __A ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Optional[Any] =[
'safety_checker/pytorch_model.bin',
'safety_checker/model.safetensors',
'vae/diffusion_pytorch_model.bin',
'vae/diffusion_pytorch_model.safetensors',
'text_encoder/pytorch_model.bin',
'text_encoder/model.safetensors',
'unet/diffusion_pytorch_model.bin',
'unet/diffusion_pytorch_model.safetensors',
]
self.assertTrue(is_safetensors_compatible(lowerCamelCase__ ) )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Any =[
'unet/diffusion_pytorch_model.bin',
'unet/diffusion_pytorch_model.safetensors',
]
self.assertTrue(is_safetensors_compatible(lowerCamelCase__ ) )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : List[str] =[
'safety_checker/pytorch_model.bin',
'safety_checker/model.safetensors',
'vae/diffusion_pytorch_model.bin',
'vae/diffusion_pytorch_model.safetensors',
'text_encoder/pytorch_model.bin',
'text_encoder/model.safetensors',
'unet/diffusion_pytorch_model.bin',
# Removed: 'unet/diffusion_pytorch_model.safetensors',
]
self.assertFalse(is_safetensors_compatible(lowerCamelCase__ ) )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : List[str] =[
'text_encoder/pytorch_model.bin',
'text_encoder/model.safetensors',
]
self.assertTrue(is_safetensors_compatible(lowerCamelCase__ ) )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Any =[
'safety_checker/pytorch_model.bin',
'safety_checker/model.safetensors',
'vae/diffusion_pytorch_model.bin',
'vae/diffusion_pytorch_model.safetensors',
'text_encoder/pytorch_model.bin',
# Removed: 'text_encoder/model.safetensors',
'unet/diffusion_pytorch_model.bin',
'unet/diffusion_pytorch_model.safetensors',
]
self.assertFalse(is_safetensors_compatible(lowerCamelCase__ ) )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Tuple =[
'safety_checker/pytorch_model.fp16.bin',
'safety_checker/model.fp16.safetensors',
'vae/diffusion_pytorch_model.fp16.bin',
'vae/diffusion_pytorch_model.fp16.safetensors',
'text_encoder/pytorch_model.fp16.bin',
'text_encoder/model.fp16.safetensors',
'unet/diffusion_pytorch_model.fp16.bin',
'unet/diffusion_pytorch_model.fp16.safetensors',
]
__UpperCamelCase : List[Any] ='fp16'
self.assertTrue(is_safetensors_compatible(lowerCamelCase__ , variant=lowerCamelCase__ ) )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : str =[
'unet/diffusion_pytorch_model.fp16.bin',
'unet/diffusion_pytorch_model.fp16.safetensors',
]
__UpperCamelCase : str ='fp16'
self.assertTrue(is_safetensors_compatible(lowerCamelCase__ , variant=lowerCamelCase__ ) )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Optional[int] =[
'unet/diffusion_pytorch_model.bin',
'unet/diffusion_pytorch_model.safetensors',
]
__UpperCamelCase : int ='fp16'
self.assertTrue(is_safetensors_compatible(lowerCamelCase__ , variant=lowerCamelCase__ ) )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Any =[
'safety_checker/pytorch_model.fp16.bin',
'safety_checker/model.fp16.safetensors',
'vae/diffusion_pytorch_model.fp16.bin',
'vae/diffusion_pytorch_model.fp16.safetensors',
'text_encoder/pytorch_model.fp16.bin',
'text_encoder/model.fp16.safetensors',
'unet/diffusion_pytorch_model.fp16.bin',
# Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
]
__UpperCamelCase : str ='fp16'
self.assertFalse(is_safetensors_compatible(lowerCamelCase__ , variant=lowerCamelCase__ ) )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : List[str] =[
'text_encoder/pytorch_model.fp16.bin',
'text_encoder/model.fp16.safetensors',
]
__UpperCamelCase : Any ='fp16'
self.assertTrue(is_safetensors_compatible(lowerCamelCase__ , variant=lowerCamelCase__ ) )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : List[Any] =[
'text_encoder/pytorch_model.bin',
'text_encoder/model.safetensors',
]
__UpperCamelCase : Tuple ='fp16'
self.assertTrue(is_safetensors_compatible(lowerCamelCase__ , variant=lowerCamelCase__ ) )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Any =[
'safety_checker/pytorch_model.fp16.bin',
'safety_checker/model.fp16.safetensors',
'vae/diffusion_pytorch_model.fp16.bin',
'vae/diffusion_pytorch_model.fp16.safetensors',
'text_encoder/pytorch_model.fp16.bin',
# 'text_encoder/model.fp16.safetensors',
'unet/diffusion_pytorch_model.fp16.bin',
'unet/diffusion_pytorch_model.fp16.safetensors',
]
__UpperCamelCase : Any ='fp16'
self.assertFalse(is_safetensors_compatible(lowerCamelCase__ , variant=lowerCamelCase__ ) )
| 245 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def UpperCAmelCase_ ( __lowerCamelCase : Optional[Any] ):
lowercase_ :Dict = SwinConfig(image_size=1_92 )
if "base" in model_name:
lowercase_ :List[str] = 6
lowercase_ :List[str] = 1_28
lowercase_ :Optional[int] = (2, 2, 18, 2)
lowercase_ :Union[str, Any] = (4, 8, 16, 32)
elif "large" in model_name:
lowercase_ :Union[str, Any] = 12
lowercase_ :int = 1_92
lowercase_ :Any = (2, 2, 18, 2)
lowercase_ :Optional[int] = (6, 12, 24, 48)
else:
raise ValueError("Model not supported, only supports base and large variants" )
lowercase_ :Optional[Any] = window_size
lowercase_ :Any = embed_dim
lowercase_ :List[str] = depths
lowercase_ :Optional[Any] = num_heads
return config
def UpperCAmelCase_ ( __lowerCamelCase : int ):
if "encoder.mask_token" in name:
lowercase_ :Tuple = name.replace("encoder.mask_token" ,"embeddings.mask_token" )
if "encoder.patch_embed.proj" in name:
lowercase_ :List[str] = name.replace("encoder.patch_embed.proj" ,"embeddings.patch_embeddings.projection" )
if "encoder.patch_embed.norm" in name:
lowercase_ :int = name.replace("encoder.patch_embed.norm" ,"embeddings.norm" )
if "attn.proj" in name:
lowercase_ :Tuple = name.replace("attn.proj" ,"attention.output.dense" )
if "attn" in name:
lowercase_ :Tuple = name.replace("attn" ,"attention.self" )
if "norm1" in name:
lowercase_ :List[str] = name.replace("norm1" ,"layernorm_before" )
if "norm2" in name:
lowercase_ :str = name.replace("norm2" ,"layernorm_after" )
if "mlp.fc1" in name:
lowercase_ :Optional[Any] = name.replace("mlp.fc1" ,"intermediate.dense" )
if "mlp.fc2" in name:
lowercase_ :List[str] = name.replace("mlp.fc2" ,"output.dense" )
if name == "encoder.norm.weight":
lowercase_ :List[Any] = "layernorm.weight"
if name == "encoder.norm.bias":
lowercase_ :Dict = "layernorm.bias"
if "decoder" in name:
pass
else:
lowercase_ :Union[str, Any] = "swin." + name
return name
def UpperCAmelCase_ ( __lowerCamelCase : int ,__lowerCamelCase : Tuple ):
for key in orig_state_dict.copy().keys():
lowercase_ :Union[str, Any] = orig_state_dict.pop(__lowerCAmelCase )
if "attn_mask" in key:
pass
elif "qkv" in key:
lowercase_ :Dict = key.split("." )
lowercase_ :List[str] = int(key_split[2] )
lowercase_ :Dict = int(key_split[4] )
lowercase_ :Tuple = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
lowercase_ :Dict = val[:dim, :]
lowercase_ :str = val[
dim : dim * 2, :
]
lowercase_ :Any = val[-dim:, :]
else:
lowercase_ :Tuple = val[
:dim
]
lowercase_ :Optional[Any] = val[
dim : dim * 2
]
lowercase_ :List[str] = val[
-dim:
]
else:
lowercase_ :Optional[int] = val
return orig_state_dict
def UpperCAmelCase_ ( __lowerCamelCase : Optional[int] ,__lowerCamelCase : Optional[Any] ,__lowerCamelCase : Optional[Any] ,__lowerCamelCase : Tuple ):
lowercase_ :Dict = torch.load(__lowerCAmelCase ,map_location="cpu" )["model"]
lowercase_ :List[str] = get_swin_config(__lowerCAmelCase )
lowercase_ :int = SwinForMaskedImageModeling(__lowerCAmelCase )
model.eval()
lowercase_ :List[Any] = convert_state_dict(__lowerCAmelCase ,__lowerCAmelCase )
model.load_state_dict(__lowerCAmelCase )
lowercase_ :Optional[Any] = "http://images.cocodataset.org/val2017/000000039769.jpg"
lowercase_ :Union[str, Any] = ViTImageProcessor(size={"height": 1_92, "width": 1_92} )
lowercase_ :Optional[int] = Image.open(requests.get(__lowerCAmelCase ,stream=__lowerCAmelCase ).raw )
lowercase_ :List[str] = image_processor(images=__lowerCAmelCase ,return_tensors="pt" )
with torch.no_grad():
lowercase_ :Tuple = model(**__lowerCAmelCase ).logits
print(outputs.keys() )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
print(F'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(__lowerCAmelCase )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(__lowerCAmelCase )
if push_to_hub:
print(F'Pushing model and image processor for {model_name} to hub' )
model.push_to_hub(F'microsoft/{model_name}' )
image_processor.push_to_hub(F'microsoft/{model_name}' )
if __name__ == "__main__":
lowerCAmelCase : Tuple =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''swin-base-simmim-window6-192''',
type=str,
choices=['''swin-base-simmim-window6-192''', '''swin-large-simmim-window12-192'''],
help='''Name of the Swin SimMIM model you\'d like to convert.''',
)
parser.add_argument(
'''--checkpoint_path''',
default='''/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth''',
type=str,
help='''Path to the original PyTorch checkpoint (.pth file).''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
lowerCAmelCase : Tuple =parser.parse_args()
convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
| 223 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ : Dict = {
"facebook/mask2former-swin-small-coco-instance": (
"https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"
)
# See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
class lowerCAmelCase__ ( __lowercase ):
a__ : Any = """mask2former"""
a__ : Dict = ["""swin"""]
a__ : Any = {"""hidden_size""": """hidden_dim"""}
def __init__( self : Any , SCREAMING_SNAKE_CASE__ : Optional[Dict] = None , SCREAMING_SNAKE_CASE__ : int = 2_56 , SCREAMING_SNAKE_CASE__ : int = 2_56 , SCREAMING_SNAKE_CASE__ : int = 2_56 , SCREAMING_SNAKE_CASE__ : int = 10_24 , SCREAMING_SNAKE_CASE__ : str = "relu" , SCREAMING_SNAKE_CASE__ : int = 6 , SCREAMING_SNAKE_CASE__ : int = 10 , SCREAMING_SNAKE_CASE__ : int = 8 , SCREAMING_SNAKE_CASE__ : float = 0.0 , SCREAMING_SNAKE_CASE__ : int = 20_48 , SCREAMING_SNAKE_CASE__ : bool = False , SCREAMING_SNAKE_CASE__ : bool = False , SCREAMING_SNAKE_CASE__ : int = 4 , SCREAMING_SNAKE_CASE__ : int = 2_55 , SCREAMING_SNAKE_CASE__ : int = 1_00 , SCREAMING_SNAKE_CASE__ : float = 0.1 , SCREAMING_SNAKE_CASE__ : float = 2.0 , SCREAMING_SNAKE_CASE__ : float = 5.0 , SCREAMING_SNAKE_CASE__ : float = 5.0 , SCREAMING_SNAKE_CASE__ : int = 1_25_44 , SCREAMING_SNAKE_CASE__ : float = 3.0 , SCREAMING_SNAKE_CASE__ : float = 0.75 , SCREAMING_SNAKE_CASE__ : float = 0.02 , SCREAMING_SNAKE_CASE__ : float = 1.0 , SCREAMING_SNAKE_CASE__ : bool = True , SCREAMING_SNAKE_CASE__ : List[int] = [4, 8, 16, 32] , SCREAMING_SNAKE_CASE__ : bool = None , **SCREAMING_SNAKE_CASE__ : Tuple , ) -> str:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.''' )
__lowerCamelCase = CONFIG_MAPPING['''swin'''](
image_size=2_24 , in_channels=3 , patch_size=4 , embed_dim=96 , depths=[2, 2, 18, 2] , num_heads=[3, 6, 12, 24] , window_size=7 , drop_path_rate=0.3 , use_absolute_embeddings=SCREAMING_SNAKE_CASE__ , out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] , )
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase = backbone_config.pop('''model_type''' )
__lowerCamelCase = CONFIG_MAPPING[backbone_model_type]
__lowerCamelCase = config_class.from_dict(SCREAMING_SNAKE_CASE__ )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '''
f'''Supported model types: {','.join(self.backbones_supported )}''' )
__lowerCamelCase = backbone_config
__lowerCamelCase = feature_size
__lowerCamelCase = mask_feature_size
__lowerCamelCase = hidden_dim
__lowerCamelCase = encoder_feedforward_dim
__lowerCamelCase = activation_function
__lowerCamelCase = encoder_layers
__lowerCamelCase = decoder_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = dropout
__lowerCamelCase = dim_feedforward
__lowerCamelCase = pre_norm
__lowerCamelCase = enforce_input_projection
__lowerCamelCase = common_stride
__lowerCamelCase = ignore_value
__lowerCamelCase = num_queries
__lowerCamelCase = no_object_weight
__lowerCamelCase = class_weight
__lowerCamelCase = mask_weight
__lowerCamelCase = dice_weight
__lowerCamelCase = train_num_points
__lowerCamelCase = oversample_ratio
__lowerCamelCase = importance_sample_ratio
__lowerCamelCase = init_std
__lowerCamelCase = init_xavier_std
__lowerCamelCase = use_auxiliary_loss
__lowerCamelCase = feature_strides
__lowerCamelCase = output_auxiliary_logits
__lowerCamelCase = decoder_layers
super().__init__(**SCREAMING_SNAKE_CASE__ )
@classmethod
def __A ( cls : Any , SCREAMING_SNAKE_CASE__ : PretrainedConfig , **SCREAMING_SNAKE_CASE__ : Optional[Any] ) -> List[Any]:
return cls(
backbone_config=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
def __A ( self : Any ) -> Dict[str, any]:
__lowerCamelCase = copy.deepcopy(self.__dict__ )
__lowerCamelCase = self.backbone_config.to_dict()
__lowerCamelCase = self.__class__.model_type
return output
| 270 | 0 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
UpperCamelCase : List[Any] = inspect.getfile(accelerate.test_utils )
UpperCamelCase : Union[str, Any] = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ["""scripts""", """external_deps""", """test_metrics.py"""] )
from accelerate.test_utils.scripts.external_deps import test_metrics # noqa: F401
UpperCamelCase : Optional[Any] = test_metrics
@require_cpu
def a_ ( self ):
debug_launcher(self.test_metrics.main , num_processes=1 )
@require_cpu
def a_ ( self ):
debug_launcher(self.test_metrics.main )
@require_single_gpu
def a_ ( self ):
self.test_metrics.main()
@require_multi_gpu
def a_ ( self ):
print(f'Found {torch.cuda.device_count()} devices.' )
UpperCamelCase : Union[str, Any] = ["""torchrun""", f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(SCREAMING_SNAKE_CASE_ , env=os.environ.copy() )
| 27 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def a_ ( self ):
UpperCamelCase : Tuple = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/sd2-inpaint/init_image.png""" )
UpperCamelCase : int = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
UpperCamelCase : Dict = """xvjiarui/stable-diffusion-2-inpainting"""
UpperCamelCase , UpperCamelCase : List[str] = FlaxStableDiffusionInpaintPipeline.from_pretrained(SCREAMING_SNAKE_CASE_ , safety_checker=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = """Face of a yellow cat, high resolution, sitting on a park bench"""
UpperCamelCase : List[str] = jax.random.PRNGKey(0 )
UpperCamelCase : Tuple = 50
UpperCamelCase : Dict = jax.device_count()
UpperCamelCase : Optional[int] = num_samples * [prompt]
UpperCamelCase : int = num_samples * [init_image]
UpperCamelCase : List[Any] = num_samples * [mask_image]
UpperCamelCase , UpperCamelCase , UpperCamelCase : Optional[Any] = pipeline.prepare_inputs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# shard inputs and rng
UpperCamelCase : Optional[int] = replicate(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = jax.random.split(SCREAMING_SNAKE_CASE_ , jax.device_count() )
UpperCamelCase : str = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = pipeline(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , jit=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = output.images.reshape(SCREAMING_SNAKE_CASE_ , 512 , 512 , 3 )
UpperCamelCase : List[Any] = images[0, 253:256, 253:256, -1]
UpperCamelCase : List[Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
UpperCamelCase : Dict = jnp.array(
[0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 27 | 1 |
def _snake_case( SCREAMING_SNAKE_CASE__ : int = 4000000 ) -> int:
'''simple docstring'''
A__ = [0, 1]
A__ = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
A__ = 0
for j in range(len(SCREAMING_SNAKE_CASE__ ) - 1 ):
if fib[j] % 2 == 0:
total += fib[j]
return total
if __name__ == "__main__":
print(f"""{solution() = }""")
| 7 |
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 lowercase__ ( _UpperCAmelCase ):
a_ =["""image_processor""", """tokenizer"""]
a_ ="""LayoutLMv2ImageProcessor"""
a_ =("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")
def __init__( self , __UpperCAmelCase=None , __UpperCAmelCase=None , **__UpperCAmelCase )-> Tuple:
'''simple docstring'''
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , __UpperCAmelCase , )
lowerCAmelCase__ = kwargs.pop("feature_extractor" )
lowerCAmelCase__ = 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__(__UpperCAmelCase , __UpperCAmelCase )
def __call__( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = True , __UpperCAmelCase = False , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = 0 , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = False , __UpperCAmelCase = False , __UpperCAmelCase = False , __UpperCAmelCase = False , __UpperCAmelCase = True , __UpperCAmelCase = None , **__UpperCAmelCase , )-> BatchEncoding:
'''simple docstring'''
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." )
if return_overflowing_tokens is True and return_offsets_mapping is False:
raise ValueError("You cannot return overflowing tokens without returning the offsets mapping." )
# first, apply the image processor
lowerCAmelCase__ = self.image_processor(images=__UpperCAmelCase , return_tensors=__UpperCAmelCase )
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
lowerCAmelCase__ = [text] # add batch dimension (as the image processor always adds a batch dimension)
lowerCAmelCase__ = features["words"]
lowerCAmelCase__ = 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=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase , padding=__UpperCAmelCase , truncation=__UpperCAmelCase , max_length=__UpperCAmelCase , stride=__UpperCAmelCase , pad_to_multiple_of=__UpperCAmelCase , return_token_type_ids=__UpperCAmelCase , return_attention_mask=__UpperCAmelCase , return_overflowing_tokens=__UpperCAmelCase , return_special_tokens_mask=__UpperCAmelCase , return_offsets_mapping=__UpperCAmelCase , return_length=__UpperCAmelCase , verbose=__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase , )
# add pixel values
lowerCAmelCase__ = features.pop("pixel_values" )
if return_overflowing_tokens is True:
lowerCAmelCase__ = self.get_overflowing_images(__UpperCAmelCase , encoded_inputs["overflow_to_sample_mapping"] )
lowerCAmelCase__ = images
return encoded_inputs
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase )-> str:
'''simple docstring'''
lowerCAmelCase__ = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx] )
if len(__UpperCAmelCase ) != len(__UpperCAmelCase ):
raise ValueError(
"Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
F" {len(__UpperCAmelCase )} and {len(__UpperCAmelCase )}" )
return images_with_overflow
def UpperCAmelCase ( self , *__UpperCAmelCase , **__UpperCAmelCase )-> Union[str, Any]:
'''simple docstring'''
return self.tokenizer.batch_decode(*__UpperCAmelCase , **__UpperCAmelCase )
def UpperCAmelCase ( self , *__UpperCAmelCase , **__UpperCAmelCase )-> Dict:
'''simple docstring'''
return self.tokenizer.decode(*__UpperCAmelCase , **__UpperCAmelCase )
@property
def UpperCAmelCase ( self )-> Optional[int]:
'''simple docstring'''
return ["input_ids", "bbox", "attention_mask", "image"]
@property
def UpperCAmelCase ( self )-> Union[str, Any]:
'''simple docstring'''
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , __UpperCAmelCase , )
return self.image_processor_class
@property
def UpperCAmelCase ( self )-> str:
'''simple docstring'''
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , __UpperCAmelCase , )
return self.image_processor
| 340 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase_ = {
'configuration_chinese_clip': [
'CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ChineseCLIPConfig',
'ChineseCLIPOnnxConfig',
'ChineseCLIPTextConfig',
'ChineseCLIPVisionConfig',
],
'processing_chinese_clip': ['ChineseCLIPProcessor'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['ChineseCLIPFeatureExtractor']
lowerCAmelCase_ = ['ChineseCLIPImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'ChineseCLIPModel',
'ChineseCLIPPreTrainedModel',
'ChineseCLIPTextModel',
'ChineseCLIPVisionModel',
]
if TYPE_CHECKING:
from .configuration_chinese_clip import (
CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
ChineseCLIPConfig,
ChineseCLIPOnnxConfig,
ChineseCLIPTextConfig,
ChineseCLIPVisionConfig,
)
from .processing_chinese_clip import ChineseCLIPProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_chinese_clip import (
CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
ChineseCLIPModel,
ChineseCLIPPreTrainedModel,
ChineseCLIPTextModel,
ChineseCLIPVisionModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 368 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCamelCase ( metaclass=__lowerCAmelCase ):
snake_case_ = ['''note_seq''']
def __init__( self, *lowercase_, **lowercase_ ) -> str:
requires_backends(self, ['note_seq'] )
@classmethod
def _lowerCamelCase ( cls, *lowercase_, **lowercase_ ) -> Union[str, Any]:
requires_backends(cls, ['note_seq'] )
@classmethod
def _lowerCamelCase ( cls, *lowercase_, **lowercase_ ) -> List[Any]:
requires_backends(cls, ['note_seq'] )
| 332 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthez import BarthezTokenizer
else:
lowerCAmelCase : Tuple =None
lowerCAmelCase : List[Any] =logging.get_logger(__name__)
lowerCAmelCase : Any ={'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
lowerCAmelCase : Dict ={
'''vocab_file''': {
'''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
'''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
'''moussaKam/barthez-orangesum-title''': (
'''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
),
},
'''tokenizer_file''': {
'''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
'''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
'''moussaKam/barthez-orangesum-title''': (
'''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
),
},
}
lowerCAmelCase : Tuple ={
'''moussaKam/mbarthez''': 1_024,
'''moussaKam/barthez''': 1_024,
'''moussaKam/barthez-orangesum-title''': 1_024,
}
lowerCAmelCase : Tuple ='''▁'''
class a_ ( _lowerCAmelCase ):
__A = VOCAB_FILES_NAMES
__A = PRETRAINED_VOCAB_FILES_MAP
__A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A = ["input_ids", "attention_mask"]
__A = BarthezTokenizer
def __init__( self : List[str] , lowercase : Tuple=None , lowercase : Any=None , lowercase : Optional[int]="<s>" , lowercase : Union[str, Any]="</s>" , lowercase : List[Any]="</s>" , lowercase : List[Any]="<s>" , lowercase : Optional[Any]="<unk>" , lowercase : str="<pad>" , lowercase : Optional[int]="<mask>" , **lowercase : Optional[Any] , ):
"""simple docstring"""
lowercase_ :str = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else mask_token
super().__init__(
lowercase , tokenizer_file=lowercase , bos_token=lowercase , eos_token=lowercase , unk_token=lowercase , sep_token=lowercase , cls_token=lowercase , pad_token=lowercase , mask_token=lowercase , **lowercase , )
lowercase_ :Union[str, Any] = vocab_file
lowercase_ :Optional[Any] = False if not self.vocab_file else True
def lowercase__ ( self : Union[str, Any] , lowercase : List[int] , lowercase : Optional[List[int]] = None ):
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowercase_ :Any = [self.cls_token_id]
lowercase_ :Any = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowercase__ ( self : Optional[int] , lowercase : List[int] , lowercase : Optional[List[int]] = None ):
"""simple docstring"""
lowercase_ :Optional[Any] = [self.sep_token_id]
lowercase_ :Tuple = [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 lowercase__ ( self : int , lowercase : str , lowercase : Optional[str] = None ):
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer." )
if not os.path.isdir(lowercase ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
lowercase_ :Optional[int] = os.path.join(
lowercase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase ):
copyfile(self.vocab_file , lowercase )
return (out_vocab_file,)
| 223 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase : Optional[Any] ={
'''configuration_nllb_moe''': [
'''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''NllbMoeConfig''',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : Tuple =[
'''NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''NllbMoeForConditionalGeneration''',
'''NllbMoeModel''',
'''NllbMoePreTrainedModel''',
'''NllbMoeTop2Router''',
'''NllbMoeSparseMLP''',
]
if TYPE_CHECKING:
from .configuration_nllb_moe import (
NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
NllbMoeConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nllb_moe import (
NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
NllbMoeForConditionalGeneration,
NllbMoeModel,
NllbMoePreTrainedModel,
NllbMoeSparseMLP,
NllbMoeTopaRouter,
)
else:
import sys
lowerCAmelCase : Tuple =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 223 | 1 |
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLModel,
)
class _snake_case :
def __init__( self: List[str] , __lowerCamelCase: int , ) -> Tuple:
__UpperCAmelCase : Optional[int] = parent
__UpperCAmelCase : Any = 13
__UpperCAmelCase : List[Any] = 7
__UpperCAmelCase : Tuple = 30
__UpperCAmelCase : Union[str, Any] = self.seq_length + self.mem_len
__UpperCAmelCase : List[Any] = 15
__UpperCAmelCase : Union[str, Any] = True
__UpperCAmelCase : str = True
__UpperCAmelCase : List[Any] = 99
__UpperCAmelCase : Any = [10, 50, 80]
__UpperCAmelCase : Union[str, Any] = 32
__UpperCAmelCase : List[str] = 32
__UpperCAmelCase : int = 4
__UpperCAmelCase : List[Any] = 8
__UpperCAmelCase : Union[str, Any] = 1_28
__UpperCAmelCase : str = 2
__UpperCAmelCase : str = 2
__UpperCAmelCase : Optional[int] = None
__UpperCAmelCase : Dict = 1
__UpperCAmelCase : Dict = 0
__UpperCAmelCase : Union[str, Any] = 3
__UpperCAmelCase : Optional[Any] = self.vocab_size - 1
__UpperCAmelCase : Union[str, Any] = 0.01
def _lowerCamelCase ( self: Union[str, Any] ) -> Optional[int]:
__UpperCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCAmelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCAmelCase : List[str] = None
if self.use_labels:
__UpperCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCAmelCase : str = TransfoXLConfig(
vocab_size=self.vocab_size , mem_len=self.mem_len , clamp_len=self.clamp_len , cutoffs=self.cutoffs , d_model=self.hidden_size , d_embed=self.d_embed , n_head=self.num_attention_heads , d_head=self.d_head , d_inner=self.d_inner , div_val=self.div_val , n_layer=self.num_hidden_layers , eos_token_id=self.eos_token_id , pad_token_id=self.vocab_size - 1 , init_range=self.init_range , num_labels=self.num_labels , )
return (config, input_ids_a, input_ids_a, lm_labels)
def _lowerCamelCase ( self: str ) -> List[Any]:
random.seed(self.seed )
tf.random.set_seed(self.seed )
def _lowerCamelCase ( self: Union[str, Any] , __lowerCamelCase: Tuple , __lowerCamelCase: Any , __lowerCamelCase: Union[str, Any] , __lowerCamelCase: int ) -> Any:
__UpperCAmelCase : Optional[int] = TFTransfoXLModel(a__ )
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = model(a__ ).to_tuple()
__UpperCAmelCase : Union[str, Any] = {"input_ids": input_ids_a, "mems": mems_a}
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = model(a__ ).to_tuple()
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def _lowerCamelCase ( self: List[Any] , __lowerCamelCase: int , __lowerCamelCase: List[str] , __lowerCamelCase: List[str] , __lowerCamelCase: Dict ) -> int:
__UpperCAmelCase : Optional[Any] = TFTransfoXLLMHeadModel(a__ )
__UpperCAmelCase , __UpperCAmelCase : List[Any] = model(a__ ).to_tuple()
__UpperCAmelCase : Optional[int] = {"input_ids": input_ids_a, "labels": lm_labels}
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = model(a__ ).to_tuple()
__UpperCAmelCase , __UpperCAmelCase : Optional[Any] = model([input_ids_a, mems_a] ).to_tuple()
__UpperCAmelCase : Union[str, Any] = {"input_ids": input_ids_a, "mems": mems_a, "labels": lm_labels}
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = model(a__ ).to_tuple()
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def _lowerCamelCase ( self: List[Any] , __lowerCamelCase: str , __lowerCamelCase: Optional[int] , __lowerCamelCase: Dict , __lowerCamelCase: List[Any] ) -> Union[str, Any]:
__UpperCAmelCase : Optional[Any] = TFTransfoXLForSequenceClassification(a__ )
__UpperCAmelCase : Optional[Any] = model(a__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowerCamelCase ( self: List[str] ) -> Optional[Any]:
__UpperCAmelCase : Optional[Any] = self.prepare_config_and_inputs()
((__UpperCAmelCase) , (__UpperCAmelCase) , (__UpperCAmelCase) , (__UpperCAmelCase)) : Optional[int] = config_and_inputs
__UpperCAmelCase : Optional[int] = {"input_ids": input_ids_a}
return config, inputs_dict
@require_tf
class _snake_case ( __a , __a , unittest.TestCase ):
lowerCamelCase__: Optional[int] = (
(TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
)
lowerCamelCase__: Optional[Any] = () if is_tf_available() else ()
lowerCamelCase__: Any = (
{
"""feature-extraction""": TFTransfoXLModel,
"""text-classification""": TFTransfoXLForSequenceClassification,
"""text-generation""": TFTransfoXLLMHeadModel,
"""zero-shot""": TFTransfoXLForSequenceClassification,
}
if is_tf_available()
else {}
)
# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
lowerCamelCase__: List[str] = False
lowerCamelCase__: str = False
lowerCamelCase__: str = False
lowerCamelCase__: List[Any] = False
def _lowerCamelCase ( self: int , __lowerCamelCase: int , __lowerCamelCase: List[str] , __lowerCamelCase: Any , __lowerCamelCase: Optional[Any] , __lowerCamelCase: int ) -> int:
if pipeline_test_casse_name == "TextGenerationPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
# tokenizer.
return True
return False
def _lowerCamelCase ( self: int ) -> Optional[Any]:
__UpperCAmelCase : Union[str, Any] = TFTransfoXLModelTester(self )
__UpperCAmelCase : List[Any] = ConfigTester(self , config_class=a__ , d_embed=37 )
def _lowerCamelCase ( self: Optional[Any] ) -> Union[str, Any]:
self.config_tester.run_common_tests()
def _lowerCamelCase ( self: int ) -> str:
self.model_tester.set_seed()
__UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_model(*a__ )
def _lowerCamelCase ( self: Optional[int] ) -> Tuple:
self.model_tester.set_seed()
__UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_lm_head(*a__ )
def _lowerCamelCase ( self: Any ) -> int:
__UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*a__ )
def _lowerCamelCase ( self: Optional[int] ) -> int:
__UpperCAmelCase , __UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase : Any = [TFTransfoXLForSequenceClassification]
for model_class in self.all_model_classes:
__UpperCAmelCase : Union[str, Any] = model_class(a__ )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class in list_other_models_with_output_ebd:
__UpperCAmelCase : Union[str, Any] = model.get_output_embeddings()
assert isinstance(a__ , tf.keras.layers.Layer )
__UpperCAmelCase : List[str] = model.get_bias()
assert name is None
else:
__UpperCAmelCase : int = model.get_output_embeddings()
assert x is None
__UpperCAmelCase : List[Any] = model.get_bias()
assert name is None
def _lowerCamelCase ( self: Dict ) -> str:
# TODO JP: Make TransfoXL XLA compliant
pass
@slow
def _lowerCamelCase ( self: List[Any] ) -> str:
for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__UpperCAmelCase : Union[str, Any] = TFTransfoXLModel.from_pretrained(a__ )
self.assertIsNotNone(a__ )
@unittest.skip(reason="This model doesn\'t play well with fit() due to not returning a single loss." )
def _lowerCamelCase ( self: Any ) -> List[str]:
pass
@require_tf
class _snake_case ( unittest.TestCase ):
@unittest.skip("Skip test until #12651 is resolved." )
@slow
def _lowerCamelCase ( self: int ) -> Tuple:
__UpperCAmelCase : Dict = TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103" )
# fmt: off
__UpperCAmelCase : Union[str, Any] = tf.convert_to_tensor([[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0]] , dtype=tf.intaa ) # noqa: E231
# fmt: on
# In 1991 , the remains of Russian Tsar Nicholas II and his family
# ( except for Alexei and Maria ) are discovered .
# The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
# remainder of the story . 1883 Western Siberia ,
# a young Grigori Rasputin is asked by his father and a group of men to perform magic .
# Rasputin has a vision and denounces one of the men as a horse thief . Although his
# father initially slaps him for making such an accusation , Rasputin watches as the
# man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
# the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
# with people , even a bishop , begging for his blessing . <eod> </s> <eos>
# fmt: off
__UpperCAmelCase : Dict = [33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0,33,1,18_57,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,28,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,0] # noqa: E231
# fmt: on
# In 1991, the remains of Russian Tsar Nicholas II and his family (
# except for Alexei and Maria ) are discovered. The voice of young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
# 1883 Western Siberia, a young Grigori Rasputin is asked by his father
# and a group of men to perform magic. Rasputin has a vision and
# denounces one of the men as a horse thief. Although his father initially
# slaps him for making such an accusation, Rasputin watches as the man
# is chased outside and beaten. Twenty years later, Rasputin sees a vision
# of the Virgin Mary, prompting him to become a priest.
# Rasputin quickly becomes famous, with people, even a bishop, begging for
# his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
# Nicholas II and his family were discovered. The voice of <unk> young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>
__UpperCAmelCase : Union[str, Any] = model.generate(a__ , max_length=2_00 , do_sample=a__ )
self.assertListEqual(output_ids[0].numpy().tolist() , a__ )
| 360 | import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer
class _snake_case ( _lowercase ):
lowerCamelCase__: Any = ["image_processor", "tokenizer"]
lowerCamelCase__: Optional[Any] = "BlipImageProcessor"
lowerCamelCase__: Optional[int] = "AutoTokenizer"
def __init__( self: List[str] , __lowerCamelCase: str , __lowerCamelCase: List[str] , __lowerCamelCase: Optional[Any] ) -> Dict:
super().__init__(__lowerCamelCase , __lowerCamelCase )
# add QFormer tokenizer
__UpperCAmelCase : Dict = qformer_tokenizer
def __call__( self: Any , __lowerCamelCase: ImageInput = None , __lowerCamelCase: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , __lowerCamelCase: bool = True , __lowerCamelCase: Union[bool, str, PaddingStrategy] = False , __lowerCamelCase: Union[bool, str, TruncationStrategy] = None , __lowerCamelCase: Optional[int] = None , __lowerCamelCase: int = 0 , __lowerCamelCase: Optional[int] = None , __lowerCamelCase: Optional[bool] = None , __lowerCamelCase: bool = False , __lowerCamelCase: bool = False , __lowerCamelCase: bool = False , __lowerCamelCase: bool = False , __lowerCamelCase: bool = False , __lowerCamelCase: bool = True , __lowerCamelCase: Optional[Union[str, TensorType]] = None , **__lowerCamelCase: Dict , ) -> BatchFeature:
if images is None and text is None:
raise ValueError("You have to specify at least images or text." )
__UpperCAmelCase : str = BatchFeature()
if text is not None:
__UpperCAmelCase : Any = self.tokenizer(
text=__lowerCamelCase , add_special_tokens=__lowerCamelCase , padding=__lowerCamelCase , truncation=__lowerCamelCase , max_length=__lowerCamelCase , stride=__lowerCamelCase , pad_to_multiple_of=__lowerCamelCase , return_attention_mask=__lowerCamelCase , return_overflowing_tokens=__lowerCamelCase , return_special_tokens_mask=__lowerCamelCase , return_offsets_mapping=__lowerCamelCase , return_token_type_ids=__lowerCamelCase , return_length=__lowerCamelCase , verbose=__lowerCamelCase , return_tensors=__lowerCamelCase , **__lowerCamelCase , )
encoding.update(__lowerCamelCase )
__UpperCAmelCase : Dict = self.qformer_tokenizer(
text=__lowerCamelCase , add_special_tokens=__lowerCamelCase , padding=__lowerCamelCase , truncation=__lowerCamelCase , max_length=__lowerCamelCase , stride=__lowerCamelCase , pad_to_multiple_of=__lowerCamelCase , return_attention_mask=__lowerCamelCase , return_overflowing_tokens=__lowerCamelCase , return_special_tokens_mask=__lowerCamelCase , return_offsets_mapping=__lowerCamelCase , return_token_type_ids=__lowerCamelCase , return_length=__lowerCamelCase , verbose=__lowerCamelCase , return_tensors=__lowerCamelCase , **__lowerCamelCase , )
__UpperCAmelCase : int = qformer_text_encoding.pop("input_ids" )
__UpperCAmelCase : Optional[int] = qformer_text_encoding.pop("attention_mask" )
if images is not None:
__UpperCAmelCase : Union[str, Any] = self.image_processor(__lowerCamelCase , return_tensors=__lowerCamelCase )
encoding.update(__lowerCamelCase )
return encoding
def _lowerCamelCase ( self: Any , *__lowerCamelCase: Any , **__lowerCamelCase: Any ) -> Optional[Any]:
return self.tokenizer.batch_decode(*__lowerCamelCase , **__lowerCamelCase )
def _lowerCamelCase ( self: Tuple , *__lowerCamelCase: Any , **__lowerCamelCase: Dict ) -> Tuple:
return self.tokenizer.decode(*__lowerCamelCase , **__lowerCamelCase )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def _lowerCamelCase ( self: List[str] ) -> Tuple:
__UpperCAmelCase : str = self.tokenizer.model_input_names
__UpperCAmelCase : Dict = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def _lowerCamelCase ( self: Union[str, Any] , __lowerCamelCase: Union[str, Any] , **__lowerCamelCase: Optional[Any] ) -> str:
if os.path.isfile(__lowerCamelCase ):
raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file''' )
os.makedirs(__lowerCamelCase , exist_ok=__lowerCamelCase )
__UpperCAmelCase : List[str] = os.path.join(__lowerCamelCase , "qformer_tokenizer" )
self.qformer_tokenizer.save_pretrained(__lowerCamelCase )
return super().save_pretrained(__lowerCamelCase , **__lowerCamelCase )
@classmethod
def _lowerCamelCase ( cls: Tuple , __lowerCamelCase: Tuple , **__lowerCamelCase: Optional[int] ) -> Union[str, Any]:
__UpperCAmelCase : List[Any] = AutoTokenizer.from_pretrained(__lowerCamelCase , subfolder="qformer_tokenizer" )
__UpperCAmelCase : List[Any] = cls._get_arguments_from_pretrained(__lowerCamelCase , **__lowerCamelCase )
args.append(__lowerCamelCase )
return cls(*__lowerCamelCase )
| 342 | 0 |
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 ..auto import CONFIG_MAPPING
__snake_case : List[Any] =logging.get_logger(__name__)
__snake_case : str ={
"""microsoft/table-transformer-detection""": (
"""https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
),
}
class lowerCamelCase__ ( lowerCamelCase__):
'''simple docstring'''
snake_case_ ="""table-transformer"""
snake_case_ =["""past_key_values"""]
snake_case_ ={
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
}
def __init__(self ,__lowerCamelCase=True ,__lowerCamelCase=None ,__lowerCamelCase=3 ,__lowerCamelCase=1_00 ,__lowerCamelCase=6 ,__lowerCamelCase=20_48 ,__lowerCamelCase=8 ,__lowerCamelCase=6 ,__lowerCamelCase=20_48 ,__lowerCamelCase=8 ,__lowerCamelCase=0.0 ,__lowerCamelCase=0.0 ,__lowerCamelCase=True ,__lowerCamelCase="relu" ,__lowerCamelCase=2_56 ,__lowerCamelCase=0.1 ,__lowerCamelCase=0.0 ,__lowerCamelCase=0.0 ,__lowerCamelCase=0.02 ,__lowerCamelCase=1.0 ,__lowerCamelCase=False ,__lowerCamelCase="sine" ,__lowerCamelCase="resnet50" ,__lowerCamelCase=True ,__lowerCamelCase=False ,__lowerCamelCase=1 ,__lowerCamelCase=5 ,__lowerCamelCase=2 ,__lowerCamelCase=1 ,__lowerCamelCase=1 ,__lowerCamelCase=5 ,__lowerCamelCase=2 ,__lowerCamelCase=0.1 ,**__lowerCamelCase ,) -> Dict:
"""simple docstring"""
if backbone_config is not None and use_timm_backbone:
raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''' )
if not use_timm_backbone:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
lowerCAmelCase__ : str = CONFIG_MAPPING["""resnet"""](out_features=['''stage4'''] )
elif isinstance(UpperCAmelCase__ ,UpperCAmelCase__ ):
lowerCAmelCase__ : Optional[int] = backbone_config.get('''model_type''' )
lowerCAmelCase__ : List[str] = CONFIG_MAPPING[backbone_model_type]
lowerCAmelCase__ : Optional[Any] = config_class.from_dict(UpperCAmelCase__ )
# set timm attributes to None
lowerCAmelCase__ : Union[str, Any] = None, None, None
lowerCAmelCase__ : List[Any] = use_timm_backbone
lowerCAmelCase__ : List[Any] = backbone_config
lowerCAmelCase__ : Dict = num_channels
lowerCAmelCase__ : Union[str, Any] = num_queries
lowerCAmelCase__ : Optional[Any] = d_model
lowerCAmelCase__ : Any = encoder_ffn_dim
lowerCAmelCase__ : str = encoder_layers
lowerCAmelCase__ : List[str] = encoder_attention_heads
lowerCAmelCase__ : Tuple = decoder_ffn_dim
lowerCAmelCase__ : Optional[Any] = decoder_layers
lowerCAmelCase__ : str = decoder_attention_heads
lowerCAmelCase__ : List[Any] = dropout
lowerCAmelCase__ : List[Any] = attention_dropout
lowerCAmelCase__ : Optional[int] = activation_dropout
lowerCAmelCase__ : Tuple = activation_function
lowerCAmelCase__ : int = init_std
lowerCAmelCase__ : str = init_xavier_std
lowerCAmelCase__ : Tuple = encoder_layerdrop
lowerCAmelCase__ : Union[str, Any] = decoder_layerdrop
lowerCAmelCase__ : Optional[Any] = encoder_layers
lowerCAmelCase__ : Any = auxiliary_loss
lowerCAmelCase__ : List[Any] = position_embedding_type
lowerCAmelCase__ : List[Any] = backbone
lowerCAmelCase__ : Optional[Any] = use_pretrained_backbone
lowerCAmelCase__ : Optional[Any] = dilation
# Hungarian matcher
lowerCAmelCase__ : List[Any] = class_cost
lowerCAmelCase__ : Tuple = bbox_cost
lowerCAmelCase__ : Dict = giou_cost
# Loss coefficients
lowerCAmelCase__ : Dict = mask_loss_coefficient
lowerCAmelCase__ : Optional[Any] = dice_loss_coefficient
lowerCAmelCase__ : Tuple = bbox_loss_coefficient
lowerCAmelCase__ : List[Any] = giou_loss_coefficient
lowerCAmelCase__ : List[Any] = eos_coefficient
super().__init__(is_encoder_decoder=UpperCAmelCase__ ,**UpperCAmelCase__ )
@property
def lowerCAmelCase__ (self ) -> int:
"""simple docstring"""
return self.encoder_attention_heads
@property
def lowerCAmelCase__ (self ) -> int:
"""simple docstring"""
return self.d_model
class lowerCamelCase__ ( lowerCamelCase__):
'''simple docstring'''
snake_case_ =version.parse("""1.11""")
@property
def lowerCAmelCase__ (self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''pixel_mask''', {0: '''batch'''}),
] )
@property
def lowerCAmelCase__ (self ) -> float:
"""simple docstring"""
return 1e-5
@property
def lowerCAmelCase__ (self ) -> int:
"""simple docstring"""
return 12
| 129 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def __lowercase ( _A ) -> List[Tuple[int, ...]]:
SCREAMING_SNAKE_CASE : Optional[int] = []
if isinstance(_A , _A ):
for v in tree.values():
shapes.extend(_fetch_dims(_A ) )
elif isinstance(_A , (list, tuple) ):
for t in tree:
shapes.extend(_fetch_dims(_A ) )
elif isinstance(_A , torch.Tensor ):
shapes.append(tree.shape )
else:
raise ValueError("""Not supported""" )
return shapes
@torch.jit.ignore
def __lowercase ( _A , _A ) -> Tuple[int, ...]:
SCREAMING_SNAKE_CASE : List[Any] = []
for d in reversed(_A ):
idx.append(flat_idx % d )
SCREAMING_SNAKE_CASE : Tuple = flat_idx // d
return tuple(reversed(_A ) )
@torch.jit.ignore
def __lowercase ( _A , _A , _A , _A = None , _A = None , ) -> List[Tuple[slice, ...]]:
# start_edges and end_edges both indicate whether, starting from any given
# dimension, the start/end index is at the top/bottom edge of the
# corresponding tensor, modeled as a tree
def reduce_edge_list(_A ) -> None:
SCREAMING_SNAKE_CASE : int = True
for i in range(len(_A ) ):
SCREAMING_SNAKE_CASE : Dict = -1 * (i + 1)
l[reversed_idx] &= tally
SCREAMING_SNAKE_CASE : Any = l[reversed_idx]
if start_edges is None:
SCREAMING_SNAKE_CASE : Tuple = [s == 0 for s in start]
reduce_edge_list(_A )
if end_edges is None:
SCREAMING_SNAKE_CASE : Tuple = [e == (d - 1) for e, d in zip(_A , _A )]
reduce_edge_list(_A )
# Base cases. Either start/end are empty and we're done, or the final,
# one-dimensional tensor can be simply sliced
if len(_A ) == 0:
return [()]
elif len(_A ) == 1:
return [(slice(start[0] , end[0] + 1 ),)]
SCREAMING_SNAKE_CASE : List[Tuple[slice, ...]] = []
SCREAMING_SNAKE_CASE : List[slice] = []
# Dimensions common to start and end can be selected directly
for s, e in zip(_A , _A ):
if s == e:
path_list.append(slice(_A , s + 1 ) )
else:
break
SCREAMING_SNAKE_CASE : Tuple[slice, ...] = tuple(_A )
SCREAMING_SNAKE_CASE : List[str] = len(_A )
# start == end, and we're done
if divergence_idx == len(_A ):
return [path]
def upper() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
SCREAMING_SNAKE_CASE : List[str] = start[divergence_idx]
return tuple(
path + (slice(_A , sdi + 1 ),) + s
for s in _get_minimal_slice_set(
start[divergence_idx + 1 :] , [d - 1 for d in dims[divergence_idx + 1 :]] , dims[divergence_idx + 1 :] , start_edges=start_edges[divergence_idx + 1 :] , end_edges=[True for _ in end_edges[divergence_idx + 1 :]] , ) )
def lower() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
SCREAMING_SNAKE_CASE : Tuple = end[divergence_idx]
return tuple(
path + (slice(_A , edi + 1 ),) + s
for s in _get_minimal_slice_set(
[0 for _ in start[divergence_idx + 1 :]] , end[divergence_idx + 1 :] , dims[divergence_idx + 1 :] , start_edges=[True for _ in start_edges[divergence_idx + 1 :]] , end_edges=end_edges[divergence_idx + 1 :] , ) )
# If both start and end are at the edges of the subtree rooted at
# divergence_idx, we can just select the whole subtree at once
if start_edges[divergence_idx] and end_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] + 1 ),) )
# If just start is at the edge, we can grab almost all of the subtree,
# treating only the ragged bottom edge as an edge case
elif start_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] ),) )
slices.extend(lower() )
# Analogous to the previous case, but the top is ragged this time
elif end_edges[divergence_idx]:
slices.extend(upper() )
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] + 1 ),) )
# If both sides of the range are ragged, we need to handle both sides
# separately. If there's contiguous meat in between them, we can index it
# in one big chunk
else:
slices.extend(upper() )
SCREAMING_SNAKE_CASE : int = end[divergence_idx] - start[divergence_idx]
if middle_ground > 1:
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] ),) )
slices.extend(lower() )
return slices
@torch.jit.ignore
def __lowercase ( _A , _A , _A , _A ) -> torch.Tensor:
SCREAMING_SNAKE_CASE : Tuple = t.shape[:no_batch_dims]
SCREAMING_SNAKE_CASE : Union[str, Any] = list(_flat_idx_to_idx(_A , _A ) )
# _get_minimal_slice_set is inclusive
SCREAMING_SNAKE_CASE : Any = list(_flat_idx_to_idx(flat_end - 1 , _A ) )
# Get an ordered list of slices to perform
SCREAMING_SNAKE_CASE : List[Any] = _get_minimal_slice_set(
_A , _A , _A , )
SCREAMING_SNAKE_CASE : List[Any] = [t[s] for s in slices]
return torch.cat([s.view((-1,) + t.shape[no_batch_dims:] ) for s in sliced_tensors] )
def __lowercase ( _A , _A , _A , _A , _A = False , _A = None , _A = False , ) -> Any:
if not (len(_A ) > 0):
raise ValueError("""Must provide at least one input""" )
SCREAMING_SNAKE_CASE : Tuple = [shape[:no_batch_dims] for shape in _fetch_dims(_A )]
SCREAMING_SNAKE_CASE : str = tuple([max(_A ) for s in zip(*_A )] )
def _prep_inputs(_A ) -> torch.Tensor:
if not low_mem:
if not sum(t.shape[:no_batch_dims] ) == no_batch_dims:
SCREAMING_SNAKE_CASE : List[Any] = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
SCREAMING_SNAKE_CASE : Union[str, Any] = t.reshape(-1 , *t.shape[no_batch_dims:] )
else:
SCREAMING_SNAKE_CASE : Optional[Any] = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
return t
SCREAMING_SNAKE_CASE : Dict[str, Any] = tensor_tree_map(_prep_inputs , _A )
SCREAMING_SNAKE_CASE : Optional[int] = None
if _out is not None:
SCREAMING_SNAKE_CASE : Optional[int] = tensor_tree_map(lambda _A : t.view([-1] + list(t.shape[no_batch_dims:] ) ) , _out )
SCREAMING_SNAKE_CASE : Optional[int] = 1
for d in orig_batch_dims:
flat_batch_dim *= d
SCREAMING_SNAKE_CASE : Tuple = flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)
def _select_chunk(_A ) -> torch.Tensor:
return t[i : i + chunk_size] if t.shape[0] != 1 else t
SCREAMING_SNAKE_CASE : Union[str, Any] = 0
SCREAMING_SNAKE_CASE : Union[str, Any] = prepped_outputs
for _ in range(_A ):
# Chunk the input
if not low_mem:
SCREAMING_SNAKE_CASE : int = _select_chunk
else:
SCREAMING_SNAKE_CASE : Optional[int] = partial(
_chunk_slice , flat_start=_A , flat_end=min(_A , i + chunk_size ) , no_batch_dims=len(_A ) , )
SCREAMING_SNAKE_CASE : Dict[str, Any] = tensor_tree_map(_A , _A )
# Run the layer on the chunk
SCREAMING_SNAKE_CASE : Tuple = layer(**_A )
# Allocate space for the output
if out is None:
SCREAMING_SNAKE_CASE : List[str] = tensor_tree_map(lambda _A : t.new_zeros((flat_batch_dim,) + t.shape[1:] ) , _A )
# Put the chunk in its pre-allocated space
if isinstance(_A , _A ):
def assign(_A , _A ) -> None:
for k, v in da.items():
if isinstance(_A , _A ):
assign(_A , da[k] )
else:
if _add_into_out:
v[i : i + chunk_size] += da[k]
else:
SCREAMING_SNAKE_CASE : Optional[Any] = da[k]
assign(_A , _A )
elif isinstance(_A , _A ):
for xa, xa in zip(_A , _A ):
if _add_into_out:
xa[i : i + chunk_size] += xa
else:
SCREAMING_SNAKE_CASE : str = xa
elif isinstance(_A , torch.Tensor ):
if _add_into_out:
out[i : i + chunk_size] += output_chunk
else:
SCREAMING_SNAKE_CASE : List[Any] = output_chunk
else:
raise ValueError("""Not supported""" )
i += chunk_size
SCREAMING_SNAKE_CASE : Any = tensor_tree_map(lambda _A : t.view(orig_batch_dims + t.shape[1:] ) , _A )
return out
class a__ :
"""simple docstring"""
def __init__( self : Optional[Any] , UpperCAmelCase__ : int = 5_1_2 , ) ->int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = max_chunk_size
SCREAMING_SNAKE_CASE : Optional[int] = None
SCREAMING_SNAKE_CASE : Optional[tuple] = None
def _lowercase ( self : List[Any] , UpperCAmelCase__ : Callable , UpperCAmelCase__ : tuple , UpperCAmelCase__ : int ) ->int:
"""simple docstring"""
logging.info("""Tuning chunk size...""" )
if min_chunk_size >= self.max_chunk_size:
return min_chunk_size
SCREAMING_SNAKE_CASE : List[int] = [2**l for l in range(int(math.log(self.max_chunk_size , 2 ) ) + 1 )]
SCREAMING_SNAKE_CASE : Dict = [c for c in candidates if c > min_chunk_size]
SCREAMING_SNAKE_CASE : List[str] = [min_chunk_size] + candidates
candidates[-1] += 4
def test_chunk_size(UpperCAmelCase__ : int ) -> bool:
try:
with torch.no_grad():
fn(*UpperCAmelCase__ , chunk_size=UpperCAmelCase__ )
return True
except RuntimeError:
return False
SCREAMING_SNAKE_CASE : List[str] = 0
SCREAMING_SNAKE_CASE : List[str] = len(UpperCAmelCase__ ) - 1
while i > min_viable_chunk_size_index:
SCREAMING_SNAKE_CASE : int = test_chunk_size(candidates[i] )
if not viable:
SCREAMING_SNAKE_CASE : Tuple = (min_viable_chunk_size_index + i) // 2
else:
SCREAMING_SNAKE_CASE : List[str] = i
SCREAMING_SNAKE_CASE : List[str] = (i + len(UpperCAmelCase__ ) - 1) // 2
return candidates[min_viable_chunk_size_index]
def _lowercase ( self : List[Any] , UpperCAmelCase__ : Iterable , UpperCAmelCase__ : Iterable ) ->bool:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = True
for aa, aa in zip(UpperCAmelCase__ , UpperCAmelCase__ ):
assert type(UpperCAmelCase__ ) == type(UpperCAmelCase__ )
if isinstance(UpperCAmelCase__ , (list, tuple) ):
consistent &= self._compare_arg_caches(UpperCAmelCase__ , UpperCAmelCase__ )
elif isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
SCREAMING_SNAKE_CASE : Optional[Any] = [v for _, v in sorted(aa.items() , key=lambda UpperCAmelCase__ : x[0] )]
SCREAMING_SNAKE_CASE : List[str] = [v for _, v in sorted(aa.items() , key=lambda UpperCAmelCase__ : x[0] )]
consistent &= self._compare_arg_caches(UpperCAmelCase__ , UpperCAmelCase__ )
else:
consistent &= aa == aa
return consistent
def _lowercase ( self : List[str] , UpperCAmelCase__ : Callable , UpperCAmelCase__ : tuple , UpperCAmelCase__ : int , ) ->int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = True
SCREAMING_SNAKE_CASE : tuple = tree_map(lambda UpperCAmelCase__ : a.shape if isinstance(UpperCAmelCase__ , torch.Tensor ) else a , UpperCAmelCase__ , UpperCAmelCase__ )
if self.cached_arg_data is not None:
# If args have changed shape/value, we need to re-tune
assert len(self.cached_arg_data ) == len(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : Optional[Any] = self._compare_arg_caches(self.cached_arg_data , UpperCAmelCase__ )
else:
# Otherwise, we can reuse the precomputed value
SCREAMING_SNAKE_CASE : List[Any] = False
if not consistent:
SCREAMING_SNAKE_CASE : List[Any] = self._determine_favorable_chunk_size(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , )
SCREAMING_SNAKE_CASE : Union[str, Any] = arg_data
assert self.cached_chunk_size is not None
return self.cached_chunk_size
| 245 | 0 |
"""simple docstring"""
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
__SCREAMING_SNAKE_CASE : Dict = datasets.logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Any = '\\n@InProceedings{moosavi2019minimum,\n author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},\n title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},\n year = {2019},\n booktitle = {Proceedings of the 57th Annual Meeting of\n the Association for Computational Linguistics (Volume 1: Long Papers)},\n publisher = {Association for Computational Linguistics},\n address = {Florence, Italy},\n}\n\n@inproceedings{10.3115/1072399.1072405,\nauthor = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},\ntitle = {A Model-Theoretic Coreference Scoring Scheme},\nyear = {1995},\nisbn = {1558604022},\npublisher = {Association for Computational Linguistics},\naddress = {USA},\nurl = {https://doi.org/10.3115/1072399.1072405},\ndoi = {10.3115/1072399.1072405},\nbooktitle = {Proceedings of the 6th Conference on Message Understanding},\npages = {45–52},\nnumpages = {8},\nlocation = {Columbia, Maryland},\nseries = {MUC6 ’95}\n}\n\n@INPROCEEDINGS{Bagga98algorithmsfor,\n author = {Amit Bagga and Breck Baldwin},\n title = {Algorithms for Scoring Coreference Chains},\n booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},\n year = {1998},\n pages = {563--566}\n}\n\n@INPROCEEDINGS{Luo05oncoreference,\n author = {Xiaoqiang Luo},\n title = {On coreference resolution performance metrics},\n booktitle = {In Proc. of HLT/EMNLP},\n year = {2005},\n pages = {25--32},\n publisher = {URL}\n}\n\n@inproceedings{moosavi-strube-2016-coreference,\n title = "Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric",\n author = "Moosavi, Nafise Sadat and\n Strube, Michael",\n booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",\n month = aug,\n year = "2016",\n address = "Berlin, Germany",\n publisher = "Association for Computational Linguistics",\n url = "https://www.aclweb.org/anthology/P16-1060",\n doi = "10.18653/v1/P16-1060",\n pages = "632--642",\n}\n\n'
__SCREAMING_SNAKE_CASE : Any = '\\nCoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which\nimplements of the common evaluation metrics including MUC [Vilain et al, 1995],\nB-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],\nLEA [Moosavi and Strube, 2016] and the averaged CoNLL score\n(the average of the F1 values of MUC, B-cubed and CEAFe)\n[Denis and Baldridge, 2009a; Pradhan et al., 2011].\n\nThis wrapper of CoVal currently only work with CoNLL line format:\nThe CoNLL format has one word per line with all the annotation for this word in column separated by spaces:\nColumn Type Description\n1 Document ID This is a variation on the document filename\n2 Part number Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.\n3 Word number\n4 Word itself This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.\n5 Part-of-Speech\n6 Parse bit This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column.\n7 Predicate lemma The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a "-"\n8 Predicate Frameset ID This is the PropBank frameset ID of the predicate in Column 7.\n9 Word sense This is the word sense of the word in Column 3.\n10 Speaker/Author This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.\n11 Named Entities These columns identifies the spans representing various named entities.\n12:N Predicate Arguments There is one column each of predicate argument structure information for the predicate mentioned in Column 7.\nN Coreference Coreference chain information encoded in a parenthesis structure.\nMore informations on the format can be found here (section "*_conll File Format"): http://www.conll.cemantix.org/2012/data.html\n\nDetails on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md\n\nCoVal code was written by @ns-moosavi.\nSome parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py\nThe test suite is taken from https://github.com/conll/reference-coreference-scorers/\nMention evaluation and the test suite are added by @andreasvc.\nParsing CoNLL files is developed by Leo Born.\n'
__SCREAMING_SNAKE_CASE : Dict = '\nCalculates coreference evaluation metrics.\nArgs:\n predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.\n Each prediction is a word with its annotations as a string made of columns joined with spaces.\n Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n See the details on the format in the description of the metric.\n references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.\n Each reference is a word with its annotations as a string made of columns joined with spaces.\n Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n See the details on the format in the description of the metric.\n keep_singletons: After extracting all mentions of key or system files,\n mentions whose corresponding coreference chain is of size one,\n are considered as singletons. The default evaluation mode will include\n singletons in evaluations if they are included in the key or the system files.\n By setting \'keep_singletons=False\', all singletons in the key and system files\n will be excluded from the evaluation.\n NP_only: Most of the recent coreference resolvers only resolve NP mentions and\n leave out the resolution of VPs. By setting the \'NP_only\' option, the scorer will only evaluate the resolution of NPs.\n min_span: By setting \'min_span\', the scorer reports the results based on automatically detected minimum spans.\n Minimum spans are determined using the MINA algorithm.\n\nReturns:\n \'mentions\': mentions\n \'muc\': MUC metric [Vilain et al, 1995]\n \'bcub\': B-cubed [Bagga and Baldwin, 1998]\n \'ceafe\': CEAFe [Luo et al., 2005]\n \'lea\': LEA [Moosavi and Strube, 2016]\n \'conll_score\': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)\n\nExamples:\n\n >>> coval = datasets.load_metric(\'coval\')\n >>> words = [\'bc/cctv/00/cctv_0005 0 0 Thank VBP (TOP(S(VP* thank 01 1 Xu_li * (V*) * -\',\n ... \'bc/cctv/00/cctv_0005 0 1 you PRP (NP*) - - - Xu_li * (ARG1*) (ARG0*) (116)\',\n ... \'bc/cctv/00/cctv_0005 0 2 everyone NN (NP*) - - - Xu_li * (ARGM-DIS*) * (116)\',\n ... \'bc/cctv/00/cctv_0005 0 3 for IN (PP* - - - Xu_li * (ARG2* * -\',\n ... \'bc/cctv/00/cctv_0005 0 4 watching VBG (S(VP*)))) watch 01 1 Xu_li * *) (V*) -\',\n ... \'bc/cctv/00/cctv_0005 0 5 . . *)) - - - Xu_li * * * -\']\n >>> references = [words]\n >>> predictions = [words]\n >>> results = coval.compute(predictions=predictions, references=references)\n >>> print(results) # doctest:+ELLIPSIS\n {\'mentions/recall\': 1.0,[...] \'conll_score\': 100.0}\n'
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE="dummy_doc" ) -> Tuple:
snake_case_ = {doc: key_lines}
snake_case_ = {doc: sys_lines}
snake_case_ = {}
snake_case_ = 0
snake_case_ = 0
snake_case_ = 0
snake_case_ = 0
snake_case_ = 0
snake_case_ = 0
snake_case_ , snake_case_ = reader.get_doc_mentions(_SCREAMING_SNAKE_CASE , key_doc_lines[doc] , _SCREAMING_SNAKE_CASE )
key_singletons_num += singletons_num
if NP_only or min_span:
snake_case_ = reader.set_annotated_parse_trees(_SCREAMING_SNAKE_CASE , key_doc_lines[doc] , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
snake_case_ , snake_case_ = reader.get_doc_mentions(_SCREAMING_SNAKE_CASE , sys_doc_lines[doc] , _SCREAMING_SNAKE_CASE )
sys_singletons_num += singletons_num
if NP_only or min_span:
snake_case_ = reader.set_annotated_parse_trees(_SCREAMING_SNAKE_CASE , key_doc_lines[doc] , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if remove_nested:
snake_case_ , snake_case_ = reader.remove_nested_coref_mentions(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
key_nested_coref_num += nested_mentions
key_removed_nested_clusters += removed_clusters
snake_case_ , snake_case_ = reader.remove_nested_coref_mentions(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
sys_nested_coref_num += nested_mentions
sys_removed_nested_clusters += removed_clusters
snake_case_ = reader.get_mention_assignments(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
snake_case_ = reader.get_mention_assignments(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
snake_case_ = (key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)
if remove_nested:
logger.info(
"""Number of removed nested coreferring mentions in the key """
f"""annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}""" )
logger.info(
"""Number of resulting singleton clusters in the key """
f"""annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}""" )
if not keep_singletons:
logger.info(
f"""{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system """
"""files, respectively""" )
return doc_coref_infos
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
snake_case_ = get_coref_infos(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
snake_case_ = {}
snake_case_ = 0
snake_case_ = 0
for name, metric in metrics:
snake_case_ , snake_case_ , snake_case_ = evaluator.evaluate_documents(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , beta=1 )
if name in ["muc", "bcub", "ceafe"]:
conll += fa
conll_subparts_num += 1
output_scores.update({f"""{name}/recall""": recall, f"""{name}/precision""": precision, f"""{name}/f1""": fa} )
logger.info(
name.ljust(10 ) , f"""Recall: {recall * 100:.2f}""" , f""" Precision: {precision * 100:.2f}""" , f""" F1: {fa * 100:.2f}""" , )
if conll_subparts_num == 3:
snake_case_ = (conll / 3) * 100
logger.info(f"""CoNLL score: {conll:.2f}""" )
output_scores.update({"""conll_score""": conll} )
return output_scores
def _a ( _SCREAMING_SNAKE_CASE ) -> str:
snake_case_ = False
for line in key_lines:
if not line.startswith("""#""" ):
if len(line.split() ) > 6:
snake_case_ = line.split()[5]
if not parse_col == "-":
snake_case_ = True
break
else:
break
return has_gold_parse
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class __A (datasets.Metric):
'''simple docstring'''
def lowerCAmelCase ( self : List[str] ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Sequence(datasets.Value("""string""" ) ),
"""references""": datasets.Sequence(datasets.Value("""string""" ) ),
} ) , codebase_urls=["""https://github.com/ns-moosavi/coval"""] , reference_urls=[
"""https://github.com/ns-moosavi/coval""",
"""https://www.aclweb.org/anthology/P16-1060""",
"""http://www.conll.cemantix.org/2012/data.html""",
] , )
def lowerCAmelCase ( self : str , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : int=True , UpperCAmelCase_ : List[str]=False , UpperCAmelCase_ : Any=False , UpperCAmelCase_ : int=False ):
"""simple docstring"""
snake_case_ = [
("""mentions""", evaluator.mentions),
("""muc""", evaluator.muc),
("""bcub""", evaluator.b_cubed),
("""ceafe""", evaluator.ceafe),
("""lea""", evaluator.lea),
]
if min_span:
snake_case_ = util.check_gold_parse_annotation(UpperCAmelCase_ )
if not has_gold_parse:
raise NotImplementedError("""References should have gold parse annotation to use 'min_span'.""" )
# util.parse_key_file(key_file)
# key_file = key_file + ".parsed"
snake_case_ = evaluate(
key_lines=UpperCAmelCase_ , sys_lines=UpperCAmelCase_ , metrics=UpperCAmelCase_ , NP_only=UpperCAmelCase_ , remove_nested=UpperCAmelCase_ , keep_singletons=UpperCAmelCase_ , min_span=UpperCAmelCase_ , )
return score
| 367 |
"""simple docstring"""
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , ) -> Optional[Any]:
if attention_mask is None:
snake_case_ = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
snake_case_ = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
snake_case_ = torch.ones(config.encoder_layers , config.encoder_attention_heads , device=_SCREAMING_SNAKE_CASE )
if decoder_head_mask is None:
snake_case_ = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=_SCREAMING_SNAKE_CASE )
if cross_attn_head_mask is None:
snake_case_ = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=_SCREAMING_SNAKE_CASE )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
class __A :
'''simple docstring'''
def __init__( self : str , UpperCAmelCase_ : int , UpperCAmelCase_ : str=13 , UpperCAmelCase_ : Optional[int]=7 , UpperCAmelCase_ : Optional[int]=True , UpperCAmelCase_ : Optional[Any]=False , UpperCAmelCase_ : int=99 , UpperCAmelCase_ : Union[str, Any]=16 , UpperCAmelCase_ : str=2 , UpperCAmelCase_ : List[Any]=4 , UpperCAmelCase_ : int=4 , UpperCAmelCase_ : Optional[Any]="relu" , UpperCAmelCase_ : int=0.1 , UpperCAmelCase_ : List[Any]=0.1 , UpperCAmelCase_ : List[Any]=0.0 , UpperCAmelCase_ : List[str]=0.0 , UpperCAmelCase_ : List[str]=20 , UpperCAmelCase_ : Dict=2 , UpperCAmelCase_ : List[Any]=1 , UpperCAmelCase_ : Optional[Any]=0 , ) ->Dict:
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_labels
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = encoder_layerdrop
snake_case_ = decoder_layerdrop
snake_case_ = max_position_embeddings
snake_case_ = eos_token_id
snake_case_ = pad_token_id
snake_case_ = bos_token_id
def lowerCAmelCase ( self : Tuple ) ->List[str]:
"""simple docstring"""
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = self.eos_token_id # Eos Token
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for M2M100 the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
snake_case_ = input_ids.clamp(self.pad_token_id + 1 )
snake_case_ = decoder_input_ids.clamp(self.pad_token_id + 1 )
snake_case_ = self.get_config()
snake_case_ = prepare_mam_aaa_inputs_dict(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
return config, inputs_dict
def lowerCAmelCase ( self : Optional[int] ) ->Union[str, Any]:
"""simple docstring"""
return MaMaaaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , encoder_layerdrop=self.encoder_layerdrop , decoder_layerdrop=self.decoder_layerdrop , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , )
def lowerCAmelCase ( self : int ) ->Optional[int]:
"""simple docstring"""
snake_case_ , snake_case_ = self.prepare_config_and_inputs()
return config, inputs_dict
def lowerCAmelCase ( self : Dict , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Dict ) ->Dict:
"""simple docstring"""
snake_case_ = MaMaaaModel(config=UpperCAmelCase_ ).get_decoder().to(UpperCAmelCase_ ).eval()
snake_case_ = inputs_dict["""input_ids"""]
snake_case_ = inputs_dict["""attention_mask"""]
snake_case_ = inputs_dict["""head_mask"""]
# first forward pass
snake_case_ = model(UpperCAmelCase_ , attention_mask=UpperCAmelCase_ , head_mask=UpperCAmelCase_ , use_cache=UpperCAmelCase_ )
snake_case_ , snake_case_ = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
snake_case_ = ids_tensor((self.batch_size, 3) , config.vocab_size )
snake_case_ = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
snake_case_ = torch.cat([input_ids, next_tokens] , dim=-1 )
snake_case_ = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
snake_case_ = model(UpperCAmelCase_ , attention_mask=UpperCAmelCase_ )["""last_hidden_state"""]
snake_case_ = model(UpperCAmelCase_ , attention_mask=UpperCAmelCase_ , past_key_values=UpperCAmelCase_ )[
"""last_hidden_state"""
]
# select random slice
snake_case_ = ids_tensor((1,) , output_from_past.shape[-1] ).item()
snake_case_ = output_from_no_past[:, -3:, random_slice_idx].detach()
snake_case_ = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(UpperCAmelCase_ , UpperCAmelCase_ , atol=1E-2 ) )
def lowerCAmelCase ( self : Dict , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Dict ) ->int:
"""simple docstring"""
snake_case_ = MaMaaaModel(config=UpperCAmelCase_ ).to(UpperCAmelCase_ ).eval()
snake_case_ = model(**UpperCAmelCase_ )
snake_case_ = outputs.encoder_last_hidden_state
snake_case_ = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ = model.get_encoder()
encoder.save_pretrained(UpperCAmelCase_ )
snake_case_ = MaMaaaEncoder.from_pretrained(UpperCAmelCase_ ).to(UpperCAmelCase_ )
snake_case_ = encoder(inputs_dict["""input_ids"""] , attention_mask=inputs_dict["""attention_mask"""] )[
0
]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3 )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ = model.get_decoder()
decoder.save_pretrained(UpperCAmelCase_ )
snake_case_ = MaMaaaDecoder.from_pretrained(UpperCAmelCase_ ).to(UpperCAmelCase_ )
snake_case_ = decoder(
input_ids=inputs_dict["""decoder_input_ids"""] , attention_mask=inputs_dict["""decoder_attention_mask"""] , encoder_hidden_states=UpperCAmelCase_ , encoder_attention_mask=inputs_dict["""attention_mask"""] , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3 )
@require_torch
class __A (snake_case__ , snake_case__ , snake_case__ , unittest.TestCase):
'''simple docstring'''
__lowercase: Optional[Any] = (
(
MaMaaaModel,
MaMaaaForConditionalGeneration,
)
if is_torch_available()
else ()
)
__lowercase: Union[str, Any] = (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
__lowercase: Tuple = (
{
"""conversational""": MaMaaaForConditionalGeneration,
"""feature-extraction""": MaMaaaModel,
"""summarization""": MaMaaaForConditionalGeneration,
"""text2text-generation""": MaMaaaForConditionalGeneration,
"""translation""": MaMaaaForConditionalGeneration,
}
if is_torch_available()
else {}
)
__lowercase: Dict = True
__lowercase: List[Any] = True
__lowercase: Union[str, Any] = False
__lowercase: Optional[int] = False
def lowerCAmelCase ( self : Union[str, Any] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : int , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : int ) ->str:
"""simple docstring"""
if pipeline_test_casse_name == "TranslationPipelineTests":
# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
return True
return False
def lowerCAmelCase ( self : int ) ->Dict:
"""simple docstring"""
snake_case_ = MaMaaaModelTester(self )
snake_case_ = ConfigTester(self , config_class=UpperCAmelCase_ )
def lowerCAmelCase ( self : Optional[Any] ) ->Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def lowerCAmelCase ( self : Union[str, Any] ) ->Optional[Any]:
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
snake_case_ = model_class(UpperCAmelCase_ )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(UpperCAmelCase_ )
snake_case_ , snake_case_ = model_class.from_pretrained(UpperCAmelCase_ , output_loading_info=UpperCAmelCase_ )
self.assertEqual(info["""missing_keys"""] , [] )
def lowerCAmelCase ( self : Optional[int] ) ->List[Any]:
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*UpperCAmelCase_ )
def lowerCAmelCase ( self : str ) ->List[str]:
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*UpperCAmelCase_ )
def lowerCAmelCase ( self : str ) ->List[str]:
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
snake_case_ = model_class(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
snake_case_ = copy.deepcopy(self._prepare_for_class(UpperCAmelCase_ , UpperCAmelCase_ ) )
if not self.is_encoder_decoder:
snake_case_ = inputs["""input_ids"""]
del inputs["input_ids"]
else:
snake_case_ = inputs["""input_ids"""]
snake_case_ = inputs.get("""decoder_input_ids""" , UpperCAmelCase_ )
del inputs["input_ids"]
inputs.pop("""decoder_input_ids""" , UpperCAmelCase_ )
snake_case_ = model.get_input_embeddings()
if not self.is_encoder_decoder:
snake_case_ = wte(UpperCAmelCase_ )
else:
snake_case_ = wte(UpperCAmelCase_ )
snake_case_ = wte(UpperCAmelCase_ )
with torch.no_grad():
model(**UpperCAmelCase_ )[0]
def lowerCAmelCase ( self : Any ) ->Any:
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs()
snake_case_ = input_dict["""input_ids"""]
snake_case_ = input_ids.ne(1 ).to(UpperCAmelCase_ )
snake_case_ = MaMaaaForConditionalGeneration(UpperCAmelCase_ ).eval().to(UpperCAmelCase_ )
if torch_device == "cuda":
model.half()
model.generate(UpperCAmelCase_ , attention_mask=UpperCAmelCase_ )
model.generate(num_beams=4 , do_sample=UpperCAmelCase_ , early_stopping=UpperCAmelCase_ , num_return_sequences=3 )
def _a ( _SCREAMING_SNAKE_CASE ) -> Optional[int]:
return torch.tensor(_SCREAMING_SNAKE_CASE , dtype=torch.long , device=_SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE : Tuple = 1E-4
@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class __A (unittest.TestCase):
'''simple docstring'''
@cached_property
def lowerCAmelCase ( self : Dict ) ->str:
"""simple docstring"""
return MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M""" )
def lowerCAmelCase ( self : str ) ->Any:
"""simple docstring"""
snake_case_ = MaMaaaModel.from_pretrained("""facebook/m2m100_418M""" ).to(UpperCAmelCase_ )
snake_case_ = _long_tensor([[128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38, 2]] )
snake_case_ = _long_tensor([[2, 128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38]] )
snake_case_ = prepare_mam_aaa_inputs_dict(model.config , UpperCAmelCase_ , UpperCAmelCase_ )
with torch.no_grad():
snake_case_ = model(**UpperCAmelCase_ )[0]
snake_case_ = torch.Size((1, 11, 1_024) )
self.assertEqual(output.shape , UpperCAmelCase_ )
# change to expected output here
snake_case_ = torch.tensor(
[[-0.7_780, -0.1_676, 0.1_038], [-6.7_556, -1.3_992, 0.0_567], [-7.5_383, -0.5_920, -0.2_779]] , device=UpperCAmelCase_ )
self.assertTrue(torch.allclose(output[:, :3, :3] , UpperCAmelCase_ , atol=UpperCAmelCase_ ) )
def lowerCAmelCase ( self : Optional[int] ) ->Any:
"""simple docstring"""
snake_case_ = MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M""" ).to(UpperCAmelCase_ )
# change to intended input
snake_case_ = _long_tensor([[128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38, 2]] )
snake_case_ = _long_tensor([[2, 128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38]] )
snake_case_ = prepare_mam_aaa_inputs_dict(model.config , UpperCAmelCase_ , UpperCAmelCase_ )
with torch.no_grad():
snake_case_ = model(**UpperCAmelCase_ )[0]
snake_case_ = torch.Size((1, 11, model.config.vocab_size) )
self.assertEqual(output.shape , UpperCAmelCase_ )
# change to expected output here
snake_case_ = torch.tensor(
[[-1.0_448, -1.0_411, 3.7_992], [-3.2_191, -3.2_386, -1.3_451], [-3.6_210, -3.5_993, 0.4_925]] , device=UpperCAmelCase_ )
self.assertTrue(torch.allclose(output[:, :3, :3] , UpperCAmelCase_ , atol=UpperCAmelCase_ ) )
def lowerCAmelCase ( self : Dict ) ->str:
"""simple docstring"""
snake_case_ = MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M""" ).to(UpperCAmelCase_ )
snake_case_ = MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M""" , src_lang="""fr""" , tgt_lang="""en""" )
snake_case_ = [
"""L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
"""Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
"""Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"""
""" Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"""
""" l'ampleur de la surveillance américaine sur l'ensemble des communications en France.""",
]
# The below article tests that we don't add any hypotheses outside of the top n_beams
snake_case_ = tokenizer(UpperCAmelCase_ , padding=UpperCAmelCase_ , return_tensors="""pt""" )
snake_case_ = model.generate(
input_ids=dct["""input_ids"""].to(UpperCAmelCase_ ) , attention_mask=dct["""attention_mask"""].to(UpperCAmelCase_ ) , num_beams=5 , forced_bos_token_id=tokenizer.get_lang_id("""en""" ) , )
snake_case_ = [
"""The NSA case highlights the total absence of intelligence debate""",
"""I think there are two levels of response from the French government.""",
"""When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."""
""" Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"""
""" communications in France.""",
]
snake_case_ = tokenizer.batch_decode(
hypotheses_batch.tolist() , clean_up_tokenization_spaces=UpperCAmelCase_ , skip_special_tokens=UpperCAmelCase_ )
assert generated == expected_en
| 233 | 0 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__lowercase : Dict = abspath(join(dirname(dirname(__file__)), 'src'))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[str] ):
from diffusers.utils.testing_utils import pytest_addoption_shared
pytest_addoption_shared(_SCREAMING_SNAKE_CASE )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int ):
from diffusers.utils.testing_utils import pytest_terminal_summary_main
__a : Union[str, Any] = terminalreporter.config.getoption('--make-reports' )
if make_reports:
pytest_terminal_summary_main(_SCREAMING_SNAKE_CASE , id=_SCREAMING_SNAKE_CASE )
| 27 |
'''simple docstring'''
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes # name
# #SBATCH --nodes=2 # nodes
# #SBATCH --ntasks-per-node=1 # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10 # number of cores per tasks
# #SBATCH --gres=gpu:4 # number of gpus
# #SBATCH --time 0:05:00 # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#
import fcntl
import os
import socket
import torch
import torch.distributed as dist
def lowerCamelCase (*_SCREAMING_SNAKE_CASE : int ):
with open(_SCREAMING_SNAKE_CASE , 'r' ) as fh:
fcntl.flock(_SCREAMING_SNAKE_CASE , fcntl.LOCK_EX )
try:
print(*_SCREAMING_SNAKE_CASE )
finally:
fcntl.flock(_SCREAMING_SNAKE_CASE , fcntl.LOCK_UN )
__lowercase : Dict = int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
__lowercase : Tuple = torch.device('cuda', local_rank)
__lowercase : Optional[int] = socket.gethostname()
__lowercase : List[str] = f'''[{hostname}-{local_rank}]'''
try:
# test distributed
dist.init_process_group('nccl')
dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
dist.barrier()
# test cuda is available and can allocate memory
torch.cuda.is_available()
torch.ones(1).cuda(local_rank)
# global rank
__lowercase : str = dist.get_rank()
__lowercase : Union[str, Any] = dist.get_world_size()
printflock(f'''{gpu} is OK (global rank: {rank}/{world_size})''')
dist.barrier()
if rank == 0:
printflock(f'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')
except Exception:
printflock(f'''{gpu} is broken''')
raise
| 27 | 1 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
't5-small': 'https://huggingface.co/t5-small/resolve/main/config.json',
't5-base': 'https://huggingface.co/t5-base/resolve/main/config.json',
't5-large': 'https://huggingface.co/t5-large/resolve/main/config.json',
't5-3b': 'https://huggingface.co/t5-3b/resolve/main/config.json',
't5-11b': 'https://huggingface.co/t5-11b/resolve/main/config.json',
}
class _lowercase ( __UpperCAmelCase ):
lowercase_ = 't5'
lowercase_ = ['past_key_values']
lowercase_ = {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}
def __init__( self , UpperCAmelCase_=32128 , UpperCAmelCase_=512 , UpperCAmelCase_=64 , UpperCAmelCase_=2048 , UpperCAmelCase_=6 , UpperCAmelCase_=None , UpperCAmelCase_=8 , UpperCAmelCase_=32 , UpperCAmelCase_=128 , UpperCAmelCase_=0.1 , UpperCAmelCase_=1E-6 , UpperCAmelCase_=1.0 , UpperCAmelCase_="relu" , UpperCAmelCase_=True , UpperCAmelCase_=True , UpperCAmelCase_=0 , UpperCAmelCase_=1 , **UpperCAmelCase_ , ) -> Dict:
lowerCamelCase : int = vocab_size
lowerCamelCase : Dict = d_model
lowerCamelCase : List[Any] = d_kv
lowerCamelCase : int = d_ff
lowerCamelCase : Dict = num_layers
lowerCamelCase : Any = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
lowerCamelCase : Optional[int] = num_heads
lowerCamelCase : List[Any] = relative_attention_num_buckets
lowerCamelCase : Optional[Any] = relative_attention_max_distance
lowerCamelCase : str = dropout_rate
lowerCamelCase : Union[str, Any] = layer_norm_epsilon
lowerCamelCase : Tuple = initializer_factor
lowerCamelCase : Tuple = feed_forward_proj
lowerCamelCase : str = use_cache
lowerCamelCase : Optional[int] = self.feed_forward_proj.split('-' )
lowerCamelCase : Optional[Any] = act_info[-1]
lowerCamelCase : str = act_info[0] == 'gated'
if len(UpperCAmelCase_ ) > 1 and act_info[0] != "gated" or len(UpperCAmelCase_ ) > 2:
raise ValueError(
F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
'\'gated-gelu\' or \'relu\'' )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
lowerCamelCase : Any = 'gelu_new'
super().__init__(
pad_token_id=UpperCAmelCase_ , eos_token_id=UpperCAmelCase_ , is_encoder_decoder=UpperCAmelCase_ , **UpperCAmelCase_ , )
class _lowercase ( __UpperCAmelCase ):
@property
def _UpperCamelCase ( self ) -> Mapping[str, Mapping[int, str]]:
lowerCamelCase : List[Any] = {
'input_ids': {0: 'batch', 1: 'encoder_sequence'},
'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
}
if self.use_past:
lowerCamelCase : Optional[Any] = 'past_encoder_sequence + sequence'
lowerCamelCase : Optional[Any] = {0: 'batch'}
lowerCamelCase : str = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
else:
lowerCamelCase : Dict = {0: 'batch', 1: 'decoder_sequence'}
lowerCamelCase : List[Any] = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(UpperCAmelCase_ , direction='inputs' )
return common_inputs
@property
def _UpperCamelCase ( self ) -> int:
return 13
| 358 |
"""simple docstring"""
def UpperCAmelCase ( a_ ):
'''simple docstring'''
lowerCamelCase : List[Any] = 1
for i in range(1, num + 1 ):
fact *= i
return fact
def UpperCAmelCase ( a_ ):
'''simple docstring'''
lowerCamelCase : Optional[Any] = 0
while number > 0:
lowerCamelCase : str = number % 10
sum_of_digits += last_digit
lowerCamelCase : Tuple = number // 10 # Removing the last_digit from the given number
return sum_of_digits
def UpperCAmelCase ( a_ = 100 ):
'''simple docstring'''
lowerCamelCase : Optional[Any] = factorial(a_ )
lowerCamelCase : int = split_and_add(a_ )
return result
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 205 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer
__lowerCamelCase : Any = logging.get_logger(__name__)
__lowerCamelCase : Optional[Any] = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__lowerCamelCase : Optional[Any] = {
'''vocab_file''': {
'''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/vocab.json''',
'''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/vocab.json''',
'''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json''',
'''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/vocab.json''',
'''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json''',
'''roberta-large-openai-detector''': (
'''https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'''
),
},
'''merges_file''': {
'''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/merges.txt''',
'''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/merges.txt''',
'''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt''',
'''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/merges.txt''',
'''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt''',
'''roberta-large-openai-detector''': (
'''https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'''
),
},
'''tokenizer_file''': {
'''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/tokenizer.json''',
'''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/tokenizer.json''',
'''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json''',
'''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json''',
'''roberta-base-openai-detector''': (
'''https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'''
),
'''roberta-large-openai-detector''': (
'''https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'''
),
},
}
__lowerCamelCase : Tuple = {
'''roberta-base''': 5_12,
'''roberta-large''': 5_12,
'''roberta-large-mnli''': 5_12,
'''distilroberta-base''': 5_12,
'''roberta-base-openai-detector''': 5_12,
'''roberta-large-openai-detector''': 5_12,
}
class a__ ( A__ ):
A = VOCAB_FILES_NAMES
A = PRETRAINED_VOCAB_FILES_MAP
A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A = ['input_ids', 'attention_mask']
A = RobertaTokenizer
def __init__( self : Optional[int],_A : str=None,_A : Any=None,_A : Tuple=None,_A : Optional[Any]="replace",_A : int="<s>",_A : int="</s>",_A : Tuple="</s>",_A : Optional[int]="<s>",_A : List[Any]="<unk>",_A : Optional[Any]="<pad>",_A : Dict="<mask>",_A : List[str]=False,_A : Optional[Any]=True,**_A : int,):
"""simple docstring"""
super().__init__(
_A,_A,tokenizer_file=_A,errors=_A,bos_token=_A,eos_token=_A,sep_token=_A,cls_token=_A,unk_token=_A,pad_token=_A,mask_token=_A,add_prefix_space=_A,trim_offsets=_A,**_A,)
SCREAMING_SNAKE_CASE_ : Optional[int] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space",_A ) != add_prefix_space:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = getattr(_A,pre_tok_state.pop("type" ) )
SCREAMING_SNAKE_CASE_ : Optional[int] = add_prefix_space
SCREAMING_SNAKE_CASE_ : Optional[int] = pre_tok_class(**_A )
SCREAMING_SNAKE_CASE_ : List[Any] = add_prefix_space
SCREAMING_SNAKE_CASE_ : Optional[int] = "post_processor"
SCREAMING_SNAKE_CASE_ : Optional[int] = getattr(self.backend_tokenizer,_A,_A )
if tokenizer_component_instance:
SCREAMING_SNAKE_CASE_ : Dict = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
SCREAMING_SNAKE_CASE_ : Tuple = tuple(state["sep"] )
if "cls" in state:
SCREAMING_SNAKE_CASE_ : Optional[Any] = tuple(state["cls"] )
SCREAMING_SNAKE_CASE_ : Optional[int] = False
if state.get("add_prefix_space",_A ) != add_prefix_space:
SCREAMING_SNAKE_CASE_ : List[Any] = add_prefix_space
SCREAMING_SNAKE_CASE_ : int = True
if state.get("trim_offsets",_A ) != trim_offsets:
SCREAMING_SNAKE_CASE_ : List[Any] = trim_offsets
SCREAMING_SNAKE_CASE_ : List[Any] = True
if changes_to_apply:
SCREAMING_SNAKE_CASE_ : int = getattr(_A,state.pop("type" ) )
SCREAMING_SNAKE_CASE_ : str = component_class(**_A )
setattr(self.backend_tokenizer,_A,_A )
@property
def __UpperCamelCase ( self : Any ):
"""simple docstring"""
if self._mask_token is None:
if self.verbose:
logger.error("Using mask_token, but it is not set yet." )
return None
return str(self._mask_token )
@mask_token.setter
def __UpperCamelCase ( self : Tuple,_A : Union[str, Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = AddedToken(_A,lstrip=_A,rstrip=_A ) if isinstance(_A,_A ) else value
SCREAMING_SNAKE_CASE_ : Union[str, Any] = value
def __UpperCamelCase ( self : Union[str, Any],*_A : int,**_A : str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = kwargs.get("is_split_into_words",_A )
assert self.add_prefix_space or not is_split_into_words, (
F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*_A,**_A )
def __UpperCamelCase ( self : Optional[int],*_A : int,**_A : Union[str, Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = kwargs.get("is_split_into_words",_A )
assert self.add_prefix_space or not is_split_into_words, (
F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
"to use it with pretokenized inputs."
)
return super()._encode_plus(*_A,**_A )
def __UpperCamelCase ( self : int,_A : str,_A : Optional[str] = None ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = self._tokenizer.model.save(_A,name=_A )
return tuple(_A )
def __UpperCamelCase ( self : Dict,_A : Optional[int],_A : List[Any]=None ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def __UpperCamelCase ( self : str,_A : List[int],_A : Optional[List[int]] = None ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = [self.sep_token_id]
SCREAMING_SNAKE_CASE_ : 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]
| 18 |
"""simple docstring"""
import argparse
import copy
def lowercase__ ( snake_case_ :Tuple ):
__UpperCAmelCase = {}
with open(snake_case_ ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
__UpperCAmelCase = []
_list.append([line.split()[1], line.split()[2]] )
__UpperCAmelCase = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
__UpperCAmelCase = []
_list.append([line.split()[0], line.split()[2]] )
__UpperCAmelCase = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def lowercase__ ( snake_case_ :Dict , snake_case_ :Optional[Any] ):
with open(snake_case_ ) as f:
__UpperCAmelCase = f.read(1 )
__UpperCAmelCase = start_node
__UpperCAmelCase = []
__UpperCAmelCase = start_node
__UpperCAmelCase = 0
while visiting not in first_solution:
__UpperCAmelCase = 10_000
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(snake_case_ ) and k[0] not in first_solution:
__UpperCAmelCase = k[1]
__UpperCAmelCase = k[0]
first_solution.append(snake_case_ )
__UpperCAmelCase = distance_of_first_solution + int(snake_case_ )
__UpperCAmelCase = best_node
first_solution.append(snake_case_ )
__UpperCAmelCase = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
__UpperCAmelCase = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 10_000
)
return first_solution, distance_of_first_solution
def lowercase__ ( snake_case_ :int , snake_case_ :Tuple ):
__UpperCAmelCase = []
for n in solution[1:-1]:
__UpperCAmelCase = solution.index(snake_case_ )
for kn in solution[1:-1]:
__UpperCAmelCase = solution.index(snake_case_ )
if n == kn:
continue
__UpperCAmelCase = copy.deepcopy(snake_case_ )
__UpperCAmelCase = kn
__UpperCAmelCase = n
__UpperCAmelCase = 0
for k in _tmp[:-1]:
__UpperCAmelCase = _tmp[_tmp.index(snake_case_ ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
__UpperCAmelCase = distance + int(i[1] )
_tmp.append(snake_case_ )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
__UpperCAmelCase = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda snake_case_ : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def lowercase__ ( snake_case_ :str , snake_case_ :Union[str, Any] , snake_case_ :Optional[int] , snake_case_ :Dict , snake_case_ :int ):
__UpperCAmelCase = 1
__UpperCAmelCase = first_solution
__UpperCAmelCase = []
__UpperCAmelCase = distance_of_first_solution
__UpperCAmelCase = solution
while count <= iters:
__UpperCAmelCase = find_neighborhood(snake_case_ , snake_case_ )
__UpperCAmelCase = 0
__UpperCAmelCase = neighborhood[index_of_best_solution]
__UpperCAmelCase = len(snake_case_ ) - 1
__UpperCAmelCase = False
while not found:
__UpperCAmelCase = 0
while i < len(snake_case_ ):
if best_solution[i] != solution[i]:
__UpperCAmelCase = best_solution[i]
__UpperCAmelCase = solution[i]
break
__UpperCAmelCase = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
__UpperCAmelCase = True
__UpperCAmelCase = best_solution[:-1]
__UpperCAmelCase = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
__UpperCAmelCase = cost
__UpperCAmelCase = solution
else:
__UpperCAmelCase = index_of_best_solution + 1
__UpperCAmelCase = neighborhood[index_of_best_solution]
if len(snake_case_ ) >= size:
tabu_list.pop(0 )
__UpperCAmelCase = count + 1
return best_solution_ever, best_cost
def lowercase__ ( snake_case_ :str=None ):
__UpperCAmelCase = generate_neighbours(args.File )
__UpperCAmelCase , __UpperCAmelCase = generate_first_solution(
args.File , snake_case_ )
__UpperCAmelCase , __UpperCAmelCase = tabu_search(
snake_case_ , snake_case_ , snake_case_ , args.Iterations , args.Size , )
print(F'''Best solution: {best_sol}, with total distance: {best_cost}.''' )
if __name__ == "__main__":
_lowercase : List[str] = argparse.ArgumentParser(description='Tabu Search')
parser.add_argument(
'-f',
'--File',
type=str,
help='Path to the file containing the data',
required=True,
)
parser.add_argument(
'-i',
'--Iterations',
type=int,
help='How many iterations the algorithm should perform',
required=True,
)
parser.add_argument(
'-s', '--Size', type=int, help='Size of the tabu list', required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 332 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__lowerCamelCase : Tuple = logging.get_logger(__name__)
__lowerCamelCase : Union[str, Any] = '''▁'''
__lowerCamelCase : str = {'''vocab_file''': '''sentencepiece.bpe.model'''}
__lowerCamelCase : Optional[Any] = {
'''vocab_file''': {
'''facebook/nllb-200-distilled-600M''': (
'''https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'''
),
}
}
__lowerCamelCase : Any = {
'''facebook/nllb-200-distilled-600M''': 1024,
}
# fmt: off
__lowerCamelCase : Tuple = ['''ace_Arab''', '''ace_Latn''', '''acm_Arab''', '''acq_Arab''', '''aeb_Arab''', '''afr_Latn''', '''ajp_Arab''', '''aka_Latn''', '''amh_Ethi''', '''apc_Arab''', '''arb_Arab''', '''ars_Arab''', '''ary_Arab''', '''arz_Arab''', '''asm_Beng''', '''ast_Latn''', '''awa_Deva''', '''ayr_Latn''', '''azb_Arab''', '''azj_Latn''', '''bak_Cyrl''', '''bam_Latn''', '''ban_Latn''', '''bel_Cyrl''', '''bem_Latn''', '''ben_Beng''', '''bho_Deva''', '''bjn_Arab''', '''bjn_Latn''', '''bod_Tibt''', '''bos_Latn''', '''bug_Latn''', '''bul_Cyrl''', '''cat_Latn''', '''ceb_Latn''', '''ces_Latn''', '''cjk_Latn''', '''ckb_Arab''', '''crh_Latn''', '''cym_Latn''', '''dan_Latn''', '''deu_Latn''', '''dik_Latn''', '''dyu_Latn''', '''dzo_Tibt''', '''ell_Grek''', '''eng_Latn''', '''epo_Latn''', '''est_Latn''', '''eus_Latn''', '''ewe_Latn''', '''fao_Latn''', '''pes_Arab''', '''fij_Latn''', '''fin_Latn''', '''fon_Latn''', '''fra_Latn''', '''fur_Latn''', '''fuv_Latn''', '''gla_Latn''', '''gle_Latn''', '''glg_Latn''', '''grn_Latn''', '''guj_Gujr''', '''hat_Latn''', '''hau_Latn''', '''heb_Hebr''', '''hin_Deva''', '''hne_Deva''', '''hrv_Latn''', '''hun_Latn''', '''hye_Armn''', '''ibo_Latn''', '''ilo_Latn''', '''ind_Latn''', '''isl_Latn''', '''ita_Latn''', '''jav_Latn''', '''jpn_Jpan''', '''kab_Latn''', '''kac_Latn''', '''kam_Latn''', '''kan_Knda''', '''kas_Arab''', '''kas_Deva''', '''kat_Geor''', '''knc_Arab''', '''knc_Latn''', '''kaz_Cyrl''', '''kbp_Latn''', '''kea_Latn''', '''khm_Khmr''', '''kik_Latn''', '''kin_Latn''', '''kir_Cyrl''', '''kmb_Latn''', '''kon_Latn''', '''kor_Hang''', '''kmr_Latn''', '''lao_Laoo''', '''lvs_Latn''', '''lij_Latn''', '''lim_Latn''', '''lin_Latn''', '''lit_Latn''', '''lmo_Latn''', '''ltg_Latn''', '''ltz_Latn''', '''lua_Latn''', '''lug_Latn''', '''luo_Latn''', '''lus_Latn''', '''mag_Deva''', '''mai_Deva''', '''mal_Mlym''', '''mar_Deva''', '''min_Latn''', '''mkd_Cyrl''', '''plt_Latn''', '''mlt_Latn''', '''mni_Beng''', '''khk_Cyrl''', '''mos_Latn''', '''mri_Latn''', '''zsm_Latn''', '''mya_Mymr''', '''nld_Latn''', '''nno_Latn''', '''nob_Latn''', '''npi_Deva''', '''nso_Latn''', '''nus_Latn''', '''nya_Latn''', '''oci_Latn''', '''gaz_Latn''', '''ory_Orya''', '''pag_Latn''', '''pan_Guru''', '''pap_Latn''', '''pol_Latn''', '''por_Latn''', '''prs_Arab''', '''pbt_Arab''', '''quy_Latn''', '''ron_Latn''', '''run_Latn''', '''rus_Cyrl''', '''sag_Latn''', '''san_Deva''', '''sat_Beng''', '''scn_Latn''', '''shn_Mymr''', '''sin_Sinh''', '''slk_Latn''', '''slv_Latn''', '''smo_Latn''', '''sna_Latn''', '''snd_Arab''', '''som_Latn''', '''sot_Latn''', '''spa_Latn''', '''als_Latn''', '''srd_Latn''', '''srp_Cyrl''', '''ssw_Latn''', '''sun_Latn''', '''swe_Latn''', '''swh_Latn''', '''szl_Latn''', '''tam_Taml''', '''tat_Cyrl''', '''tel_Telu''', '''tgk_Cyrl''', '''tgl_Latn''', '''tha_Thai''', '''tir_Ethi''', '''taq_Latn''', '''taq_Tfng''', '''tpi_Latn''', '''tsn_Latn''', '''tso_Latn''', '''tuk_Latn''', '''tum_Latn''', '''tur_Latn''', '''twi_Latn''', '''tzm_Tfng''', '''uig_Arab''', '''ukr_Cyrl''', '''umb_Latn''', '''urd_Arab''', '''uzn_Latn''', '''vec_Latn''', '''vie_Latn''', '''war_Latn''', '''wol_Latn''', '''xho_Latn''', '''ydd_Hebr''', '''yor_Latn''', '''yue_Hant''', '''zho_Hans''', '''zho_Hant''', '''zul_Latn''']
class __snake_case ( lowerCamelCase_ ):
lowerCAmelCase_ = VOCAB_FILES_NAMES
lowerCAmelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase_ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase_ = ["input_ids", "attention_mask"]
lowerCAmelCase_ = []
lowerCAmelCase_ = []
def __init__( self : List[str] , _lowercase : Union[str, Any] , _lowercase : Union[str, Any]="<s>" , _lowercase : Any="</s>" , _lowercase : Optional[int]="</s>" , _lowercase : Dict="<s>" , _lowercase : Tuple="<unk>" , _lowercase : Tuple="<pad>" , _lowercase : Optional[Any]="<mask>" , _lowercase : Any=None , _lowercase : Optional[int]=None , _lowercase : Union[str, Any]=None , _lowercase : Optional[Dict[str, Any]] = None , _lowercase : List[str]=None , _lowercase : int=False , **_lowercase : Optional[int] , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else mask_token
SCREAMING_SNAKE_CASE__ = {} if sp_model_kwargs is None else sp_model_kwargs
SCREAMING_SNAKE_CASE__ = legacy_behaviour
super().__init__(
bos_token=_lowercase , eos_token=_lowercase , unk_token=_lowercase , sep_token=_lowercase , cls_token=_lowercase , pad_token=_lowercase , mask_token=_lowercase , tokenizer_file=_lowercase , src_lang=_lowercase , tgt_lang=_lowercase , additional_special_tokens=_lowercase , sp_model_kwargs=self.sp_model_kwargs , legacy_behaviour=_lowercase , **_lowercase , )
SCREAMING_SNAKE_CASE__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(_lowercase ) )
SCREAMING_SNAKE_CASE__ = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
# spm | '<unk>' | '<s>' | '</s>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'
# Mimic fairseq token-to-id alignment for the first 4 token
SCREAMING_SNAKE_CASE__ = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
SCREAMING_SNAKE_CASE__ = 1
SCREAMING_SNAKE_CASE__ = len(self.sp_model )
SCREAMING_SNAKE_CASE__ = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_lowercase )
}
SCREAMING_SNAKE_CASE__ = {v: k for k, v in self.lang_code_to_id.items()}
SCREAMING_SNAKE_CASE__ = len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
SCREAMING_SNAKE_CASE__ = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
SCREAMING_SNAKE_CASE__ = list(self.lang_code_to_id.keys() )
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
self._additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in self._additional_special_tokens] )
SCREAMING_SNAKE_CASE__ = src_lang if src_lang is not None else """eng_Latn"""
SCREAMING_SNAKE_CASE__ = self.lang_code_to_id[self._src_lang]
SCREAMING_SNAKE_CASE__ = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
def __getstate__( self : Any ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.__dict__.copy()
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : int , _lowercase : Union[str, Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
SCREAMING_SNAKE_CASE__ = {}
SCREAMING_SNAKE_CASE__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
@property
def __a ( self : Optional[int] ):
"""simple docstring"""
return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1 # Plus 1 for the mask token
@property
def __a ( self : Dict ):
"""simple docstring"""
return self._src_lang
@src_lang.setter
def __a ( self : int , _lowercase : str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def __a ( self : Union[str, Any] , _lowercase : List[int] , _lowercase : Optional[List[int]] = None , _lowercase : bool = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowercase , token_ids_a=_lowercase , already_has_special_tokens=_lowercase )
SCREAMING_SNAKE_CASE__ = [1] * len(self.prefix_tokens )
SCREAMING_SNAKE_CASE__ = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(_lowercase )) + suffix_ones
return prefix_ones + ([0] * len(_lowercase )) + ([0] * len(_lowercase )) + suffix_ones
def __a ( self : List[str] , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def __a ( self : List[str] , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ = [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 : Dict , _lowercase : List[Any] , _lowercase : str , _lowercase : Optional[str] , _lowercase : Optional[str] , **_lowercase : int ):
"""simple docstring"""
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
SCREAMING_SNAKE_CASE__ = src_lang
SCREAMING_SNAKE_CASE__ = self(_lowercase , add_special_tokens=_lowercase , return_tensors=_lowercase , **_lowercase )
SCREAMING_SNAKE_CASE__ = self.convert_tokens_to_ids(_lowercase )
SCREAMING_SNAKE_CASE__ = tgt_lang_id
return inputs
def __a ( self : str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = {self.convert_ids_to_tokens(_lowercase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __a ( self : Any , _lowercase : str ):
"""simple docstring"""
return self.sp_model.encode(_lowercase , out_type=_lowercase )
def __a ( self : Tuple , _lowercase : Any ):
"""simple docstring"""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
SCREAMING_SNAKE_CASE__ = self.sp_model.PieceToId(_lowercase )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def __a ( self : str , _lowercase : int ):
"""simple docstring"""
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 __a ( self : List[str] , _lowercase : Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = """""".join(_lowercase ).replace(_lowercase , """ """ ).strip()
return out_string
def __a ( self : List[Any] , _lowercase : str , _lowercase : Optional[str] = None ):
"""simple docstring"""
if not os.path.isdir(_lowercase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
SCREAMING_SNAKE_CASE__ = os.path.join(
_lowercase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowercase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowercase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowercase , """wb""" ) as fi:
SCREAMING_SNAKE_CASE__ = self.sp_model.serialized_model_proto()
fi.write(_lowercase )
return (out_vocab_file,)
def __a ( self : Any , _lowercase : List[str] , _lowercase : str = "eng_Latn" , _lowercase : Optional[List[str]] = None , _lowercase : str = "fra_Latn" , **_lowercase : Any , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = src_lang
SCREAMING_SNAKE_CASE__ = tgt_lang
return super().prepare_seqaseq_batch(_lowercase , _lowercase , **_lowercase )
def __a ( self : Any ):
"""simple docstring"""
return self.set_src_lang_special_tokens(self.src_lang )
def __a ( self : Dict ):
"""simple docstring"""
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def __a ( self : Optional[Any] , _lowercase : List[str] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.lang_code_to_id[src_lang]
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE__ = [self.cur_lang_code]
SCREAMING_SNAKE_CASE__ = [self.eos_token_id]
def __a ( self : int , _lowercase : str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.lang_code_to_id[lang]
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE__ = [self.cur_lang_code]
SCREAMING_SNAKE_CASE__ = [self.eos_token_id]
| 359 | import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
renew_vae_resnet_paths,
)
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : Optional[int] , __UpperCamelCase : str ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = checkpoint
SCREAMING_SNAKE_CASE__ = {}
SCREAMING_SNAKE_CASE__ = vae_state_dict["""encoder.conv_in.weight"""]
SCREAMING_SNAKE_CASE__ = vae_state_dict["""encoder.conv_in.bias"""]
SCREAMING_SNAKE_CASE__ = vae_state_dict["""encoder.conv_out.weight"""]
SCREAMING_SNAKE_CASE__ = vae_state_dict["""encoder.conv_out.bias"""]
SCREAMING_SNAKE_CASE__ = vae_state_dict["""encoder.norm_out.weight"""]
SCREAMING_SNAKE_CASE__ = vae_state_dict["""encoder.norm_out.bias"""]
SCREAMING_SNAKE_CASE__ = vae_state_dict["""decoder.conv_in.weight"""]
SCREAMING_SNAKE_CASE__ = vae_state_dict["""decoder.conv_in.bias"""]
SCREAMING_SNAKE_CASE__ = vae_state_dict["""decoder.conv_out.weight"""]
SCREAMING_SNAKE_CASE__ = vae_state_dict["""decoder.conv_out.bias"""]
SCREAMING_SNAKE_CASE__ = vae_state_dict["""decoder.norm_out.weight"""]
SCREAMING_SNAKE_CASE__ = vae_state_dict["""decoder.norm_out.bias"""]
SCREAMING_SNAKE_CASE__ = vae_state_dict["""quant_conv.weight"""]
SCREAMING_SNAKE_CASE__ = vae_state_dict["""quant_conv.bias"""]
SCREAMING_SNAKE_CASE__ = vae_state_dict["""post_quant_conv.weight"""]
SCREAMING_SNAKE_CASE__ = vae_state_dict["""post_quant_conv.bias"""]
# Retrieves the keys for the encoder down blocks only
SCREAMING_SNAKE_CASE__ = len({""".""".join(layer.split(""".""" )[:3] ) for layer in vae_state_dict if """encoder.down""" in layer} )
SCREAMING_SNAKE_CASE__ = {
layer_id: [key for key in vae_state_dict if f"""down.{layer_id}""" in key] for layer_id in range(__UpperCamelCase )
}
# Retrieves the keys for the decoder up blocks only
SCREAMING_SNAKE_CASE__ = len({""".""".join(layer.split(""".""" )[:3] ) for layer in vae_state_dict if """decoder.up""" in layer} )
SCREAMING_SNAKE_CASE__ = {
layer_id: [key for key in vae_state_dict if f"""up.{layer_id}""" in key] for layer_id in range(__UpperCamelCase )
}
for i in range(__UpperCamelCase ):
SCREAMING_SNAKE_CASE__ = [key for key in down_blocks[i] if f"""down.{i}""" in key and f"""down.{i}.downsample""" not in key]
if f"""encoder.down.{i}.downsample.conv.weight""" in vae_state_dict:
SCREAMING_SNAKE_CASE__ = vae_state_dict.pop(
f"""encoder.down.{i}.downsample.conv.weight""" )
SCREAMING_SNAKE_CASE__ = vae_state_dict.pop(
f"""encoder.down.{i}.downsample.conv.bias""" )
SCREAMING_SNAKE_CASE__ = renew_vae_resnet_paths(__UpperCamelCase )
SCREAMING_SNAKE_CASE__ = {"""old""": f"""down.{i}.block""", """new""": f"""down_blocks.{i}.resnets"""}
assign_to_checkpoint(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , additional_replacements=[meta_path] , config=__UpperCamelCase )
SCREAMING_SNAKE_CASE__ = [key for key in vae_state_dict if """encoder.mid.block""" in key]
SCREAMING_SNAKE_CASE__ = 2
for i in range(1 , num_mid_res_blocks + 1 ):
SCREAMING_SNAKE_CASE__ = [key for key in mid_resnets if f"""encoder.mid.block_{i}""" in key]
SCREAMING_SNAKE_CASE__ = renew_vae_resnet_paths(__UpperCamelCase )
SCREAMING_SNAKE_CASE__ = {"""old""": f"""mid.block_{i}""", """new""": f"""mid_block.resnets.{i - 1}"""}
assign_to_checkpoint(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , additional_replacements=[meta_path] , config=__UpperCamelCase )
SCREAMING_SNAKE_CASE__ = [key for key in vae_state_dict if """encoder.mid.attn""" in key]
SCREAMING_SNAKE_CASE__ = renew_vae_attention_paths(__UpperCamelCase )
SCREAMING_SNAKE_CASE__ = {"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
assign_to_checkpoint(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , additional_replacements=[meta_path] , config=__UpperCamelCase )
conv_attn_to_linear(__UpperCamelCase )
for i in range(__UpperCamelCase ):
SCREAMING_SNAKE_CASE__ = num_up_blocks - 1 - i
SCREAMING_SNAKE_CASE__ = [
key for key in up_blocks[block_id] if f"""up.{block_id}""" in key and f"""up.{block_id}.upsample""" not in key
]
if f"""decoder.up.{block_id}.upsample.conv.weight""" in vae_state_dict:
SCREAMING_SNAKE_CASE__ = vae_state_dict[
f"""decoder.up.{block_id}.upsample.conv.weight"""
]
SCREAMING_SNAKE_CASE__ = vae_state_dict[
f"""decoder.up.{block_id}.upsample.conv.bias"""
]
SCREAMING_SNAKE_CASE__ = renew_vae_resnet_paths(__UpperCamelCase )
SCREAMING_SNAKE_CASE__ = {"""old""": f"""up.{block_id}.block""", """new""": f"""up_blocks.{i}.resnets"""}
assign_to_checkpoint(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , additional_replacements=[meta_path] , config=__UpperCamelCase )
SCREAMING_SNAKE_CASE__ = [key for key in vae_state_dict if """decoder.mid.block""" in key]
SCREAMING_SNAKE_CASE__ = 2
for i in range(1 , num_mid_res_blocks + 1 ):
SCREAMING_SNAKE_CASE__ = [key for key in mid_resnets if f"""decoder.mid.block_{i}""" in key]
SCREAMING_SNAKE_CASE__ = renew_vae_resnet_paths(__UpperCamelCase )
SCREAMING_SNAKE_CASE__ = {"""old""": f"""mid.block_{i}""", """new""": f"""mid_block.resnets.{i - 1}"""}
assign_to_checkpoint(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , additional_replacements=[meta_path] , config=__UpperCamelCase )
SCREAMING_SNAKE_CASE__ = [key for key in vae_state_dict if """decoder.mid.attn""" in key]
SCREAMING_SNAKE_CASE__ = renew_vae_attention_paths(__UpperCamelCase )
SCREAMING_SNAKE_CASE__ = {"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
assign_to_checkpoint(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , additional_replacements=[meta_path] , config=__UpperCamelCase )
conv_attn_to_linear(__UpperCamelCase )
return new_checkpoint
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : str , __UpperCamelCase : str , ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = requests.get(
""" https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml""" )
SCREAMING_SNAKE_CASE__ = io.BytesIO(r.content )
SCREAMING_SNAKE_CASE__ = OmegaConf.load(__UpperCamelCase )
SCREAMING_SNAKE_CASE__ = 5_12
SCREAMING_SNAKE_CASE__ = """cuda""" if torch.cuda.is_available() else """cpu"""
if checkpoint_path.endswith("""safetensors""" ):
from safetensors import safe_open
SCREAMING_SNAKE_CASE__ = {}
with safe_open(__UpperCamelCase , framework="""pt""" , device="""cpu""" ) as f:
for key in f.keys():
SCREAMING_SNAKE_CASE__ = f.get_tensor(__UpperCamelCase )
else:
SCREAMING_SNAKE_CASE__ = torch.load(__UpperCamelCase , map_location=__UpperCamelCase )["""state_dict"""]
# Convert the VAE model.
SCREAMING_SNAKE_CASE__ = create_vae_diffusers_config(__UpperCamelCase , image_size=__UpperCamelCase )
SCREAMING_SNAKE_CASE__ = custom_convert_ldm_vae_checkpoint(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE__ = AutoencoderKL(**__UpperCamelCase )
vae.load_state_dict(__UpperCamelCase )
vae.save_pretrained(__UpperCamelCase )
if __name__ == "__main__":
__lowerCamelCase : List[Any] = argparse.ArgumentParser()
parser.add_argument('''--vae_pt_path''', default=None, type=str, required=True, help='''Path to the VAE.pt to convert.''')
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the VAE.pt to convert.''')
__lowerCamelCase : Optional[int] = parser.parse_args()
vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
| 204 | 0 |
"""simple docstring"""
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
lowerCAmelCase__ : Dict = '<<<<<<< This should probably be modified because it mentions: '
lowerCAmelCase__ : int = '=======\n>>>>>>>\n'
lowerCAmelCase__ : Optional[int] = [
'TextEncoderConfig',
'ByteTextEncoder',
'SubwordTextEncoder',
'encoder_config',
'maybe_build_from_corpus',
'manual_dir',
]
lowerCAmelCase__ : Dict = [
# (pattern, replacement)
# Order is important here for some replacements
(r'tfds\.core', r'datasets'),
(r'tf\.io\.gfile\.GFile', r'open'),
(r'tf\.([\w\d]+)', r'datasets.Value(\'\1\')'),
(r'tfds\.features\.Text\(\)', r'datasets.Value(\'string\')'),
(r'tfds\.features\.Text\(', r'datasets.Value(\'string\'),'),
(r'features\s*=\s*tfds.features.FeaturesDict\(', r'features=datasets.Features('),
(r'tfds\.features\.FeaturesDict\(', r'dict('),
(r'The TensorFlow Datasets Authors', r'The TensorFlow Datasets Authors and the HuggingFace Datasets Authors'),
(r'tfds\.', r'datasets.'),
(r'dl_manager\.manual_dir', r'self.config.data_dir'),
(r'self\.builder_config', r'self.config'),
]
def a_ ( lowerCamelCase ):
return ConvertCommand(args.tfds_path , args.datasets_directory )
class snake_case ( __UpperCAmelCase ):
"""simple docstring"""
@staticmethod
def __lowerCAmelCase ( lowerCamelCase__ : ArgumentParser ):
UpperCAmelCase__ = parser.add_parser(
'convert' ,help='Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.' ,)
train_parser.add_argument(
'--tfds_path' ,type=lowerCamelCase__ ,required=lowerCamelCase__ ,help='Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.' ,)
train_parser.add_argument(
'--datasets_directory' ,type=lowerCamelCase__ ,required=lowerCamelCase__ ,help='Path to the HuggingFace Datasets folder.' )
train_parser.set_defaults(func=lowerCamelCase__ )
def __init__( self : List[Any] ,lowerCamelCase__ : str ,lowerCamelCase__ : str ,*lowerCamelCase__ : Union[str, Any] ):
UpperCAmelCase__ = get_logger('datasets-cli/converting' )
UpperCAmelCase__ = tfds_path
UpperCAmelCase__ = datasets_directory
def __lowerCAmelCase ( self : List[Any] ):
if os.path.isdir(self._tfds_path ):
UpperCAmelCase__ = os.path.abspath(self._tfds_path )
elif os.path.isfile(self._tfds_path ):
UpperCAmelCase__ = os.path.dirname(self._tfds_path )
else:
raise ValueError('--tfds_path is neither a directory nor a file. Please check path.' )
UpperCAmelCase__ = os.path.abspath(self._datasets_directory )
self._logger.info(f'''Converting datasets from {abs_tfds_path} to {abs_datasets_path}''' )
UpperCAmelCase__ = []
UpperCAmelCase__ = []
UpperCAmelCase__ = {}
if os.path.isdir(self._tfds_path ):
UpperCAmelCase__ = os.listdir(lowerCamelCase__ )
else:
UpperCAmelCase__ = [os.path.basename(self._tfds_path )]
for f_name in file_names:
self._logger.info(f'''Looking at file {f_name}''' )
UpperCAmelCase__ = os.path.join(lowerCamelCase__ ,lowerCamelCase__ )
UpperCAmelCase__ = os.path.join(lowerCamelCase__ ,lowerCamelCase__ )
if not os.path.isfile(lowerCamelCase__ ) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
self._logger.info('Skipping file' )
continue
with open(lowerCamelCase__ ,encoding='utf-8' ) as f:
UpperCAmelCase__ = f.readlines()
UpperCAmelCase__ = []
UpperCAmelCase__ = False
UpperCAmelCase__ = False
UpperCAmelCase__ = []
for line in lines:
UpperCAmelCase__ = line
# Convert imports
if "import tensorflow.compat.v2 as tf" in out_line:
continue
elif "@tfds.core" in out_line:
continue
elif "builder=self" in out_line:
continue
elif "import tensorflow_datasets.public_api as tfds" in out_line:
UpperCAmelCase__ = 'import datasets\n'
elif "import tensorflow" in out_line:
# order is important here
UpperCAmelCase__ = ''
continue
elif "from absl import logging" in out_line:
UpperCAmelCase__ = 'from datasets import logging\n'
elif "getLogger" in out_line:
UpperCAmelCase__ = out_line.replace('getLogger' ,'get_logger' )
elif any(expression in out_line for expression in TO_HIGHLIGHT ):
UpperCAmelCase__ = True
UpperCAmelCase__ = list(filter(lambda lowerCamelCase__ : e in out_line ,lowerCamelCase__ ) )
out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(lowerCamelCase__ ) + '\n' )
out_lines.append(lowerCamelCase__ )
out_lines.append(lowerCamelCase__ )
continue
else:
for pattern, replacement in TO_CONVERT:
UpperCAmelCase__ = re.sub(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )
# Take care of saving utilities (to later move them together with main script)
if "tensorflow_datasets" in out_line:
UpperCAmelCase__ = re.match(R'from\stensorflow_datasets.*import\s([^\.\r\n]+)' ,lowerCamelCase__ )
tfds_imports.extend(imp.strip() for imp in match.group(1 ).split(',' ) )
UpperCAmelCase__ = 'from . import ' + match.group(1 )
# Check we have not forget anything
if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
raise ValueError(f'''Error converting {out_line.strip()}''' )
if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
UpperCAmelCase__ = True
out_lines.append(lowerCamelCase__ )
if is_builder or "wmt" in f_name:
# We create a new directory for each dataset
UpperCAmelCase__ = f_name.replace('.py' ,'' )
UpperCAmelCase__ = os.path.join(lowerCamelCase__ ,lowerCamelCase__ )
UpperCAmelCase__ = os.path.join(lowerCamelCase__ ,lowerCamelCase__ )
os.makedirs(lowerCamelCase__ ,exist_ok=lowerCamelCase__ )
self._logger.info(f'''Adding directory {output_dir}''' )
imports_to_builder_map.update({imp: output_dir for imp in tfds_imports} )
else:
# Utilities will be moved at the end
utils_files.append(lowerCamelCase__ )
if needs_manual_update:
with_manual_update.append(lowerCamelCase__ )
with open(lowerCamelCase__ ,'w' ,encoding='utf-8' ) as f:
f.writelines(lowerCamelCase__ )
self._logger.info(f'''Converted in {output_file}''' )
for utils_file in utils_files:
try:
UpperCAmelCase__ = os.path.basename(lowerCamelCase__ )
UpperCAmelCase__ = imports_to_builder_map[f_name.replace('.py' ,'' )]
self._logger.info(f'''Moving {dest_folder} to {utils_file}''' )
shutil.copy(lowerCamelCase__ ,lowerCamelCase__ )
except KeyError:
self._logger.error(f'''Cannot find destination folder for {utils_file}. Please copy manually.''' )
if with_manual_update:
for file_path in with_manual_update:
self._logger.warning(
f'''You need to manually update file {file_path} to remove configurations using \'TextEncoderConfig\'.''' )
| 98 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
TimesformerForVideoClassification,
TimesformerModel,
)
from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class snake_case__ :
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__=13 , lowerCAmelCase__=10 , lowerCAmelCase__=3 , lowerCAmelCase__=2 , lowerCAmelCase__=2 , lowerCAmelCase__=True , lowerCAmelCase__=True , lowerCAmelCase__=32 , lowerCAmelCase__=5 , lowerCAmelCase__=4 , lowerCAmelCase__=37 , lowerCAmelCase__="gelu" , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.1 , lowerCAmelCase__=10 , lowerCAmelCase__=0.0_2 , lowerCAmelCase__="divided_space_time" , lowerCAmelCase__=None , ) -> List[str]:
__magic_name__ : int = parent
__magic_name__ : Tuple = batch_size
__magic_name__ : int = image_size
__magic_name__ : str = num_channels
__magic_name__ : Dict = patch_size
__magic_name__ : Tuple = num_frames
__magic_name__ : List[Any] = is_training
__magic_name__ : List[Any] = use_labels
__magic_name__ : Dict = hidden_size
__magic_name__ : List[Any] = num_hidden_layers
__magic_name__ : str = num_attention_heads
__magic_name__ : List[Any] = intermediate_size
__magic_name__ : Dict = hidden_act
__magic_name__ : List[Any] = hidden_dropout_prob
__magic_name__ : Union[str, Any] = attention_probs_dropout_prob
__magic_name__ : Tuple = attention_type
__magic_name__ : List[str] = initializer_range
__magic_name__ : Optional[Any] = scope
__magic_name__ : Tuple = num_labels
# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
__magic_name__ : str = (image_size // patch_size) ** 2
__magic_name__ : Any = (num_frames) * self.num_patches_per_frame + 1
def __magic_name__ ( self ) -> Dict:
__magic_name__ : Optional[Any] = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
__magic_name__ : str = None
if self.use_labels:
__magic_name__ : List[Any] = ids_tensor([self.batch_size] , self.num_labels )
__magic_name__ : Optional[Any] = self.get_config()
return config, pixel_values, labels
def __magic_name__ ( self ) -> str:
__magic_name__ : Dict = TimesformerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , 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 , initializer_range=self.initializer_range , attention_type=self.attention_type , )
__magic_name__ : Optional[Any] = self.num_labels
return config
def __magic_name__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Optional[Any]:
__magic_name__ : List[Any] = TimesformerModel(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
__magic_name__ : Optional[Any] = model(lowerCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Any:
__magic_name__ : int = TimesformerForVideoClassification(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
__magic_name__ : List[Any] = model(lowerCAmelCase__ )
# verify the logits shape
__magic_name__ : List[Any] = torch.Size((self.batch_size, self.num_labels) )
self.parent.assertEqual(result.logits.shape , lowerCAmelCase__ )
def __magic_name__ ( self ) -> Any:
__magic_name__ : Union[str, Any] = self.prepare_config_and_inputs()
__magic_name__ ,__magic_name__ ,__magic_name__ : Tuple = config_and_inputs
__magic_name__ : Optional[int] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class snake_case__ ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
lowercase__ : Tuple = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
lowercase__ : Union[str, Any] = (
{'''feature-extraction''': TimesformerModel, '''video-classification''': TimesformerForVideoClassification}
if is_torch_available()
else {}
)
lowercase__ : int = False
lowercase__ : str = False
lowercase__ : Tuple = False
lowercase__ : Any = False
def __magic_name__ ( self ) -> List[Any]:
__magic_name__ : List[Any] = TimesformerModelTester(self )
__magic_name__ : List[str] = ConfigTester(
self , config_class=lowerCAmelCase__ , has_text_modality=lowerCAmelCase__ , hidden_size=37 )
def __magic_name__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=False ) -> List[str]:
__magic_name__ : List[str] = copy.deepcopy(lowerCAmelCase__ )
if return_labels:
if model_class in get_values(lowerCAmelCase__ ):
__magic_name__ : Union[str, Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
return inputs_dict
def __magic_name__ ( self ) -> List[str]:
self.config_tester.run_common_tests()
@unittest.skip(reason="""TimeSformer does not use inputs_embeds""" )
def __magic_name__ ( self ) -> str:
pass
def __magic_name__ ( self ) -> Optional[int]:
__magic_name__ ,__magic_name__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ : List[Any] = model_class(lowerCAmelCase__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__magic_name__ : Tuple = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCAmelCase__ , nn.Linear ) )
def __magic_name__ ( self ) -> Optional[Any]:
__magic_name__ ,__magic_name__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ : Dict = model_class(lowerCAmelCase__ )
__magic_name__ : Optional[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__magic_name__ : Optional[int] = [*signature.parameters.keys()]
__magic_name__ : Any = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowerCAmelCase__ )
def __magic_name__ ( self ) -> List[Any]:
__magic_name__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def __magic_name__ ( self ) -> Union[str, Any]:
__magic_name__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_video_classification(*lowerCAmelCase__ )
@slow
def __magic_name__ ( self ) -> Optional[int]:
for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__magic_name__ : List[str] = TimesformerModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
def __magic_name__ ( self ) -> List[Any]:
if not self.has_attentions:
pass
else:
__magic_name__ ,__magic_name__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
__magic_name__ : Optional[int] = True
for model_class in self.all_model_classes:
__magic_name__ : Tuple = self.model_tester.seq_length
__magic_name__ : int = self.model_tester.num_frames
__magic_name__ : Any = True
__magic_name__ : Tuple = False
__magic_name__ : Optional[int] = True
__magic_name__ : str = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
__magic_name__ : List[str] = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
__magic_name__ : List[str] = outputs.attentions
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__magic_name__ : Optional[Any] = True
__magic_name__ : Optional[Any] = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
__magic_name__ : Optional[int] = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
__magic_name__ : int = outputs.attentions
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
__magic_name__ : Union[str, Any] = len(lowerCAmelCase__ )
# Check attention is always last and order is fine
__magic_name__ : str = True
__magic_name__ : Optional[Any] = True
__magic_name__ : int = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
__magic_name__ : List[str] = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
self.assertEqual(out_len + 1 , len(lowerCAmelCase__ ) )
__magic_name__ : Union[str, Any] = outputs.attentions
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
def __magic_name__ ( self ) -> Any:
def check_hidden_states_output(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
__magic_name__ : Union[str, Any] = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
__magic_name__ : int = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
__magic_name__ : Optional[Any] = outputs.hidden_states
__magic_name__ : str = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(lowerCAmelCase__ ) , lowerCAmelCase__ )
__magic_name__ : str = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
__magic_name__ ,__magic_name__ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ : Optional[Any] = True
check_hidden_states_output(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__magic_name__ : Union[str, Any] = True
check_hidden_states_output(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
def UpperCamelCase ( ):
"""simple docstring"""
__magic_name__ : List[Any] = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""", filename="""eating_spaghetti.npy""", repo_type="""dataset""" )
__magic_name__ : List[str] = np.load(_A )
return list(_A )
@require_torch
@require_vision
class snake_case__ ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self ) -> Optional[Any]:
# logits were tested with a different mean and std, so we use the same here
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def __magic_name__ ( self ) -> List[Any]:
__magic_name__ : Dict = TimesformerForVideoClassification.from_pretrained("""facebook/timesformer-base-finetuned-k400""" ).to(
lowerCAmelCase__ )
__magic_name__ : str = self.default_image_processor
__magic_name__ : Any = prepare_video()
__magic_name__ : Dict = image_processor(video[:8] , return_tensors="""pt""" ).to(lowerCAmelCase__ )
# forward pass
with torch.no_grad():
__magic_name__ : int = model(**lowerCAmelCase__ )
# verify the logits
__magic_name__ : Optional[int] = torch.Size((1, 4_00) )
self.assertEqual(outputs.logits.shape , lowerCAmelCase__ )
__magic_name__ : Union[str, Any] = torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5] ).to(lowerCAmelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCAmelCase__ , atol=1e-4 ) )
| 342 | 0 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"
# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
a = 'facebook/wmt19-en-de'
a = FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
a = FSMTConfig.from_pretrained(mname)
config.update(
dict(
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
)
a = FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')
# Test
a = tokenizer(['''Making tiny model'''], return_tensors='''pt''')
a = tiny_model(**batch)
print('''test output:''', len(outputs.logits[0]))
# Save
a = 'tiny-wmt19-en-de'
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(F'''Generated {mname_tiny}''')
# Upload
# transformers-cli upload tiny-wmt19-en-de | 356 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
a = [8, 5, 9, 7]
a = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class lowercase_ :
'''simple docstring'''
def __init__( self : str , _UpperCAmelCase : list[int] , _UpperCAmelCase : list[list[int]] , _UpperCAmelCase : list[list[int]] , ):
_A = claim_vector
_A = allocated_resources_table
_A = maximum_claim_table
def lowerCAmelCase_ ( self : Tuple ):
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def lowerCAmelCase_ ( self : Tuple ):
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def lowerCAmelCase_ ( self : List[Any] ):
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(_UpperCAmelCase ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def lowerCAmelCase_ ( self : List[Any] ):
return {self.__need().index(_UpperCAmelCase ): i for i in self.__need()}
def lowerCAmelCase_ ( self : List[str] , **_UpperCAmelCase : int ):
_A = self.__need()
_A = self.__allocated_resources_table
_A = self.__available_resources()
_A = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print('_' * 50 + '\n' )
while need_list:
_A = False
for each_need in need_list:
_A = True
for index, need in enumerate(_UpperCAmelCase ):
if need > available_resources[index]:
_A = False
break
if execution:
_A = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
_A = original_need_index
print(F'''Process {process_number + 1} is executing.''' )
# remove the process run from stack
need_list.remove(_UpperCAmelCase )
# update available/freed resources stack
_A = np.array(_UpperCAmelCase ) + np.array(
alloc_resources_table[process_number] )
print(
'Updated available resource stack for processes: '
+ ' '.join([str(_UpperCAmelCase ) for x in available_resources] ) )
break
if safe:
print('The process is in a safe state.\n' )
else:
print('System in unsafe state. Aborting...\n' )
break
def lowerCAmelCase_ ( self : Union[str, Any] ):
print(' ' * 9 + 'Allocated Resource Table' )
for item in self.__allocated_resources_table:
print(
F'''P{self.__allocated_resources_table.index(_UpperCAmelCase ) + 1}'''
+ ' '.join(F'''{it:>8}''' for it in item )
+ '\n' )
print(' ' * 9 + 'System Resource Table' )
for item in self.__maximum_claim_table:
print(
F'''P{self.__maximum_claim_table.index(_UpperCAmelCase ) + 1}'''
+ ' '.join(F'''{it:>8}''' for it in item )
+ '\n' )
print(
'Current Usage by Active Processes: '
+ ' '.join(str(_UpperCAmelCase ) for x in self.__claim_vector ) )
print(
'Initial Available Resources: '
+ ' '.join(str(_UpperCAmelCase ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 271 | 0 |
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
lowerCAmelCase__ : List[Any] =argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
)
# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
parser.add_argument(
'''--original_config_file''',
default=None,
type=str,
help='''The YAML config file corresponding to the original architecture.''',
)
parser.add_argument(
'''--num_in_channels''',
default=None,
type=int,
help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
)
parser.add_argument(
'''--scheduler_type''',
default='''pndm''',
type=str,
help='''Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']''',
)
parser.add_argument(
'''--pipeline_type''',
default=None,
type=str,
help=(
'''The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''''
'''. If `None` pipeline will be automatically inferred.'''
),
)
parser.add_argument(
'''--image_size''',
default=None,
type=int,
help=(
'''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
''' Base. Use 768 for Stable Diffusion v2.'''
),
)
parser.add_argument(
'''--prediction_type''',
default=None,
type=str,
help=(
'''The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'''
''' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'''
),
)
parser.add_argument(
'''--extract_ema''',
action='''store_true''',
help=(
'''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
),
)
parser.add_argument(
'''--upcast_attention''',
action='''store_true''',
help=(
'''Whether the attention computation should always be upcasted. This is necessary when running stable'''
''' diffusion 2.1.'''
),
)
parser.add_argument(
'''--from_safetensors''',
action='''store_true''',
help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
)
parser.add_argument(
'''--to_safetensors''',
action='''store_true''',
help='''Whether to store pipeline in safetensors format or not.''',
)
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
parser.add_argument(
'''--stable_unclip''',
type=str,
default=None,
required=False,
help='''Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.''',
)
parser.add_argument(
'''--stable_unclip_prior''',
type=str,
default=None,
required=False,
help='''Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.''',
)
parser.add_argument(
'''--clip_stats_path''',
type=str,
help='''Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.''',
required=False,
)
parser.add_argument(
'''--controlnet''', action='''store_true''', default=None, help='''Set flag if this is a controlnet checkpoint.'''
)
parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
parser.add_argument(
'''--vae_path''',
type=str,
default=None,
required=False,
help='''Set to a path, hub id to an already converted vae to not convert it again.''',
)
lowerCAmelCase__ : Any =parser.parse_args()
lowerCAmelCase__ : Dict =download_from_original_stable_diffusion_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
prediction_type=args.prediction_type,
model_type=args.pipeline_type,
extract_ema=args.extract_ema,
scheduler_type=args.scheduler_type,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
stable_unclip=args.stable_unclip,
stable_unclip_prior=args.stable_unclip_prior,
clip_stats_path=args.clip_stats_path,
controlnet=args.controlnet,
vae_path=args.vae_path,
)
if args.half:
pipe.to(torch_dtype=torch.floataa)
if args.controlnet:
# only save the controlnet model
pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
else:
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 257 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
lowerCamelCase : List[str] = {
'''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 lowerCAmelCase ( __a ):
'''simple docstring'''
_A : List[Any] = '''canine'''
def __init__( self : List[str] , __a : Optional[int]=768 , __a : Any=12 , __a : Any=12 , __a : Dict=3072 , __a : Dict="gelu" , __a : List[Any]=0.1 , __a : List[Any]=0.1 , __a : Tuple=16384 , __a : List[Any]=16 , __a : List[Any]=0.02 , __a : Optional[Any]=1E-12 , __a : Dict=0 , __a : List[Any]=0xe_0_0_0 , __a : Optional[int]=0xe_0_0_1 , __a : Any=4 , __a : Dict=4 , __a : Optional[int]=8 , __a : Any=16384 , __a : Optional[Any]=128 , **__a : List[str] , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , **__a )
__lowercase : int = max_position_embeddings
__lowercase : List[str] = hidden_size
__lowercase : List[Any] = num_hidden_layers
__lowercase : Dict = num_attention_heads
__lowercase : int = intermediate_size
__lowercase : Dict = hidden_act
__lowercase : Tuple = hidden_dropout_prob
__lowercase : Union[str, Any] = attention_probs_dropout_prob
__lowercase : Union[str, Any] = initializer_range
__lowercase : Any = type_vocab_size
__lowercase : int = layer_norm_eps
# Character config:
__lowercase : int = downsampling_rate
__lowercase : str = upsampling_kernel_size
__lowercase : Union[str, Any] = num_hash_functions
__lowercase : Optional[Any] = num_hash_buckets
__lowercase : Optional[int] = local_transformer_stride | 233 | 0 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
__UpperCAmelCase = '''python tqdm regex requests packaging filelock numpy tokenizers'''.split()
if sys.version_info < (3, 7):
pkgs_to_check_at_runtime.append('''dataclasses''')
if sys.version_info < (3, 8):
pkgs_to_check_at_runtime.append('''importlib_metadata''')
for pkg in pkgs_to_check_at_runtime:
if pkg in deps:
if pkg == "tokenizers":
# must be loaded here, or else tqdm check may fail
from .utils import is_tokenizers_available
if not is_tokenizers_available():
continue # not required, check version only if installed
require_version_core(deps[pkg])
else:
raise ValueError(f"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")
def __lowerCamelCase ( __magic_name__ : Tuple , __magic_name__ : Any=None ):
require_version(deps[pkg] , __magic_name__ )
| 42 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class lowerCamelCase__ ( _a ):
@require_torch
def _lowerCamelCase ( self : Union[str, Any] ):
# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
# while running an external program
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
a__: Dict ="\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n "
a__: Union[str, Any] ="\nmname = \"hf-internal-testing/tiny-random-bert\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task=\"fill-mask\", model=mname)\nprint(\"success\")\n "
a__: Dict ="\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError(\"Offline mode is enabled, we shouldn't access internet\")\nsocket.socket = offline_socket\n "
# Force fetching the files so that we can use the cache
a__: Tuple ="hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(_a )
BertModel.from_pretrained(_a )
BertTokenizer.from_pretrained(_a )
pipeline(task="fill-mask" , model=_a )
# baseline - just load from_pretrained with normal network
a__: Optional[int] =[sys.executable, "-c", "\n".join([load, run, mock] )]
# should succeed
a__: str =self.get_env()
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
a__: Union[str, Any] ="1"
a__: Any =subprocess.run(_a , env=_a , check=_a , capture_output=_a )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("success" , result.stdout.decode() )
@require_torch
def _lowerCamelCase ( self : str ):
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
a__: Optional[int] ="\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n "
a__: Tuple ="\nmname = \"hf-internal-testing/tiny-random-bert\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task=\"fill-mask\", model=mname)\nprint(\"success\")\n "
a__: List[Any] ="\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error(\"Faking flaky internet\")\nsocket.socket = offline_socket\n "
# Force fetching the files so that we can use the cache
a__: str ="hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(_a )
BertModel.from_pretrained(_a )
BertTokenizer.from_pretrained(_a )
pipeline(task="fill-mask" , model=_a )
# baseline - just load from_pretrained with normal network
a__: Any =[sys.executable, "-c", "\n".join([load, run, mock] )]
# should succeed
a__: Optional[Any] =self.get_env()
a__: Union[str, Any] =subprocess.run(_a , env=_a , check=_a , capture_output=_a )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("success" , result.stdout.decode() )
@require_torch
def _lowerCamelCase ( self : List[str] ):
# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
# while running an external program
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
a__: Tuple ="\nfrom transformers import BertConfig, BertModel, BertTokenizer\n "
a__: str ="\nmname = \"hf-internal-testing/tiny-random-bert-sharded\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint(\"success\")\n "
a__: int ="\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError(\"Offline mode is enabled\")\nsocket.socket = offline_socket\n "
# baseline - just load from_pretrained with normal network
a__: Union[str, Any] =[sys.executable, "-c", "\n".join([load, run] )]
# should succeed
a__: Optional[Any] =self.get_env()
a__: Optional[Any] =subprocess.run(_a , env=_a , check=_a , capture_output=_a )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("success" , result.stdout.decode() )
# next emulate no network
a__: int =[sys.executable, "-c", "\n".join([load, mock, run] )]
# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
# env["TRANSFORMERS_OFFLINE"] = "0"
# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
# self.assertEqual(result.returncode, 1, result.stderr)
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
a__: Tuple ="1"
a__: Dict =subprocess.run(_a , env=_a , check=_a , capture_output=_a )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("success" , result.stdout.decode() )
@require_torch
def _lowerCamelCase ( self : Optional[Any] ):
a__: Dict ="\nfrom transformers import pipeline\n "
a__: Union[str, Any] ="\nmname = \"hf-internal-testing/tiny-random-bert\"\npipe = pipeline(model=mname)\n "
a__: List[str] ="\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error(\"Offline mode is enabled\")\nsocket.socket = offline_socket\n "
a__: Dict =self.get_env()
a__: Optional[Any] ="1"
a__: Dict =[sys.executable, "-c", "\n".join([load, mock, run] )]
a__: Optional[int] =subprocess.run(_a , env=_a , check=_a , capture_output=_a )
self.assertEqual(result.returncode , 1 , result.stderr )
self.assertIn(
"You cannot infer task automatically within `pipeline` when using offline mode" , result.stderr.decode().replace("\n" , "" ) , )
@require_torch
def _lowerCamelCase ( self : Optional[int] ):
a__: Optional[Any] ="\nfrom transformers import AutoModel\n "
a__: str ="\nmname = \"hf-internal-testing/test_dynamic_model\"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint(\"success\")\n "
# baseline - just load from_pretrained with normal network
a__: Tuple =[sys.executable, "-c", "\n".join([load, run] )]
# should succeed
a__: Any =self.get_env()
a__: int =subprocess.run(_a , env=_a , check=_a , capture_output=_a )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("success" , result.stdout.decode() )
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
a__: List[Any] ="1"
a__: int =subprocess.run(_a , env=_a , check=_a , capture_output=_a )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("success" , result.stdout.decode() )
| 42 | 1 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
lowerCAmelCase__ :int = logging.get_logger(__name__)
class __a ( UpperCAmelCase ):
def __init__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
warnings.warn(
'The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use SegformerImageProcessor instead.' , _SCREAMING_SNAKE_CASE , )
super().__init__(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
| 329 |
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
lowercase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( SCREAMING_SNAKE_CASE ):
_a = ["""input_features"""]
def __init__( self , lowerCAmelCase=80 , lowerCAmelCase=16_000 , lowerCAmelCase=160 , lowerCAmelCase=30 , lowerCAmelCase=400 , lowerCAmelCase=0.0 , lowerCAmelCase=False , **lowerCAmelCase , ) -> Any:
'''simple docstring'''
super().__init__(
feature_size=lowerCAmelCase , sampling_rate=lowerCAmelCase , padding_value=lowerCAmelCase , return_attention_mask=lowerCAmelCase , **lowerCAmelCase , )
_lowercase =n_fft
_lowercase =hop_length
_lowercase =chunk_length
_lowercase =chunk_length * sampling_rate
_lowercase =self.n_samples // hop_length
_lowercase =sampling_rate
_lowercase =mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=lowerCAmelCase , min_frequency=0.0 , max_frequency=8000.0 , sampling_rate=lowerCAmelCase , norm='slaney' , mel_scale='slaney' , )
def A__ ( self , lowerCAmelCase ) -> np.ndarray:
'''simple docstring'''
_lowercase =spectrogram(
lowerCAmelCase , 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' , )
_lowercase =log_spec[:, :-1]
_lowercase =np.maximum(lowerCAmelCase , log_spec.max() - 8.0 )
_lowercase =(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 A__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = 0.0 ) -> List[np.ndarray]:
'''simple docstring'''
if attention_mask is not None:
_lowercase =np.array(lowerCAmelCase , np.intaa )
_lowercase =[]
for vector, length in zip(lowerCAmelCase , attention_mask.sum(-1 ) ):
_lowercase =(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7 )
if length < normed_slice.shape[0]:
_lowercase =padding_value
normed_input_values.append(lowerCAmelCase )
else:
_lowercase =[(x - x.mean()) / np.sqrt(x.var() + 1e-7 ) for x in input_values]
return normed_input_values
def __call__( self , lowerCAmelCase , lowerCAmelCase = True , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = "max_length" , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , **lowerCAmelCase , ) -> BatchFeature:
'''simple docstring'''
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.' )
_lowercase =isinstance(lowerCAmelCase , 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}''' )
_lowercase =is_batched_numpy or (
isinstance(lowerCAmelCase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
_lowercase =[np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(lowerCAmelCase , np.ndarray ):
_lowercase =np.asarray(lowerCAmelCase , dtype=np.floataa )
elif isinstance(lowerCAmelCase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
_lowercase =raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
_lowercase =[np.asarray([raw_speech] ).T]
_lowercase =BatchFeature({'input_features': raw_speech} )
# convert into correct format for padding
_lowercase =self.pad(
lowerCAmelCase , padding=lowerCAmelCase , max_length=max_length if max_length else self.n_samples , truncation=lowerCAmelCase , pad_to_multiple_of=lowerCAmelCase , return_attention_mask=return_attention_mask or do_normalize , )
# zero-mean and unit-variance normalization
if do_normalize:
_lowercase =self.zero_mean_unit_var_norm(
padded_inputs['input_features'] , attention_mask=padded_inputs['attention_mask'] , padding_value=self.padding_value , )
_lowercase =np.stack(padded_inputs['input_features'] , axis=0 )
# make sure list is in array format
_lowercase =padded_inputs.get('input_features' ).transpose(2 , 0 , 1 )
_lowercase =[self._np_extract_fbank_features(lowerCAmelCase ) for waveform in input_features[0]]
if isinstance(input_features[0] , lowerCAmelCase ):
_lowercase =[np.asarray(lowerCAmelCase , dtype=np.floataa ) for feature in input_features]
else:
_lowercase =input_features
if return_attention_mask:
# rescale from sample (48000) to feature (3000)
_lowercase =padded_inputs['attention_mask'][:, :: self.hop_length]
if return_tensors is not None:
_lowercase =padded_inputs.convert_to_tensors(lowerCAmelCase )
return padded_inputs
def A__ ( self ) -> Dict[str, Any]:
'''simple docstring'''
_lowercase =copy.deepcopy(self.__dict__ )
_lowercase =self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
return output
| 205 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ = {'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ = [
'''WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''WavLMForAudioFrameClassification''',
'''WavLMForCTC''',
'''WavLMForSequenceClassification''',
'''WavLMForXVector''',
'''WavLMModel''',
'''WavLMPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavlm import (
WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
WavLMForAudioFrameClassification,
WavLMForCTC,
WavLMForSequenceClassification,
WavLMForXVector,
WavLMModel,
WavLMPreTrainedModel,
)
else:
import sys
a__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 15 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from accelerate.test_utils import execute_subprocess_async
def __UpperCAmelCase ( __a : Dict=None ) -> str:
"""simple docstring"""
if subparsers is not None:
_a : Union[str, Any] = subparsers.add_parser('''test''' )
else:
_a : List[str] = argparse.ArgumentParser('''Accelerate test command''' )
parser.add_argument(
'''--config_file''' ,default=__a ,help=(
'''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
'''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
'''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
'''with \'huggingface\'.'''
) ,)
if subparsers is not None:
parser.set_defaults(func=__a )
return parser
def __UpperCAmelCase ( __a : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
_a : Dict = os.path.sep.join(__file__.split(os.path.sep )[:-2] + ['''test_utils''', '''scripts''', '''test_script.py'''] )
if args.config_file is None:
_a : List[Any] = script_name
else:
_a : Union[str, Any] = F"""--config_file={args.config_file} {script_name}"""
_a : str = ['''accelerate-launch'''] + test_args.split()
_a : str = execute_subprocess_async(__a ,env=os.environ.copy() )
if result.returncode == 0:
print('''Test is a success! You are ready for your distributed training!''' )
def __UpperCAmelCase ( ) -> List[Any]:
"""simple docstring"""
_a : Optional[int] = test_command_parser()
_a : List[Any] = parser.parse_args()
test_command(__a )
if __name__ == "__main__":
main()
| 15 | 1 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class UpperCAmelCase ( A_ ):
@slow
@require_torch
def _SCREAMING_SNAKE_CASE (self : List[str] ) -> Optional[int]:
'''simple docstring'''
snake_case : Tuple = EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny" , "prajjwal1/bert-tiny" )
snake_case : Optional[int] = BertTokenizer.from_pretrained("bert-base-uncased" )
snake_case : List[str] = bertabert.config.encoder.vocab_size
snake_case : List[str] = tokenizer.sep_token_id
snake_case : str = tokenizer.cls_token_id
snake_case : Optional[int] = 1_28
snake_case : int = datasets.load_dataset("cnn_dailymail" , "3.0.0" , split="train[:1%]" )
snake_case : List[Any] = datasets.load_dataset("cnn_dailymail" , "3.0.0" , split="validation[:1%]" )
snake_case : int = train_dataset.select(range(32 ) )
snake_case : List[str] = val_dataset.select(range(16 ) )
snake_case : int = 4
def _map_to_encoder_decoder_inputs(snake_case__ : List[Any] ):
# Tokenizer will automatically set [BOS] <text> [EOS]
snake_case : int = tokenizer(batch["article"] , padding="max_length" , truncation=A_ , max_length=5_12 )
snake_case : Union[str, Any] = tokenizer(batch["highlights"] , padding="max_length" , truncation=A_ , max_length=1_28 )
snake_case : Any = inputs.input_ids
snake_case : Union[str, Any] = inputs.attention_mask
snake_case : Dict = outputs.input_ids
snake_case : List[str] = outputs.input_ids.copy()
snake_case : Dict = [
[-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
]
snake_case : Optional[Any] = outputs.attention_mask
assert all(len(A_ ) == 5_12 for x in inputs.input_ids )
assert all(len(A_ ) == 1_28 for x in outputs.input_ids )
return batch
def _compute_metrics(snake_case__ : Tuple ):
snake_case : Tuple = pred.label_ids
snake_case : Optional[Any] = pred.predictions
# all unnecessary tokens are removed
snake_case : int = tokenizer.batch_decode(A_ , skip_special_tokens=A_ )
snake_case : Union[str, Any] = tokenizer.batch_decode(A_ , skip_special_tokens=A_ )
snake_case : Dict = sum([int(pred_str[i] == label_str[i] ) for i in range(len(A_ ) )] ) / len(A_ )
return {"accuracy": accuracy}
# map train dataset
snake_case : Union[str, Any] = train_dataset.map(
_map_to_encoder_decoder_inputs , batched=A_ , batch_size=A_ , remove_columns=["article", "highlights"] , )
train_dataset.set_format(
type="torch" , columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"] , )
# same for validation dataset
snake_case : Optional[int] = val_dataset.map(
_map_to_encoder_decoder_inputs , batched=A_ , batch_size=A_ , remove_columns=["article", "highlights"] , )
val_dataset.set_format(
type="torch" , columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"] , )
snake_case : List[str] = self.get_auto_remove_tmp_dir()
snake_case : Dict = SeqaSeqTrainingArguments(
output_dir=A_ , per_device_train_batch_size=A_ , per_device_eval_batch_size=A_ , predict_with_generate=A_ , evaluation_strategy="steps" , do_train=A_ , do_eval=A_ , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
snake_case : Optional[int] = SeqaSeqTrainer(
model=A_ , args=A_ , compute_metrics=_compute_metrics , train_dataset=A_ , eval_dataset=A_ , tokenizer=A_ , )
# start training
trainer.train()
| 59 |
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
lowerCamelCase : Any = logging.get_logger(__name__)
class A( UpperCamelCase ):
'''simple docstring'''
def __init__( self : Dict , *A_ : Optional[int] , **A_ : int ) -> None:
"""simple docstring"""
warnings.warn(
'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use DonutImageProcessor instead.' , A_ , )
super().__init__(*A_ , **A_ )
| 204 | 0 |
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
_lowerCamelCase = 'http://www.mocksite.com/file1.txt'
_lowerCamelCase = '"text": ["foo", "foo"]'
_lowerCamelCase = '6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'
class a :
'''simple docstring'''
lowerCAmelCase : str = 200
lowerCAmelCase : List[str] = {'Content-Length': '100'}
lowerCAmelCase : Union[str, Any] = {}
def lowerCamelCase_ ( self : List[str] , **__snake_case : int ):
return [bytes(__snake_case , '''utf-8''' )]
def SCREAMING_SNAKE_CASE ( *__UpperCamelCase : Optional[int] , **__UpperCamelCase : Dict ) -> List[str]:
return MockResponse()
@pytest.mark.parametrize('''urls_type''' , [str, list, dict] )
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int , __UpperCamelCase : Dict , __UpperCamelCase : Optional[int] ) -> List[Any]:
import requests
monkeypatch.setattr(__UpperCamelCase , '''request''' , __UpperCamelCase )
UpperCAmelCase_ = URL
if issubclass(__UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase_ = url
elif issubclass(__UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase_ = [url]
elif issubclass(__UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase_ = {'''train''': url}
UpperCAmelCase_ = '''dummy'''
UpperCAmelCase_ = '''downloads'''
UpperCAmelCase_ = tmp_path
UpperCAmelCase_ = DownloadConfig(
cache_dir=os.path.join(__UpperCamelCase , __UpperCamelCase ) , use_etag=__UpperCamelCase , )
UpperCAmelCase_ = DownloadManager(dataset_name=__UpperCamelCase , download_config=__UpperCamelCase )
UpperCAmelCase_ = dl_manager.download(__UpperCamelCase )
UpperCAmelCase_ = urls
for downloaded_paths in [downloaded_paths]:
if isinstance(__UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase_ = [downloaded_paths]
UpperCAmelCase_ = [urls]
elif isinstance(__UpperCamelCase , __UpperCamelCase ):
assert "train" in downloaded_paths.keys()
UpperCAmelCase_ = downloaded_paths.values()
UpperCAmelCase_ = urls.values()
assert downloaded_paths
for downloaded_path, input_url in zip(__UpperCamelCase , __UpperCamelCase ):
assert downloaded_path == dl_manager.downloaded_paths[input_url]
UpperCAmelCase_ = Path(__UpperCamelCase )
UpperCAmelCase_ = downloaded_path.parts
assert parts[-1] == HASH
assert parts[-2] == cache_subdir
assert downloaded_path.exists()
UpperCAmelCase_ = downloaded_path.read_text()
assert content == CONTENT
UpperCAmelCase_ = downloaded_path.with_suffix('''.json''' )
assert metadata_downloaded_path.exists()
UpperCAmelCase_ = json.loads(metadata_downloaded_path.read_text() )
assert metadata_content == {"url": URL, "etag": None}
@pytest.mark.parametrize('''paths_type''' , [str, list, dict] )
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int , __UpperCamelCase : List[Any] , __UpperCamelCase : List[str] ) -> int:
UpperCAmelCase_ = str(__UpperCamelCase )
if issubclass(__UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase_ = filename
elif issubclass(__UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase_ = [filename]
elif issubclass(__UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase_ = {'''train''': filename}
UpperCAmelCase_ = '''dummy'''
UpperCAmelCase_ = xz_file.parent
UpperCAmelCase_ = '''extracted'''
UpperCAmelCase_ = DownloadConfig(
cache_dir=__UpperCamelCase , use_etag=__UpperCamelCase , )
UpperCAmelCase_ = DownloadManager(dataset_name=__UpperCamelCase , download_config=__UpperCamelCase )
UpperCAmelCase_ = dl_manager.extract(__UpperCamelCase )
UpperCAmelCase_ = paths
for extracted_paths in [extracted_paths]:
if isinstance(__UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase_ = [extracted_paths]
UpperCAmelCase_ = [paths]
elif isinstance(__UpperCamelCase , __UpperCamelCase ):
assert "train" in extracted_paths.keys()
UpperCAmelCase_ = extracted_paths.values()
UpperCAmelCase_ = paths.values()
assert extracted_paths
for extracted_path, input_path in zip(__UpperCamelCase , __UpperCamelCase ):
assert extracted_path == dl_manager.extracted_paths[input_path]
UpperCAmelCase_ = Path(__UpperCamelCase )
UpperCAmelCase_ = extracted_path.parts
assert parts[-1] == hash_url_to_filename(__UpperCamelCase , etag=__UpperCamelCase )
assert parts[-2] == extracted_subdir
assert extracted_path.exists()
UpperCAmelCase_ = extracted_path.read_text()
UpperCAmelCase_ = text_file.read_text()
assert extracted_file_content == expected_file_content
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : List[str] , __UpperCamelCase : List[Any] ) -> Optional[Any]:
assert path.endswith('''.jsonl''' )
for num_items, line in enumerate(__UpperCamelCase , start=1 ):
UpperCAmelCase_ = json.loads(line.decode('''utf-8''' ) )
assert item.keys() == {"col_1", "col_2", "col_3"}
assert num_items == 4
@pytest.mark.parametrize('''archive_jsonl''' , ['''tar_jsonl_path''', '''zip_jsonl_path'''] )
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Tuple , __UpperCamelCase : Union[str, Any] ) -> Union[str, Any]:
UpperCAmelCase_ = request.getfixturevalue(__UpperCamelCase )
UpperCAmelCase_ = DownloadManager()
for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(__UpperCamelCase ) , start=1 ):
_test_jsonl(__UpperCamelCase , __UpperCamelCase )
assert num_jsonl == 2
@pytest.mark.parametrize('''archive_nested_jsonl''' , ['''tar_nested_jsonl_path''', '''zip_nested_jsonl_path'''] )
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int , __UpperCamelCase : str ) -> Optional[int]:
UpperCAmelCase_ = request.getfixturevalue(__UpperCamelCase )
UpperCAmelCase_ = DownloadManager()
for num_tar, (path, file) in enumerate(dl_manager.iter_archive(__UpperCamelCase ) , start=1 ):
for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(__UpperCamelCase ) , start=1 ):
_test_jsonl(__UpperCamelCase , __UpperCamelCase )
assert num_tar == 1
assert num_jsonl == 2
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Optional[Any] ) -> Optional[Any]:
UpperCAmelCase_ = DownloadManager()
for num_file, file in enumerate(dl_manager.iter_files(__UpperCamelCase ) , start=1 ):
assert os.path.basename(__UpperCamelCase ) == ("test.txt" if num_file == 1 else "train.txt")
assert num_file == 2
| 177 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCamelCase = {
'configuration_conditional_detr': [
'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ConditionalDetrConfig',
'ConditionalDetrOnnxConfig',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = ['ConditionalDetrFeatureExtractor']
_lowerCamelCase = ['ConditionalDetrImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
'ConditionalDetrForObjectDetection',
'ConditionalDetrForSegmentation',
'ConditionalDetrModel',
'ConditionalDetrPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
ConditionalDetrConfig,
ConditionalDetrOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
from .image_processing_conditional_detr import ConditionalDetrImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrModel,
ConditionalDetrPreTrainedModel,
)
else:
import sys
_lowerCamelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 177 | 1 |
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase = '''▁'''
UpperCamelCase = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class snake_case_ ( __A ,unittest.TestCase ):
__A : Optional[int] = BigBirdTokenizer
__A : Any = BigBirdTokenizerFast
__A : Dict = True
__A : Optional[Any] = True
def __UpperCamelCase ( self : Union[str, Any] ) -> Optional[Any]:
super().setUp()
lowercase__ : Union[str, Any] = self.tokenizer_class(lowercase_ , keep_accents=lowercase_ )
tokenizer.save_pretrained(self.tmpdirname )
def __UpperCamelCase ( self : Tuple ) -> List[Any]:
lowercase__ : List[Any] = "<s>"
lowercase__ : Optional[Any] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_ ) , lowercase_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_ ) , lowercase_ )
def __UpperCamelCase ( self : Optional[Any] ) -> int:
lowercase__ : 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] , "[MASK]" )
self.assertEqual(len(lowercase_ ) , 10_04 )
def __UpperCamelCase ( self : List[Any] ) -> str:
self.assertEqual(self.get_tokenizer().vocab_size , 10_00 )
def __UpperCamelCase ( self : Optional[int] ) -> Tuple:
if not self.test_rust_tokenizer:
return
lowercase__ : Dict = self.get_tokenizer()
lowercase__ : Any = self.get_rust_tokenizer()
lowercase__ : str = "I was born in 92000, and this is falsé."
lowercase__ : Optional[int] = tokenizer.tokenize(lowercase_ )
lowercase__ : Any = rust_tokenizer.tokenize(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
lowercase__ : List[str] = tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
lowercase__ : List[str] = rust_tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
lowercase__ : str = self.get_rust_tokenizer()
lowercase__ : Union[str, Any] = tokenizer.encode(lowercase_ )
lowercase__ : Optional[Any] = rust_tokenizer.encode(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
def __UpperCamelCase ( self : Dict ) -> str:
lowercase__ : Optional[Any] = BigBirdTokenizer(lowercase_ , keep_accents=lowercase_ )
lowercase__ : List[str] = tokenizer.tokenize("This is a test" )
self.assertListEqual(lowercase_ , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowercase_ ) , [2_85, 46, 10, 1_70, 3_82] , )
lowercase__ : Dict = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
lowercase_ , [
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",
".",
] , )
lowercase__ : Tuple = tokenizer.convert_tokens_to_ids(lowercase_ )
self.assertListEqual(
lowercase_ , [8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
lowercase__ : Tuple = tokenizer.convert_ids_to_tokens(lowercase_ )
self.assertListEqual(
lowercase_ , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
@cached_property
def __UpperCamelCase ( self : List[Any] ) -> Optional[int]:
return BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base" )
@slow
def __UpperCamelCase ( self : Optional[int] ) -> int:
lowercase__ : Optional[Any] = "Hello World!"
lowercase__ : Optional[int] = [65, 1_85_36, 22_60, 1_01, 66]
self.assertListEqual(lowercase_ , self.big_tokenizer.encode(lowercase_ ) )
@slow
def __UpperCamelCase ( self : Tuple ) -> Optional[Any]:
lowercase__ : Tuple = (
"This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
)
# fmt: off
lowercase__ : Optional[Any] = [65, 8_71, 4_19, 3_58, 9_46, 9_91, 25_21, 4_52, 3_58, 13_57, 3_87, 77_51, 35_36, 1_12, 9_85, 4_56, 1_26, 8_65, 9_38, 54_00, 57_34, 4_58, 13_68, 4_67, 7_86, 24_62, 52_46, 11_59, 6_33, 8_65, 45_19, 4_57, 5_82, 8_52, 25_57, 4_27, 9_16, 5_08, 4_05, 3_43_24, 4_97, 3_91, 4_08, 1_13_42, 12_44, 3_85, 1_00, 9_38, 9_85, 4_56, 5_74, 3_62, 1_25_97, 32_00, 31_29, 11_72, 66] # noqa: E231
# fmt: on
self.assertListEqual(lowercase_ , self.big_tokenizer.encode(lowercase_ ) )
@require_torch
@slow
def __UpperCamelCase ( self : List[Any] ) -> int:
import torch
from transformers import BigBirdConfig, BigBirdModel
# Build sequence
lowercase__ : Optional[int] = list(self.big_tokenizer.get_vocab().keys() )[:10]
lowercase__ : str = " ".join(lowercase_ )
lowercase__ : List[str] = self.big_tokenizer.encode_plus(lowercase_ , return_tensors="pt" , return_token_type_ids=lowercase_ )
lowercase__ : int = self.big_tokenizer.batch_encode_plus(
[sequence + " " + sequence] , return_tensors="pt" , return_token_type_ids=lowercase_ )
lowercase__ : Optional[int] = BigBirdConfig(attention_type="original_full" )
lowercase__ : List[str] = BigBirdModel(lowercase_ )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**lowercase_ )
model(**lowercase_ )
@slow
def __UpperCamelCase ( self : str ) -> Optional[int]:
lowercase__ : List[Any] = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base" )
lowercase__ : str = tokenizer.decode(tokenizer("Paris is the [MASK]." ).input_ids )
self.assertTrue(decoded_text == "[CLS] Paris is the[MASK].[SEP]" )
@slow
def __UpperCamelCase ( self : Optional[int] ) -> Optional[Any]:
# fmt: off
lowercase__ : str = {"input_ids": [[65, 3_92_86, 4_58, 3_63_35, 20_01, 4_56, 1_30_73, 1_32_66, 4_55, 1_13, 77_46, 17_41, 1_11_57, 3_91, 1_30_73, 1_32_66, 4_55, 1_13, 39_67, 3_54_12, 1_13, 49_36, 1_09, 38_70, 23_77, 1_13, 3_00_84, 4_57_20, 4_58, 1_34, 1_74_96, 1_12, 5_03, 1_16_72, 1_13, 1_18, 1_12, 56_65, 1_33_47, 3_86_87, 1_12, 14_96, 3_13_89, 1_12, 32_68, 4_72_64, 1_34, 9_62, 1_12, 1_63_77, 80_35, 2_31_30, 4_30, 1_21_69, 1_55_18, 2_85_92, 4_58, 1_46, 4_16_97, 1_09, 3_91, 1_21_69, 1_55_18, 1_66_89, 4_58, 1_46, 4_13_58, 1_09, 4_52, 7_26, 40_34, 1_11, 7_63, 3_54_12, 50_82, 3_88, 19_03, 1_11, 90_51, 3_91, 28_70, 4_89_18, 19_00, 11_23, 5_50, 9_98, 1_12, 95_86, 1_59_85, 4_55, 3_91, 4_10, 2_29_55, 3_76_36, 1_14, 66], [65, 4_48, 1_74_96, 4_19, 36_63, 3_85, 7_63, 1_13, 2_75_33, 28_70, 32_83, 1_30_43, 16_39, 2_47_13, 5_23, 6_56, 2_40_13, 1_85_50, 25_21, 5_17, 2_70_14, 2_12_44, 4_20, 12_12, 14_65, 3_91, 9_27, 48_33, 3_88, 5_78, 1_17_86, 1_14, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 4_84, 21_69, 76_87, 2_19_32, 1_81_46, 7_26, 3_63, 1_70_32, 33_91, 1_14, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowercase_ , model_name="google/bigbird-roberta-base" , revision="215c99f1600e06f83acce68422f2035b2b5c3510" , )
| 87 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
__lowerCAmelCase = TypeVar("""T""")
class UpperCAmelCase__ ( Generic[T] ):
"""simple docstring"""
def __init__( self : Tuple ,_a : T ):
'''simple docstring'''
_a : List[str] = data
_a : Node[T] | None = None
def __str__( self : Dict ):
'''simple docstring'''
return F"""{self.data}"""
class UpperCAmelCase__ ( Generic[T] ):
"""simple docstring"""
def __init__( self : Optional[int] ):
'''simple docstring'''
_a : Node[T] | None = None
def __iter__( self : str ):
'''simple docstring'''
_a : Tuple = self.top
while node:
yield node.data
_a : int = node.next
def __str__( self : str ):
'''simple docstring'''
return "->".join([str(_a ) for item in self] )
def __len__( self : Optional[Any] ):
'''simple docstring'''
return len(tuple(iter(self ) ) )
def __lowercase ( self : str ):
'''simple docstring'''
return self.top is None
def __lowercase ( self : List[Any] ,_a : T ):
'''simple docstring'''
_a : int = Node(_a )
if not self.is_empty():
_a : Optional[Any] = self.top
_a : List[str] = node
def __lowercase ( self : Tuple ):
'''simple docstring'''
if self.is_empty():
raise IndexError('pop from empty stack' )
assert isinstance(self.top ,_a )
_a : List[Any] = self.top
_a : int = self.top.next
return pop_node.data
def __lowercase ( self : List[str] ):
'''simple docstring'''
if self.is_empty():
raise IndexError('peek from empty stack' )
assert self.top is not None
return self.top.data
def __lowercase ( self : List[str] ):
'''simple docstring'''
_a : Optional[int] = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 271 | 0 |
from math import isqrt, loga
def A ( lowercase ) -> list[int]:
'''simple docstring'''
UpperCamelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , lowercase , lowercase ):
UpperCamelCase = False
return [i for i in range(2 , lowercase ) if is_prime[i]]
def A ( lowercase = 800_800 , lowercase = 800_800 ) -> int:
'''simple docstring'''
UpperCamelCase = degree * loga(lowercase )
UpperCamelCase = int(lowercase )
UpperCamelCase = calculate_prime_numbers(lowercase )
UpperCamelCase = 0
UpperCamelCase = 0
UpperCamelCase = len(lowercase ) - 1
while left < right:
while (
prime_numbers[right] * loga(prime_numbers[left] )
+ prime_numbers[left] * loga(prime_numbers[right] )
> upper_bound
):
right -= 1
hybrid_integers_count += right - left
left += 1
return hybrid_integers_count
if __name__ == "__main__":
print(F'''{solution() = }''')
| 110 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
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 LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer
class lowercase :
def __init__( self , A_ , A_=13 , A_=7 , A_=True , A_=True , A_=False , A_=True , A_=99 , A_=32 , A_=5 , A_=4 , A_=37 , A_="gelu" , A_=0.1 , A_=0.1 , A_=512 , A_=16 , A_=2 , A_=0.02 , A_=3 , A_=4 , A_=None , ) -> Tuple:
"""simple docstring"""
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = seq_length
UpperCamelCase = is_training
UpperCamelCase = use_input_mask
UpperCamelCase = use_token_type_ids
UpperCamelCase = use_labels
UpperCamelCase = vocab_size
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_act
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = max_position_embeddings
UpperCamelCase = type_vocab_size
UpperCamelCase = type_sequence_label_size
UpperCamelCase = initializer_range
UpperCamelCase = num_labels
UpperCamelCase = num_choices
UpperCamelCase = scope
def __UpperCamelCase ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase = None
if self.use_input_mask:
UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase = None
if self.use_token_type_ids:
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
if self.use_labels:
UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
return LlamaConfig(
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 , )
def __UpperCamelCase ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ) -> int:
"""simple docstring"""
UpperCamelCase = LlamaModel(config=A_ )
model.to(A_ )
model.eval()
UpperCamelCase = model(A_ , attention_mask=A_ )
UpperCamelCase = model(A_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCamelCase ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = True
UpperCamelCase = LlamaModel(A_ )
model.to(A_ )
model.eval()
UpperCamelCase = model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , encoder_attention_mask=A_ , )
UpperCamelCase = model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , )
UpperCamelCase = model(A_ , attention_mask=A_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCamelCase ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , ) -> str:
"""simple docstring"""
UpperCamelCase = LlamaForCausalLM(config=A_ )
model.to(A_ )
model.eval()
UpperCamelCase = model(A_ , attention_mask=A_ , labels=A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCamelCase ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = True
UpperCamelCase = True
UpperCamelCase = LlamaForCausalLM(config=A_ )
model.to(A_ )
model.eval()
# first forward pass
UpperCamelCase = model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , encoder_attention_mask=A_ , use_cache=A_ , )
UpperCamelCase = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
UpperCamelCase = ids_tensor((self.batch_size, 3) , config.vocab_size )
UpperCamelCase = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
UpperCamelCase = torch.cat([input_ids, next_tokens] , dim=-1 )
UpperCamelCase = torch.cat([input_mask, next_mask] , dim=-1 )
UpperCamelCase = model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , encoder_attention_mask=A_ , output_hidden_states=A_ , )['hidden_states'][0]
UpperCamelCase = model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , encoder_attention_mask=A_ , past_key_values=A_ , output_hidden_states=A_ , )['hidden_states'][0]
# select random slice
UpperCamelCase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
UpperCamelCase = output_from_no_past[:, -3:, random_slice_idx].detach()
UpperCamelCase = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A_ , A_ , atol=1e-3 ) )
def __UpperCamelCase ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) = config_and_inputs
UpperCamelCase = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , unittest.TestCase ):
__lowercase : str = (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
__lowercase : str = (LlamaForCausalLM,) if is_torch_available() else ()
__lowercase : Any = (
{
"feature-extraction": LlamaModel,
"text-classification": LlamaForSequenceClassification,
"text-generation": LlamaForCausalLM,
"zero-shot": LlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
__lowercase : int = False
__lowercase : Optional[int] = False
def __UpperCamelCase ( self ) -> int:
"""simple docstring"""
UpperCamelCase = LlamaModelTester(self )
UpperCamelCase = ConfigTester(self , config_class=A_ , hidden_size=37 )
def __UpperCamelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def __UpperCamelCase ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A_ )
def __UpperCamelCase ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
UpperCamelCase = type
self.model_tester.create_and_check_model(*A_ )
def __UpperCamelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = 3
UpperCamelCase = input_dict['input_ids']
UpperCamelCase = input_ids.ne(1 ).to(A_ )
UpperCamelCase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
UpperCamelCase = LlamaForSequenceClassification(A_ )
model.to(A_ )
model.eval()
UpperCamelCase = model(A_ , attention_mask=A_ , labels=A_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __UpperCamelCase ( self ) -> Dict:
"""simple docstring"""
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = 3
UpperCamelCase = 'single_label_classification'
UpperCamelCase = input_dict['input_ids']
UpperCamelCase = input_ids.ne(1 ).to(A_ )
UpperCamelCase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
UpperCamelCase = LlamaForSequenceClassification(A_ )
model.to(A_ )
model.eval()
UpperCamelCase = model(A_ , attention_mask=A_ , labels=A_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __UpperCamelCase ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = 3
UpperCamelCase = 'multi_label_classification'
UpperCamelCase = input_dict['input_ids']
UpperCamelCase = input_ids.ne(1 ).to(A_ )
UpperCamelCase = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
UpperCamelCase = LlamaForSequenceClassification(A_ )
model.to(A_ )
model.eval()
UpperCamelCase = model(A_ , attention_mask=A_ , labels=A_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('LLaMA buffers include complex numbers, which breaks this test' )
def __UpperCamelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def __UpperCamelCase ( self , A_ ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = ids_tensor([1, 10] , config.vocab_size )
UpperCamelCase = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
UpperCamelCase = LlamaModel(A_ )
original_model.to(A_ )
original_model.eval()
UpperCamelCase = original_model(A_ ).last_hidden_state
UpperCamelCase = original_model(A_ ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
UpperCamelCase = {'type': scaling_type, 'factor': 10.0}
UpperCamelCase = LlamaModel(A_ )
scaled_model.to(A_ )
scaled_model.eval()
UpperCamelCase = scaled_model(A_ ).last_hidden_state
UpperCamelCase = scaled_model(A_ ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(A_ , A_ , atol=1e-5 ) )
else:
self.assertFalse(torch.allclose(A_ , A_ , atol=1e-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(A_ , A_ , atol=1e-5 ) )
@require_torch
class lowercase ( unittest.TestCase ):
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def __UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
UpperCamelCase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf' , device_map='auto' )
UpperCamelCase = model(torch.tensor([input_ids] ) )
# Expected mean on dim = -1
UpperCamelCase = torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]] )
torch.testing.assert_close(out.mean(-1 ) , A_ , atol=1e-2 , rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
UpperCamelCase = torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , A_ , atol=1e-5 , rtol=1e-5 )
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def __UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
UpperCamelCase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf' , device_map='auto' )
UpperCamelCase = model(torch.tensor(A_ ) )
# Expected mean on dim = -1
UpperCamelCase = torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]] )
torch.testing.assert_close(out.mean(-1 ) , A_ , atol=1e-2 , rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
UpperCamelCase = torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , A_ , atol=1e-5 , rtol=1e-5 )
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def __UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
UpperCamelCase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf' , device_map='auto' )
UpperCamelCase = model(torch.tensor(A_ ) )
# Expected mean on dim = -1
UpperCamelCase = torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]] )
torch.testing.assert_close(out.mean(-1 ) , A_ , atol=1e-2 , rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
UpperCamelCase = torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513] )
# fmt: on
torch.testing.assert_close(out.mean(-1 ) , A_ , atol=1e-2 , rtol=1e-2 )
@unittest.skip(
'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test' )
@slow
def __UpperCamelCase ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
UpperCamelCase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf' , device_map='auto' )
UpperCamelCase = model(torch.tensor(A_ ) )
UpperCamelCase = torch.tensor(
[[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]] , dtype=torch.floataa )
torch.testing.assert_close(out.mean(-1 ) , A_ , atol=1e-2 , rtol=1e-2 )
# fmt: off
UpperCamelCase = torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , A_ , atol=1e-5 , rtol=1e-5 )
@unittest.skip('Model is curently gated' )
@slow
def __UpperCamelCase ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = 'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
UpperCamelCase = 'Simply put, the theory of relativity states that '
UpperCamelCase = LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf' )
UpperCamelCase = tokenizer.encode(A_ , return_tensors='pt' )
UpperCamelCase = LlamaForCausalLM.from_pretrained(
'meta-llama/Llama-2-13b-chat-hf' , device_map='sequential' , use_safetensors=A_ )
# greedy generation outputs
UpperCamelCase = model.generate(A_ , max_new_tokens=64 , top_p=A_ , temperature=1 , do_sample=A_ )
UpperCamelCase = tokenizer.decode(generated_ids[0] , skip_special_tokens=A_ )
self.assertEqual(A_ , A_ )
| 110 | 1 |
'''simple docstring'''
from math import factorial
def SCREAMING_SNAKE_CASE__ ( __A , __A , __A ) -> float:
if successes > trials:
raise ValueError('successes must be lower or equal to trials' )
if trials < 0 or successes < 0:
raise ValueError('the function is defined for non-negative integers' )
if not isinstance(__A , __A ) or not isinstance(__A , __A ):
raise ValueError('the function is defined for non-negative integers' )
if not 0 < prob < 1:
raise ValueError('prob has to be in range of 1 - 0' )
_snake_case = (prob**successes) * ((1 - prob) ** (trials - successes))
# Calculate the binomial coefficient: n! / k!(n-k)!
_snake_case = float(factorial(__A ) )
coefficient /= factorial(__A ) * factorial(trials - successes )
return probability * coefficient
if __name__ == "__main__":
from doctest import testmod
testmod()
print("Probability of 2 successes out of 4 trails")
print("with probability of 0.75 is:", end=" ")
print(binomial_distribution(2, 4, 0.75))
| 42 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A = 100 ) -> int:
_snake_case = n * (n + 1) * (2 * n + 1) / 6
_snake_case = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 42 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A : Optional[Any] = {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Optional[Any] = ['YolosFeatureExtractor']
__A : Dict = ['YolosImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Any = [
'YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST',
'YolosForObjectDetection',
'YolosModel',
'YolosPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_yolos import YolosFeatureExtractor
from .image_processing_yolos import YolosImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_yolos import (
YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
YolosForObjectDetection,
YolosModel,
YolosPreTrainedModel,
)
else:
import sys
__A : Optional[int] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 366 |
__A : List[Any] = [
9_99,
8_00,
7_99,
6_00,
5_99,
5_00,
4_00,
3_99,
3_77,
3_55,
3_33,
3_11,
2_88,
2_66,
2_44,
2_22,
2_00,
1_99,
1_77,
1_55,
1_33,
1_11,
88,
66,
44,
22,
0,
]
__A : int = [
9_99,
9_76,
9_52,
9_28,
9_05,
8_82,
8_58,
8_57,
8_10,
7_62,
7_15,
7_14,
5_72,
4_29,
4_28,
2_86,
2_85,
2_38,
1_90,
1_43,
1_42,
1_18,
95,
71,
47,
24,
0,
]
__A : Any = [
9_99,
9_88,
9_77,
9_66,
9_55,
9_44,
9_33,
9_22,
9_11,
9_00,
8_99,
8_79,
8_59,
8_40,
8_20,
8_00,
7_99,
7_66,
7_33,
7_00,
6_99,
6_50,
6_00,
5_99,
5_00,
4_99,
4_00,
3_99,
3_50,
3_00,
2_99,
2_66,
2_33,
2_00,
1_99,
1_79,
1_59,
1_40,
1_20,
1_00,
99,
88,
77,
66,
55,
44,
33,
22,
11,
0,
]
__A : Dict = [
9_99,
9_95,
9_92,
9_89,
9_85,
9_81,
9_78,
9_75,
9_71,
9_67,
9_64,
9_61,
9_57,
9_56,
9_51,
9_47,
9_42,
9_37,
9_33,
9_28,
9_23,
9_19,
9_14,
9_13,
9_08,
9_03,
8_97,
8_92,
8_87,
8_81,
8_76,
8_71,
8_70,
8_64,
8_58,
8_52,
8_46,
8_40,
8_34,
8_28,
8_27,
8_20,
8_13,
8_06,
7_99,
7_92,
7_85,
7_84,
7_77,
7_70,
7_63,
7_56,
7_49,
7_42,
7_41,
7_33,
7_24,
7_16,
7_07,
6_99,
6_98,
6_88,
6_77,
6_66,
6_56,
6_55,
6_45,
6_34,
6_23,
6_13,
6_12,
5_98,
5_84,
5_70,
5_69,
5_55,
5_41,
5_27,
5_26,
5_05,
4_84,
4_83,
4_62,
4_40,
4_39,
3_96,
3_95,
3_52,
3_51,
3_08,
3_07,
2_64,
2_63,
2_20,
2_19,
1_76,
1_32,
88,
44,
0,
]
__A : List[str] = [
9_99,
9_97,
9_95,
9_92,
9_90,
9_88,
9_86,
9_84,
9_81,
9_79,
9_77,
9_75,
9_72,
9_70,
9_68,
9_66,
9_64,
9_61,
9_59,
9_57,
9_56,
9_54,
9_51,
9_49,
9_46,
9_44,
9_41,
9_39,
9_36,
9_34,
9_31,
9_29,
9_26,
9_24,
9_21,
9_19,
9_16,
9_14,
9_13,
9_10,
9_07,
9_05,
9_02,
8_99,
8_96,
8_93,
8_91,
8_88,
8_85,
8_82,
8_79,
8_77,
8_74,
8_71,
8_70,
8_67,
8_64,
8_61,
8_58,
8_55,
8_52,
8_49,
8_46,
8_43,
8_40,
8_37,
8_34,
8_31,
8_28,
8_27,
8_24,
8_21,
8_17,
8_14,
8_11,
8_08,
8_04,
8_01,
7_98,
7_95,
7_91,
7_88,
7_85,
7_84,
7_80,
7_77,
7_74,
7_70,
7_66,
7_63,
7_60,
7_56,
7_52,
7_49,
7_46,
7_42,
7_41,
7_37,
7_33,
7_30,
7_26,
7_22,
7_18,
7_14,
7_10,
7_07,
7_03,
6_99,
6_98,
6_94,
6_90,
6_85,
6_81,
6_77,
6_73,
6_69,
6_64,
6_60,
6_56,
6_55,
6_50,
6_46,
6_41,
6_36,
6_32,
6_27,
6_22,
6_18,
6_13,
6_12,
6_07,
6_02,
5_96,
5_91,
5_86,
5_80,
5_75,
5_70,
5_69,
5_63,
5_57,
5_51,
5_45,
5_39,
5_33,
5_27,
5_26,
5_19,
5_12,
5_05,
4_98,
4_91,
4_84,
4_83,
4_74,
4_66,
4_57,
4_49,
4_40,
4_39,
4_28,
4_18,
4_07,
3_96,
3_95,
3_81,
3_66,
3_52,
3_51,
3_30,
3_08,
3_07,
2_86,
2_64,
2_63,
2_42,
2_20,
2_19,
1_76,
1_75,
1_32,
1_31,
88,
44,
0,
]
__A : List[str] = [
9_99,
9_91,
9_82,
9_74,
9_66,
9_58,
9_50,
9_41,
9_33,
9_25,
9_16,
9_08,
9_00,
8_99,
8_74,
8_50,
8_25,
8_00,
7_99,
7_00,
6_00,
5_00,
4_00,
3_00,
2_00,
1_00,
0,
]
__A : Dict = [
9_99,
9_92,
9_85,
9_78,
9_71,
9_64,
9_57,
9_49,
9_42,
9_35,
9_28,
9_21,
9_14,
9_07,
9_00,
8_99,
8_79,
8_59,
8_40,
8_20,
8_00,
7_99,
7_66,
7_33,
7_00,
6_99,
6_50,
6_00,
5_99,
5_00,
4_99,
4_00,
3_99,
3_00,
2_99,
2_00,
1_99,
1_00,
99,
0,
]
__A : str = [
9_99,
9_96,
9_92,
9_89,
9_85,
9_82,
9_79,
9_75,
9_72,
9_68,
9_65,
9_61,
9_58,
9_55,
9_51,
9_48,
9_44,
9_41,
9_38,
9_34,
9_31,
9_27,
9_24,
9_20,
9_17,
9_14,
9_10,
9_07,
9_03,
9_00,
8_99,
8_91,
8_84,
8_76,
8_69,
8_61,
8_53,
8_46,
8_38,
8_30,
8_23,
8_15,
8_08,
8_00,
7_99,
7_88,
7_77,
7_66,
7_55,
7_44,
7_33,
7_22,
7_11,
7_00,
6_99,
6_88,
6_77,
6_66,
6_55,
6_44,
6_33,
6_22,
6_11,
6_00,
5_99,
5_85,
5_71,
5_57,
5_42,
5_28,
5_14,
5_00,
4_99,
4_85,
4_71,
4_57,
4_42,
4_28,
4_14,
4_00,
3_99,
3_79,
3_59,
3_40,
3_20,
3_00,
2_99,
2_79,
2_59,
2_40,
2_20,
2_00,
1_99,
1_66,
1_33,
1_00,
99,
66,
33,
0,
]
| 49 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE :int = {'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE :Dict = [
'WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST',
'WavLMForAudioFrameClassification',
'WavLMForCTC',
'WavLMForSequenceClassification',
'WavLMForXVector',
'WavLMModel',
'WavLMPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavlm import (
WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
WavLMForAudioFrameClassification,
WavLMForCTC,
WavLMForSequenceClassification,
WavLMForXVector,
WavLMModel,
WavLMPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE :str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 15 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCAmelCase ( a_ ) -> List[str]:
"""simple docstring"""
__A = args.pruning_method
__A = args.threshold
__A = args.model_name_or_path.rstrip("/" )
__A = args.target_model_path
print(F'''Load fine-pruned model from {model_name_or_path}''' )
__A = torch.load(os.path.join(a_ , "pytorch_model.bin" ) )
__A = {}
for name, tensor in model.items():
if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
__A = tensor
print(F'''Copied layer {name}''' )
elif "classifier" in name or "qa_output" in name:
__A = tensor
print(F'''Copied layer {name}''' )
elif "bias" in name:
__A = tensor
print(F'''Copied layer {name}''' )
else:
if pruning_method == "magnitude":
__A = MagnitudeBinarizer.apply(inputs=a_ , threshold=a_ )
__A = tensor * mask
print(F'''Pruned layer {name}''' )
elif pruning_method == "topK":
if "mask_scores" in name:
continue
__A = name[:-6]
__A = model[F'''{prefix_}mask_scores''']
__A = TopKBinarizer.apply(a_ , a_ )
__A = tensor * mask
print(F'''Pruned layer {name}''' )
elif pruning_method == "sigmoied_threshold":
if "mask_scores" in name:
continue
__A = name[:-6]
__A = model[F'''{prefix_}mask_scores''']
__A = ThresholdBinarizer.apply(a_ , a_ , a_ )
__A = tensor * mask
print(F'''Pruned layer {name}''' )
elif pruning_method == "l0":
if "mask_scores" in name:
continue
__A = name[:-6]
__A = model[F'''{prefix_}mask_scores''']
__A , __A = -0.1, 1.1
__A = torch.sigmoid(a_ )
__A = s * (r - l) + l
__A = s_bar.clamp(min=0.0 , max=1.0 )
__A = tensor * mask
print(F'''Pruned layer {name}''' )
else:
raise ValueError("Unknown pruning method" )
if target_model_path is None:
__A = os.path.join(
os.path.dirname(a_ ) , F'''bertarized_{os.path.basename(a_ )}''' )
if not os.path.isdir(a_ ):
shutil.copytree(a_ , a_ )
print(F'''\nCreated folder {target_model_path}''' )
torch.save(a_ , os.path.join(a_ , "pytorch_model.bin" ) )
print("\nPruned model saved! See you later!" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :Tuple = argparse.ArgumentParser()
parser.add_argument(
'--pruning_method',
choices=['l0', 'magnitude', 'topK', 'sigmoied_threshold'],
type=str,
required=True,
help=(
'Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'
' sigmoied_threshold = Soft movement pruning)'
),
)
parser.add_argument(
'--threshold',
type=float,
required=False,
help=(
'For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'
'For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'
'Not needed for `l0`'
),
)
parser.add_argument(
'--model_name_or_path',
type=str,
required=True,
help='Folder containing the model that was previously fine-pruned',
)
parser.add_argument(
'--target_model_path',
default=None,
type=str,
required=False,
help='Folder containing the model that was previously fine-pruned',
)
SCREAMING_SNAKE_CASE :str = parser.parse_args()
main(args)
| 15 | 1 |
import argparse
from collections import defaultdict
import yaml
lowerCAmelCase = 'docs/source/en/_toctree.yml'
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = defaultdict(SCREAMING_SNAKE_CASE )
lowercase__ = []
lowercase__ = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({'''local''': doc['''local'''], '''title''': doc['''title''']} )
else:
new_doc_list.append(SCREAMING_SNAKE_CASE )
lowercase__ = new_doc_list
lowercase__ = [key for key, value in counts.items() if value > 1]
lowercase__ = []
for duplicate_key in duplicates:
lowercase__ = list({doc['''title'''] for doc in doc_list if doc['''local'''] == duplicate_key} )
if len(SCREAMING_SNAKE_CASE ) > 1:
raise ValueError(
f'{duplicate_key} is present several times in the documentation table of content at '
'''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
'''others.''' )
# Only add this once
new_doc.append({'''local''': duplicate_key, '''title''': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if '''local''' not in counts or counts[doc['''local''']] == 1] )
lowercase__ = sorted(SCREAMING_SNAKE_CASE , key=lambda SCREAMING_SNAKE_CASE : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(SCREAMING_SNAKE_CASE ) > 1:
raise ValueError('''{doc_list} has two \'overview\' docs which is not allowed.''' )
overview_doc.extend(SCREAMING_SNAKE_CASE )
# Sort
return overview_doc
def _a ( SCREAMING_SNAKE_CASE=False ):
"""simple docstring"""
with open(SCREAMING_SNAKE_CASE , encoding='''utf-8''' ) as f:
lowercase__ = yaml.safe_load(f.read() )
# Get to the API doc
lowercase__ = 0
while content[api_idx]["title"] != "API":
api_idx += 1
lowercase__ = content[api_idx]['''sections''']
# Then to the model doc
lowercase__ = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
lowercase__ = api_doc[scheduler_idx]['''sections''']
lowercase__ = clean_doc_toc(SCREAMING_SNAKE_CASE )
lowercase__ = False
if new_scheduler_doc != scheduler_doc:
lowercase__ = True
if overwrite:
lowercase__ = new_scheduler_doc
if diff:
if overwrite:
lowercase__ = api_doc
with open(SCREAMING_SNAKE_CASE , '''w''' , encoding='''utf-8''' ) as f:
f.write(yaml.dump(SCREAMING_SNAKE_CASE , allow_unicode=SCREAMING_SNAKE_CASE ) )
else:
raise ValueError(
'''The model doc part of the table of content is not properly sorted, run `make style` to fix this.''' )
def _a ( SCREAMING_SNAKE_CASE=False ):
"""simple docstring"""
with open(SCREAMING_SNAKE_CASE , encoding='''utf-8''' ) as f:
lowercase__ = yaml.safe_load(f.read() )
# Get to the API doc
lowercase__ = 0
while content[api_idx]["title"] != "API":
api_idx += 1
lowercase__ = content[api_idx]['''sections''']
# Then to the model doc
lowercase__ = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
lowercase__ = False
lowercase__ = api_doc[pipeline_idx]['''sections''']
lowercase__ = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
lowercase__ = pipeline_doc['''section''']
lowercase__ = clean_doc_toc(SCREAMING_SNAKE_CASE )
if overwrite:
lowercase__ = new_sub_pipeline_doc
new_pipeline_docs.append(SCREAMING_SNAKE_CASE )
# sort overall pipeline doc
lowercase__ = clean_doc_toc(SCREAMING_SNAKE_CASE )
if new_pipeline_docs != pipeline_docs:
lowercase__ = True
if overwrite:
lowercase__ = new_pipeline_docs
if diff:
if overwrite:
lowercase__ = api_doc
with open(SCREAMING_SNAKE_CASE , '''w''' , encoding='''utf-8''' ) as f:
f.write(yaml.dump(SCREAMING_SNAKE_CASE , allow_unicode=SCREAMING_SNAKE_CASE ) )
else:
raise ValueError(
'''The model doc part of the table of content is not properly sorted, run `make style` to fix this.''' )
if __name__ == "__main__":
lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
lowerCAmelCase = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 359 |
from abc import ABC, abstractmethod
from typing import List, Optional
class _a ( UpperCamelCase__ ):
def __init__( self: Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
self.test()
def lowerCamelCase_ ( self: List[Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ = 0
lowercase__ = False
while not completed:
if counter == 1:
self.reset()
lowercase__ = self.advance()
if not self.does_advance(UpperCamelCase_ ):
raise Exception(
'''Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.''' )
lowercase__ , lowercase__ , lowercase__ = self.update(UpperCamelCase_ )
counter += 1
if counter > 10_000:
raise Exception('''update() does not fulfill the constraint.''' )
if self.remaining() != 0:
raise Exception('''Custom Constraint is not defined correctly.''' )
@abstractmethod
def lowerCamelCase_ ( self: int ) -> Any:
"""simple docstring"""
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def lowerCamelCase_ ( self: Tuple , UpperCamelCase_: int ) -> Optional[int]:
"""simple docstring"""
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def lowerCamelCase_ ( self: Optional[int] , UpperCamelCase_: int ) -> str:
"""simple docstring"""
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def lowerCamelCase_ ( self: Tuple ) -> List[Any]:
"""simple docstring"""
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def lowerCamelCase_ ( self: Any ) -> Tuple:
"""simple docstring"""
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def lowerCamelCase_ ( self: Tuple , UpperCamelCase_: int=False ) -> Any:
"""simple docstring"""
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
class _a ( UpperCamelCase__ ):
def __init__( self: str , UpperCamelCase_: List[int] ) -> Tuple:
"""simple docstring"""
super(UpperCamelCase_ , self ).__init__()
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ) or len(UpperCamelCase_ ) == 0:
raise ValueError(f'`token_ids` has to be a non-empty list, but is {token_ids}.' )
if any((not isinstance(UpperCamelCase_ , UpperCamelCase_ ) or token_id < 0) for token_id in token_ids ):
raise ValueError(f'Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.' )
lowercase__ = token_ids
lowercase__ = len(self.token_ids )
lowercase__ = -1 # the index of the currently fulfilled step
lowercase__ = False
def lowerCamelCase_ ( self: Tuple ) -> Tuple:
"""simple docstring"""
if self.completed:
return None
return self.token_ids[self.fulfilled_idx + 1]
def lowerCamelCase_ ( self: Union[str, Any] , UpperCamelCase_: int ) -> Optional[Any]:
"""simple docstring"""
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
raise ValueError(f'`token_id` has to be an `int`, but is {token_id} of type {type(UpperCamelCase_ )}' )
if self.completed:
return False
return token_id == self.token_ids[self.fulfilled_idx + 1]
def lowerCamelCase_ ( self: Optional[int] , UpperCamelCase_: int ) -> Dict:
"""simple docstring"""
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
raise ValueError(f'`token_id` has to be an `int`, but is {token_id} of type {type(UpperCamelCase_ )}' )
lowercase__ = False
lowercase__ = False
lowercase__ = False
if self.does_advance(UpperCamelCase_ ):
self.fulfilled_idx += 1
lowercase__ = True
if self.fulfilled_idx == (self.seqlen - 1):
lowercase__ = True
lowercase__ = completed
else:
# failed to make progress.
lowercase__ = True
self.reset()
return stepped, completed, reset
def lowerCamelCase_ ( self: Tuple ) -> int:
"""simple docstring"""
lowercase__ = False
lowercase__ = 0
def lowerCamelCase_ ( self: Any ) -> Dict:
"""simple docstring"""
return self.seqlen - (self.fulfilled_idx + 1)
def lowerCamelCase_ ( self: Any , UpperCamelCase_: Union[str, Any]=False ) -> Tuple:
"""simple docstring"""
lowercase__ = PhrasalConstraint(self.token_ids )
if stateful:
lowercase__ = self.seqlen
lowercase__ = self.fulfilled_idx
lowercase__ = self.completed
return new_constraint
class _a :
def __init__( self: Union[str, Any] , UpperCamelCase_: List[List[int]] , UpperCamelCase_: int=True ) -> int:
"""simple docstring"""
lowercase__ = max([len(UpperCamelCase_ ) for one in nested_token_ids] )
lowercase__ = {}
for token_ids in nested_token_ids:
lowercase__ = root
for tidx, token_id in enumerate(UpperCamelCase_ ):
if token_id not in level:
lowercase__ = {}
lowercase__ = level[token_id]
if no_subsets and self.has_subsets(UpperCamelCase_ , UpperCamelCase_ ):
raise ValueError(
'''Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'''
f' {nested_token_ids}.' )
lowercase__ = root
def lowerCamelCase_ ( self: int , UpperCamelCase_: str ) -> Any:
"""simple docstring"""
lowercase__ = self.trie
for current_token in current_seq:
lowercase__ = start[current_token]
lowercase__ = list(start.keys() )
return next_tokens
def lowerCamelCase_ ( self: Any , UpperCamelCase_: Tuple ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.next_tokens(UpperCamelCase_ )
return len(UpperCamelCase_ ) == 0
def lowerCamelCase_ ( self: List[str] , UpperCamelCase_: Optional[Any] ) -> Any:
"""simple docstring"""
lowercase__ = list(root.values() )
if len(UpperCamelCase_ ) == 0:
return 1
else:
return sum([self.count_leaves(UpperCamelCase_ ) for nn in next_nodes] )
def lowerCamelCase_ ( self: Any , UpperCamelCase_: Union[str, Any] , UpperCamelCase_: int ) -> Tuple:
"""simple docstring"""
lowercase__ = self.count_leaves(UpperCamelCase_ )
return len(UpperCamelCase_ ) != leaf_count
class _a ( UpperCamelCase__ ):
def __init__( self: Optional[int] , UpperCamelCase_: List[List[int]] ) -> List[Any]:
"""simple docstring"""
super(UpperCamelCase_ , self ).__init__()
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ) or len(UpperCamelCase_ ) == 0:
raise ValueError(f'`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.' )
if any(not isinstance(UpperCamelCase_ , UpperCamelCase_ ) for token_ids in nested_token_ids ):
raise ValueError(f'`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.' )
if any(
any((not isinstance(UpperCamelCase_ , UpperCamelCase_ ) or token_id < 0) for token_id in token_ids )
for token_ids in nested_token_ids ):
raise ValueError(
f'Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.' )
lowercase__ = DisjunctiveTrie(UpperCamelCase_ )
lowercase__ = nested_token_ids
lowercase__ = self.trie.max_height
lowercase__ = []
lowercase__ = False
def lowerCamelCase_ ( self: Union[str, Any] ) -> str:
"""simple docstring"""
lowercase__ = self.trie.next_tokens(self.current_seq )
if len(UpperCamelCase_ ) == 0:
return None
else:
return token_list
def lowerCamelCase_ ( self: str , UpperCamelCase_: int ) -> Tuple:
"""simple docstring"""
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
raise ValueError(f'`token_id` is supposed to be type `int`, but is {token_id} of type {type(UpperCamelCase_ )}' )
lowercase__ = self.trie.next_tokens(self.current_seq )
return token_id in next_tokens
def lowerCamelCase_ ( self: int , UpperCamelCase_: int ) -> Dict:
"""simple docstring"""
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
raise ValueError(f'`token_id` is supposed to be type `int`, but is {token_id} of type {type(UpperCamelCase_ )}' )
lowercase__ = False
lowercase__ = False
lowercase__ = False
if self.does_advance(UpperCamelCase_ ):
self.current_seq.append(UpperCamelCase_ )
lowercase__ = True
else:
lowercase__ = True
self.reset()
lowercase__ = self.trie.reached_leaf(self.current_seq )
lowercase__ = completed
return stepped, completed, reset
def lowerCamelCase_ ( self: List[str] ) -> List[Any]:
"""simple docstring"""
lowercase__ = False
lowercase__ = []
def lowerCamelCase_ ( self: List[str] ) -> Optional[Any]:
"""simple docstring"""
if self.completed:
# since this can be completed without reaching max height
return 0
else:
return self.seqlen - len(self.current_seq )
def lowerCamelCase_ ( self: Any , UpperCamelCase_: Optional[Any]=False ) -> str:
"""simple docstring"""
lowercase__ = DisjunctiveConstraint(self.token_ids )
if stateful:
lowercase__ = self.seqlen
lowercase__ = self.current_seq
lowercase__ = self.completed
return new_constraint
class _a :
def __init__( self: int , UpperCamelCase_: List[Constraint] ) -> Dict:
"""simple docstring"""
lowercase__ = constraints
# max # of steps required to fulfill a given constraint
lowercase__ = max([c.seqlen for c in constraints] )
lowercase__ = len(UpperCamelCase_ )
lowercase__ = False
self.init_state()
def lowerCamelCase_ ( self: List[str] ) -> str:
"""simple docstring"""
lowercase__ = []
lowercase__ = None
lowercase__ = [constraint.copy(stateful=UpperCamelCase_ ) for constraint in self.constraints]
def lowerCamelCase_ ( self: str ) -> Any:
"""simple docstring"""
lowercase__ = 0
if self.inprogress_constraint:
# extra points for having a constraint mid-fulfilled
add += self.max_seqlen - self.inprogress_constraint.remaining()
return (len(self.complete_constraints ) * self.max_seqlen) + add
def lowerCamelCase_ ( self: Union[str, Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ = []
if self.inprogress_constraint is None:
for constraint in self.pending_constraints: # "pending" == "unfulfilled yet"
lowercase__ = constraint.advance()
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
token_list.append(UpperCamelCase_ )
elif isinstance(UpperCamelCase_ , UpperCamelCase_ ):
token_list.extend(UpperCamelCase_ )
else:
lowercase__ = self.inprogress_constraint.advance()
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
token_list.append(UpperCamelCase_ )
elif isinstance(UpperCamelCase_ , UpperCamelCase_ ):
token_list.extend(UpperCamelCase_ )
if len(UpperCamelCase_ ) == 0:
return None
else:
return token_list
def lowerCamelCase_ ( self: Optional[Any] , UpperCamelCase_: Optional[List[int]] ) -> Optional[int]:
"""simple docstring"""
self.init_state()
if token_ids is not None:
for token in token_ids:
# completes or steps **one** constraint
lowercase__ , lowercase__ = self.add(UpperCamelCase_ )
# the entire list of constraints are fulfilled
if self.completed:
break
def lowerCamelCase_ ( self: Optional[Any] , UpperCamelCase_: int ) -> int:
"""simple docstring"""
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
raise ValueError(f'`token_id` should be an `int`, but is `{token_id}`.' )
lowercase__ , lowercase__ = False, False
if self.completed:
lowercase__ = True
lowercase__ = False
return complete, stepped
if self.inprogress_constraint is not None:
# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
# job, simply update the state
lowercase__ , lowercase__ , lowercase__ = self.inprogress_constraint.update(UpperCamelCase_ )
if reset:
# 1. If the next token breaks the progress, then we must restart.
# e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".
# But that doesn't mean we self.init_state(), since we only reset the state for this particular
# constraint, not the full list of constraints.
self.pending_constraints.append(self.inprogress_constraint.copy(stateful=UpperCamelCase_ ) )
lowercase__ = None
if complete:
# 2. If the next token completes the constraint, move it to completed list, set
# inprogress to None. If there are no pending constraints either, then this full list of constraints
# is complete.
self.complete_constraints.append(self.inprogress_constraint )
lowercase__ = None
if len(self.pending_constraints ) == 0:
# we're done!
lowercase__ = True
else:
# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
# of constraints?
for cidx, pending_constraint in enumerate(self.pending_constraints ):
if pending_constraint.does_advance(UpperCamelCase_ ):
lowercase__ , lowercase__ , lowercase__ = pending_constraint.update(UpperCamelCase_ )
if not stepped:
raise Exception(
'''`constraint.update(token_id)` is not yielding incremental progress, '''
'''even though `constraint.does_advance(token_id)` is true.''' )
if complete:
self.complete_constraints.append(UpperCamelCase_ )
lowercase__ = None
if not complete and stepped:
lowercase__ = pending_constraint
if complete or stepped:
# If we made any progress at all, then it's at least not a "pending constraint".
lowercase__ = (
self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
)
if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None:
# If there's no longer any pending after this and no inprogress either, then we must be
# complete.
lowercase__ = True
break # prevent accidentally stepping through multiple constraints with just one token.
return complete, stepped
def lowerCamelCase_ ( self: Any , UpperCamelCase_: Optional[Any]=True ) -> Dict:
"""simple docstring"""
lowercase__ = ConstraintListState(self.constraints ) # we actually never though self.constraints objects
# throughout this process. So it's at initialization state.
if stateful:
lowercase__ = [
constraint.copy(stateful=UpperCamelCase_ ) for constraint in self.complete_constraints
]
if self.inprogress_constraint is not None:
lowercase__ = self.inprogress_constraint.copy(stateful=UpperCamelCase_ )
lowercase__ = [constraint.copy() for constraint in self.pending_constraints]
return new_state
| 93 | 0 |
"""simple docstring"""
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def SCREAMING_SNAKE_CASE__ ( ) -> Union[str, Any]:
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
lowercase__: int = '''__test_patch_submodule_mock__'''
with patch_submodule(_test_patching , '''os.path.join''' , __UpperCAmelCase ):
# Every way to access os.path.join must be patched, and the rest must stay untouched
# check os.path.join
assert isinstance(_test_patching.os , _PatchedModuleObj )
assert isinstance(_test_patching.os.path , _PatchedModuleObj )
assert _test_patching.os.path.join is mock
# check path.join
assert isinstance(_test_patching.path , _PatchedModuleObj )
assert _test_patching.path.join is mock
# check join
assert _test_patching.join is mock
# check that the other attributes are untouched
assert _test_patching.os.rename is original_rename
assert _test_patching.path.dirname is original_dirname
assert _test_patching.os.path.dirname is original_dirname
# Even renamed modules or objects must be patched
# check renamed_os.path.join
assert isinstance(_test_patching.renamed_os , _PatchedModuleObj )
assert isinstance(_test_patching.renamed_os.path , _PatchedModuleObj )
assert _test_patching.renamed_os.path.join is mock
# check renamed_path.join
assert isinstance(_test_patching.renamed_path , _PatchedModuleObj )
assert _test_patching.renamed_path.join is mock
# check renamed_join
assert _test_patching.renamed_join is mock
# check that the other attributes are untouched
assert _test_patching.renamed_os.rename is original_rename
assert _test_patching.renamed_path.dirname is original_dirname
assert _test_patching.renamed_os.path.dirname is original_dirname
# check that everthing is back to normal when the patch is over
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
def SCREAMING_SNAKE_CASE__ ( ) -> Optional[Any]:
assert _test_patching.open is open
lowercase__: Any = '''__test_patch_submodule_builtin_mock__'''
# _test_patching has "open" in its globals
assert _test_patching.open is open
with patch_submodule(_test_patching , '''open''' , __UpperCAmelCase ):
assert _test_patching.open is mock
# check that everthing is back to normal when the patch is over
assert _test_patching.open is open
def SCREAMING_SNAKE_CASE__ ( ) -> int:
# pandas.read_csv is not present in _test_patching
lowercase__: Union[str, Any] = '''__test_patch_submodule_missing_mock__'''
with patch_submodule(_test_patching , '''pandas.read_csv''' , __UpperCAmelCase ):
pass
def SCREAMING_SNAKE_CASE__ ( ) -> List[str]:
# builtin should always be mocked even if they're not in the globals
# in case they're loaded at one point
lowercase__: Tuple = '''__test_patch_submodule_missing_builtin_mock__'''
# _test_patching doesn't have "len" in its globals
assert getattr(_test_patching , '''len''' , __UpperCAmelCase ) is None
with patch_submodule(_test_patching , '''len''' , __UpperCAmelCase ):
assert _test_patching.len is mock
assert _test_patching.len is len
def SCREAMING_SNAKE_CASE__ ( ) -> List[Any]:
lowercase__: Union[str, Any] = '''__test_patch_submodule_start_and_stop_mock__'''
lowercase__: int = patch_submodule(_test_patching , '''open''' , __UpperCAmelCase )
assert _test_patching.open is open
patch.start()
assert _test_patching.open is mock
patch.stop()
assert _test_patching.open is open
def SCREAMING_SNAKE_CASE__ ( ) -> Union[str, Any]:
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
lowercase__: List[Any] = '''__test_patch_submodule_successive_join__'''
lowercase__: Tuple = '''__test_patch_submodule_successive_dirname__'''
lowercase__: List[str] = '''__test_patch_submodule_successive_rename__'''
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
with patch_submodule(_test_patching , '''os.path.join''' , __UpperCAmelCase ):
with patch_submodule(_test_patching , '''os.rename''' , __UpperCAmelCase ):
with patch_submodule(_test_patching , '''os.path.dirname''' , __UpperCAmelCase ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
# try another order
with patch_submodule(_test_patching , '''os.rename''' , __UpperCAmelCase ):
with patch_submodule(_test_patching , '''os.path.join''' , __UpperCAmelCase ):
with patch_submodule(_test_patching , '''os.path.dirname''' , __UpperCAmelCase ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
def SCREAMING_SNAKE_CASE__ ( ) -> Any:
lowercase__: Union[str, Any] = '''__test_patch_submodule_doesnt_exist_mock__'''
with patch_submodule(_test_patching , '''__module_that_doesn_exist__.__attribute_that_doesn_exist__''' , __UpperCAmelCase ):
pass
with patch_submodule(_test_patching , '''os.__attribute_that_doesn_exist__''' , __UpperCAmelCase ):
pass
| 177 | """simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig
__A = [
"openmmlab/upernet-convnext-tiny",
# See all UperNet models at https://huggingface.co/models?filter=upernet
]
# General docstring
__A = "UperNetConfig"
class UpperCAmelCase (nn.Module ):
"""simple docstring"""
def __init__( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = 0 , _UpperCAmelCase = False , _UpperCAmelCase = 1 , ):
super().__init__()
lowercase__: List[Any] = nn.Convad(
in_channels=_UpperCAmelCase , out_channels=_UpperCAmelCase , kernel_size=_UpperCAmelCase , padding=_UpperCAmelCase , bias=_UpperCAmelCase , dilation=_UpperCAmelCase , )
lowercase__: List[Any] = nn.BatchNormad(_UpperCAmelCase )
lowercase__: int = nn.ReLU()
def _snake_case ( self , _UpperCAmelCase ):
lowercase__: Dict = self.conv(_UpperCAmelCase )
lowercase__: Optional[int] = self.batch_norm(_UpperCAmelCase )
lowercase__: List[Any] = self.activation(_UpperCAmelCase )
return output
class UpperCAmelCase (nn.Module ):
"""simple docstring"""
def __init__( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
super().__init__()
lowercase__: int = [
nn.AdaptiveAvgPoolad(_UpperCAmelCase ),
UperNetConvModule(_UpperCAmelCase , _UpperCAmelCase , kernel_size=1 ),
]
for i, layer in enumerate(self.layers ):
self.add_module(str(_UpperCAmelCase ) , _UpperCAmelCase )
def _snake_case ( self , _UpperCAmelCase ):
lowercase__: Any = input
for layer in self.layers:
lowercase__: Any = layer(_UpperCAmelCase )
return hidden_state
class UpperCAmelCase (nn.Module ):
"""simple docstring"""
def __init__( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
super().__init__()
lowercase__: int = pool_scales
lowercase__: Optional[Any] = align_corners
lowercase__: Optional[int] = in_channels
lowercase__: Optional[Any] = channels
lowercase__: List[Any] = []
for i, pool_scale in enumerate(_UpperCAmelCase ):
lowercase__: Optional[int] = UperNetPyramidPoolingBlock(pool_scale=_UpperCAmelCase , in_channels=_UpperCAmelCase , channels=_UpperCAmelCase )
self.blocks.append(_UpperCAmelCase )
self.add_module(str(_UpperCAmelCase ) , _UpperCAmelCase )
def _snake_case ( self , _UpperCAmelCase ):
lowercase__: Union[str, Any] = []
for ppm in self.blocks:
lowercase__: Tuple = ppm(_UpperCAmelCase )
lowercase__: Any = nn.functional.interpolate(
_UpperCAmelCase , size=x.size()[2:] , mode='''bilinear''' , align_corners=self.align_corners )
ppm_outs.append(_UpperCAmelCase )
return ppm_outs
class UpperCAmelCase (nn.Module ):
"""simple docstring"""
def __init__( self , _UpperCAmelCase , _UpperCAmelCase ):
super().__init__()
lowercase__: Optional[int] = config
lowercase__: int = config.pool_scales # e.g. (1, 2, 3, 6)
lowercase__: Optional[int] = in_channels
lowercase__: List[str] = config.hidden_size
lowercase__: List[str] = False
lowercase__: List[str] = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
# PSP Module
lowercase__: Dict = UperNetPyramidPoolingModule(
self.pool_scales , self.in_channels[-1] , self.channels , align_corners=self.align_corners , )
lowercase__: int = UperNetConvModule(
self.in_channels[-1] + len(self.pool_scales ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
# FPN Module
lowercase__: List[Any] = nn.ModuleList()
lowercase__: Union[str, Any] = nn.ModuleList()
for in_channels in self.in_channels[:-1]: # skip the top layer
lowercase__: int = UperNetConvModule(_UpperCAmelCase , self.channels , kernel_size=1 )
lowercase__: Dict = UperNetConvModule(self.channels , self.channels , kernel_size=3 , padding=1 )
self.lateral_convs.append(_UpperCAmelCase )
self.fpn_convs.append(_UpperCAmelCase )
lowercase__: Any = UperNetConvModule(
len(self.in_channels ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
def _snake_case ( self ):
self.apply(self._init_weights )
def _snake_case ( self , _UpperCAmelCase ):
if isinstance(_UpperCAmelCase , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def _snake_case ( self , _UpperCAmelCase ):
lowercase__: List[str] = inputs[-1]
lowercase__: str = [x]
psp_outs.extend(self.psp_modules(_UpperCAmelCase ) )
lowercase__: Dict = torch.cat(_UpperCAmelCase , dim=1 )
lowercase__: Tuple = self.bottleneck(_UpperCAmelCase )
return output
def _snake_case ( self , _UpperCAmelCase ):
# build laterals
lowercase__: Dict = [lateral_conv(encoder_hidden_states[i] ) for i, lateral_conv in enumerate(self.lateral_convs )]
laterals.append(self.psp_forward(_UpperCAmelCase ) )
# build top-down path
lowercase__: int = len(_UpperCAmelCase )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
lowercase__: str = laterals[i - 1].shape[2:]
lowercase__: Optional[int] = laterals[i - 1] + nn.functional.interpolate(
laterals[i] , size=_UpperCAmelCase , mode='''bilinear''' , align_corners=self.align_corners )
# build outputs
lowercase__: str = [self.fpn_convs[i](laterals[i] ) for i in range(used_backbone_levels - 1 )]
# append psp feature
fpn_outs.append(laterals[-1] )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
lowercase__: Any = nn.functional.interpolate(
fpn_outs[i] , size=fpn_outs[0].shape[2:] , mode='''bilinear''' , align_corners=self.align_corners )
lowercase__: int = torch.cat(_UpperCAmelCase , dim=1 )
lowercase__: Tuple = self.fpn_bottleneck(_UpperCAmelCase )
lowercase__: Dict = self.classifier(_UpperCAmelCase )
return output
class UpperCAmelCase (nn.Module ):
"""simple docstring"""
def __init__( self , _UpperCAmelCase , _UpperCAmelCase = 2 , _UpperCAmelCase = 3 , _UpperCAmelCase = 1 ):
super().__init__()
lowercase__: Optional[Any] = config
lowercase__: Optional[Any] = config.auxiliary_in_channels
lowercase__: List[Any] = config.auxiliary_channels
lowercase__: Tuple = config.auxiliary_num_convs
lowercase__: Any = config.auxiliary_concat_input
lowercase__: Optional[int] = in_index
lowercase__: Tuple = (kernel_size // 2) * dilation
lowercase__: Tuple = []
convs.append(
UperNetConvModule(
self.in_channels , self.channels , kernel_size=_UpperCAmelCase , padding=_UpperCAmelCase , dilation=_UpperCAmelCase ) )
for i in range(self.num_convs - 1 ):
convs.append(
UperNetConvModule(
self.channels , self.channels , kernel_size=_UpperCAmelCase , padding=_UpperCAmelCase , dilation=_UpperCAmelCase ) )
if self.num_convs == 0:
lowercase__: List[Any] = nn.Identity()
else:
lowercase__: Union[str, Any] = nn.Sequential(*_UpperCAmelCase )
if self.concat_input:
lowercase__: Dict = UperNetConvModule(
self.in_channels + self.channels , self.channels , kernel_size=_UpperCAmelCase , padding=kernel_size // 2 )
lowercase__: Union[str, Any] = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
def _snake_case ( self ):
self.apply(self._init_weights )
def _snake_case ( self , _UpperCAmelCase ):
if isinstance(_UpperCAmelCase , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def _snake_case ( self , _UpperCAmelCase ):
# just take the relevant feature maps
lowercase__: Dict = encoder_hidden_states[self.in_index]
lowercase__: Optional[int] = self.convs(_UpperCAmelCase )
if self.concat_input:
lowercase__: Optional[int] = self.conv_cat(torch.cat([hidden_states, output] , dim=1 ) )
lowercase__: Dict = self.classifier(_UpperCAmelCase )
return output
class UpperCAmelCase (_UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase :Dict = UperNetConfig
_UpperCAmelCase :int = "pixel_values"
_UpperCAmelCase :Optional[Any] = True
def _snake_case ( self , _UpperCAmelCase ):
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
module.backbone.init_weights()
module.decode_head.init_weights()
module.auxiliary_head.init_weights()
def _snake_case ( self ):
self.backbone.init_weights()
self.decode_head.init_weights()
self.auxiliary_head.init_weights()
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase=False ):
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
lowercase__: Any = value
__A = R"\n Parameters:\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n config ([`UperNetConfig`]): 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"
__A = R"\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using\n [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See\n `attentions` under returned tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under\n returned tensors for 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(
"UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes." ,_UpperCAmelCase ,)
class UpperCAmelCase (_UpperCAmelCase ):
"""simple docstring"""
def __init__( self , _UpperCAmelCase ):
super().__init__(_UpperCAmelCase )
lowercase__: Optional[int] = AutoBackbone.from_config(config.backbone_config )
# Semantic segmentation head(s)
lowercase__: Any = UperNetHead(_UpperCAmelCase , in_channels=self.backbone.channels )
lowercase__: Tuple = UperNetFCNHead(_UpperCAmelCase ) if config.use_auxiliary_head else None
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format('''batch_size, sequence_length''' ) )
@replace_return_docstrings(output_type=_UpperCAmelCase , config_class=_CONFIG_FOR_DOC )
def _snake_case ( self , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = None , ):
lowercase__: Tuple = return_dict if return_dict is not None else self.config.use_return_dict
lowercase__: Any = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase__: str = output_attentions if output_attentions is not None else self.config.output_attentions
lowercase__: List[str] = self.backbone.forward_with_filtered_kwargs(
_UpperCAmelCase , output_hidden_states=_UpperCAmelCase , output_attentions=_UpperCAmelCase )
lowercase__: Tuple = outputs.feature_maps
lowercase__: Union[str, Any] = self.decode_head(_UpperCAmelCase )
lowercase__: str = nn.functional.interpolate(_UpperCAmelCase , size=pixel_values.shape[2:] , mode='''bilinear''' , align_corners=_UpperCAmelCase )
lowercase__: Any = None
if self.auxiliary_head is not None:
lowercase__: Union[str, Any] = self.auxiliary_head(_UpperCAmelCase )
lowercase__: Tuple = nn.functional.interpolate(
_UpperCAmelCase , size=pixel_values.shape[2:] , mode='''bilinear''' , align_corners=_UpperCAmelCase )
lowercase__: List[Any] = None
if labels is not None:
if self.config.num_labels == 1:
raise ValueError('''The number of labels should be greater than one''' )
else:
# compute weighted loss
lowercase__: List[str] = CrossEntropyLoss(ignore_index=self.config.loss_ignore_index )
lowercase__: Optional[Any] = loss_fct(_UpperCAmelCase , _UpperCAmelCase )
lowercase__: Dict = loss_fct(_UpperCAmelCase , _UpperCAmelCase )
lowercase__: int = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss
if not return_dict:
if output_hidden_states:
lowercase__: Tuple = (logits,) + outputs[1:]
else:
lowercase__: Optional[int] = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SemanticSegmenterOutput(
loss=_UpperCAmelCase , logits=_UpperCAmelCase , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 177 | 1 |
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
SCREAMING_SNAKE_CASE__ = sys.version_info >= (3, 10)
def lowerCAmelCase__ ( _UpperCamelCase : int=None , _UpperCamelCase : int=None ) -> Optional[int]:
"""simple docstring"""
return field(default_factory=lambda: default , metadata=_a )
@dataclass
class lowerCAmelCase_ :
"""simple docstring"""
_lowerCAmelCase : int
_lowerCAmelCase : float
_lowerCAmelCase : str
_lowerCAmelCase : bool
@dataclass
class lowerCAmelCase_ :
"""simple docstring"""
_lowerCAmelCase : int = 42
_lowerCAmelCase : str = field(default="""toto""" , metadata={"""help""": """help message"""} )
@dataclass
class lowerCAmelCase_ :
"""simple docstring"""
_lowerCAmelCase : bool = False
_lowerCAmelCase : bool = True
_lowerCAmelCase : Optional[bool] = None
class lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCAmelCase : Tuple = 'titi'
_lowerCAmelCase : List[str] = 'toto'
class lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCAmelCase : Tuple = 'titi'
_lowerCAmelCase : List[Any] = 'toto'
_lowerCAmelCase : Union[str, Any] = 42
@dataclass
class lowerCAmelCase_ :
"""simple docstring"""
_lowerCAmelCase : BasicEnum = "toto"
def snake_case ( self ):
"""simple docstring"""
snake_case = BasicEnum(self.foo )
@dataclass
class lowerCAmelCase_ :
"""simple docstring"""
_lowerCAmelCase : MixedTypeEnum = "toto"
def snake_case ( self ):
"""simple docstring"""
snake_case = MixedTypeEnum(self.foo )
@dataclass
class lowerCAmelCase_ :
"""simple docstring"""
_lowerCAmelCase : Optional[int] = None
_lowerCAmelCase : Optional[float] = field(default=SCREAMING_SNAKE_CASE__ , metadata={"""help""": """help message"""} )
_lowerCAmelCase : Optional[str] = None
_lowerCAmelCase : Optional[List[str]] = list_field(default=[] )
_lowerCAmelCase : Optional[List[int]] = list_field(default=[] )
@dataclass
class lowerCAmelCase_ :
"""simple docstring"""
_lowerCAmelCase : List[int] = list_field(default=[] )
_lowerCAmelCase : List[int] = list_field(default=[1, 2, 3] )
_lowerCAmelCase : List[str] = list_field(default=["""Hallo""", """Bonjour""", """Hello"""] )
_lowerCAmelCase : List[float] = list_field(default=[0.1, 0.2, 0.3] )
@dataclass
class lowerCAmelCase_ :
"""simple docstring"""
_lowerCAmelCase : List[int] = field()
_lowerCAmelCase : str = field()
_lowerCAmelCase : BasicEnum = field()
def snake_case ( self ):
"""simple docstring"""
snake_case = BasicEnum(self.required_enum )
@dataclass
class lowerCAmelCase_ :
"""simple docstring"""
_lowerCAmelCase : int
_lowerCAmelCase : "BasicEnum" = field()
_lowerCAmelCase : "Optional[bool]" = None
_lowerCAmelCase : "str" = field(default="""toto""" , metadata={"""help""": """help message"""} )
_lowerCAmelCase : "List[str]" = list_field(default=["""Hallo""", """Bonjour""", """Hello"""] )
if is_python_no_less_than_3_10:
@dataclass
class lowerCAmelCase_ :
"""simple docstring"""
_lowerCAmelCase : bool = False
_lowerCAmelCase : bool = True
_lowerCAmelCase : bool | None = None
@dataclass
class lowerCAmelCase_ :
"""simple docstring"""
_lowerCAmelCase : int | None = None
_lowerCAmelCase : float | None = field(default=SCREAMING_SNAKE_CASE__ , metadata={"""help""": """help message"""} )
_lowerCAmelCase : str | None = None
_lowerCAmelCase : list[str] | None = list_field(default=[] )
_lowerCAmelCase : list[int] | None = list_field(default=[] )
class lowerCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def snake_case ( self , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
snake_case = {k: v for k, v in vars(_SCREAMING_SNAKE_CASE ).items() if k != "container"}
snake_case = {k: v for k, v in vars(_SCREAMING_SNAKE_CASE ).items() if k != "container"}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get('choices' , _SCREAMING_SNAKE_CASE ) and yy.get('choices' , _SCREAMING_SNAKE_CASE ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx['type'](_SCREAMING_SNAKE_CASE ) , yy['type'](_SCREAMING_SNAKE_CASE ) )
del xx["type"], yy["type"]
self.assertEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def snake_case ( self ):
"""simple docstring"""
snake_case = HfArgumentParser(_SCREAMING_SNAKE_CASE )
snake_case = argparse.ArgumentParser()
expected.add_argument('--foo' , type=_SCREAMING_SNAKE_CASE , required=_SCREAMING_SNAKE_CASE )
expected.add_argument('--bar' , type=_SCREAMING_SNAKE_CASE , required=_SCREAMING_SNAKE_CASE )
expected.add_argument('--baz' , type=_SCREAMING_SNAKE_CASE , required=_SCREAMING_SNAKE_CASE )
expected.add_argument('--flag' , type=_SCREAMING_SNAKE_CASE , default=_SCREAMING_SNAKE_CASE , const=_SCREAMING_SNAKE_CASE , nargs='?' )
self.argparsersEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
snake_case = ["--foo", "1", "--baz", "quux", "--bar", "0.5"]
(snake_case ) = parser.parse_args_into_dataclasses(_SCREAMING_SNAKE_CASE , look_for_args_file=_SCREAMING_SNAKE_CASE )
self.assertFalse(example.flag )
def snake_case ( self ):
"""simple docstring"""
snake_case = HfArgumentParser(_SCREAMING_SNAKE_CASE )
snake_case = argparse.ArgumentParser()
expected.add_argument('--foo' , default=42 , type=_SCREAMING_SNAKE_CASE )
expected.add_argument('--baz' , default='toto' , type=_SCREAMING_SNAKE_CASE , help='help message' )
self.argparsersEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def snake_case ( self ):
"""simple docstring"""
snake_case = argparse.ArgumentParser()
expected.add_argument('--foo' , type=_SCREAMING_SNAKE_CASE , default=_SCREAMING_SNAKE_CASE , const=_SCREAMING_SNAKE_CASE , nargs='?' )
expected.add_argument('--baz' , type=_SCREAMING_SNAKE_CASE , default=_SCREAMING_SNAKE_CASE , const=_SCREAMING_SNAKE_CASE , nargs='?' )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument('--no_baz' , action='store_false' , default=_SCREAMING_SNAKE_CASE , dest='baz' )
expected.add_argument('--opt' , type=_SCREAMING_SNAKE_CASE , default=_SCREAMING_SNAKE_CASE )
snake_case = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(_SCREAMING_SNAKE_CASE )
for dataclass_type in dataclass_types:
snake_case = HfArgumentParser(_SCREAMING_SNAKE_CASE )
self.argparsersEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
snake_case = parser.parse_args([] )
self.assertEqual(_SCREAMING_SNAKE_CASE , Namespace(foo=_SCREAMING_SNAKE_CASE , baz=_SCREAMING_SNAKE_CASE , opt=_SCREAMING_SNAKE_CASE ) )
snake_case = parser.parse_args(['--foo', '--no_baz'] )
self.assertEqual(_SCREAMING_SNAKE_CASE , Namespace(foo=_SCREAMING_SNAKE_CASE , baz=_SCREAMING_SNAKE_CASE , opt=_SCREAMING_SNAKE_CASE ) )
snake_case = parser.parse_args(['--foo', '--baz'] )
self.assertEqual(_SCREAMING_SNAKE_CASE , Namespace(foo=_SCREAMING_SNAKE_CASE , baz=_SCREAMING_SNAKE_CASE , opt=_SCREAMING_SNAKE_CASE ) )
snake_case = parser.parse_args(['--foo', 'True', '--baz', 'True', '--opt', 'True'] )
self.assertEqual(_SCREAMING_SNAKE_CASE , Namespace(foo=_SCREAMING_SNAKE_CASE , baz=_SCREAMING_SNAKE_CASE , opt=_SCREAMING_SNAKE_CASE ) )
snake_case = parser.parse_args(['--foo', 'False', '--baz', 'False', '--opt', 'False'] )
self.assertEqual(_SCREAMING_SNAKE_CASE , Namespace(foo=_SCREAMING_SNAKE_CASE , baz=_SCREAMING_SNAKE_CASE , opt=_SCREAMING_SNAKE_CASE ) )
def snake_case ( self ):
"""simple docstring"""
snake_case = HfArgumentParser(_SCREAMING_SNAKE_CASE )
snake_case = argparse.ArgumentParser()
expected.add_argument(
'--foo' , default='toto' , choices=['titi', 'toto', 42] , type=make_choice_type_function(['titi', 'toto', 42] ) , )
self.argparsersEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
snake_case = parser.parse_args([] )
self.assertEqual(args.foo , 'toto' )
snake_case = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
snake_case = parser.parse_args(['--foo', 'titi'] )
self.assertEqual(args.foo , 'titi' )
snake_case = parser.parse_args_into_dataclasses(['--foo', 'titi'] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
snake_case = parser.parse_args(['--foo', '42'] )
self.assertEqual(args.foo , 42 )
snake_case = parser.parse_args_into_dataclasses(['--foo', '42'] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def snake_case ( self ):
"""simple docstring"""
@dataclass
class lowerCAmelCase_ :
"""simple docstring"""
_lowerCAmelCase : Literal["titi", "toto", 42] = "toto"
snake_case = HfArgumentParser(_SCREAMING_SNAKE_CASE )
snake_case = argparse.ArgumentParser()
expected.add_argument(
'--foo' , default='toto' , choices=('titi', 'toto', 42) , type=make_choice_type_function(['titi', 'toto', 42] ) , )
self.argparsersEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
snake_case = parser.parse_args([] )
self.assertEqual(args.foo , 'toto' )
snake_case = parser.parse_args(['--foo', 'titi'] )
self.assertEqual(args.foo , 'titi' )
snake_case = parser.parse_args(['--foo', '42'] )
self.assertEqual(args.foo , 42 )
def snake_case ( self ):
"""simple docstring"""
snake_case = HfArgumentParser(_SCREAMING_SNAKE_CASE )
snake_case = argparse.ArgumentParser()
expected.add_argument('--foo_int' , nargs='+' , default=[] , type=_SCREAMING_SNAKE_CASE )
expected.add_argument('--bar_int' , nargs='+' , default=[1, 2, 3] , type=_SCREAMING_SNAKE_CASE )
expected.add_argument('--foo_str' , nargs='+' , default=['Hallo', 'Bonjour', 'Hello'] , type=_SCREAMING_SNAKE_CASE )
expected.add_argument('--foo_float' , nargs='+' , default=[0.1, 0.2, 0.3] , type=_SCREAMING_SNAKE_CASE )
self.argparsersEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
snake_case = parser.parse_args([] )
self.assertEqual(
_SCREAMING_SNAKE_CASE , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=['Hallo', 'Bonjour', 'Hello'] , foo_float=[0.1, 0.2, 0.3] ) , )
snake_case = parser.parse_args('--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7'.split() )
self.assertEqual(_SCREAMING_SNAKE_CASE , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=['a', 'b', 'c'] , foo_float=[0.1, 0.7] ) )
def snake_case ( self ):
"""simple docstring"""
snake_case = argparse.ArgumentParser()
expected.add_argument('--foo' , default=_SCREAMING_SNAKE_CASE , type=_SCREAMING_SNAKE_CASE )
expected.add_argument('--bar' , default=_SCREAMING_SNAKE_CASE , type=_SCREAMING_SNAKE_CASE , help='help message' )
expected.add_argument('--baz' , default=_SCREAMING_SNAKE_CASE , type=_SCREAMING_SNAKE_CASE )
expected.add_argument('--ces' , nargs='+' , default=[] , type=_SCREAMING_SNAKE_CASE )
expected.add_argument('--des' , nargs='+' , default=[] , type=_SCREAMING_SNAKE_CASE )
snake_case = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(_SCREAMING_SNAKE_CASE )
for dataclass_type in dataclass_types:
snake_case = HfArgumentParser(_SCREAMING_SNAKE_CASE )
self.argparsersEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
snake_case = parser.parse_args([] )
self.assertEqual(_SCREAMING_SNAKE_CASE , Namespace(foo=_SCREAMING_SNAKE_CASE , bar=_SCREAMING_SNAKE_CASE , baz=_SCREAMING_SNAKE_CASE , ces=[] , des=[] ) )
snake_case = parser.parse_args('--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3'.split() )
self.assertEqual(_SCREAMING_SNAKE_CASE , Namespace(foo=12 , bar=3.14 , baz='42' , ces=['a', 'b', 'c'] , des=[1, 2, 3] ) )
def snake_case ( self ):
"""simple docstring"""
snake_case = HfArgumentParser(_SCREAMING_SNAKE_CASE )
snake_case = argparse.ArgumentParser()
expected.add_argument('--required_list' , nargs='+' , type=_SCREAMING_SNAKE_CASE , required=_SCREAMING_SNAKE_CASE )
expected.add_argument('--required_str' , type=_SCREAMING_SNAKE_CASE , required=_SCREAMING_SNAKE_CASE )
expected.add_argument(
'--required_enum' , type=make_choice_type_function(['titi', 'toto'] ) , choices=['titi', 'toto'] , required=_SCREAMING_SNAKE_CASE , )
self.argparsersEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def snake_case ( self ):
"""simple docstring"""
snake_case = HfArgumentParser(_SCREAMING_SNAKE_CASE )
snake_case = argparse.ArgumentParser()
expected.add_argument('--foo' , type=_SCREAMING_SNAKE_CASE , required=_SCREAMING_SNAKE_CASE )
expected.add_argument(
'--required_enum' , type=make_choice_type_function(['titi', 'toto'] ) , choices=['titi', 'toto'] , required=_SCREAMING_SNAKE_CASE , )
expected.add_argument('--opt' , type=_SCREAMING_SNAKE_CASE , default=_SCREAMING_SNAKE_CASE )
expected.add_argument('--baz' , default='toto' , type=_SCREAMING_SNAKE_CASE , help='help message' )
expected.add_argument('--foo_str' , nargs='+' , default=['Hallo', 'Bonjour', 'Hello'] , type=_SCREAMING_SNAKE_CASE )
self.argparsersEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def snake_case ( self ):
"""simple docstring"""
snake_case = HfArgumentParser(_SCREAMING_SNAKE_CASE )
snake_case = {
"foo": 12,
"bar": 3.14,
"baz": "42",
"flag": True,
}
snake_case = parser.parse_dict(_SCREAMING_SNAKE_CASE )[0]
snake_case = BasicExample(**_SCREAMING_SNAKE_CASE )
self.assertEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def snake_case ( self ):
"""simple docstring"""
snake_case = HfArgumentParser(_SCREAMING_SNAKE_CASE )
snake_case = {
"foo": 12,
"bar": 3.14,
"baz": "42",
"flag": True,
"extra": 42,
}
self.assertRaises(_SCREAMING_SNAKE_CASE , parser.parse_dict , _SCREAMING_SNAKE_CASE , allow_extra_keys=_SCREAMING_SNAKE_CASE )
def snake_case ( self ):
"""simple docstring"""
snake_case = HfArgumentParser(_SCREAMING_SNAKE_CASE )
snake_case = {
"foo": 12,
"bar": 3.14,
"baz": "42",
"flag": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case = os.path.join(_SCREAMING_SNAKE_CASE , 'temp_json' )
os.mkdir(_SCREAMING_SNAKE_CASE )
with open(temp_local_path + '.json' , 'w+' ) as f:
json.dump(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
snake_case = parser.parse_yaml_file(Path(temp_local_path + '.json' ) )[0]
snake_case = BasicExample(**_SCREAMING_SNAKE_CASE )
self.assertEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def snake_case ( self ):
"""simple docstring"""
snake_case = HfArgumentParser(_SCREAMING_SNAKE_CASE )
snake_case = {
"foo": 12,
"bar": 3.14,
"baz": "42",
"flag": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case = os.path.join(_SCREAMING_SNAKE_CASE , 'temp_yaml' )
os.mkdir(_SCREAMING_SNAKE_CASE )
with open(temp_local_path + '.yaml' , 'w+' ) as f:
yaml.dump(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
snake_case = parser.parse_yaml_file(Path(temp_local_path + '.yaml' ) )[0]
snake_case = BasicExample(**_SCREAMING_SNAKE_CASE )
self.assertEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def snake_case ( self ):
"""simple docstring"""
snake_case = HfArgumentParser(_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
| 364 | """simple docstring"""
import cmath
import math
def lowerCAmelCase__ ( _UpperCamelCase : float , _UpperCamelCase : float , _UpperCamelCase : float , _UpperCamelCase : float ) -> complex:
"""simple docstring"""
snake_case = math.radians(_UpperCamelCase )
snake_case = math.radians(_UpperCamelCase )
# Convert voltage and current to rectangular form
snake_case = cmath.rect(_UpperCamelCase , _UpperCamelCase )
snake_case = cmath.rect(_UpperCamelCase , _UpperCamelCase )
# Calculate apparent power
return voltage_rect * current_rect
if __name__ == "__main__":
import doctest
doctest.testmod()
| 149 | 0 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
lowerCAmelCase = logging.get_logger(__name__)
class _a :
def __init__( self: str , UpperCamelCase_: Any , UpperCamelCase_: str ) -> Any:
"""simple docstring"""
lowercase__ = question_encoder
lowercase__ = generator
lowercase__ = self.question_encoder
def lowerCamelCase_ ( self: List[Any] , UpperCamelCase_: Optional[Any] ) -> Optional[int]:
"""simple docstring"""
if os.path.isfile(UpperCamelCase_ ):
raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file' )
os.makedirs(UpperCamelCase_ , exist_ok=UpperCamelCase_ )
lowercase__ = os.path.join(UpperCamelCase_ , '''question_encoder_tokenizer''' )
lowercase__ = os.path.join(UpperCamelCase_ , '''generator_tokenizer''' )
self.question_encoder.save_pretrained(UpperCamelCase_ )
self.generator.save_pretrained(UpperCamelCase_ )
@classmethod
def lowerCamelCase_ ( cls: Any , UpperCamelCase_: List[str] , **UpperCamelCase_: Dict ) -> List[Any]:
"""simple docstring"""
from ..auto.tokenization_auto import AutoTokenizer
lowercase__ = kwargs.pop('''config''' , UpperCamelCase_ )
if config is None:
lowercase__ = RagConfig.from_pretrained(UpperCamelCase_ )
lowercase__ = AutoTokenizer.from_pretrained(
UpperCamelCase_ , config=config.question_encoder , subfolder='''question_encoder_tokenizer''' )
lowercase__ = AutoTokenizer.from_pretrained(
UpperCamelCase_ , config=config.generator , subfolder='''generator_tokenizer''' )
return cls(question_encoder=UpperCamelCase_ , generator=UpperCamelCase_ )
def __call__( self: str , *UpperCamelCase_: Union[str, Any] , **UpperCamelCase_: Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
return self.current_tokenizer(*UpperCamelCase_ , **UpperCamelCase_ )
def lowerCamelCase_ ( self: Any , *UpperCamelCase_: List[Any] , **UpperCamelCase_: str ) -> Any:
"""simple docstring"""
return self.generator.batch_decode(*UpperCamelCase_ , **UpperCamelCase_ )
def lowerCamelCase_ ( self: Any , *UpperCamelCase_: str , **UpperCamelCase_: Tuple ) -> Optional[Any]:
"""simple docstring"""
return self.generator.decode(*UpperCamelCase_ , **UpperCamelCase_ )
def lowerCamelCase_ ( self: Tuple ) -> List[str]:
"""simple docstring"""
lowercase__ = self.question_encoder
def lowerCamelCase_ ( self: str ) -> str:
"""simple docstring"""
lowercase__ = self.generator
def lowerCamelCase_ ( self: Union[str, Any] , UpperCamelCase_: List[str] , UpperCamelCase_: Optional[List[str]] = None , UpperCamelCase_: Optional[int] = None , UpperCamelCase_: Optional[int] = None , UpperCamelCase_: str = "longest" , UpperCamelCase_: str = None , UpperCamelCase_: bool = True , **UpperCamelCase_: Union[str, Any] , ) -> BatchEncoding:
"""simple docstring"""
warnings.warn(
'''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
'''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
'''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
'''details''' , UpperCamelCase_ , )
if max_length is None:
lowercase__ = self.current_tokenizer.model_max_length
lowercase__ = self(
UpperCamelCase_ , add_special_tokens=UpperCamelCase_ , return_tensors=UpperCamelCase_ , max_length=UpperCamelCase_ , padding=UpperCamelCase_ , truncation=UpperCamelCase_ , **UpperCamelCase_ , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
lowercase__ = self.current_tokenizer.model_max_length
lowercase__ = self(
text_target=UpperCamelCase_ , add_special_tokens=UpperCamelCase_ , return_tensors=UpperCamelCase_ , padding=UpperCamelCase_ , max_length=UpperCamelCase_ , truncation=UpperCamelCase_ , **UpperCamelCase_ , )
lowercase__ = labels['''input_ids''']
return model_inputs
| 110 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
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
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class _a ( UpperCamelCase__ , unittest.TestCase ):
_lowercase : Union[str, Any] = KandinskyVaaImgaImgPipeline
_lowercase : Tuple = ['''image_embeds''', '''negative_image_embeds''', '''image''']
_lowercase : Any = [
'''image_embeds''',
'''negative_image_embeds''',
'''image''',
]
_lowercase : Union[str, Any] = [
'''generator''',
'''height''',
'''width''',
'''strength''',
'''guidance_scale''',
'''num_inference_steps''',
'''return_dict''',
'''guidance_scale''',
'''num_images_per_prompt''',
'''output_type''',
'''return_dict''',
]
_lowercase : Optional[Any] = False
@property
def lowerCamelCase_ ( self: Union[str, Any] ) -> Dict:
"""simple docstring"""
return 32
@property
def lowerCamelCase_ ( self: Optional[int] ) -> Optional[Any]:
"""simple docstring"""
return 32
@property
def lowerCamelCase_ ( self: Any ) -> Any:
"""simple docstring"""
return self.time_input_dim
@property
def lowerCamelCase_ ( self: Tuple ) -> Any:
"""simple docstring"""
return self.time_input_dim * 4
@property
def lowerCamelCase_ ( self: List[Any] ) -> Optional[Any]:
"""simple docstring"""
return 100
@property
def lowerCamelCase_ ( self: int ) -> int:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = {
'''in_channels''': 4,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
lowercase__ = UNetaDConditionModel(**UpperCamelCase_ )
return model
@property
def lowerCamelCase_ ( self: Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def lowerCamelCase_ ( self: Optional[Any] ) -> int:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = VQModel(**self.dummy_movq_kwargs )
return model
def lowerCamelCase_ ( self: Optional[int] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.dummy_unet
lowercase__ = self.dummy_movq
lowercase__ = {
'''num_train_timesteps''': 1_000,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.00085,
'''beta_end''': 0.012,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
lowercase__ = DDIMScheduler(**UpperCamelCase_ )
lowercase__ = {
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def lowerCamelCase_ ( self: Optional[int] , UpperCamelCase_: Optional[Any] , UpperCamelCase_: Optional[int]=0 ) -> Optional[int]:
"""simple docstring"""
lowercase__ = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(UpperCamelCase_ ) ).to(UpperCamelCase_ )
lowercase__ = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
UpperCamelCase_ )
# create init_image
lowercase__ = floats_tensor((1, 3, 64, 64) , rng=random.Random(UpperCamelCase_ ) ).to(UpperCamelCase_ )
lowercase__ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowercase__ = Image.fromarray(np.uinta(UpperCamelCase_ ) ).convert('''RGB''' ).resize((256, 256) )
if str(UpperCamelCase_ ).startswith('''mps''' ):
lowercase__ = torch.manual_seed(UpperCamelCase_ )
else:
lowercase__ = torch.Generator(device=UpperCamelCase_ ).manual_seed(UpperCamelCase_ )
lowercase__ = {
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 10,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def lowerCamelCase_ ( self: Optional[int] ) -> Dict:
"""simple docstring"""
lowercase__ = '''cpu'''
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**UpperCamelCase_ )
lowercase__ = pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
lowercase__ = pipe(**self.get_dummy_inputs(UpperCamelCase_ ) )
lowercase__ = output.images
lowercase__ = pipe(
**self.get_dummy_inputs(UpperCamelCase_ ) , return_dict=UpperCamelCase_ , )[0]
lowercase__ = image[0, -3:, -3:, -1]
lowercase__ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase__ = np.array(
[0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'
@slow
@require_torch_gpu
class _a ( unittest.TestCase ):
def lowerCamelCase_ ( self: str ) -> List[Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self: List[str] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_img2img_frog.npy''' )
lowercase__ = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
lowercase__ = '''A red cartoon frog, 4k'''
lowercase__ = KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(UpperCamelCase_ )
lowercase__ = KandinskyVaaImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-decoder''' , torch_dtype=torch.floataa )
lowercase__ = pipeline.to(UpperCamelCase_ )
pipeline.set_progress_bar_config(disable=UpperCamelCase_ )
lowercase__ = torch.Generator(device='''cpu''' ).manual_seed(0 )
lowercase__ , lowercase__ = pipe_prior(
UpperCamelCase_ , generator=UpperCamelCase_ , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
lowercase__ = pipeline(
image=UpperCamelCase_ , image_embeds=UpperCamelCase_ , negative_image_embeds=UpperCamelCase_ , generator=UpperCamelCase_ , num_inference_steps=100 , height=768 , width=768 , strength=0.2 , output_type='''np''' , )
lowercase__ = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(UpperCamelCase_ , UpperCamelCase_ )
| 110 | 1 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCamelCase_ ( _a , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ = CTRLTokenizer
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = False
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
a = ["""adapt""", """re@@""", """a@@""", """apt""", """c@@""", """t""", """<unk>"""]
a = dict(zip(snake_case_ ,range(len(snake_case_ ) ) ) )
a = ["""#version: 0.2""", """a p""", """ap t</w>""", """r e""", """a d""", """ad apt</w>""", """"""]
a = {"""unk_token""": """<unk>"""}
a = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES['''vocab_file'''] )
a = 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(snake_case_ ) + '''\n''' )
with open(self.merges_file ,'''w''' ,encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(snake_case_ ) )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ,**__lowerCamelCase : List[str] ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CTRLTokenizer.from_pretrained(self.tmpdirname ,**snake_case_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ,__lowerCamelCase : Optional[int] ):
'''simple docstring'''
a = """adapt react readapt apt"""
a = """adapt react readapt apt"""
return input_text, output_text
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
'''simple docstring'''
a = CTRLTokenizer(self.vocab_file ,self.merges_file ,**self.special_tokens_map )
a = """adapt react readapt apt"""
a = """adapt re@@ a@@ c@@ t re@@ adapt apt""".split()
a = tokenizer.tokenize(snake_case_ )
self.assertListEqual(snake_case_ ,snake_case_ )
a = tokens + [tokenizer.unk_token]
a = [0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_ ) ,snake_case_ )
| 371 |
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
UpperCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
UpperCamelCase__ : Union[str, Any] = {
"""hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class lowerCamelCase_ ( a_ ):
SCREAMING_SNAKE_CASE_ = 'yolos'
def __init__( self : Union[str, Any] ,__lowerCamelCase : int=7_68 ,__lowerCamelCase : Dict=12 ,__lowerCamelCase : Union[str, Any]=12 ,__lowerCamelCase : List[Any]=30_72 ,__lowerCamelCase : int="gelu" ,__lowerCamelCase : int=0.0 ,__lowerCamelCase : str=0.0 ,__lowerCamelCase : Optional[Any]=0.02 ,__lowerCamelCase : int=1e-12 ,__lowerCamelCase : Any=[5_12, 8_64] ,__lowerCamelCase : Tuple=16 ,__lowerCamelCase : int=3 ,__lowerCamelCase : Tuple=True ,__lowerCamelCase : Optional[int]=1_00 ,__lowerCamelCase : List[Any]=True ,__lowerCamelCase : List[str]=False ,__lowerCamelCase : int=1 ,__lowerCamelCase : List[Any]=5 ,__lowerCamelCase : Optional[int]=2 ,__lowerCamelCase : int=5 ,__lowerCamelCase : str=2 ,__lowerCamelCase : Tuple=0.1 ,**__lowerCamelCase : List[Any] ,):
'''simple docstring'''
super().__init__(**__lowerCamelCase )
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = intermediate_size
a = hidden_act
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = initializer_range
a = layer_norm_eps
a = image_size
a = patch_size
a = num_channels
a = qkv_bias
a = num_detection_tokens
a = use_mid_position_embeddings
a = auxiliary_loss
# Hungarian matcher
a = class_cost
a = bbox_cost
a = giou_cost
# Loss coefficients
a = bbox_loss_coefficient
a = giou_loss_coefficient
a = eos_coefficient
class lowerCamelCase_ ( a_ ):
SCREAMING_SNAKE_CASE_ = version.parse('1.11' )
@property
def SCREAMING_SNAKE_CASE_ ( self : str ):
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
'''simple docstring'''
return 1e-4
@property
def SCREAMING_SNAKE_CASE_ ( self : str ):
'''simple docstring'''
return 12
| 330 | 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)
__A : Union[str, Any] = logging.getLogger()
def lowercase ( _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
_UpperCAmelCase = '''\n'''.join(_UpperCAmelCase )
Path(_UpperCAmelCase ).open('''w''' ).writelines(_UpperCAmelCase )
__A : Tuple = '''patrickvonplaten/t5-tiny-random'''
__A : List[Any] = '''sshleifer/bart-tiny-random'''
__A : int = '''sshleifer/tiny-mbart'''
__A : List[str] = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks
class _a ( __UpperCAmelCase):
"""simple docstring"""
def lowercase__ ( self : str , __UpperCamelCase : Dict )->Any:
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() ) / '''utest_input.source'''
_UpperCAmelCase = input_file_name.parent / '''utest_output.txt'''
assert not output_file_name.exists()
_UpperCAmelCase = [''' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.''']
_dump_articles(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
_UpperCAmelCase = str(Path(self.get_auto_remove_tmp_dir() ) / '''scores.json''' )
_UpperCAmelCase = '''translation_en_to_de''' if model == T5_TINY else '''summarization'''
_UpperCAmelCase = F'\n run_eval_search.py\n {model}\n {input_file_name}\n {output_file_name}\n --score_path {score_path}\n --task {task}\n --num_beams 2\n --length_penalty 2.0\n '.split()
with patch.object(__SCREAMING_SNAKE_CASE , '''argv''' , __SCREAMING_SNAKE_CASE ):
run_generate()
assert Path(__SCREAMING_SNAKE_CASE ).exists()
# os.remove(Path(output_file_name))
def lowercase__ ( self : Tuple )->str:
self.run_eval_tester(__SCREAMING_SNAKE_CASE )
@parameterized.expand([BART_TINY, MBART_TINY] )
@slow
def lowercase__ ( self : Optional[Any] , __UpperCamelCase : Dict )->List[Any]:
self.run_eval_tester(__SCREAMING_SNAKE_CASE )
@parameterized.expand([T5_TINY, MBART_TINY] )
@slow
def lowercase__ ( self : str , __UpperCamelCase : Any )->Union[str, Any]:
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() ) / '''utest_input.source'''
_UpperCAmelCase = input_file_name.parent / '''utest_output.txt'''
assert not output_file_name.exists()
_UpperCAmelCase = {
'''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!''',
],
}
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() )
_UpperCAmelCase = str(tmp_dir / '''scores.json''' )
_UpperCAmelCase = str(tmp_dir / '''val.target''' )
_dump_articles(__SCREAMING_SNAKE_CASE , text['''en'''] )
_dump_articles(__SCREAMING_SNAKE_CASE , text['''de'''] )
_UpperCAmelCase = '''translation_en_to_de''' if model == T5_TINY else '''summarization'''
_UpperCAmelCase = F'\n run_eval_search.py\n {model}\n {str(__SCREAMING_SNAKE_CASE )}\n {str(__SCREAMING_SNAKE_CASE )}\n --score_path {score_path}\n --reference_path {reference_path}\n --task {task}\n '.split()
testargs.extend(['''--search''', '''num_beams=1:2 length_penalty=0.9:1.0'''] )
with patch.object(__SCREAMING_SNAKE_CASE , '''argv''' , __SCREAMING_SNAKE_CASE ):
with CaptureStdout() as cs:
run_search()
_UpperCAmelCase = [''' num_beams | length_penalty''', model, '''Best score args''']
_UpperCAmelCase = ['''Info''']
if "translation" in task:
expected_strings.append('''bleu''' )
else:
expected_strings.extend(__SCREAMING_SNAKE_CASE )
for w in expected_strings:
assert w in cs.out
for w in un_expected_strings:
assert w not in cs.out
assert Path(__SCREAMING_SNAKE_CASE ).exists()
os.remove(Path(__SCREAMING_SNAKE_CASE ) )
| 260 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING
__snake_case :List[Any] = logging.get_logger(__name__)
@add_end_docstrings(__UpperCAmelCase )
class _A ( __UpperCAmelCase ):
def __init__( self : Dict , **__SCREAMING_SNAKE_CASE : Tuple):
'''simple docstring'''
super().__init__(**__SCREAMING_SNAKE_CASE)
requires_backends(self , '''vision''')
requires_backends(self , '''torch''')
if self.framework != "pt":
raise ValueError(F'The {self.__class__} is only available in PyTorch.')
self.check_model_type(__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : Any , **__SCREAMING_SNAKE_CASE : Any):
'''simple docstring'''
__a = {}
__a = {}
__a = {}
# preprocess args
if "points_per_batch" in kwargs:
__a = kwargs['''points_per_batch''']
if "points_per_crop" in kwargs:
__a = kwargs['''points_per_crop''']
if "crops_n_layers" in kwargs:
__a = kwargs['''crops_n_layers''']
if "crop_overlap_ratio" in kwargs:
__a = kwargs['''crop_overlap_ratio''']
if "crop_n_points_downscale_factor" in kwargs:
__a = kwargs['''crop_n_points_downscale_factor''']
# postprocess args
if "pred_iou_thresh" in kwargs:
__a = kwargs['''pred_iou_thresh''']
if "stability_score_offset" in kwargs:
__a = kwargs['''stability_score_offset''']
if "mask_threshold" in kwargs:
__a = kwargs['''mask_threshold''']
if "stability_score_thresh" in kwargs:
__a = kwargs['''stability_score_thresh''']
if "crops_nms_thresh" in kwargs:
__a = kwargs['''crops_nms_thresh''']
if "output_rle_mask" in kwargs:
__a = kwargs['''output_rle_mask''']
if "output_bboxes_mask" in kwargs:
__a = kwargs['''output_bboxes_mask''']
return preprocess_kwargs, forward_params, postprocess_kwargs
def __call__( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : int , *__SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Tuple=None , __SCREAMING_SNAKE_CASE : Any=None , **__SCREAMING_SNAKE_CASE : str):
'''simple docstring'''
return super().__call__(__SCREAMING_SNAKE_CASE , *__SCREAMING_SNAKE_CASE , num_workers=__SCREAMING_SNAKE_CASE , batch_size=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : Dict , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Any=64 , __SCREAMING_SNAKE_CASE : int = 0 , __SCREAMING_SNAKE_CASE : float = 512 / 1_500 , __SCREAMING_SNAKE_CASE : Optional[int] = 32 , __SCREAMING_SNAKE_CASE : Optional[int] = 1 , ):
'''simple docstring'''
__a = load_image(__SCREAMING_SNAKE_CASE)
__a = self.image_processor.size['''longest_edge''']
__a , __a , __a , __a = self.image_processor.generate_crop_boxes(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
__a = self.image_processor(images=__SCREAMING_SNAKE_CASE , return_tensors='''pt''')
with self.device_placement():
if self.framework == "pt":
__a = self.get_inference_context()
with inference_context():
__a = self._ensure_tensor_on_device(__SCREAMING_SNAKE_CASE , device=self.device)
__a = self.model.get_image_embeddings(model_inputs.pop('''pixel_values'''))
__a = image_embeddings
__a = grid_points.shape[1]
__a = points_per_batch if points_per_batch is not None else n_points
if points_per_batch <= 0:
raise ValueError(
'''Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. '''
'''To return all points at once, set points_per_batch to None''')
for i in range(0 , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE):
__a = grid_points[:, i : i + points_per_batch, :, :]
__a = input_labels[:, i : i + points_per_batch]
__a = i == n_points - points_per_batch
yield {
"input_points": batched_points,
"input_labels": labels,
"input_boxes": crop_boxes,
"is_last": is_last,
**model_inputs,
}
def _lowerCamelCase ( self : Any , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : int=0.88 , __SCREAMING_SNAKE_CASE : List[Any]=0.95 , __SCREAMING_SNAKE_CASE : List[Any]=0 , __SCREAMING_SNAKE_CASE : int=1 , ):
'''simple docstring'''
__a = model_inputs.pop('''input_boxes''')
__a = model_inputs.pop('''is_last''')
__a = model_inputs.pop('''original_sizes''').tolist()
__a = model_inputs.pop('''reshaped_input_sizes''').tolist()
__a = self.model(**__SCREAMING_SNAKE_CASE)
# post processing happens here in order to avoid CPU GPU copies of ALL the masks
__a = model_outputs['''pred_masks''']
__a = self.image_processor.post_process_masks(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , binarize=__SCREAMING_SNAKE_CASE)
__a = model_outputs['''iou_scores''']
__a , __a , __a = self.image_processor.filter_masks(
masks[0] , iou_scores[0] , original_sizes[0] , input_boxes[0] , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , )
return {
"masks": masks,
"is_last": is_last,
"boxes": boxes,
"iou_scores": iou_scores,
}
def _lowerCamelCase ( self : int , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Dict=False , __SCREAMING_SNAKE_CASE : Tuple=False , __SCREAMING_SNAKE_CASE : int=0.7 , ):
'''simple docstring'''
__a = []
__a = []
__a = []
for model_output in model_outputs:
all_scores.append(model_output.pop('''iou_scores'''))
all_masks.extend(model_output.pop('''masks'''))
all_boxes.append(model_output.pop('''boxes'''))
__a = torch.cat(__SCREAMING_SNAKE_CASE)
__a = torch.cat(__SCREAMING_SNAKE_CASE)
__a , __a , __a , __a = self.image_processor.post_process_for_mask_generation(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
__a = defaultdict(__SCREAMING_SNAKE_CASE)
for output in model_outputs:
for k, v in output.items():
extra[k].append(__SCREAMING_SNAKE_CASE)
__a = {}
if output_rle_mask:
__a = rle_mask
if output_bboxes_mask:
__a = bounding_boxes
return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
| 49 | 0 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCamelCase ( lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE_ = "ClapFeatureExtractor"
SCREAMING_SNAKE_CASE_ = ("RobertaTokenizer", "RobertaTokenizerFast")
def __init__( self, lowerCAmelCase__, lowerCAmelCase__) -> List[str]:
super().__init__(lowerCAmelCase__, lowerCAmelCase__)
def __call__( self, lowerCAmelCase__=None, lowerCAmelCase__=None, lowerCAmelCase__=None, **lowerCAmelCase__) -> List[Any]:
snake_case_ = kwargs.pop('sampling_rate', lowerCAmelCase__)
if text is None and audios is None:
raise ValueError('You have to specify either text or audios. Both cannot be none.')
if text is not None:
snake_case_ = self.tokenizer(lowerCAmelCase__, return_tensors=lowerCAmelCase__, **lowerCAmelCase__)
if audios is not None:
snake_case_ = self.feature_extractor(
lowerCAmelCase__, sampling_rate=lowerCAmelCase__, return_tensors=lowerCAmelCase__, **lowerCAmelCase__)
if text is not None and audios is not None:
snake_case_ = audio_features.input_features
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**lowerCAmelCase__), tensor_type=lowerCAmelCase__)
def a_ ( self, *lowerCAmelCase__, **lowerCAmelCase__) -> Tuple:
return self.tokenizer.batch_decode(*lowerCAmelCase__, **lowerCAmelCase__)
def a_ ( self, *lowerCAmelCase__, **lowerCAmelCase__) -> str:
return self.tokenizer.decode(*lowerCAmelCase__, **lowerCAmelCase__)
@property
def a_ ( self) -> int:
snake_case_ = self.tokenizer.model_input_names
snake_case_ = self.feature_extractor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names))
| 312 | """simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def UpperCAmelCase ( UpperCAmelCase ) -> Dict:
# vision encoder
if "img_encoder.pos_embed" in name:
snake_case_ = name.replace('img_encoder.pos_embed' , 'vision_model.embeddings.position_embeddings' )
if "img_encoder.patch_embed.proj" in name:
snake_case_ = name.replace('img_encoder.patch_embed.proj' , 'vision_model.embeddings.patch_embeddings.projection' )
if "img_encoder.patch_embed.norm" in name:
snake_case_ = name.replace('img_encoder.patch_embed.norm' , 'vision_model.embeddings.layernorm' )
if "img_encoder.layers" in name:
snake_case_ = name.replace('img_encoder.layers' , 'vision_model.encoder.stages' )
if "blocks" in name and "res" not in name:
snake_case_ = name.replace('blocks' , 'layers' )
if "attn" in name and "pre_assign" not in name:
snake_case_ = name.replace('attn' , 'self_attn' )
if "proj" in name and "self_attn" in name and "text" not in name:
snake_case_ = name.replace('proj' , 'out_proj' )
if "pre_assign_attn.attn.proj" in name:
snake_case_ = name.replace('pre_assign_attn.attn.proj' , 'pre_assign_attn.attn.out_proj' )
if "norm1" in name:
snake_case_ = name.replace('norm1' , 'layer_norm1' )
if "norm2" in name and "pre_assign" not in name:
snake_case_ = name.replace('norm2' , 'layer_norm2' )
if "img_encoder.norm" in name:
snake_case_ = name.replace('img_encoder.norm' , 'vision_model.layernorm' )
# text encoder
if "text_encoder.token_embedding" in name:
snake_case_ = name.replace('text_encoder.token_embedding' , 'text_model.embeddings.token_embedding' )
if "text_encoder.positional_embedding" in name:
snake_case_ = name.replace('text_encoder.positional_embedding' , 'text_model.embeddings.position_embedding.weight' )
if "text_encoder.transformer.resblocks." in name:
snake_case_ = name.replace('text_encoder.transformer.resblocks.' , 'text_model.encoder.layers.' )
if "ln_1" in name:
snake_case_ = name.replace('ln_1' , 'layer_norm1' )
if "ln_2" in name:
snake_case_ = name.replace('ln_2' , 'layer_norm2' )
if "c_fc" in name:
snake_case_ = name.replace('c_fc' , 'fc1' )
if "c_proj" in name:
snake_case_ = name.replace('c_proj' , 'fc2' )
if "text_encoder" in name:
snake_case_ = name.replace('text_encoder' , 'text_model' )
if "ln_final" in name:
snake_case_ = name.replace('ln_final' , 'final_layer_norm' )
# projection layers
if "img_projector.linear_hidden." in name:
snake_case_ = name.replace('img_projector.linear_hidden.' , 'visual_projection.' )
if "img_projector.linear_out." in name:
snake_case_ = name.replace('img_projector.linear_out.' , 'visual_projection.3.' )
if "text_projector.linear_hidden" in name:
snake_case_ = name.replace('text_projector.linear_hidden' , 'text_projection' )
if "text_projector.linear_out" in name:
snake_case_ = name.replace('text_projector.linear_out' , 'text_projection.3' )
return name
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> Optional[Any]:
for key in orig_state_dict.copy().keys():
snake_case_ = orig_state_dict.pop(UpperCAmelCase )
if "qkv" in key:
# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
snake_case_ = key.split('.' )
snake_case_ , snake_case_ = int(key_split[2] ), int(key_split[4] )
snake_case_ = config.vision_config.hidden_size
if "weight" in key:
snake_case_ = val[:dim, :]
snake_case_ = val[dim : dim * 2, :]
snake_case_ = val[-dim:, :]
else:
snake_case_ = val[:dim]
snake_case_ = val[dim : dim * 2]
snake_case_ = val[-dim:]
elif "in_proj" in key:
# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
snake_case_ = key.split('.' )
snake_case_ = int(key_split[3] )
snake_case_ = config.text_config.hidden_size
if "weight" in key:
snake_case_ = val[:dim, :]
snake_case_ = val[
dim : dim * 2, :
]
snake_case_ = val[-dim:, :]
else:
snake_case_ = val[:dim]
snake_case_ = val[dim : dim * 2]
snake_case_ = val[-dim:]
else:
snake_case_ = rename_key(UpperCAmelCase )
# squeeze if necessary
if (
"text_projection.0" in new_name
or "text_projection.3" in new_name
or "visual_projection.0" in new_name
or "visual_projection.3" in new_name
):
snake_case_ = val.squeeze_()
else:
snake_case_ = val
return orig_state_dict
def UpperCAmelCase ( ) -> Any:
snake_case_ = 'http://images.cocodataset.org/val2017/000000039769.jpg'
snake_case_ = Image.open(requests.get(UpperCAmelCase , stream=UpperCAmelCase ).raw )
return im
@torch.no_grad()
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase="groupvit-gcc-yfcc" , UpperCAmelCase=False ) -> int:
snake_case_ = GroupViTConfig()
snake_case_ = GroupViTModel(UpperCAmelCase ).eval()
snake_case_ = torch.load(UpperCAmelCase , map_location='cpu' )['model']
snake_case_ = convert_state_dict(UpperCAmelCase , UpperCAmelCase )
snake_case_ , snake_case_ = model.load_state_dict(UpperCAmelCase , strict=UpperCAmelCase )
assert missing_keys == ["text_model.embeddings.position_ids"]
assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(UpperCAmelCase ) == 0)
# verify result
snake_case_ = CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32' )
snake_case_ = prepare_img()
snake_case_ = processor(text=['a photo of a cat', 'a photo of a dog'] , images=UpperCAmelCase , padding=UpperCAmelCase , return_tensors='pt' )
with torch.no_grad():
snake_case_ = model(**UpperCAmelCase )
if model_name == "groupvit-gcc-yfcc":
snake_case_ = torch.tensor([[13.3_523, 6.3_629]] )
elif model_name == "groupvit-gcc-redcaps":
snake_case_ = torch.tensor([[16.1_873, 8.6_230]] )
else:
raise ValueError(f'Model name {model_name} not supported.' )
assert torch.allclose(outputs.logits_per_image , UpperCAmelCase , atol=1e-3 )
processor.save_pretrained(UpperCAmelCase )
model.save_pretrained(UpperCAmelCase )
print('Successfully saved processor and model to' , UpperCAmelCase )
if push_to_hub:
print('Pushing to the hub...' )
processor.push_to_hub(UpperCAmelCase , organization='nielsr' )
model.push_to_hub(UpperCAmelCase , organization='nielsr' )
if __name__ == "__main__":
__UpperCamelCase = argparse.ArgumentParser()
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to dump the processor and PyTorch model.'''
)
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to GroupViT checkpoint''')
parser.add_argument(
'''--model_name''',
default='''groupvit-gccy-fcc''',
type=str,
help='''Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'''',
)
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
help='''Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.''',
)
__UpperCamelCase = parser.parse_args()
convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 312 | 1 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}
class __lowerCAmelCase ( lowerCamelCase_ ):
'''simple docstring'''
__UpperCAmelCase : int = 'git_vision_model'
def __init__( self , _a=768 , _a=3_072 , _a=12 , _a=12 , _a=3 , _a=224 , _a=16 , _a="quick_gelu" , _a=1E-5 , _a=0.0 , _a=0.02 , **_a , ):
super().__init__(**__SCREAMING_SNAKE_CASE )
__a = hidden_size
__a = intermediate_size
__a = num_hidden_layers
__a = num_attention_heads
__a = num_channels
__a = patch_size
__a = image_size
__a = initializer_range
__a = attention_dropout
__a = layer_norm_eps
__a = hidden_act
@classmethod
def __UpperCAmelCase ( cls , _a , **_a ):
cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE )
__a = cls.get_config_dict(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
# get the vision config dict if we are loading from GITConfig
if config_dict.get('''model_type''' ) == "git":
__a = config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
class __lowerCAmelCase ( lowerCamelCase_ ):
'''simple docstring'''
__UpperCAmelCase : Tuple = 'git'
def __init__( self , _a=None , _a=30_522 , _a=768 , _a=6 , _a=12 , _a=3_072 , _a="gelu" , _a=0.1 , _a=0.1 , _a=1_024 , _a=0.02 , _a=1E-12 , _a=0 , _a="absolute" , _a=True , _a=False , _a=101 , _a=102 , _a=None , **_a , ):
super().__init__(bos_token_id=__SCREAMING_SNAKE_CASE , eos_token_id=__SCREAMING_SNAKE_CASE , pad_token_id=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
if vision_config is None:
__a = {}
logger.info('''vision_config is None. initializing the GitVisionConfig with default values.''' )
__a = GitVisionConfig(**__SCREAMING_SNAKE_CASE )
__a = vocab_size
__a = hidden_size
__a = num_hidden_layers
__a = num_attention_heads
__a = hidden_act
__a = intermediate_size
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = max_position_embeddings
__a = initializer_range
__a = layer_norm_eps
__a = position_embedding_type
__a = use_cache
__a = tie_word_embeddings
__a = num_image_with_embedding
__a = bos_token_id
__a = eos_token_id
def __UpperCAmelCase ( self ):
__a = copy.deepcopy(self.__dict__ )
__a = self.vision_config.to_dict()
__a = self.__class__.model_type
return output
| 45 |
'''simple docstring'''
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
_lowercase : Union[str, Any] = yaml.safe_load(
"\\nname: \"\"\nallow_empty: false\nallow_empty_text: true\nsubsections:\n - name: \"Dataset Card for X\" # First-level markdown heading\n allow_empty: false\n allow_empty_text: true\n subsections:\n - name: \"Table of Contents\"\n allow_empty: false\n allow_empty_text: false\n subsections: null\n - name: \"Dataset Description\"\n allow_empty: false\n allow_empty_text: false\n subsections:\n - name: \"Dataset Summary\"\n allow_empty: false\n allow_empty_text: false\n subsections: null\n - name: \"Supported Tasks and Leaderboards\"\n allow_empty: true\n allow_empty_text: true\n subsections: null\n - name: Languages\n allow_empty: false\n allow_empty_text: true\n subsections: null\n"
)
_lowercase : int = {
"name": "root",
"text": "",
"is_empty_text": True,
"subsections": [
{
"name": "Dataset Card for My Dataset",
"text": "",
"is_empty_text": True,
"subsections": [
{"name": "Table of Contents", "text": "Some text here.", "is_empty_text": False, "subsections": []},
{
"name": "Dataset Description",
"text": "Some text here.",
"is_empty_text": False,
"subsections": [
{
"name": "Dataset Summary",
"text": "Some text here.",
"is_empty_text": False,
"subsections": [],
},
{
"name": "Supported Tasks and Leaderboards",
"text": "",
"is_empty_text": True,
"subsections": [],
},
{"name": "Languages", "text": "Language Text", "is_empty_text": False, "subsections": []},
],
},
],
}
],
}
_lowercase : Optional[Any] = "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"
_lowercase : Union[str, Any] = "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n#### Extra Ignored Subsection\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"
_lowercase : Any = {
"name": "root",
"text": "",
"is_empty_text": True,
"subsections": [
{
"name": "Dataset Card for My Dataset",
"text": "",
"is_empty_text": True,
"subsections": [
{"name": "Table of Contents", "text": "Some text here.", "is_empty_text": False, "subsections": []},
{
"name": "Dataset Description",
"text": "Some text here.",
"is_empty_text": False,
"subsections": [
{
"name": "Dataset Summary",
"text": "Some text here.",
"is_empty_text": False,
"subsections": [
{
"name": "Extra Ignored Subsection",
"text": "",
"is_empty_text": True,
"subsections": [],
}
],
},
{
"name": "Supported Tasks and Leaderboards",
"text": "",
"is_empty_text": True,
"subsections": [],
},
{"name": "Languages", "text": "Language Text", "is_empty_text": False, "subsections": []},
],
},
],
}
],
}
_lowercase : str = "\\n---\n---\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"
_lowercase : List[str] = (
"The following issues were found for the README at `{path}`:\n-\tEmpty YAML markers are present in the README."
)
_lowercase : Tuple = "\\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"
_lowercase : Optional[Any] = (
"The following issues were found for the README at `{path}`:\n-\tNo YAML markers are present in the README."
)
_lowercase : Tuple = "\\n---\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"
_lowercase : Optional[int] = "The following issues were found for the README at `{path}`:\n-\tOnly the start of YAML tags present in the README."
_lowercase : List[Any] = "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"
_lowercase : Optional[Any] = "The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Summary` but it is empty.\n-\tExpected some text in section `Dataset Summary` but it is empty (text in subsections are ignored)."
_lowercase : Optional[int] = "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n"
_lowercase : Union[str, Any] = "The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Card for My Dataset` but it is empty.\n-\tSection `Dataset Card for My Dataset` expected the following subsections: `Table of Contents`, `Dataset Description`. Found 'None'."
_lowercase : Union[str, Any] = "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Languages\nLanguage Text\n"
_lowercase : int = "The following issues were found for the README at `{path}`:\n-\tSection `Dataset Description` is missing subsection: `Supported Tasks and Leaderboards`."
_lowercase : List[Any] = "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\n"
_lowercase : int = "The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Languages` but it is empty."
_lowercase : List[str] = "\\n---\nlanguage:\n- zh\n- en\n---\n\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"
_lowercase : str = "The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README."
_lowercase : Dict = "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n# Dataset Card My Dataset\n"
_lowercase : List[str] = "The following issues were found for the README at `{path}`:\n-\tThe README has several first-level headings: `Dataset Card for My Dataset`, `Dataset Card My Dataset`. Only one heading is expected. Skipping further validation for this README."
_lowercase : str = "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"
_lowercase : Union[str, Any] = "The following issues were found for the README at `{path}`:\n-\tNo first-level heading starting with `Dataset Card for` found in README. Skipping further validation for this README."
_lowercase : List[Any] = ""
_lowercase : Optional[Any] = "The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README.\n-\tNo YAML markers are present in the README."
_lowercase : List[Any] = "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"
_lowercase : Optional[Any] = "The following issues were found while parsing the README at `{path}`:\n-\tMultiple sections with the same heading `Dataset Card for My Dataset` have been found. Please keep only one of these sections."
@pytest.mark.parametrize(
'''readme_md, expected_dict''' , [
(README_CORRECT, CORRECT_DICT),
(README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
] , )
def snake_case_ ( __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Optional[Any] ):
"""simple docstring"""
assert ReadMe.from_string(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ).to_dict() == expected_dict
@pytest.mark.parametrize(
'''readme_md, expected_error''' , [
(README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
(README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
(README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
(README_EMPTY, EXPECTED_ERROR_README_EMPTY),
(README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
(README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
(README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
(README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
(README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
(README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
(README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
] , )
def snake_case_ ( __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Dict ):
"""simple docstring"""
with pytest.raises(__SCREAMING_SNAKE_CASE , match=re.escape(expected_error.format(path='''root''' ) ) ):
lowercase_ : Optional[int] = ReadMe.from_string(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
readme.validate()
@pytest.mark.parametrize(
'''readme_md, expected_error''' , [
(README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
] , )
def snake_case_ ( __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Dict ):
"""simple docstring"""
with pytest.raises(__SCREAMING_SNAKE_CASE , match=re.escape(expected_error.format(path='''root''' ) ) ):
ReadMe.from_string(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
@pytest.mark.parametrize(
'''readme_md,''' , [
(README_MULTIPLE_SAME_HEADING_1),
] , )
def snake_case_ ( __SCREAMING_SNAKE_CASE : Any ):
"""simple docstring"""
ReadMe.from_string(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , suppress_parsing_errors=__SCREAMING_SNAKE_CASE )
@pytest.mark.parametrize(
'''readme_md, expected_dict''' , [
(README_CORRECT, CORRECT_DICT),
(README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
] , )
def snake_case_ ( __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase_ : Optional[int] = Path(__SCREAMING_SNAKE_CASE ) / '''README.md'''
with open(__SCREAMING_SNAKE_CASE , '''w+''' ) as readme_file:
readme_file.write(__SCREAMING_SNAKE_CASE )
lowercase_ : Any = ReadMe.from_readme(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ).to_dict()
assert out["name"] == path
assert out["text"] == ""
assert out["is_empty_text"]
assert out["subsections"] == expected_dict["subsections"]
@pytest.mark.parametrize(
'''readme_md, expected_error''' , [
(README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
(README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
(README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
(README_EMPTY, EXPECTED_ERROR_README_EMPTY),
(README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
(README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
(README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
(README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
(README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
(README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
(README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
] , )
def snake_case_ ( __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Union[str, Any] ):
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase_ : str = Path(__SCREAMING_SNAKE_CASE ) / '''README.md'''
with open(__SCREAMING_SNAKE_CASE , '''w+''' ) as readme_file:
readme_file.write(__SCREAMING_SNAKE_CASE )
lowercase_ : List[str] = expected_error.format(path=__SCREAMING_SNAKE_CASE )
with pytest.raises(__SCREAMING_SNAKE_CASE , match=re.escape(__SCREAMING_SNAKE_CASE ) ):
lowercase_ : int = ReadMe.from_readme(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
readme.validate()
@pytest.mark.parametrize(
'''readme_md, expected_error''' , [
(README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
] , )
def snake_case_ ( __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Optional[Any] ):
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase_ : Dict = Path(__SCREAMING_SNAKE_CASE ) / '''README.md'''
with open(__SCREAMING_SNAKE_CASE , '''w+''' ) as readme_file:
readme_file.write(__SCREAMING_SNAKE_CASE )
lowercase_ : Tuple = expected_error.format(path=__SCREAMING_SNAKE_CASE )
with pytest.raises(__SCREAMING_SNAKE_CASE , match=re.escape(__SCREAMING_SNAKE_CASE ) ):
ReadMe.from_readme(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
@pytest.mark.parametrize(
'''readme_md,''' , [
(README_MULTIPLE_SAME_HEADING_1),
] , )
def snake_case_ ( __SCREAMING_SNAKE_CASE : Tuple ):
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase_ : Optional[int] = Path(__SCREAMING_SNAKE_CASE ) / '''README.md'''
with open(__SCREAMING_SNAKE_CASE , '''w+''' ) as readme_file:
readme_file.write(__SCREAMING_SNAKE_CASE )
ReadMe.from_readme(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , suppress_parsing_errors=__SCREAMING_SNAKE_CASE )
| 93 | 0 |
'''simple docstring'''
import argparse
import os
import platform
import numpy as np
import psutil
import torch
from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file
from ..utils import is_npu_available, is_xpu_available
def _lowercase ( __A=None ):
'''simple docstring'''
if subparsers is not None:
__UpperCamelCase = subparsers.add_parser("""env""" )
else:
__UpperCamelCase = argparse.ArgumentParser("""Accelerate env command""" )
parser.add_argument(
"""--config_file""" ,default=_UpperCAmelCase ,help="""The config file to use for the default values in the launching script.""" )
if subparsers is not None:
parser.set_defaults(func=_UpperCAmelCase )
return parser
def _lowercase ( __A ):
'''simple docstring'''
__UpperCamelCase = torch.__version__
__UpperCamelCase = torch.cuda.is_available()
__UpperCamelCase = is_xpu_available()
__UpperCamelCase = is_npu_available()
__UpperCamelCase = "Not found"
# Get the default from the config file.
if args.config_file is not None or os.path.isfile(_UpperCAmelCase ):
__UpperCamelCase = load_config_from_file(args.config_file ).to_dict()
__UpperCamelCase = {
"`Accelerate` version": version,
"Platform": platform.platform(),
"Python version": platform.python_version(),
"Numpy version": np.__version__,
"PyTorch version (GPU?)": f"{pt_version} ({pt_cuda_available})",
"PyTorch XPU available": str(_UpperCAmelCase ),
"PyTorch NPU available": str(_UpperCAmelCase ),
"System RAM": f"{psutil.virtual_memory().total / 1_024 ** 3:.2f} GB",
}
if pt_cuda_available:
__UpperCamelCase = torch.cuda.get_device_name()
print("""\nCopy-and-paste the text below in your GitHub issue\n""" )
print("""\n""".join([f"- {prop}: {val}" for prop, val in info.items()] ) )
print("""- `Accelerate` default config:""" if args.config_file is None else """- `Accelerate` config passed:""" )
__UpperCamelCase = (
"\n".join([f"\t- {prop}: {val}" for prop, val in accelerate_config.items()] )
if isinstance(_UpperCAmelCase ,_UpperCAmelCase )
else f"\t{accelerate_config}"
)
print(_UpperCAmelCase )
__UpperCamelCase = accelerate_config
return info
def _lowercase ( ):
'''simple docstring'''
__UpperCamelCase = env_command_parser()
__UpperCamelCase = parser.parse_args()
env_command(_UpperCAmelCase )
return 0
if __name__ == "__main__":
raise SystemExit(main())
| 365 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
a__ : List[Any] = logging.get_logger(__name__)
a__ : str = {'vocab_file': 'vocab.txt'}
a__ : Any = {
'vocab_file': {
'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt',
'YituTech/conv-bert-medium-small': (
'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'
),
'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt',
}
}
a__ : Tuple = {
'YituTech/conv-bert-base': 5_1_2,
'YituTech/conv-bert-medium-small': 5_1_2,
'YituTech/conv-bert-small': 5_1_2,
}
a__ : str = {
'YituTech/conv-bert-base': {'do_lower_case': True},
'YituTech/conv-bert-medium-small': {'do_lower_case': True},
'YituTech/conv-bert-small': {'do_lower_case': True},
}
class UpperCAmelCase__ ( UpperCAmelCase_):
__SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
__SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
__SCREAMING_SNAKE_CASE = PRETRAINED_INIT_CONFIGURATION
__SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__SCREAMING_SNAKE_CASE = ConvBertTokenizer
def __init__( self , lowercase=None , lowercase=None , lowercase=True , lowercase="[UNK]" , lowercase="[SEP]" , lowercase="[PAD]" , lowercase="[CLS]" , lowercase="[MASK]" , lowercase=True , lowercase=None , **lowercase , ) -> int:
super().__init__(
lowercase , tokenizer_file=lowercase , do_lower_case=lowercase , unk_token=lowercase , sep_token=lowercase , pad_token=lowercase , cls_token=lowercase , mask_token=lowercase , tokenize_chinese_chars=lowercase , strip_accents=lowercase , **lowercase , )
__UpperCamelCase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" , lowercase ) != do_lower_case
or normalizer_state.get("""strip_accents""" , lowercase ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" , lowercase ) != tokenize_chinese_chars
):
__UpperCamelCase = getattr(lowercase , normalizer_state.pop("""type""" ) )
__UpperCamelCase = do_lower_case
__UpperCamelCase = strip_accents
__UpperCamelCase = tokenize_chinese_chars
__UpperCamelCase = normalizer_class(**lowercase )
__UpperCamelCase = do_lower_case
def __lowerCamelCase ( self , lowercase , lowercase=None ) -> Tuple:
__UpperCamelCase = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __lowerCamelCase ( self , lowercase , lowercase = None ) -> List[int]:
__UpperCamelCase = [self.sep_token_id]
__UpperCamelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __lowerCamelCase ( self , lowercase , lowercase = None ) -> Tuple[str]:
__UpperCamelCase = self._tokenizer.model.save(lowercase , name=lowercase )
return tuple(lowercase )
| 243 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th
logging.set_verbosity_info()
__A : Union[str, Any] = logging.get_logger('''transformers.models.encodec''')
__A : Dict = {
'''quantizer.vq.layers.*._codebook.inited''': '''quantizer.layers.*.codebook.inited''',
'''quantizer.vq.layers.*._codebook.cluster_size''': '''quantizer.layers.*.codebook.cluster_size''',
'''quantizer.vq.layers.*._codebook.embed''': '''quantizer.layers.*.codebook.embed''',
'''quantizer.vq.layers.*._codebook.embed_avg''': '''quantizer.layers.*.codebook.embed_avg''',
}
__A : str = {
'''encoder.model.0.conv.conv''': '''encoder.layers.0.conv''',
'''encoder.model.1.block.1.conv.conv''': '''encoder.layers.1.block.1.conv''',
'''encoder.model.1.block.3.conv.conv''': '''encoder.layers.1.block.3.conv''',
'''encoder.model.1.shortcut.conv.conv''': '''encoder.layers.1.shortcut.conv''',
'''encoder.model.3.conv.conv''': '''encoder.layers.3.conv''',
'''encoder.model.4.block.1.conv.conv''': '''encoder.layers.4.block.1.conv''',
'''encoder.model.4.block.3.conv.conv''': '''encoder.layers.4.block.3.conv''',
'''encoder.model.4.shortcut.conv.conv''': '''encoder.layers.4.shortcut.conv''',
'''encoder.model.6.conv.conv''': '''encoder.layers.6.conv''',
'''encoder.model.7.block.1.conv.conv''': '''encoder.layers.7.block.1.conv''',
'''encoder.model.7.block.3.conv.conv''': '''encoder.layers.7.block.3.conv''',
'''encoder.model.7.shortcut.conv.conv''': '''encoder.layers.7.shortcut.conv''',
'''encoder.model.9.conv.conv''': '''encoder.layers.9.conv''',
'''encoder.model.10.block.1.conv.conv''': '''encoder.layers.10.block.1.conv''',
'''encoder.model.10.block.3.conv.conv''': '''encoder.layers.10.block.3.conv''',
'''encoder.model.10.shortcut.conv.conv''': '''encoder.layers.10.shortcut.conv''',
'''encoder.model.12.conv.conv''': '''encoder.layers.12.conv''',
'''encoder.model.13.lstm''': '''encoder.layers.13.lstm''',
'''encoder.model.15.conv.conv''': '''encoder.layers.15.conv''',
}
__A : Dict = {
'''encoder.model.0.conv.norm''': '''encoder.layers.0.norm''',
'''encoder.model.1.block.1.conv.norm''': '''encoder.layers.1.block.1.norm''',
'''encoder.model.1.block.3.conv.norm''': '''encoder.layers.1.block.3.norm''',
'''encoder.model.1.shortcut.conv.norm''': '''encoder.layers.1.shortcut.norm''',
'''encoder.model.3.conv.norm''': '''encoder.layers.3.norm''',
'''encoder.model.4.block.1.conv.norm''': '''encoder.layers.4.block.1.norm''',
'''encoder.model.4.block.3.conv.norm''': '''encoder.layers.4.block.3.norm''',
'''encoder.model.4.shortcut.conv.norm''': '''encoder.layers.4.shortcut.norm''',
'''encoder.model.6.conv.norm''': '''encoder.layers.6.norm''',
'''encoder.model.7.block.1.conv.norm''': '''encoder.layers.7.block.1.norm''',
'''encoder.model.7.block.3.conv.norm''': '''encoder.layers.7.block.3.norm''',
'''encoder.model.7.shortcut.conv.norm''': '''encoder.layers.7.shortcut.norm''',
'''encoder.model.9.conv.norm''': '''encoder.layers.9.norm''',
'''encoder.model.10.block.1.conv.norm''': '''encoder.layers.10.block.1.norm''',
'''encoder.model.10.block.3.conv.norm''': '''encoder.layers.10.block.3.norm''',
'''encoder.model.10.shortcut.conv.norm''': '''encoder.layers.10.shortcut.norm''',
'''encoder.model.12.conv.norm''': '''encoder.layers.12.norm''',
'''encoder.model.15.conv.norm''': '''encoder.layers.15.norm''',
}
__A : int = {
'''decoder.model.0.conv.conv''': '''decoder.layers.0.conv''',
'''decoder.model.1.lstm''': '''decoder.layers.1.lstm''',
'''decoder.model.3.convtr.convtr''': '''decoder.layers.3.conv''',
'''decoder.model.4.block.1.conv.conv''': '''decoder.layers.4.block.1.conv''',
'''decoder.model.4.block.3.conv.conv''': '''decoder.layers.4.block.3.conv''',
'''decoder.model.4.shortcut.conv.conv''': '''decoder.layers.4.shortcut.conv''',
'''decoder.model.6.convtr.convtr''': '''decoder.layers.6.conv''',
'''decoder.model.7.block.1.conv.conv''': '''decoder.layers.7.block.1.conv''',
'''decoder.model.7.block.3.conv.conv''': '''decoder.layers.7.block.3.conv''',
'''decoder.model.7.shortcut.conv.conv''': '''decoder.layers.7.shortcut.conv''',
'''decoder.model.9.convtr.convtr''': '''decoder.layers.9.conv''',
'''decoder.model.10.block.1.conv.conv''': '''decoder.layers.10.block.1.conv''',
'''decoder.model.10.block.3.conv.conv''': '''decoder.layers.10.block.3.conv''',
'''decoder.model.10.shortcut.conv.conv''': '''decoder.layers.10.shortcut.conv''',
'''decoder.model.12.convtr.convtr''': '''decoder.layers.12.conv''',
'''decoder.model.13.block.1.conv.conv''': '''decoder.layers.13.block.1.conv''',
'''decoder.model.13.block.3.conv.conv''': '''decoder.layers.13.block.3.conv''',
'''decoder.model.13.shortcut.conv.conv''': '''decoder.layers.13.shortcut.conv''',
'''decoder.model.15.conv.conv''': '''decoder.layers.15.conv''',
}
__A : Optional[Any] = {
'''decoder.model.0.conv.norm''': '''decoder.layers.0.norm''',
'''decoder.model.3.convtr.norm''': '''decoder.layers.3.norm''',
'''decoder.model.4.block.1.conv.norm''': '''decoder.layers.4.block.1.norm''',
'''decoder.model.4.block.3.conv.norm''': '''decoder.layers.4.block.3.norm''',
'''decoder.model.4.shortcut.conv.norm''': '''decoder.layers.4.shortcut.norm''',
'''decoder.model.6.convtr.norm''': '''decoder.layers.6.norm''',
'''decoder.model.7.block.1.conv.norm''': '''decoder.layers.7.block.1.norm''',
'''decoder.model.7.block.3.conv.norm''': '''decoder.layers.7.block.3.norm''',
'''decoder.model.7.shortcut.conv.norm''': '''decoder.layers.7.shortcut.norm''',
'''decoder.model.9.convtr.norm''': '''decoder.layers.9.norm''',
'''decoder.model.10.block.1.conv.norm''': '''decoder.layers.10.block.1.norm''',
'''decoder.model.10.block.3.conv.norm''': '''decoder.layers.10.block.3.norm''',
'''decoder.model.10.shortcut.conv.norm''': '''decoder.layers.10.shortcut.norm''',
'''decoder.model.12.convtr.norm''': '''decoder.layers.12.norm''',
'''decoder.model.13.block.1.conv.norm''': '''decoder.layers.13.block.1.norm''',
'''decoder.model.13.block.3.conv.norm''': '''decoder.layers.13.block.3.norm''',
'''decoder.model.13.shortcut.conv.norm''': '''decoder.layers.13.shortcut.norm''',
'''decoder.model.15.conv.norm''': '''decoder.layers.15.norm''',
}
__A : Tuple = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_DECODER,
}
__A : Optional[int] = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_ENCODER_48K,
**MAPPING_DECODER,
**MAPPING_DECODER_48K,
}
__A : Optional[int] = []
__A : Dict = []
def lowercase ( __snake_case : Union[str, Any] , __snake_case : Optional[int] , __snake_case : List[Any] , __snake_case : str , __snake_case : Tuple ):
for attribute in key.split('''.''' ):
lowercase_ : Union[str, Any] = getattr(__snake_case , __snake_case )
if weight_type is not None:
lowercase_ : Any = getattr(__snake_case , __snake_case ).shape
else:
lowercase_ : Union[str, Any] = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
F''' {value.shape} for {full_name}''' )
if weight_type == "weight":
lowercase_ : Tuple = value
elif weight_type == "weight_g":
lowercase_ : Optional[Any] = value
elif weight_type == "weight_v":
lowercase_ : Tuple = value
elif weight_type == "bias":
lowercase_ : Optional[int] = value
elif weight_type == "running_mean":
lowercase_ : Any = value
elif weight_type == "running_var":
lowercase_ : Optional[int] = value
elif weight_type == "num_batches_tracked":
lowercase_ : Dict = value
elif weight_type == "weight_ih_l0":
lowercase_ : Optional[int] = value
elif weight_type == "weight_hh_l0":
lowercase_ : Tuple = value
elif weight_type == "bias_ih_l0":
lowercase_ : Optional[int] = value
elif weight_type == "bias_hh_l0":
lowercase_ : Any = value
elif weight_type == "weight_ih_l1":
lowercase_ : Optional[int] = value
elif weight_type == "weight_hh_l1":
lowercase_ : str = value
elif weight_type == "bias_ih_l1":
lowercase_ : Optional[int] = value
elif weight_type == "bias_hh_l1":
lowercase_ : str = value
else:
lowercase_ : List[str] = value
logger.info(F'''{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.''' )
def lowercase ( __snake_case : int , __snake_case : Dict ):
for key in ignore_keys:
if key.endswith('''.*''' ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
lowercase_ , lowercase_ : Union[str, Any] = key.split('''.*.''' )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def lowercase ( __snake_case : Dict , __snake_case : Union[str, Any] , __snake_case : Tuple ):
lowercase_ : Dict = []
if model_name == "encodec_24khz" or "encodec_32khz":
lowercase_ : Dict = MAPPING_24K
elif model_name == "encodec_48khz":
lowercase_ : Union[str, Any] = MAPPING_48K
else:
raise ValueError(F'''Unsupported model: {model_name}''' )
for name, value in orig_dict.items():
if should_ignore(__snake_case , __snake_case ):
logger.info(F'''{name} was ignored''' )
continue
lowercase_ : List[str] = False
for key, mapped_key in MAPPING.items():
if "*" in key:
lowercase_ , lowercase_ : Dict = key.split('''.*.''' )
if prefix in name and suffix in name:
lowercase_ : List[str] = suffix
if key in name:
# HACK otherwise .embed gets initialized with .embed_avg too
if key.endswith('''embed''' ) and name.endswith('''embed_avg''' ):
continue
lowercase_ : Union[str, Any] = True
if "*" in mapped_key:
lowercase_ : str = name.split(__snake_case )[0].split('''.''' )[-2]
lowercase_ : Any = mapped_key.replace('''*''' , __snake_case )
if "weight_g" in name:
lowercase_ : Any = '''weight_g'''
elif "weight_v" in name:
lowercase_ : Optional[int] = '''weight_v'''
elif "weight_ih_l0" in name:
lowercase_ : int = '''weight_ih_l0'''
elif "weight_hh_l0" in name:
lowercase_ : int = '''weight_hh_l0'''
elif "bias_ih_l0" in name:
lowercase_ : Tuple = '''bias_ih_l0'''
elif "bias_hh_l0" in name:
lowercase_ : Union[str, Any] = '''bias_hh_l0'''
elif "weight_ih_l1" in name:
lowercase_ : List[str] = '''weight_ih_l1'''
elif "weight_hh_l1" in name:
lowercase_ : int = '''weight_hh_l1'''
elif "bias_ih_l1" in name:
lowercase_ : Tuple = '''bias_ih_l1'''
elif "bias_hh_l1" in name:
lowercase_ : Optional[Any] = '''bias_hh_l1'''
elif "bias" in name:
lowercase_ : List[Any] = '''bias'''
elif "weight" in name:
lowercase_ : Dict = '''weight'''
elif "running_mean" in name:
lowercase_ : List[str] = '''running_mean'''
elif "running_var" in name:
lowercase_ : Tuple = '''running_var'''
elif "num_batches_tracked" in name:
lowercase_ : str = '''num_batches_tracked'''
else:
lowercase_ : int = None
set_recursively(__snake_case , __snake_case , __snake_case , __snake_case , __snake_case )
continue
if not is_used:
unused_weights.append(__snake_case )
logger.warning(F'''Unused weights: {unused_weights}''' )
@torch.no_grad()
def lowercase ( __snake_case : List[str] , __snake_case : Dict , __snake_case : List[Any] , __snake_case : Union[str, Any]=None , __snake_case : Optional[int]=None , ):
if config_path is not None:
lowercase_ : List[Any] = EncodecConfig.from_pretrained(__snake_case )
else:
lowercase_ : Optional[Any] = EncodecConfig()
if model_name == "encodec_24khz":
pass # config is already correct
elif model_name == "encodec_32khz":
lowercase_ : Any = [8, 5, 4, 4]
lowercase_ : List[str] = [2.2]
lowercase_ : Optional[Any] = 6_4
lowercase_ : int = 3_2_0_0_0
lowercase_ : Union[str, Any] = 2_0_4_8
lowercase_ : Union[str, Any] = False
lowercase_ : int = False
lowercase_ : str = False
elif model_name == "encodec_48khz":
lowercase_ : str = [8, 5, 4, 2]
lowercase_ : str = [3.0, 6.0, 12.0, 24.0]
lowercase_ : str = 4_8_0_0_0
lowercase_ : Optional[Any] = 2
lowercase_ : List[str] = False
lowercase_ : int = '''time_group_norm'''
lowercase_ : List[str] = True
lowercase_ : Dict = 1.0
lowercase_ : Union[str, Any] = 0.01
else:
raise ValueError(F'''Unknown model name: {model_name}''' )
lowercase_ : Optional[Any] = EncodecModel(__snake_case )
lowercase_ : Dict = EncodecFeatureExtractor(
feature_size=config.audio_channels , sampling_rate=config.sampling_rate , chunk_length_s=config.chunk_length_s , overlap=config.overlap , )
feature_extractor.save_pretrained(__snake_case )
lowercase_ : str = torch.load(__snake_case )
if "best_state" in original_checkpoint:
# we might have a training state saved, in which case discard the yaml results and just retain the weights
lowercase_ : List[str] = original_checkpoint['''best_state''']
recursively_load_weights(__snake_case , __snake_case , __snake_case )
model.save_pretrained(__snake_case )
if repo_id:
print('''Pushing to the hub...''' )
feature_extractor.push_to_hub(__snake_case )
model.push_to_hub(__snake_case )
if __name__ == "__main__":
__A : Tuple = argparse.ArgumentParser()
parser.add_argument(
'''--model''',
default='''encodec_24khz''',
type=str,
help='''The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.''',
)
parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to original checkpoint''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
)
__A : Optional[int] = parser.parse_args()
convert_checkpoint(
args.model,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 33 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def lowerCAmelCase_ ( A_ ,A_ ,A_):
UpperCamelCase__: List[Any] = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - это здорово, не так ли?",
"de": "Maschinelles Lernen ist großartig, oder?",
}
# BLUE scores as follows:
# "pair": [fairseq, transformers]
UpperCamelCase__: str = {
"ru-en": ["[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)", "39.20"],
"en-ru": ["[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)", "33.47"],
"en-de": ["[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)", "42.83"],
"de-en": ["[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)", "41.35"],
}
UpperCamelCase__: str = F"{src_lang}-{tgt_lang}"
UpperCamelCase__: Optional[Any] = F"\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"facebook/wmt19-{src_lang}-{tgt_lang}\"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = \"{texts[src_lang]}\"\ninput_ids = tokenizer.encode(input, return_tensors=\"pt\")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n- The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair | fairseq | transformers\n-------|---------|----------\n{pair} | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=15\nmkdir -p $DATA_DIR\nsacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS\n```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\n\n\n### BibTeX entry and citation info\n\n```bibtex\n@inproceedings{{...,\n year={{2020}},\n title={{Facebook FAIR's WMT19 News Translation Task Submission}},\n author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n"
os.makedirs(A_ ,exist_ok=A_)
UpperCamelCase__: Union[str, Any] = os.path.join(A_ ,"README.md")
print(F"Generating {path}")
with open(A_ ,"w" ,encoding="utf-8") as f:
f.write(A_)
# make sure we are under the root of the project
A__: Optional[Any] = Path(__file__).resolve().parent.parent.parent
A__: Optional[int] = repo_dir / '''model_cards'''
for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
A__ , A__ , A__: Optional[Any] = model_name.split('''-''')
A__: List[str] = model_cards_dir / '''facebook''' / model_name
write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)
| 149 | 0 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
"Salesforce/instruct-blip-flan-t5": "https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json",
}
class lowercase_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase_ = '''instructblip_vision_model'''
def __init__( self : Optional[int] , __lowerCamelCase : Tuple=1_4_0_8 , __lowerCamelCase : Optional[int]=6_1_4_4 , __lowerCamelCase : str=3_9 , __lowerCamelCase : List[Any]=1_6 , __lowerCamelCase : Tuple=2_2_4 , __lowerCamelCase : List[str]=1_4 , __lowerCamelCase : List[Any]="gelu" , __lowerCamelCase : str=1e-6 , __lowerCamelCase : Any=0.0 , __lowerCamelCase : List[Any]=1e-10 , __lowerCamelCase : Any=True , **__lowerCamelCase : List[str] , ):
"""simple docstring"""
super().__init__(**A__ )
_SCREAMING_SNAKE_CASE = hidden_size
_SCREAMING_SNAKE_CASE = intermediate_size
_SCREAMING_SNAKE_CASE = num_hidden_layers
_SCREAMING_SNAKE_CASE = num_attention_heads
_SCREAMING_SNAKE_CASE = patch_size
_SCREAMING_SNAKE_CASE = image_size
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = attention_dropout
_SCREAMING_SNAKE_CASE = layer_norm_eps
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = qkv_bias
@classmethod
def lowerCAmelCase_ ( cls : str , __lowerCamelCase : Tuple , **__lowerCamelCase : List[str] ):
"""simple docstring"""
cls._set_token_in_kwargs(A__ )
_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE = cls.get_config_dict(A__ , **A__ )
# get the vision config dict if we are loading from InstructBlipConfig
if config_dict.get("model_type" ) == "instructblip":
_SCREAMING_SNAKE_CASE = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(A__ , **A__ )
class lowercase_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase_ = '''instructblip_qformer'''
def __init__( self : str , __lowerCamelCase : Optional[Any]=3_0_5_2_2 , __lowerCamelCase : List[Any]=7_6_8 , __lowerCamelCase : Optional[Any]=1_2 , __lowerCamelCase : Tuple=1_2 , __lowerCamelCase : Tuple=3_0_7_2 , __lowerCamelCase : Dict="gelu" , __lowerCamelCase : List[Any]=0.1 , __lowerCamelCase : Dict=0.1 , __lowerCamelCase : int=5_1_2 , __lowerCamelCase : Union[str, Any]=0.0_2 , __lowerCamelCase : str=1e-12 , __lowerCamelCase : Dict=0 , __lowerCamelCase : int="absolute" , __lowerCamelCase : Tuple=2 , __lowerCamelCase : Union[str, Any]=1_4_0_8 , **__lowerCamelCase : Any , ):
"""simple docstring"""
super().__init__(pad_token_id=A__ , **A__ )
_SCREAMING_SNAKE_CASE = vocab_size
_SCREAMING_SNAKE_CASE = hidden_size
_SCREAMING_SNAKE_CASE = num_hidden_layers
_SCREAMING_SNAKE_CASE = num_attention_heads
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = intermediate_size
_SCREAMING_SNAKE_CASE = hidden_dropout_prob
_SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
_SCREAMING_SNAKE_CASE = max_position_embeddings
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = layer_norm_eps
_SCREAMING_SNAKE_CASE = position_embedding_type
_SCREAMING_SNAKE_CASE = cross_attention_frequency
_SCREAMING_SNAKE_CASE = encoder_hidden_size
@classmethod
def lowerCAmelCase_ ( cls : Dict , __lowerCamelCase : Union[str, Any] , **__lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
cls._set_token_in_kwargs(A__ )
_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE = cls.get_config_dict(A__ , **A__ )
# get the qformer config dict if we are loading from InstructBlipConfig
if config_dict.get("model_type" ) == "instructblip":
_SCREAMING_SNAKE_CASE = config_dict["qformer_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(A__ , **A__ )
class lowercase_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase_ = '''instructblip'''
lowerCamelCase_ = True
def __init__( self : int , __lowerCamelCase : Optional[int]=None , __lowerCamelCase : List[str]=None , __lowerCamelCase : List[str]=None , __lowerCamelCase : int=3_2 , **__lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
super().__init__(**A__ )
if vision_config is None:
_SCREAMING_SNAKE_CASE = {}
logger.info("vision_config is None. initializing the InstructBlipVisionConfig with default values." )
if qformer_config is None:
_SCREAMING_SNAKE_CASE = {}
logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values." )
if text_config is None:
_SCREAMING_SNAKE_CASE = {}
logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`)." )
_SCREAMING_SNAKE_CASE = InstructBlipVisionConfig(**A__ )
_SCREAMING_SNAKE_CASE = InstructBlipQFormerConfig(**A__ )
_SCREAMING_SNAKE_CASE = text_config["model_type"] if "model_type" in text_config else "opt"
_SCREAMING_SNAKE_CASE = CONFIG_MAPPING[text_model_type](**A__ )
_SCREAMING_SNAKE_CASE = self.text_config.tie_word_embeddings
_SCREAMING_SNAKE_CASE = self.text_config.is_encoder_decoder
_SCREAMING_SNAKE_CASE = num_query_tokens
_SCREAMING_SNAKE_CASE = self.vision_config.hidden_size
_SCREAMING_SNAKE_CASE = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
_SCREAMING_SNAKE_CASE = 1.0
_SCREAMING_SNAKE_CASE = 0.0_2
@classmethod
def lowerCAmelCase_ ( cls : Tuple , __lowerCamelCase : Tuple , __lowerCamelCase : List[Any] , __lowerCamelCase : Any , **__lowerCamelCase : Any , ):
"""simple docstring"""
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **A__ , )
def lowerCAmelCase_ ( self : Dict ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = copy.deepcopy(self.__dict__ )
_SCREAMING_SNAKE_CASE = self.vision_config.to_dict()
_SCREAMING_SNAKE_CASE = self.qformer_config.to_dict()
_SCREAMING_SNAKE_CASE = self.text_config.to_dict()
_SCREAMING_SNAKE_CASE = self.__class__.model_type
return output
| 359 |
'''simple docstring'''
from collections.abc import Sequence
def SCREAMING_SNAKE_CASE_ ( __A : Sequence[int] | None = None ) -> int:
if nums is None or not nums:
raise ValueError("Input sequence should not be empty" )
_SCREAMING_SNAKE_CASE = nums[0]
for i in range(1 , len(__A ) ):
_SCREAMING_SNAKE_CASE = nums[i]
_SCREAMING_SNAKE_CASE = max(__A , ans + num , __A )
return ans
if __name__ == "__main__":
import doctest
doctest.testmod()
# Try on a sample input from the user
lowerCamelCase_ = int(input('Enter number of elements : ').strip())
lowerCamelCase_ = list(map(int, input('\nEnter the numbers : ').strip().split()))[:n]
print(max_subsequence_sum(array))
| 111 | 0 |
import random
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Optional[Any] = a[left_index]
__UpperCamelCase :Any = left_index + 1
for j in range(left_index + 1 , SCREAMING_SNAKE_CASE ):
if a[j] < pivot:
__UpperCamelCase , __UpperCamelCase :str = a[i], a[j]
i += 1
__UpperCamelCase , __UpperCamelCase :Optional[int] = a[i - 1], a[left_index]
return i - 1
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if left < right:
__UpperCamelCase :int = random.randint(SCREAMING_SNAKE_CASE , right - 1 )
__UpperCamelCase , __UpperCamelCase :List[str] = (
a[left],
a[pivot],
) # switches the pivot with the left most bound
__UpperCamelCase :Dict = partition(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
quick_sort_random(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) # recursive quicksort to the left of the pivot point
quick_sort_random(
SCREAMING_SNAKE_CASE , pivot_index + 1 , SCREAMING_SNAKE_CASE ) # recursive quicksort to the right of the pivot point
def lowerCamelCase ( ):
'''simple docstring'''
__UpperCamelCase :Tuple = input('''Enter numbers separated by a comma:\n''' ).strip()
__UpperCamelCase :Union[str, Any] = [int(SCREAMING_SNAKE_CASE ) for item in user_input.split(''',''' )]
quick_sort_random(SCREAMING_SNAKE_CASE , 0 , len(SCREAMING_SNAKE_CASE ) )
print(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 43 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
a_ = logging.get_logger(__name__)
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase=None , __UpperCAmelCase=None ):
'''simple docstring'''
if not conversation_id:
__lowerCamelCase = uuid.uuida()
if past_user_inputs is None:
__lowerCamelCase = []
if generated_responses is None:
__lowerCamelCase = []
__lowerCamelCase = conversation_id
__lowerCamelCase = past_user_inputs
__lowerCamelCase = generated_responses
__lowerCamelCase = text
def __eq__( self , __UpperCAmelCase ):
'''simple docstring'''
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
return False
if self.uuid == other.uuid:
return True
return (
self.new_user_input == other.new_user_input
and self.past_user_inputs == other.past_user_inputs
and self.generated_responses == other.generated_responses
)
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = False ):
'''simple docstring'''
if self.new_user_input:
if overwrite:
logger.warning(
F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten """
F"""with: \"{text}\".""" )
__lowerCamelCase = text
else:
logger.warning(
F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" new input """
F"""ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input""" )
else:
__lowerCamelCase = text
def lowerCamelCase ( self ):
'''simple docstring'''
if self.new_user_input:
self.past_user_inputs.append(self.new_user_input )
__lowerCamelCase = None
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
self.generated_responses.append(__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
for user_input, generated_response in zip(self.past_user_inputs , self.generated_responses ):
yield True, user_input
yield False, generated_response
if self.new_user_input:
yield True, self.new_user_input
def __repr__( self ):
'''simple docstring'''
__lowerCamelCase = F"""Conversation id: {self.uuid} \n"""
for is_user, text in self.iter_texts():
__lowerCamelCase = '''user''' if is_user else '''bot'''
output += F"""{name} >> {text} \n"""
return output
@add_end_docstrings(
lowerCAmelCase__ , r"""
min_length_for_response (`int`, *optional*, defaults to 32):
The minimum length (in number of tokens) for a response.
minimum_tokens (`int`, *optional*, defaults to 10):
The minimum length of tokens to leave for a response.
""" , )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase )
if self.tokenizer.pad_token_id is None:
__lowerCamelCase = self.tokenizer.eos_token
def lowerCamelCase ( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = {}
__lowerCamelCase = {}
__lowerCamelCase = {}
if min_length_for_response is not None:
__lowerCamelCase = min_length_for_response
if minimum_tokens is not None:
__lowerCamelCase = minimum_tokens
if "max_length" in generate_kwargs:
__lowerCamelCase = generate_kwargs['''max_length''']
# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
if clean_up_tokenization_spaces is not None:
__lowerCamelCase = clean_up_tokenization_spaces
if generate_kwargs:
forward_params.update(__UpperCAmelCase )
return preprocess_params, forward_params, postprocess_params
def __call__( self , __UpperCAmelCase , __UpperCAmelCase=0 , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = super().__call__(__UpperCAmelCase , num_workers=__UpperCAmelCase , **__UpperCAmelCase )
if isinstance(__UpperCAmelCase , __UpperCAmelCase ) and len(__UpperCAmelCase ) == 1:
return outputs[0]
return outputs
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=32 ):
'''simple docstring'''
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise ValueError('''ConversationalPipeline, expects Conversation as inputs''' )
if conversation.new_user_input is None:
raise ValueError(
F"""Conversation with UUID {type(conversation.uuid )} does not contain new user input to process. """
'''Add user inputs with the conversation\'s `add_user_input` method''' )
if hasattr(self.tokenizer , '''_build_conversation_input_ids''' ):
__lowerCamelCase = self.tokenizer._build_conversation_input_ids(__UpperCAmelCase )
else:
# If the tokenizer cannot handle conversations, we default to only the old version
__lowerCamelCase = self._legacy_parse_and_tokenize(__UpperCAmelCase )
if self.framework == "pt":
__lowerCamelCase = torch.LongTensor([input_ids] )
elif self.framework == "tf":
__lowerCamelCase = tf.constant([input_ids] )
return {"input_ids": input_ids, "conversation": conversation}
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=10 , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = generate_kwargs.get('''max_length''' , self.model.config.max_length )
__lowerCamelCase = model_inputs['''input_ids'''].shape[1]
if max_length - minimum_tokens < n:
logger.warning(F"""Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})""" )
__lowerCamelCase = max_length - minimum_tokens
__lowerCamelCase = model_inputs['''input_ids'''][:, -trim:]
if "attention_mask" in model_inputs:
__lowerCamelCase = model_inputs['''attention_mask'''][:, -trim:]
__lowerCamelCase = model_inputs.pop('''conversation''' )
__lowerCamelCase = max_length
__lowerCamelCase = self.model.generate(**__UpperCAmelCase , **__UpperCAmelCase )
if self.model.config.is_encoder_decoder:
__lowerCamelCase = 1
else:
__lowerCamelCase = n
return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=True ):
'''simple docstring'''
__lowerCamelCase = model_outputs['''output_ids''']
__lowerCamelCase = self.tokenizer.decode(
output_ids[0] , skip_special_tokens=__UpperCAmelCase , clean_up_tokenization_spaces=__UpperCAmelCase , )
__lowerCamelCase = model_outputs['''conversation''']
conversation.mark_processed()
conversation.append_response(__UpperCAmelCase )
return conversation
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.tokenizer.eos_token_id
__lowerCamelCase = []
for is_user, text in conversation.iter_texts():
if eos_token_id is not None:
input_ids.extend(self.tokenizer.encode(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase ) + [eos_token_id] )
else:
input_ids.extend(self.tokenizer.encode(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase ) )
if len(__UpperCAmelCase ) > self.tokenizer.model_max_length:
__lowerCamelCase = input_ids[-self.tokenizer.model_max_length :]
return input_ids
| 330 | 0 |
def UpperCamelCase ( __magic_name__ : int = 1000 ) -> int:
"""simple docstring"""
lowercase__ = -1
lowercase__ = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
lowercase__ = (n * n - 2 * a * n) // (2 * n - 2 * a)
lowercase__ = n - a - b
if c * c == (a * a + b * b):
lowercase__ = a * b * c
if candidate >= product:
lowercase__ = candidate
return product
if __name__ == "__main__":
print(F'{solution() = }')
| 146 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Optional[Any] = logging.get_logger(__name__)
A : Tuple = {
'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json',
'YituTech/conv-bert-medium-small': (
'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'
),
'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json',
# See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''convbert'''
def __init__(self : str , _UpperCAmelCase : Union[str, Any]=3_0522 , _UpperCAmelCase : Dict=768 , _UpperCAmelCase : Optional[int]=12 , _UpperCAmelCase : List[str]=12 , _UpperCAmelCase : str=3072 , _UpperCAmelCase : Tuple="gelu" , _UpperCAmelCase : Optional[int]=0.1 , _UpperCAmelCase : int=0.1 , _UpperCAmelCase : str=512 , _UpperCAmelCase : Dict=2 , _UpperCAmelCase : int=0.02 , _UpperCAmelCase : Tuple=1E-1_2 , _UpperCAmelCase : Dict=1 , _UpperCAmelCase : Optional[Any]=0 , _UpperCAmelCase : List[str]=2 , _UpperCAmelCase : str=768 , _UpperCAmelCase : List[Any]=2 , _UpperCAmelCase : Optional[Any]=9 , _UpperCAmelCase : List[Any]=1 , _UpperCAmelCase : Union[str, Any]=None , **_UpperCAmelCase : List[Any] , ) -> List[str]:
"""simple docstring"""
super().__init__(
pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase , )
lowercase__ = vocab_size
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = intermediate_size
lowercase__ = hidden_act
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = max_position_embeddings
lowercase__ = type_vocab_size
lowercase__ = initializer_range
lowercase__ = layer_norm_eps
lowercase__ = embedding_size
lowercase__ = head_ratio
lowercase__ = conv_kernel_size
lowercase__ = num_groups
lowercase__ = classifier_dropout
class A ( UpperCAmelCase__ ):
'''simple docstring'''
@property
def lowerCamelCase__ (self : Any ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
lowercase__ = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
lowercase__ = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""token_type_ids""", dynamic_axis),
] )
| 146 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
_lowercase : Optional[Any] = logging.get_logger(__name__)
@dataclass
class _UpperCAmelCase ( _lowerCAmelCase ):
a__ : int = [
"no_inference",
"no_cuda",
"no_tpu",
"no_speed",
"no_memory",
"no_env_print",
"no_multi_process",
]
def __init__( self : Optional[Any] , **_lowercase : Dict ):
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
__UpperCAmelCase = deprecated_arg[3:]
__UpperCAmelCase = not kwargs.pop(_lowercase )
logger.warning(
F'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
F''' {positive_arg}={kwargs[positive_arg]}''' )
__UpperCAmelCase = kwargs.pop('''tpu_name''' , self.tpu_name )
__UpperCAmelCase = kwargs.pop('''device_idx''' , self.device_idx )
__UpperCAmelCase = kwargs.pop('''eager_mode''' , self.eager_mode )
__UpperCAmelCase = kwargs.pop('''use_xla''' , self.use_xla )
super().__init__(**_lowercase )
a__ : str = field(
default=_lowerCAmelCase , metadata={"help": "Name of TPU"} , )
a__ : int = field(
default=0 , metadata={"help": "CPU / GPU device index. Defaults to 0."} , )
a__ : bool = field(default=_lowerCAmelCase , metadata={"help": "Benchmark models in eager model."} )
a__ : bool = field(
default=_lowerCAmelCase , metadata={
"help": "Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."
} , )
@cached_property
def a ( self : List[str] ):
requires_backends(self , ['''tf'''] )
__UpperCAmelCase = None
if self.tpu:
try:
if self.tpu_name:
__UpperCAmelCase = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
else:
__UpperCAmelCase = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
__UpperCAmelCase = None
return tpu
@cached_property
def a ( self : Optional[int] ):
requires_backends(self , ['''tf'''] )
if self.is_tpu:
tf.config.experimental_connect_to_cluster(self._setup_tpu )
tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )
__UpperCAmelCase = tf.distribute.TPUStrategy(self._setup_tpu )
else:
# currently no multi gpu is allowed
if self.is_gpu:
# TODO: Currently only single GPU is supported
tf.config.set_visible_devices(self.gpu_list[self.device_idx] , '''GPU''' )
__UpperCAmelCase = tf.distribute.OneDeviceStrategy(device=F'''/gpu:{self.device_idx}''' )
else:
tf.config.set_visible_devices([] , '''GPU''' ) # disable GPU
__UpperCAmelCase = tf.distribute.OneDeviceStrategy(device=F'''/cpu:{self.device_idx}''' )
return strategy
@property
def a ( self : Tuple ):
requires_backends(self , ['''tf'''] )
return self._setup_tpu is not None
@property
def a ( self : List[str] ):
requires_backends(self , ['''tf'''] )
return self._setup_strategy
@property
def a ( self : Union[str, Any] ):
requires_backends(self , ['''tf'''] )
return tf.config.list_physical_devices('''GPU''' )
@property
def a ( self : str ):
requires_backends(self , ['''tf'''] )
if self.cuda:
return len(self.gpu_list )
return 0
@property
def a ( self : Union[str, Any] ):
return self.n_gpu > 0
| 332 |
"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class _UpperCAmelCase ( unittest.TestCase ):
def a ( self : Dict , _lowercase : Union[str, Any] ):
for model_result in results.values():
for batch_size, sequence_length in zip(model_result['''bs'''] , model_result['''ss'''] ):
__UpperCAmelCase = model_result['''result'''][batch_size][sequence_length]
self.assertIsNotNone(_lowercase )
def a ( self : str ):
__UpperCAmelCase = '''sshleifer/tiny-gpt2'''
__UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , )
__UpperCAmelCase = PyTorchBenchmark(_lowercase )
__UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a ( self : List[str] ):
__UpperCAmelCase = '''sgugger/tiny-distilbert-classification'''
__UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , only_pretrain_model=_lowercase , )
__UpperCAmelCase = PyTorchBenchmark(_lowercase )
__UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a ( self : str ):
__UpperCAmelCase = '''sshleifer/tiny-gpt2'''
__UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , torchscript=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , )
__UpperCAmelCase = PyTorchBenchmark(_lowercase )
__UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(torch_device == '''cpu''' , '''Cant do half precision''' )
def a ( self : Optional[Any] ):
__UpperCAmelCase = '''sshleifer/tiny-gpt2'''
__UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , fpaa=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , )
__UpperCAmelCase = PyTorchBenchmark(_lowercase )
__UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a ( self : int ):
__UpperCAmelCase = '''sshleifer/tiny-gpt2'''
__UpperCAmelCase = AutoConfig.from_pretrained(_lowercase )
# set architectures equal to `None`
__UpperCAmelCase = None
__UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , )
__UpperCAmelCase = PyTorchBenchmark(_lowercase , configs=[config] )
__UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a ( self : Tuple ):
__UpperCAmelCase = '''sshleifer/tiny-gpt2'''
__UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , )
__UpperCAmelCase = PyTorchBenchmark(_lowercase )
__UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
@unittest.skipIf(torch_device == '''cpu''' , '''Can\'t do half precision''' )
def a ( self : Optional[Any] ):
__UpperCAmelCase = '''sshleifer/tiny-gpt2'''
__UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , fpaa=_lowercase , multi_process=_lowercase , )
__UpperCAmelCase = PyTorchBenchmark(_lowercase )
__UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def a ( self : Any ):
__UpperCAmelCase = '''sshleifer/tiny-gpt2'''
__UpperCAmelCase = AutoConfig.from_pretrained(_lowercase )
__UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , )
__UpperCAmelCase = PyTorchBenchmark(_lowercase , configs=[config] )
__UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a ( self : str ):
__UpperCAmelCase = '''sshleifer/tinier_bart'''
__UpperCAmelCase = AutoConfig.from_pretrained(_lowercase )
__UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , )
__UpperCAmelCase = PyTorchBenchmark(_lowercase , configs=[config] )
__UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a ( self : Union[str, Any] ):
__UpperCAmelCase = '''sshleifer/tiny-gpt2'''
__UpperCAmelCase = AutoConfig.from_pretrained(_lowercase )
__UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , )
__UpperCAmelCase = PyTorchBenchmark(_lowercase , configs=[config] )
__UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def a ( self : int ):
__UpperCAmelCase = '''sshleifer/tinier_bart'''
__UpperCAmelCase = AutoConfig.from_pretrained(_lowercase )
__UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , )
__UpperCAmelCase = PyTorchBenchmark(_lowercase , configs=[config] )
__UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def a ( self : Optional[Any] ):
__UpperCAmelCase = '''sshleifer/tiny-gpt2'''
with tempfile.TemporaryDirectory() as tmp_dir:
__UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , save_to_csv=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(_lowercase , '''inf_time.csv''' ) , train_memory_csv_file=os.path.join(_lowercase , '''train_mem.csv''' ) , inference_memory_csv_file=os.path.join(_lowercase , '''inf_mem.csv''' ) , train_time_csv_file=os.path.join(_lowercase , '''train_time.csv''' ) , env_info_csv_file=os.path.join(_lowercase , '''env.csv''' ) , multi_process=_lowercase , )
__UpperCAmelCase = PyTorchBenchmark(_lowercase )
benchmark.run()
self.assertTrue(Path(os.path.join(_lowercase , '''inf_time.csv''' ) ).exists() )
self.assertTrue(Path(os.path.join(_lowercase , '''train_time.csv''' ) ).exists() )
self.assertTrue(Path(os.path.join(_lowercase , '''inf_mem.csv''' ) ).exists() )
self.assertTrue(Path(os.path.join(_lowercase , '''train_mem.csv''' ) ).exists() )
self.assertTrue(Path(os.path.join(_lowercase , '''env.csv''' ) ).exists() )
def a ( self : List[Any] ):
__UpperCAmelCase = '''sshleifer/tiny-gpt2'''
def _check_summary_is_not_empty(_lowercase : str ):
self.assertTrue(hasattr(_lowercase , '''sequential''' ) )
self.assertTrue(hasattr(_lowercase , '''cumulative''' ) )
self.assertTrue(hasattr(_lowercase , '''current''' ) )
self.assertTrue(hasattr(_lowercase , '''total''' ) )
with tempfile.TemporaryDirectory() as tmp_dir:
__UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(_lowercase , '''log.txt''' ) , log_print=_lowercase , trace_memory_line_by_line=_lowercase , multi_process=_lowercase , )
__UpperCAmelCase = PyTorchBenchmark(_lowercase )
__UpperCAmelCase = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
_check_summary_is_not_empty(result.train_summary )
self.assertTrue(Path(os.path.join(_lowercase , '''log.txt''' ) ).exists() )
| 332 | 1 |
_A = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def __UpperCamelCase ( ):
lowerCAmelCase_ = input('''Enter message: ''' )
lowerCAmelCase_ = input('''Enter key [alphanumeric]: ''' )
lowerCAmelCase_ = input('''Encrypt/Decrypt [e/d]: ''' )
if mode.lower().startswith('''e''' ):
lowerCAmelCase_ = '''encrypt'''
lowerCAmelCase_ = encrypt_message(_A , _A )
elif mode.lower().startswith('''d''' ):
lowerCAmelCase_ = '''decrypt'''
lowerCAmelCase_ = decrypt_message(_A , _A )
print(f"\n{mode.title()}ed message:" )
print(_A )
def __UpperCamelCase ( _A , _A ):
return translate_message(_A , _A , '''encrypt''' )
def __UpperCamelCase ( _A , _A ):
return translate_message(_A , _A , '''decrypt''' )
def __UpperCamelCase ( _A , _A , _A ):
lowerCAmelCase_ = []
lowerCAmelCase_ = 0
lowerCAmelCase_ = key.upper()
for symbol in message:
lowerCAmelCase_ = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(_A )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(_A ):
lowerCAmelCase_ = 0
else:
translated.append(_A )
return "".join(_A )
if __name__ == "__main__":
main()
| 355 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
_A = logging.get_logger(__name__)
_A = TypeVar('''DatasetType''', Dataset, IterableDataset)
def __UpperCamelCase ( _A , _A = None , _A = None , _A = None , _A = None , _A = "first_exhausted" , ):
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('''Unable to interleave an empty list of datasets.''' )
for i, dataset in enumerate(_A ):
if not isinstance(_A , (Dataset, IterableDataset) ):
if isinstance(_A , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
'''is an empty dataset dictionary.''' )
raise ValueError(
f"Dataset at position {i} has at least one split: {list(_A )}\n"
f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_A ) )}']" )
raise ValueError(
f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_A ).__name__}." )
if i == 0:
lowerCAmelCase_ , lowerCAmelCase_ = (
(Dataset, IterableDataset) if isinstance(_A , _A ) else (IterableDataset, Dataset)
)
elif not isinstance(_A , _A ):
raise ValueError(
f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects." )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(f"{stopping_strategy} is not supported. Please enter a valid stopping_strategy." )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
_A , _A , _A , info=_A , split=_A , stopping_strategy=_A )
else:
return _interleave_iterable_datasets(
_A , _A , _A , info=_A , split=_A , stopping_strategy=_A )
def __UpperCamelCase ( _A , _A = None , _A = None , _A = 0 , ):
if not dsets:
raise ValueError('''Unable to concatenate an empty list of datasets.''' )
for i, dataset in enumerate(_A ):
if not isinstance(_A , (Dataset, IterableDataset) ):
if isinstance(_A , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
'''is an empty dataset dictionary.''' )
raise ValueError(
f"Dataset at position {i} has at least one split: {list(_A )}\n"
f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_A ) )}']" )
raise ValueError(
f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_A ).__name__}." )
if i == 0:
lowerCAmelCase_ , lowerCAmelCase_ = (
(Dataset, IterableDataset) if isinstance(_A , _A ) else (IterableDataset, Dataset)
)
elif not isinstance(_A , _A ):
raise ValueError(
f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects." )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(_A , info=_A , split=_A , axis=_A )
else:
return _concatenate_iterable_datasets(_A , info=_A , split=_A , axis=_A )
| 167 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import VivitImageProcessor
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
def __init__( self : int , A : Dict , A : Optional[int]=7 , A : Tuple=3 , A : Optional[Any]=10 , A : int=18 , A : Dict=30 , A : List[str]=400 , A : int=True , A : Optional[Any]=None , A : Optional[Any]=True , A : List[Any]=[0.5, 0.5, 0.5] , A : List[str]=[0.5, 0.5, 0.5] , A : Optional[int]=None , ):
_UpperCAmelCase : Dict = size if size is not None else {"shortest_edge": 18}
_UpperCAmelCase : Optional[Any] = crop_size if crop_size is not None else {"height": 18, "width": 18}
_UpperCAmelCase : Tuple = parent
_UpperCAmelCase : Any = batch_size
_UpperCAmelCase : Optional[int] = num_channels
_UpperCAmelCase : Optional[Any] = num_frames
_UpperCAmelCase : Any = image_size
_UpperCAmelCase : Dict = min_resolution
_UpperCAmelCase : Any = max_resolution
_UpperCAmelCase : Optional[int] = do_resize
_UpperCAmelCase : str = size
_UpperCAmelCase : List[Any] = do_normalize
_UpperCAmelCase : Any = image_mean
_UpperCAmelCase : Tuple = image_std
_UpperCAmelCase : Any = crop_size
def _A ( self : List[Any] ):
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class lowerCamelCase_ (snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: Dict = VivitImageProcessor if is_vision_available() else None
def _A ( self : int ):
_UpperCAmelCase : Tuple = VivitImageProcessingTester(self )
@property
def _A ( self : Optional[Any] ):
return self.image_processor_tester.prepare_image_processor_dict()
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : str = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A , "image_mean" ) )
self.assertTrue(hasattr(A , "image_std" ) )
self.assertTrue(hasattr(A , "do_normalize" ) )
self.assertTrue(hasattr(A , "do_resize" ) )
self.assertTrue(hasattr(A , "do_center_crop" ) )
self.assertTrue(hasattr(A , "size" ) )
def _A ( self : List[Any] ):
_UpperCAmelCase : Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 18} )
self.assertEqual(image_processor.crop_size , {"height": 18, "width": 18} )
_UpperCAmelCase : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"shortest_edge": 42} )
self.assertEqual(image_processor.crop_size , {"height": 84, "width": 84} )
def _A ( self : Tuple ):
# Initialize image_processing
_UpperCAmelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL videos
_UpperCAmelCase : Any = prepare_video_inputs(self.image_processor_tester , equal_resolution=A )
for video in video_inputs:
self.assertIsInstance(A , A )
self.assertIsInstance(video[0] , Image.Image )
# Test not batched input
_UpperCAmelCase : str = image_processing(video_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : List[Any] = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def _A ( self : List[Any] ):
# Initialize image_processing
_UpperCAmelCase : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_UpperCAmelCase : List[Any] = prepare_video_inputs(self.image_processor_tester , equal_resolution=A , numpify=A )
for video in video_inputs:
self.assertIsInstance(A , A )
self.assertIsInstance(video[0] , np.ndarray )
# Test not batched input
_UpperCAmelCase : Tuple = image_processing(video_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : Optional[int] = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def _A ( self : List[Any] ):
# Initialize image_processing
_UpperCAmelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_UpperCAmelCase : Optional[int] = prepare_video_inputs(self.image_processor_tester , equal_resolution=A , torchify=A )
for video in video_inputs:
self.assertIsInstance(A , A )
self.assertIsInstance(video[0] , torch.Tensor )
# Test not batched input
_UpperCAmelCase : Optional[Any] = image_processing(video_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : List[Any] = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
| 31 |
"""simple docstring"""
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class snake_case ( unittest.TestCase ):
a_ : Any = JukeboxTokenizer
a_ : Any = {
"""artist""": """Zac Brown Band""",
"""genres""": """Country""",
"""lyrics""": """I met a traveller from an antique land,
Who said \"Two vast and trunkless legs of stone
Stand in the desert. . . . Near them, on the sand,
Half sunk a shattered visage lies, whose frown,
And wrinkled lip, and sneer of cold command,
Tell that its sculptor well those passions read
Which yet survive, stamped on these lifeless things,
The hand that mocked them, and the heart that fed;
And on the pedestal, these words appear:
My name is Ozymandias, King of Kings;
Look on my Works, ye Mighty, and despair!
Nothing beside remains. Round the decay
Of that colossal Wreck, boundless and bare
The lone and level sands stretch far away
""",
}
@require_torch
def UpperCAmelCase__ ( self) ->Any:
import torch
a_ = JukeboxTokenizer.from_pretrained("openai/jukebox-1b-lyrics")
a_ = tokenizer(**self.metas)["input_ids"]
# fmt: off
a_ = [
torch.tensor([[
0, 0, 0, 71_69, 5_07, 9, 76, 39, 31, 46, 76, 27,
76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
76, 76]]),
torch.tensor([[0, 0, 0, 10_69, 11]]),
torch.tensor([[0, 0, 0, 10_69, 11]]),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0]))
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1]))
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2]))
@require_torch
def UpperCAmelCase__ ( self) ->Tuple:
import torch
a_ = JukeboxTokenizer.from_pretrained("openai/jukebox-5b-lyrics")
a_ = tokenizer(**self.metas)["input_ids"]
# fmt: off
a_ = [
torch.tensor([[
0, 0, 0, 10_69, 11, -1, -1, -1, -1, 9, 77, 39,
31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
77, 77, 77, 77, 77, 77]]),
torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]),
torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0]))
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1]))
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2])) | 243 | 0 |
import numpy as np
class __a :
def __init__( self ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = (0, 0)
_UpperCAmelCase = None
_UpperCAmelCase = 0
_UpperCAmelCase = 0
_UpperCAmelCase = 0
def __eq__( self , _SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
return self.position == cell.position
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
print(self.position )
class __a :
def __init__( self , _SCREAMING_SNAKE_CASE=(5, 5) ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = np.zeros(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = world_size[0]
_UpperCAmelCase = world_size[1]
def UpperCAmelCase__ ( self ) -> List[str]:
"""simple docstring"""
print(self.w )
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = [
(-1, -1),
(-1, 0),
(-1, 1),
(0, -1),
(0, 1),
(1, -1),
(1, 0),
(1, 1),
]
_UpperCAmelCase = cell.position[0]
_UpperCAmelCase = cell.position[1]
_UpperCAmelCase = []
for n in neughbour_cord:
_UpperCAmelCase = current_x + n[0]
_UpperCAmelCase = current_y + n[1]
if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
_UpperCAmelCase = Cell()
_UpperCAmelCase = (x, y)
_UpperCAmelCase = cell
neighbours.append(_SCREAMING_SNAKE_CASE )
return neighbours
def lowerCAmelCase__ ( a__: List[str] , a__: Any , a__: List[Any] ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = []
_open.append(a__ )
while _open:
_UpperCAmelCase = np.argmin([n.f for n in _open] )
_UpperCAmelCase = _open[min_f]
_closed.append(_open.pop(a__ ) )
if current == goal:
break
for n in world.get_neigbours(a__ ):
for c in _closed:
if c == n:
continue
_UpperCAmelCase = current.g + 1
_UpperCAmelCase , _UpperCAmelCase = n.position
_UpperCAmelCase , _UpperCAmelCase = goal.position
_UpperCAmelCase = (ya - ya) ** 2 + (xa - xa) ** 2
_UpperCAmelCase = n.h + n.g
for c in _open:
if c == n and c.f < n.f:
continue
_open.append(a__ )
_UpperCAmelCase = []
while current.parent is not None:
path.append(current.position )
_UpperCAmelCase = current.parent
path.append(current.position )
return path[::-1]
if __name__ == "__main__":
lowerCAmelCase__ :int = Gridworld()
# Start position and goal
lowerCAmelCase__ :List[str] = Cell()
lowerCAmelCase__ :str = (0, 0)
lowerCAmelCase__ :int = Cell()
lowerCAmelCase__ :Dict = (4, 4)
print(f'''path from {start.position} to {goal.position}''')
lowerCAmelCase__ :List[str] = astar(world, start, goal)
# Just for visual reasons.
for i in s:
lowerCAmelCase__ :Dict = 1
print(world.w)
| 356 |
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
lowerCAmelCase__ :Tuple = 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''')
lowerCAmelCase__ :Optional[int] = parser.parse_args()
if args.model_type == "bert":
lowerCAmelCase__ :Tuple = BertForMaskedLM.from_pretrained(args.model_name)
lowerCAmelCase__ :Optional[int] = '''bert'''
else:
raise ValueError('''args.model_type should be "bert".''')
lowerCAmelCase__ :Any = model.state_dict()
lowerCAmelCase__ :Dict = {}
for w in ["word_embeddings", "position_embeddings"]:
lowerCAmelCase__ :List[Any] = state_dict[f'''{prefix}.embeddings.{w}.weight''']
for w in ["weight", "bias"]:
lowerCAmelCase__ :Union[str, Any] = state_dict[f'''{prefix}.embeddings.LayerNorm.{w}''']
lowerCAmelCase__ :str = 0
for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
for w in ["weight", "bias"]:
lowerCAmelCase__ :Any = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
]
lowerCAmelCase__ :List[Any] = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
]
lowerCAmelCase__ :List[str] = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
]
lowerCAmelCase__ :List[Any] = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
]
lowerCAmelCase__ :int = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
]
lowerCAmelCase__ :List[Any] = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
]
lowerCAmelCase__ :List[Any] = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
]
lowerCAmelCase__ :List[Any] = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
]
std_idx += 1
lowerCAmelCase__ :Optional[int] = state_dict['''cls.predictions.decoder.weight''']
lowerCAmelCase__ :List[str] = state_dict['''cls.predictions.bias''']
if args.vocab_transform:
for w in ["weight", "bias"]:
lowerCAmelCase__ :Any = state_dict[f'''cls.predictions.transform.dense.{w}''']
lowerCAmelCase__ :List[str] = 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)
| 185 | 0 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _snake_case ( _snake_case ):
SCREAMING_SNAKE_CASE__ = 'ClapFeatureExtractor'
SCREAMING_SNAKE_CASE__ = ('RobertaTokenizer', 'RobertaTokenizerFast')
def __init__( self , _lowerCamelCase , _lowerCamelCase ):
super().__init__(_lowerCamelCase , _lowerCamelCase )
def __call__( self , _lowerCamelCase=None , _lowerCamelCase=None , _lowerCamelCase=None , **_lowerCamelCase ):
a :Dict = kwargs.pop('''sampling_rate''' , _lowerCamelCase )
if text is None and audios is None:
raise ValueError('''You have to specify either text or audios. Both cannot be none.''' )
if text is not None:
a :Optional[int] = self.tokenizer(_lowerCamelCase , return_tensors=_lowerCamelCase , **_lowerCamelCase )
if audios is not None:
a :Tuple = self.feature_extractor(
_lowerCamelCase , sampling_rate=_lowerCamelCase , return_tensors=_lowerCamelCase , **_lowerCamelCase )
if text is not None and audios is not None:
a :Union[str, Any] = audio_features.input_features
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**_lowerCamelCase ) , tensor_type=_lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self , *_lowerCamelCase , **_lowerCamelCase ):
return self.tokenizer.batch_decode(*_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self , *_lowerCamelCase , **_lowerCamelCase ):
return self.tokenizer.decode(*_lowerCamelCase , **_lowerCamelCase )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
a :List[Any] = self.tokenizer.model_input_names
a :str = self.feature_extractor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names ) )
| 94 |
__UpperCAmelCase : int = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages
| 111 | 0 |
"""simple docstring"""
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class UpperCamelCase_ (__A ):
__magic_name__ = ['''vqvae''']
def __init__( self : Dict , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[Any] , ) -> Tuple:
super().__init__()
self.register_modules(unet=_snake_case , scheduler=_snake_case , mel=_snake_case , vqvae=_snake_case )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> int:
return 50 if isinstance(self.scheduler , _snake_case ) else 1_000
@torch.no_grad()
def __call__( self : Optional[int] , lowerCAmelCase_ : Dict = 1 , lowerCAmelCase_ : str = None , lowerCAmelCase_ : Any = None , lowerCAmelCase_ : List[str] = 0 , lowerCAmelCase_ : Dict = 0 , lowerCAmelCase_ : List[str] = None , lowerCAmelCase_ : Optional[Any] = None , lowerCAmelCase_ : Tuple = 0 , lowerCAmelCase_ : Union[str, Any] = 0 , lowerCAmelCase_ : List[str] = None , lowerCAmelCase_ : Dict = 0 , lowerCAmelCase_ : Dict = None , lowerCAmelCase_ : Union[str, Any] = None , lowerCAmelCase_ : str=True , ) -> Union[
Union[AudioPipelineOutput, ImagePipelineOutput],
Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
]:
UpperCAmelCase_ : Tuple = steps or self.get_default_steps()
self.scheduler.set_timesteps(_snake_case )
UpperCAmelCase_ : Union[str, Any] = step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size ) == int:
UpperCAmelCase_ : Optional[Any] = (self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
UpperCAmelCase_ : Optional[Any] = randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
) , generator=_snake_case , device=self.device , )
UpperCAmelCase_ : List[Any] = noise
UpperCAmelCase_ : Dict = None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(_snake_case , _snake_case )
UpperCAmelCase_ : Any = self.mel.audio_slice_to_image(_snake_case )
UpperCAmelCase_ : Optional[int] = np.frombuffer(input_image.tobytes() , dtype="uint8" ).reshape(
(input_image.height, input_image.width) )
UpperCAmelCase_ : Optional[int] = (input_image / 255) * 2 - 1
UpperCAmelCase_ : Optional[Any] = torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float ).to(self.device )
if self.vqvae is not None:
UpperCAmelCase_ : int = self.vqvae.encode(torch.unsqueeze(_snake_case , 0 ) ).latent_dist.sample(
generator=_snake_case )[0]
UpperCAmelCase_ : List[str] = self.vqvae.config.scaling_factor * input_images
if start_step > 0:
UpperCAmelCase_ : Optional[int] = self.scheduler.add_noise(_snake_case , _snake_case , self.scheduler.timesteps[start_step - 1] )
UpperCAmelCase_ : str = (
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
UpperCAmelCase_ : Any = int(mask_start_secs * pixels_per_second )
UpperCAmelCase_ : int = int(mask_end_secs * pixels_per_second )
UpperCAmelCase_ : int = self.scheduler.add_noise(_snake_case , _snake_case , torch.tensor(self.scheduler.timesteps[start_step:] ) )
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
if isinstance(self.unet , _snake_case ):
UpperCAmelCase_ : List[str] = self.unet(_snake_case , _snake_case , _snake_case )["sample"]
else:
UpperCAmelCase_ : Optional[int] = self.unet(_snake_case , _snake_case )["sample"]
if isinstance(self.scheduler , _snake_case ):
UpperCAmelCase_ : Tuple = self.scheduler.step(
model_output=_snake_case , timestep=_snake_case , sample=_snake_case , eta=_snake_case , generator=_snake_case , )["prev_sample"]
else:
UpperCAmelCase_ : List[str] = self.scheduler.step(
model_output=_snake_case , timestep=_snake_case , sample=_snake_case , generator=_snake_case , )["prev_sample"]
if mask is not None:
if mask_start > 0:
UpperCAmelCase_ : Any = mask[:, step, :, :mask_start]
if mask_end > 0:
UpperCAmelCase_ : Tuple = mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
UpperCAmelCase_ : Tuple = 1 / self.vqvae.config.scaling_factor * images
UpperCAmelCase_ : int = self.vqvae.decode(_snake_case )["sample"]
UpperCAmelCase_ : Any = (images / 2 + 0.5).clamp(0 , 1 )
UpperCAmelCase_ : List[Any] = images.cpu().permute(0 , 2 , 3 , 1 ).numpy()
UpperCAmelCase_ : List[Any] = (images * 255).round().astype("uint8" )
UpperCAmelCase_ : str = list(
(Image.fromarray(_[:, :, 0] ) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(_snake_case , mode="RGB" ).convert("L" ) for _ in images) )
UpperCAmelCase_ : Tuple = [self.mel.image_to_audio(_snake_case ) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(_snake_case )[:, np.newaxis, :] ) , **ImagePipelineOutput(_snake_case ) )
@torch.no_grad()
def _SCREAMING_SNAKE_CASE ( self : int , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Dict = 50 ) -> np.ndarray:
assert isinstance(self.scheduler , _snake_case )
self.scheduler.set_timesteps(_snake_case )
UpperCAmelCase_ : List[Any] = np.array(
[np.frombuffer(image.tobytes() , dtype="uint8" ).reshape((1, image.height, image.width) ) for image in images] )
UpperCAmelCase_ : List[Any] = (sample / 255) * 2 - 1
UpperCAmelCase_ : Tuple = torch.Tensor(_snake_case ).to(self.device )
for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,) ) ):
UpperCAmelCase_ : List[Any] = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
UpperCAmelCase_ : int = self.scheduler.alphas_cumprod[t]
UpperCAmelCase_ : Union[str, Any] = (
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
UpperCAmelCase_ : Optional[int] = 1 - alpha_prod_t
UpperCAmelCase_ : Any = self.unet(_snake_case , _snake_case )["sample"]
UpperCAmelCase_ : Tuple = (1 - alpha_prod_t_prev) ** 0.5 * model_output
UpperCAmelCase_ : List[Any] = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
UpperCAmelCase_ : str = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output
return sample
@staticmethod
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase_ : Any , lowerCAmelCase_ : Any , lowerCAmelCase_ : Tuple ) -> torch.Tensor:
UpperCAmelCase_ : List[Any] = acos(torch.dot(torch.flatten(_snake_case ) , torch.flatten(_snake_case ) ) / torch.norm(_snake_case ) / torch.norm(_snake_case ) )
return sin((1 - alpha) * theta ) * xa / sin(_snake_case ) + sin(alpha * theta ) * xa / sin(_snake_case )
| 361 |
"""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 ..auto import CONFIG_MAPPING
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''microsoft/table-transformer-detection''': (
'''https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'''
),
}
class UpperCamelCase_ (__A ):
__magic_name__ = '''table-transformer'''
__magic_name__ = ['''past_key_values''']
__magic_name__ = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
}
def __init__( self : List[Any] , lowerCAmelCase_ : List[Any]=True , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : Optional[Any]=3 , lowerCAmelCase_ : Optional[Any]=100 , lowerCAmelCase_ : Optional[int]=6 , lowerCAmelCase_ : List[Any]=2_048 , lowerCAmelCase_ : Tuple=8 , lowerCAmelCase_ : Dict=6 , lowerCAmelCase_ : List[Any]=2_048 , lowerCAmelCase_ : Optional[int]=8 , lowerCAmelCase_ : Tuple=0.0 , lowerCAmelCase_ : str=0.0 , lowerCAmelCase_ : str=True , lowerCAmelCase_ : Optional[int]="relu" , lowerCAmelCase_ : List[Any]=256 , lowerCAmelCase_ : Optional[Any]=0.1 , lowerCAmelCase_ : Optional[int]=0.0 , lowerCAmelCase_ : Union[str, Any]=0.0 , lowerCAmelCase_ : Dict=0.0_2 , lowerCAmelCase_ : Any=1.0 , lowerCAmelCase_ : Any=False , lowerCAmelCase_ : Dict="sine" , lowerCAmelCase_ : Optional[Any]="resnet50" , lowerCAmelCase_ : int=True , lowerCAmelCase_ : Union[str, Any]=False , lowerCAmelCase_ : int=1 , lowerCAmelCase_ : int=5 , lowerCAmelCase_ : Union[str, Any]=2 , lowerCAmelCase_ : Any=1 , lowerCAmelCase_ : List[str]=1 , lowerCAmelCase_ : List[Any]=5 , lowerCAmelCase_ : Optional[Any]=2 , lowerCAmelCase_ : Optional[Any]=0.1 , **lowerCAmelCase_ : Dict , ) -> Union[str, Any]:
if backbone_config is not None and use_timm_backbone:
raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`." )
if not use_timm_backbone:
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
UpperCAmelCase_ : Union[str, Any] = CONFIG_MAPPING["resnet"](out_features=["stage4"] )
elif isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
UpperCAmelCase_ : Dict = backbone_config.get("model_type" )
UpperCAmelCase_ : str = CONFIG_MAPPING[backbone_model_type]
UpperCAmelCase_ : Any = config_class.from_dict(lowerCAmelCase_ )
# set timm attributes to None
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : str = None, None, None
UpperCAmelCase_ : int = use_timm_backbone
UpperCAmelCase_ : int = backbone_config
UpperCAmelCase_ : Dict = num_channels
UpperCAmelCase_ : Optional[Any] = num_queries
UpperCAmelCase_ : List[str] = d_model
UpperCAmelCase_ : Union[str, Any] = encoder_ffn_dim
UpperCAmelCase_ : Optional[Any] = encoder_layers
UpperCAmelCase_ : List[str] = encoder_attention_heads
UpperCAmelCase_ : int = decoder_ffn_dim
UpperCAmelCase_ : int = decoder_layers
UpperCAmelCase_ : Optional[int] = decoder_attention_heads
UpperCAmelCase_ : List[str] = dropout
UpperCAmelCase_ : Dict = attention_dropout
UpperCAmelCase_ : Union[str, Any] = activation_dropout
UpperCAmelCase_ : Optional[int] = activation_function
UpperCAmelCase_ : int = init_std
UpperCAmelCase_ : Any = init_xavier_std
UpperCAmelCase_ : Union[str, Any] = encoder_layerdrop
UpperCAmelCase_ : Dict = decoder_layerdrop
UpperCAmelCase_ : Union[str, Any] = encoder_layers
UpperCAmelCase_ : Any = auxiliary_loss
UpperCAmelCase_ : List[str] = position_embedding_type
UpperCAmelCase_ : Dict = backbone
UpperCAmelCase_ : Optional[int] = use_pretrained_backbone
UpperCAmelCase_ : Tuple = dilation
# Hungarian matcher
UpperCAmelCase_ : Optional[Any] = class_cost
UpperCAmelCase_ : List[Any] = bbox_cost
UpperCAmelCase_ : Optional[int] = giou_cost
# Loss coefficients
UpperCAmelCase_ : Optional[int] = mask_loss_coefficient
UpperCAmelCase_ : List[str] = dice_loss_coefficient
UpperCAmelCase_ : Union[str, Any] = bbox_loss_coefficient
UpperCAmelCase_ : Union[str, Any] = giou_loss_coefficient
UpperCAmelCase_ : Dict = eos_coefficient
super().__init__(is_encoder_decoder=lowerCAmelCase_ , **lowerCAmelCase_ )
@property
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> int:
return self.encoder_attention_heads
@property
def _SCREAMING_SNAKE_CASE ( self : List[Any] ) -> int:
return self.d_model
class UpperCamelCase_ (__A ):
__magic_name__ = version.parse('''1.11''' )
@property
def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("pixel_mask", {0: "batch"}),
] )
@property
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> float:
return 1e-5
@property
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> int:
return 12
| 253 | 0 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
def _a ( SCREAMING_SNAKE_CASE : str=None , SCREAMING_SNAKE_CASE : int=None ):
"""simple docstring"""
return field(default_factory=lambda: default , metadata=SCREAMING_SNAKE_CASE )
@dataclass
class __magic_name__ :
A: List[str] = list_field(
default=[] , metadata={
"help": (
"Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
" of all available models"
)
} , )
A: List[int] = list_field(
default=[8] , metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"})
A: List[int] = list_field(
default=[8, 3_2, 1_2_8, 5_1_2] , metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"} , )
A: bool = field(
default=__lowerCAmelCase , metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."} , )
A: bool = field(
default=__lowerCAmelCase , metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."} , )
A: bool = field(
default=__lowerCAmelCase , metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."})
A: bool = field(default=__lowerCAmelCase , metadata={"help": "Use FP16 to accelerate inference."})
A: bool = field(default=__lowerCAmelCase , metadata={"help": "Benchmark training of model"})
A: bool = field(default=__lowerCAmelCase , metadata={"help": "Verbose memory tracing"})
A: bool = field(
default=__lowerCAmelCase , metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."} , )
A: bool = field(
default=__lowerCAmelCase , metadata={
"help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
} , )
A: bool = field(default=__lowerCAmelCase , metadata={"help": "Trace memory line by line"})
A: bool = field(default=__lowerCAmelCase , metadata={"help": "Save result to a CSV file"})
A: bool = field(default=__lowerCAmelCase , metadata={"help": "Save all print statements in a log file"})
A: bool = field(default=__lowerCAmelCase , metadata={"help": "Whether to print environment information"})
A: bool = field(
default=__lowerCAmelCase , metadata={
"help": (
"Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
" multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
" for debugging / testing and on TPU."
)
} , )
A: str = field(
default=F"inference_time_{round(time())}.csv" , metadata={"help": "CSV filename used if saving time results to csv."} , )
A: str = field(
default=F"inference_memory_{round(time())}.csv" , metadata={"help": "CSV filename used if saving memory results to csv."} , )
A: str = field(
default=F"train_time_{round(time())}.csv" , metadata={"help": "CSV filename used if saving time results to csv for training."} , )
A: str = field(
default=F"train_memory_{round(time())}.csv" , metadata={"help": "CSV filename used if saving memory results to csv for training."} , )
A: str = field(
default=F"env_info_{round(time())}.csv" , metadata={"help": "CSV filename used if saving environment information."} , )
A: str = field(
default=F"log_{round(time())}.csv" , metadata={"help": "Log filename used if print statements are saved in log."} , )
A: int = field(default=3 , metadata={"help": "Times an experiment will be run."})
A: bool = field(
default=__lowerCAmelCase , metadata={
"help": (
"Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
" model weights."
)
} , )
def UpperCAmelCase__ ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
warnings.warn(
F"The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"
''' are deprecated in general and it is advised to use external Benchmarking libraries '''
''' to benchmark Transformer models.''' , lowerCamelCase__ , )
def UpperCAmelCase__ ( self : Optional[int] ) -> Tuple:
'''simple docstring'''
return json.dumps(dataclasses.asdict(self ) , indent=2 )
@property
def UpperCAmelCase__ ( self : Any ) -> List[str]:
'''simple docstring'''
if len(self.models ) <= 0:
raise ValueError(
'''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
''' bert-base-cased` or `args.models = [\'bert-base-cased\'].''' )
return self.models
@property
def UpperCAmelCase__ ( self : Optional[int] ) -> Any:
'''simple docstring'''
if not self.multi_process:
return False
elif self.is_tpu:
logger.info('''Multiprocessing is currently not possible on TPU.''' )
return False
else:
return True
| 146 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _a ( SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Any ):
"""simple docstring"""
UpperCamelCase__ : List[Any] = torch.load(SCREAMING_SNAKE_CASE , map_location='''cpu''' )
UpperCamelCase__ : str = chkpt['''model''']
# We have the base model one level deeper than the original XLM repository
UpperCamelCase__ : int = {}
for k, v in state_dict.items():
if "pred_layer" in k:
UpperCamelCase__ : Optional[int] = v
else:
UpperCamelCase__ : Tuple = v
UpperCamelCase__ : Union[str, Any] = chkpt['''params''']
UpperCamelCase__ : Optional[Any] = {n: v for n, v in config.items() if not isinstance(SCREAMING_SNAKE_CASE , (torch.FloatTensor, numpy.ndarray) )}
UpperCamelCase__ : Dict = chkpt['''dico_word2id''']
UpperCamelCase__ : Dict = {s + '''</w>''' if s.find('''@@''' ) == -1 and i > 13 else s.replace('''@@''' , '''''' ): i for s, i in vocab.items()}
# Save pytorch-model
UpperCamelCase__ : List[str] = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
UpperCamelCase__ : Optional[Any] = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
UpperCamelCase__ : Any = pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']
print(F"Save PyTorch model to {pytorch_weights_dump_path}" )
torch.save(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
print(F"Save configuration file to {pytorch_config_dump_path}" )
with open(SCREAMING_SNAKE_CASE , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(SCREAMING_SNAKE_CASE , indent=2 ) + '''\n''' )
print(F"Save vocab file to {pytorch_config_dump_path}" )
with open(SCREAMING_SNAKE_CASE , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(SCREAMING_SNAKE_CASE , indent=2 ) + '''\n''' )
if __name__ == "__main__":
__UpperCamelCase : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--xlm_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
__UpperCamelCase : Optional[int] = parser.parse_args()
convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)
| 146 | 1 |
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .notes_encoder import SpectrogramNotesEncoder
from .continous_encoder import SpectrogramContEncoder
from .pipeline_spectrogram_diffusion import (
SpectrogramContEncoder,
SpectrogramDiffusionPipeline,
TaFilmDecoder,
)
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 .midi_utils import MidiProcessor
| 301 |
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
UpperCAmelCase__ : Tuple = logging.get_logger(__name__)
UpperCAmelCase__ : Union[str, Any] = {
'google/mobilenet_v1_1.0_224': 'https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json',
'google/mobilenet_v1_0.75_192': 'https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__UpperCamelCase : Optional[int] = '''mobilenet_v1'''
def __init__( self : Optional[int] , lowerCAmelCase_ : Any=3 , lowerCAmelCase_ : str=2_2_4 , lowerCAmelCase_ : List[str]=1.0 , lowerCAmelCase_ : Any=8 , lowerCAmelCase_ : Tuple="relu6" , lowerCAmelCase_ : List[Any]=True , lowerCAmelCase_ : Optional[int]=0.999 , lowerCAmelCase_ : List[str]=0.02 , lowerCAmelCase_ : List[Any]=0.001 , **lowerCAmelCase_ : Optional[Any] , ):
"""simple docstring"""
super().__init__(**lowerCAmelCase_ )
if depth_multiplier <= 0:
raise ValueError('''depth_multiplier must be greater than zero.''' )
_A: Any = num_channels
_A: Optional[int] = image_size
_A: Optional[Any] = depth_multiplier
_A: Tuple = min_depth
_A: Any = hidden_act
_A: Dict = tf_padding
_A: List[Any] = classifier_dropout_prob
_A: Tuple = initializer_range
_A: Tuple = layer_norm_eps
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__UpperCamelCase : Dict = version.parse('''1.11''' )
@property
def __magic_name__ ( self : Union[str, Any] ):
"""simple docstring"""
return OrderedDict([('''pixel_values''', {0: '''batch'''})] )
@property
def __magic_name__ ( self : Optional[Any] ):
"""simple docstring"""
if self.task == "image-classification":
return OrderedDict([('''logits''', {0: '''batch'''})] )
else:
return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})] )
@property
def __magic_name__ ( self : Dict ):
"""simple docstring"""
return 1e-4
| 301 | 1 |
def UpperCamelCase__ ( A__ = 1000 ) -> int:
snake_case__ : List[Any] = 2**power
snake_case__ : List[str] = str(_UpperCAmelCase )
snake_case__ : Tuple = list(_UpperCAmelCase )
snake_case__ : Union[str, Any] = 0
for i in list_num:
sum_of_num += int(_UpperCAmelCase )
return sum_of_num
if __name__ == "__main__":
lowerCAmelCase__ : List[Any] = int(input('''Enter the power of 2: ''').strip())
print('''2 ^ ''', power, ''' = ''', 2**power)
lowerCAmelCase__ : Optional[int] = solution(power)
print('''Sum of the digits is: ''', result)
| 143 |
"""simple docstring"""
from __future__ import annotations
from typing import Generic, TypeVar
_lowerCamelCase : Any = TypeVar('T')
class lowercase ( Generic[T]):
def __init__( self : Tuple , _lowerCamelCase : T ):
"""simple docstring"""
A_ : Union[str, Any] = data
A_ : List[Any] = self
A_ : Optional[Any] = 0
class lowercase ( Generic[T]):
def __init__( self : int ):
"""simple docstring"""
A_ : dict[T, DisjointSetTreeNode[T]] = {}
def a_ ( self : List[str] , _lowerCamelCase : T ):
"""simple docstring"""
A_ : List[str] = DisjointSetTreeNode(_lowerCamelCase )
def a_ ( self : Dict , _lowerCamelCase : T ):
"""simple docstring"""
A_ : Any = self.map[data]
if elem_ref != elem_ref.parent:
A_ : Any = self.find_set(elem_ref.parent.data )
return elem_ref.parent
def a_ ( self : Union[str, Any] , _lowerCamelCase : DisjointSetTreeNode[T] , _lowerCamelCase : DisjointSetTreeNode[T] ):
"""simple docstring"""
if nodea.rank > nodea.rank:
A_ : List[str] = nodea
else:
A_ : Optional[Any] = nodea
if nodea.rank == nodea.rank:
nodea.rank += 1
def a_ ( self : Optional[Any] , _lowerCamelCase : T , _lowerCamelCase : T ):
"""simple docstring"""
self.link(self.find_set(_lowerCamelCase ) , self.find_set(_lowerCamelCase ) )
class lowercase ( Generic[T]):
def __init__( self : Tuple ):
"""simple docstring"""
A_ : dict[T, dict[T, int]] = {}
def a_ ( self : List[Any] , _lowerCamelCase : T ):
"""simple docstring"""
if node not in self.connections:
A_ : Tuple = {}
def a_ ( self : Optional[Any] , _lowerCamelCase : T , _lowerCamelCase : T , _lowerCamelCase : int ):
"""simple docstring"""
self.add_node(_lowerCamelCase )
self.add_node(_lowerCamelCase )
A_ : int = weight
A_ : Dict = weight
def a_ ( self : Any ):
"""simple docstring"""
A_ : Tuple = []
A_ : Tuple = set()
for start in self.connections:
for end in self.connections[start]:
if (start, end) not in seen:
seen.add((end, start) )
edges.append((start, end, self.connections[start][end]) )
edges.sort(key=lambda _lowerCamelCase : x[2] )
# creating the disjoint set
A_ : Optional[Any] = DisjointSetTree[T]()
for node in self.connections:
disjoint_set.make_set(_lowerCamelCase )
# MST generation
A_ : Any = 0
A_ : Optional[int] = 0
A_ : Union[str, Any] = GraphUndirectedWeighted[T]()
while num_edges < len(self.connections ) - 1:
A_ , A_ , A_ : int = edges[index]
index += 1
A_ : Tuple = disjoint_set.find_set(_lowerCamelCase )
A_ : int = disjoint_set.find_set(_lowerCamelCase )
if parent_u != parent_v:
num_edges += 1
graph.add_edge(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
disjoint_set.union(_lowerCamelCase , _lowerCamelCase )
return graph
| 167 | 0 |
'''simple docstring'''
from math import ceil
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Any:
'''simple docstring'''
snake_case_ = list(range(0, __UpperCAmelCase ) )
snake_case_ = [item for sublist in list(device_map.values() ) for item in sublist]
# Duplicate check
snake_case_ = []
for i in device_map_blocks:
if device_map_blocks.count(__UpperCAmelCase ) > 1 and i not in duplicate_blocks:
duplicate_blocks.append(__UpperCAmelCase )
# Missing blocks
snake_case_ = [i for i in blocks if i not in device_map_blocks]
snake_case_ = [i for i in device_map_blocks if i not in blocks]
if len(__UpperCAmelCase ) != 0:
raise ValueError(
'''Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'''
''' These attention blocks were specified more than once: ''' + str(__UpperCAmelCase ) )
if len(__UpperCAmelCase ) != 0:
raise ValueError(
'''There are attention blocks for this model that are not specified in the device_map. Add these attention '''
'''blocks to a device on the device_map: ''' + str(__UpperCAmelCase ) )
if len(__UpperCAmelCase ) != 0:
raise ValueError(
'''The device_map contains more attention blocks than this model has. Remove these from the device_map:'''
+ str(__UpperCAmelCase ) )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Dict:
'''simple docstring'''
snake_case_ = list(range(__UpperCAmelCase ) )
snake_case_ = int(ceil(n_layers / len(__UpperCAmelCase ) ) )
snake_case_ = [layers[i : i + n_blocks] for i in range(0, __UpperCAmelCase, __UpperCAmelCase )]
return dict(zip(__UpperCAmelCase, __UpperCAmelCase ) )
| 357 |
'''simple docstring'''
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class a ( _lowerCamelCase ):
snake_case_ = 42
snake_case_ = None
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase=0.9_9_9, __UpperCAmelCase="cosine", ) -> Dict:
'''simple docstring'''
if alpha_transform_type == "cosine":
def alpha_bar_fn(__UpperCAmelCase ):
return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(__UpperCAmelCase ):
return math.exp(t * -1_2.0 )
else:
raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}" )
snake_case_ = []
for i in range(__UpperCAmelCase ):
snake_case_ = i / num_diffusion_timesteps
snake_case_ = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(__UpperCAmelCase ) / alpha_bar_fn(__UpperCAmelCase ), __UpperCAmelCase ) )
return torch.tensor(__UpperCAmelCase, dtype=torch.floataa )
class a ( _lowerCamelCase , _lowerCamelCase ):
@register_to_config
def __init__( self : List[str] , lowercase_ : int = 1000 , lowercase_ : str = "fixed_small_log" , lowercase_ : bool = True , lowercase_ : Optional[float] = 1.0 , lowercase_ : str = "epsilon" , lowercase_ : str = "squaredcos_cap_v2" , ):
if beta_schedule != "squaredcos_cap_v2":
raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'''' )
snake_case_ = betas_for_alpha_bar(lowercase_ )
snake_case_ = 1.0 - self.betas
snake_case_ = torch.cumprod(self.alphas , dim=0 )
snake_case_ = torch.tensor(1.0 )
# standard deviation of the initial noise distribution
snake_case_ = 1.0
# setable values
snake_case_ = None
snake_case_ = torch.from_numpy(np.arange(0 , lowercase_ )[::-1].copy() )
snake_case_ = variance_type
def A_ ( self : Optional[Any] , lowercase_ : torch.FloatTensor , lowercase_ : Optional[int] = None ):
return sample
def A_ ( self : Optional[int] , lowercase_ : int , lowercase_ : Union[str, torch.device] = None ):
snake_case_ = num_inference_steps
snake_case_ = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
snake_case_ = (np.arange(0 , lowercase_ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
snake_case_ = torch.from_numpy(lowercase_ ).to(lowercase_ )
def A_ ( self : Optional[int] , lowercase_ : List[Any] , lowercase_ : Optional[int]=None , lowercase_ : Tuple=None , lowercase_ : Tuple=None ):
if prev_timestep is None:
snake_case_ = t - 1
snake_case_ = self.alphas_cumprod[t]
snake_case_ = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
snake_case_ = 1 - alpha_prod_t
snake_case_ = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
snake_case_ = self.betas[t]
else:
snake_case_ = 1 - alpha_prod_t / alpha_prod_t_prev
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
snake_case_ = beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
snake_case_ = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
snake_case_ = torch.log(torch.clamp(lowercase_ , min=1e-20 ) )
snake_case_ = torch.exp(0.5 * variance )
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
snake_case_ = variance.log()
snake_case_ = beta.log()
snake_case_ = (predicted_variance + 1) / 2
snake_case_ = frac * max_log + (1 - frac) * min_log
return variance
def A_ ( self : List[Any] , lowercase_ : torch.FloatTensor , lowercase_ : int , lowercase_ : torch.FloatTensor , lowercase_ : Optional[int] = None , lowercase_ : int=None , lowercase_ : bool = True , ):
snake_case_ = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
snake_case_ ,snake_case_ = torch.split(lowercase_ , sample.shape[1] , dim=1 )
else:
snake_case_ = None
# 1. compute alphas, betas
if prev_timestep is None:
snake_case_ = t - 1
snake_case_ = self.alphas_cumprod[t]
snake_case_ = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
snake_case_ = 1 - alpha_prod_t
snake_case_ = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
snake_case_ = self.betas[t]
snake_case_ = self.alphas[t]
else:
snake_case_ = 1 - alpha_prod_t / alpha_prod_t_prev
snake_case_ = 1 - beta
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
snake_case_ = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
snake_case_ = model_output
else:
raise ValueError(
F"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"
''' for the UnCLIPScheduler.''' )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
snake_case_ = torch.clamp(
lowercase_ , -self.config.clip_sample_range , self.config.clip_sample_range )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
snake_case_ = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
snake_case_ = alpha ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
snake_case_ = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
snake_case_ = 0
if t > 0:
snake_case_ = randn_tensor(
model_output.shape , dtype=model_output.dtype , generator=lowercase_ , device=model_output.device )
snake_case_ = self._get_variance(
lowercase_ , predicted_variance=lowercase_ , prev_timestep=lowercase_ , )
if self.variance_type == "fixed_small_log":
snake_case_ = variance
elif self.variance_type == "learned_range":
snake_case_ = (0.5 * variance).exp()
else:
raise ValueError(
F"variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"
''' for the UnCLIPScheduler.''' )
snake_case_ = variance * variance_noise
snake_case_ = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return UnCLIPSchedulerOutput(prev_sample=lowercase_ , pred_original_sample=lowercase_ )
def A_ ( self : Any , lowercase_ : torch.FloatTensor , lowercase_ : torch.FloatTensor , lowercase_ : torch.IntTensor , ):
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
snake_case_ = self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype )
snake_case_ = timesteps.to(original_samples.device )
snake_case_ = alphas_cumprod[timesteps] ** 0.5
snake_case_ = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
snake_case_ = sqrt_alpha_prod.unsqueeze(-1 )
snake_case_ = (1 - alphas_cumprod[timesteps]) ** 0.5
snake_case_ = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
snake_case_ = sqrt_one_minus_alpha_prod.unsqueeze(-1 )
snake_case_ = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
| 72 | 0 |
"""simple docstring"""
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
__UpperCAmelCase = [
# tf -> hf
("""/""", """."""),
("""layer_""", """layers."""),
("""kernel""", """weight"""),
("""beta""", """bias"""),
("""gamma""", """weight"""),
("""pegasus""", """model"""),
]
__UpperCAmelCase = [
(""".output.dense""", """.fc2"""),
("""intermediate.LayerNorm""", """final_layer_norm"""),
("""intermediate.dense""", """fc1"""),
]
__UpperCAmelCase = (
INIT_COMMON
+ [
("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
("""attention.output.dense""", """self_attn.out_proj"""),
("""attention.self""", """self_attn"""),
("""attention.encdec.LayerNorm""", """encoder_attn_layer_norm"""),
("""attention.encdec_output.dense""", """encoder_attn.out_proj"""),
("""attention.encdec""", """encoder_attn"""),
("""key""", """k_proj"""),
("""value""", """v_proj"""),
("""query""", """q_proj"""),
("""decoder.LayerNorm""", """decoder.layernorm_embedding"""),
]
+ END_COMMON
)
__UpperCAmelCase = (
INIT_COMMON
+ [
("""embeddings.word_embeddings""", """shared.weight"""),
("""embeddings.position_embeddings""", """embed_positions.weight"""),
("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
("""attention.output.dense""", """self_attn.output"""),
("""attention.self""", """self_attn.self"""),
("""encoder.LayerNorm""", """encoder.layernorm_embedding"""),
]
+ END_COMMON
)
__UpperCAmelCase = [
"""encdec/key/bias""",
"""encdec/query/bias""",
"""encdec/value/bias""",
"""self/key/bias""",
"""self/query/bias""",
"""self/value/bias""",
"""encdec_output/dense/bias""",
"""attention/output/dense/bias""",
]
def _snake_case ( lowercase__ : Union[str, Any] , lowercase__ : Any ) -> List[str]:
'''simple docstring'''
for tf_name, hf_name in patterns:
lowerCAmelCase_ :Optional[int] = k.replace(UpperCAmelCase_ , UpperCAmelCase_ )
return k
def _snake_case ( lowercase__ : dict , lowercase__ : dict ) -> BigBirdPegasusForConditionalGeneration:
'''simple docstring'''
lowerCAmelCase_ :int = BigBirdPegasusConfig(**UpperCAmelCase_ )
lowerCAmelCase_ :Any = BigBirdPegasusForConditionalGeneration(UpperCAmelCase_ )
lowerCAmelCase_ :Union[str, Any] = torch_model.state_dict()
lowerCAmelCase_ :Tuple = {}
# separating decoder weights
lowerCAmelCase_ :Dict = {k: tf_weights[k] for k in tf_weights if k.startswith("""pegasus/decoder""" )}
lowerCAmelCase_ :str = {k: tf_weights[k] for k in tf_weights if not k.startswith("""pegasus/decoder""" )}
for k, v in tqdm(decoder_weights.items() , """tf -> hf conversion""" ):
lowerCAmelCase_ :Tuple = [k.endswith(UpperCAmelCase_ ) for ending in KEYS_TO_IGNORE]
if any(UpperCAmelCase_ ):
continue
lowerCAmelCase_ :Tuple = DECODER_PATTERNS
lowerCAmelCase_ :Optional[int] = rename_state_dict_key(UpperCAmelCase_ , UpperCAmelCase_ )
if new_k not in state_dict:
raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ["""dense""", """query""", """key""", """value"""] ):
lowerCAmelCase_ :Union[str, Any] = v.T
lowerCAmelCase_ :str = torch.from_numpy(UpperCAmelCase_ )
assert v.shape == state_dict[new_k].shape, f"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
for k, v in tqdm(remaining_weights.items() , """tf -> hf conversion""" ):
lowerCAmelCase_ :Optional[Any] = [k.endswith(UpperCAmelCase_ ) for ending in KEYS_TO_IGNORE]
if any(UpperCAmelCase_ ):
continue
lowerCAmelCase_ :Dict = REMAINING_PATTERNS
lowerCAmelCase_ :List[str] = rename_state_dict_key(UpperCAmelCase_ , UpperCAmelCase_ )
if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ["""dense""", """query""", """key""", """value"""] ):
lowerCAmelCase_ :List[str] = v.T
lowerCAmelCase_ :List[str] = torch.from_numpy(UpperCAmelCase_ )
if k != "pegasus/embeddings/position_embeddings":
assert v.shape == state_dict[new_k].shape, f"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
lowerCAmelCase_ :Any = mapping['model.embed_positions.weight']
lowerCAmelCase_ :Union[str, Any] = mapping.pop("""model.embed_positions.weight""" )
lowerCAmelCase_ :List[str] = torch_model.load_state_dict(UpperCAmelCase_ , strict=UpperCAmelCase_ )
lowerCAmelCase_ :Any = [
k
for k in missing
if k
not in [
'final_logits_bias',
'model.encoder.embed_tokens.weight',
'model.decoder.embed_tokens.weight',
'lm_head.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 _snake_case ( lowercase__ : Union[str, Any] ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :int = tf.train.list_variables(UpperCAmelCase_ )
lowerCAmelCase_ :List[str] = {}
lowerCAmelCase_ :List[Any] = ['global_step']
for name, shape in tqdm(UpperCAmelCase_ , desc="""converting tf checkpoint to dict""" ):
lowerCAmelCase_ :Any = any(pat in name for pat in ignore_name )
if skip_key:
continue
lowerCAmelCase_ :Dict = tf.train.load_variable(UpperCAmelCase_ , UpperCAmelCase_ )
lowerCAmelCase_ :List[Any] = array
return tf_weights
def _snake_case ( lowercase__ : str , lowercase__ : str , lowercase__ : dict ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = get_tf_weights_as_numpy(UpperCAmelCase_ )
lowerCAmelCase_ :List[str] = convert_bigbird_pegasus(UpperCAmelCase_ , UpperCAmelCase_ )
torch_model.save_pretrained(UpperCAmelCase_ )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
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.')
__UpperCAmelCase = parser.parse_args()
__UpperCAmelCase = {}
convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)
| 84 |
'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
if dst_width < 0 or dst_height < 0:
raise ValueError('Destination width/height should be > 0' )
__lowerCamelCase : Dict = img
__lowerCamelCase : Any = img.shape[1]
__lowerCamelCase : Optional[int] = img.shape[0]
__lowerCamelCase : Dict = dst_width
__lowerCamelCase : str = dst_height
__lowerCamelCase : Dict = self.src_w / self.dst_w
__lowerCamelCase : List[Any] = self.src_h / self.dst_h
__lowerCamelCase : Optional[int] = (
np.ones((self.dst_h, self.dst_w, 3) , np.uinta ) * 2_55
)
def lowercase_ ( self ) -> List[Any]:
for i in range(self.dst_h ):
for j in range(self.dst_w ):
__lowerCamelCase : Union[str, Any] = self.img[self.get_y(SCREAMING_SNAKE_CASE_ )][self.get_x(SCREAMING_SNAKE_CASE_ )]
def lowercase_ ( self , SCREAMING_SNAKE_CASE_ ) -> int:
return int(self.ratio_x * x )
def lowercase_ ( self , SCREAMING_SNAKE_CASE_ ) -> int:
return int(self.ratio_y * y )
if __name__ == "__main__":
A__ , A__ : Optional[Any] = 800, 600
A__ : List[str] = imread("""image_data/lena.jpg""", 1)
A__ : List[Any] = NearestNeighbour(im, dst_w, dst_h)
n.process()
imshow(
f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
)
waitKey(0)
destroyAllWindows()
| 185 | 0 |
import re
from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class _lowerCAmelCase ( _lowerCamelCase ):
'''simple docstring'''
a_ : Union[str, Any] ="""naver-clova-ix/donut-base-finetuned-docvqa"""
a_ : Union[str, Any] =(
"""This is a tool that answers a question about an document (pdf). It takes an input named `document` which """
"""should be the document containing the information, as well as a `question` that is the question about the """
"""document. It returns a text that contains the answer to the question."""
)
a_ : Union[str, Any] ="""document_qa"""
a_ : str =AutoProcessor
a_ : Tuple =VisionEncoderDecoderModel
a_ : Optional[Any] =["""image""", """text"""]
a_ : Optional[int] =["""text"""]
def __init__( self : Optional[int] , *UpperCamelCase : Optional[int] , **UpperCamelCase : Tuple ):
'''simple docstring'''
if not is_vision_available():
raise ValueError('Pillow must be installed to use the DocumentQuestionAnsweringTool.' )
super().__init__(*lowercase_ , **lowercase_ )
def UpperCamelCase_ ( self : Any , UpperCamelCase : "Image" , UpperCamelCase : str ):
'''simple docstring'''
_snake_case : Tuple = '<s_docvqa><s_question>{user_input}</s_question><s_answer>'
_snake_case : List[str] = task_prompt.replace('{user_input}' , lowercase_ )
_snake_case : List[Any] = self.pre_processor.tokenizer(
lowercase_ , add_special_tokens=lowercase_ , return_tensors='pt' ).input_ids
_snake_case : Any = self.pre_processor(lowercase_ , return_tensors='pt' ).pixel_values
return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}
def UpperCamelCase_ ( self : str , UpperCamelCase : Dict ):
'''simple docstring'''
return self.model.generate(
inputs['pixel_values'].to(self.device ) , decoder_input_ids=inputs['decoder_input_ids'].to(self.device ) , max_length=self.model.decoder.config.max_position_embeddings , early_stopping=lowercase_ , pad_token_id=self.pre_processor.tokenizer.pad_token_id , eos_token_id=self.pre_processor.tokenizer.eos_token_id , use_cache=lowercase_ , num_beams=1 , bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]] , return_dict_in_generate=lowercase_ , ).sequences
def UpperCamelCase_ ( self : List[str] , UpperCamelCase : Tuple ):
'''simple docstring'''
_snake_case : Optional[Any] = self.pre_processor.batch_decode(lowercase_ )[0]
_snake_case : Dict = sequence.replace(self.pre_processor.tokenizer.eos_token , '' )
_snake_case : List[str] = sequence.replace(self.pre_processor.tokenizer.pad_token , '' )
_snake_case : List[str] = re.sub(R'<.*?>' , '' , lowercase_ , count=1 ).strip() # remove first task start token
_snake_case : List[Any] = self.pre_processor.tokenajson(lowercase_ )
return sequence["answer"]
| 356 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def lowerCamelCase_ ( lowerCAmelCase: Optional[Any] , lowerCAmelCase: str , lowerCAmelCase: str , lowerCAmelCase: Path , lowerCAmelCase: str = None , lowerCAmelCase: str = None , lowerCAmelCase: str = None , )-> List[Any]:
if config_name_or_path is None:
_snake_case : int = 'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'
if generator_tokenizer_name_or_path is None:
_snake_case : Optional[int] = generator_name_or_path
if question_encoder_tokenizer_name_or_path is None:
_snake_case : List[str] = question_encoder_name_or_path
_snake_case : List[str] = RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration
# Save model.
_snake_case : Any = RagConfig.from_pretrained(lowerCAmelCase )
_snake_case : Tuple = AutoConfig.from_pretrained(lowerCAmelCase )
_snake_case : Any = AutoConfig.from_pretrained(lowerCAmelCase )
_snake_case : int = gen_config
_snake_case : Tuple = question_encoder_config
_snake_case : int = model_class.from_pretrained_question_encoder_generator(
lowerCAmelCase , lowerCAmelCase , config=lowerCAmelCase )
rag_model.save_pretrained(lowerCAmelCase )
# Sanity check.
model_class.from_pretrained(lowerCAmelCase )
# Save tokenizers.
_snake_case : int = AutoTokenizer.from_pretrained(lowerCAmelCase )
gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/' )
_snake_case : str = AutoTokenizer.from_pretrained(lowerCAmelCase )
question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/' )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
parser.add_argument(
"""--model_type""",
choices=["""rag_sequence""", """rag_token"""],
required=True,
type=str,
help="""RAG model type: rag_sequence, rag_token""",
)
parser.add_argument("""--dest""", type=str, required=True, help="""Path to the output checkpoint directory.""")
parser.add_argument("""--generator_name_or_path""", type=str, required=True, help="""Generator model identifier""")
parser.add_argument(
"""--question_encoder_name_or_path""", type=str, required=True, help="""Question encoder model identifier"""
)
parser.add_argument(
"""--generator_tokenizer_name_or_path""",
type=str,
help="""Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``""",
)
parser.add_argument(
"""--question_encoder_tokenizer_name_or_path""",
type=str,
help="""Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``""",
)
parser.add_argument(
"""--config_name_or_path""",
type=str,
help=(
"""Identifier of the model config to use, if not provided, resolves to a base config for a given"""
""" ``model_type``"""
),
)
lowerCAmelCase_ = parser.parse_args()
lowerCAmelCase_ = Path(args.dest)
dest_dir.mkdir(exist_ok=True)
consolidate(
args.model_type,
args.generator_name_or_path,
args.question_encoder_name_or_path,
dest_dir,
args.config_name_or_path,
args.generator_tokenizer_name_or_path,
args.question_encoder_tokenizer_name_or_path,
)
| 260 | 0 |
'''simple docstring'''
import math
import os
import sys
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str ):
__a : str = ''
try:
with open(_SCREAMING_SNAKE_CASE , 'rb' ) as binary_file:
__a : str = binary_file.read()
for dat in data:
__a : str = F"""{dat:08b}"""
result += curr_byte
return result
except OSError:
print('File not accessible' )
sys.exit()
def lowerCamelCase (_SCREAMING_SNAKE_CASE : dict[str, str] , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : str ):
lexicon.pop(_SCREAMING_SNAKE_CASE )
__a : Tuple = last_match_id
if math.loga(_SCREAMING_SNAKE_CASE ).is_integer():
for curr_key in lexicon:
__a : List[Any] = '0' + lexicon[curr_key]
__a : Optional[Any] = bin(_SCREAMING_SNAKE_CASE )[2:]
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str ):
__a : List[Any] = {'0': '0', '1': '1'}
__a , __a : Union[str, Any] = '', ''
__a : Dict = len(_SCREAMING_SNAKE_CASE )
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
__a : Optional[int] = lexicon[curr_string]
result += last_match_id
add_key_to_lexicon(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
index += 1
__a : List[Any] = ''
while curr_string != "" and curr_string not in lexicon:
curr_string += "0"
if curr_string != "":
__a : List[Any] = lexicon[curr_string]
result += last_match_id
return result
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str ):
__a : Any = os.path.getsize(_SCREAMING_SNAKE_CASE )
__a : int = bin(_SCREAMING_SNAKE_CASE )[2:]
__a : Dict = len(_SCREAMING_SNAKE_CASE )
return "0" * (length_length - 1) + file_length_binary + compressed
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str ):
__a : Any = 8
try:
with open(_SCREAMING_SNAKE_CASE , 'wb' ) as opened_file:
__a : Optional[int] = [
to_write[i : i + byte_length]
for i in range(0 , len(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append('10000000' )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array:
opened_file.write(int(_SCREAMING_SNAKE_CASE , 2 ).to_bytes(1 , byteorder='big' ) )
except OSError:
print('File not accessible' )
sys.exit()
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str ):
__a : List[str] = read_file_binary(_SCREAMING_SNAKE_CASE )
__a : Tuple = compress_data(_SCREAMING_SNAKE_CASE )
__a : Union[str, Any] = add_file_length(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
write_file_binary(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 27 |
lowerCAmelCase : Optional[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 : Dict = [{'type': 'code', 'content': INSTALL_CONTENT}]
lowerCAmelCase : int = {
'{processor_class}': 'FakeProcessorClass',
'{model_class}': 'FakeModelClass',
'{object_class}': 'FakeObjectClass',
}
| 253 | 0 |
'''simple docstring'''
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import PIL.Image
from .features import FeatureType
lowerCAmelCase_ : Optional[List[str]] = None
lowerCAmelCase_ : List[Any] = '''<''' if sys.byteorder == '''little''' else '''>'''
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
lowerCAmelCase_ : str = [
np.dtype('''|b1'''),
np.dtype('''|u1'''),
np.dtype('''<u2'''),
np.dtype('''>u2'''),
np.dtype('''<i2'''),
np.dtype('''>i2'''),
np.dtype('''<u4'''),
np.dtype('''>u4'''),
np.dtype('''<i4'''),
np.dtype('''>i4'''),
np.dtype('''<f4'''),
np.dtype('''>f4'''),
np.dtype('''<f8'''),
np.dtype('''>f8'''),
]
@dataclass
class __lowerCAmelCase :
snake_case : bool = True
snake_case : Optional[str] = None
# Automatically constructed
snake_case : ClassVar[str] = "PIL.Image.Image"
snake_case : ClassVar[Any] = pa.struct({"""bytes""": pa.binary(), """path""": pa.string()} )
snake_case : str = field(default="""Image""" , init=__a , repr=__a )
def __call__(self ):
return self.pa_type
def snake_case_ (self , lowerCAmelCase__ ):
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
_UpperCAmelCase : List[Any] = np.array(lowerCAmelCase__ )
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
return {"path": value, "bytes": None}
elif isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
return {"path": None, "bytes": value}
elif isinstance(lowerCAmelCase__ , np.ndarray ):
# convert the image array to PNG/TIFF bytes
return encode_np_array(lowerCAmelCase__ )
elif isinstance(lowerCAmelCase__ , PIL.Image.Image ):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(lowerCAmelCase__ )
elif value.get("""path""" ) is not None and os.path.isfile(value["""path"""] ):
# we set "bytes": None to not duplicate the data if they're already available locally
return {"bytes": None, "path": value.get("""path""" )}
elif value.get("""bytes""" ) is not None or value.get("""path""" ) is not None:
# store the image bytes, and path is used to infer the image format using the file extension
return {"bytes": value.get("""bytes""" ), "path": value.get("""path""" )}
else:
raise ValueError(
F"An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}." )
def snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__=None ):
if not self.decode:
raise RuntimeError("""Decoding is disabled for this feature. Please use Image(decode=True) instead.""" )
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support decoding images, please install 'Pillow'.""" )
if token_per_repo_id is None:
_UpperCAmelCase : Optional[int] = {}
_UpperCAmelCase , _UpperCAmelCase : Dict = value["""path"""], value["""bytes"""]
if bytes_ is None:
if path is None:
raise ValueError(F"An image should have one of 'path' or 'bytes' but both are None in {value}." )
else:
if is_local_path(lowerCAmelCase__ ):
_UpperCAmelCase : int = PIL.Image.open(lowerCAmelCase__ )
else:
_UpperCAmelCase : Optional[int] = path.split("""::""" )[-1]
try:
_UpperCAmelCase : str = string_to_dict(lowerCAmelCase__ , config.HUB_DATASETS_URL )["""repo_id"""]
_UpperCAmelCase : int = token_per_repo_id.get(lowerCAmelCase__ )
except ValueError:
_UpperCAmelCase : int = None
with xopen(lowerCAmelCase__ , """rb""" , use_auth_token=lowerCAmelCase__ ) as f:
_UpperCAmelCase : Tuple = BytesIO(f.read() )
_UpperCAmelCase : List[Any] = PIL.Image.open(bytes_ )
else:
_UpperCAmelCase : Dict = PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def snake_case_ (self ):
from .features import Value
return (
self
if self.decode
else {
"bytes": Value("""binary""" ),
"path": Value("""string""" ),
}
)
def snake_case_ (self , lowerCAmelCase__ ):
if pa.types.is_string(storage.type ):
_UpperCAmelCase : Union[str, Any] = pa.array([None] * len(lowerCAmelCase__ ) , type=pa.binary() )
_UpperCAmelCase : int = pa.StructArray.from_arrays([bytes_array, storage] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
_UpperCAmelCase : Dict = pa.array([None] * len(lowerCAmelCase__ ) , type=pa.string() )
_UpperCAmelCase : int = pa.StructArray.from_arrays([storage, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("""bytes""" ) >= 0:
_UpperCAmelCase : Union[str, Any] = storage.field("""bytes""" )
else:
_UpperCAmelCase : Dict = pa.array([None] * len(lowerCAmelCase__ ) , type=pa.binary() )
if storage.type.get_field_index("""path""" ) >= 0:
_UpperCAmelCase : Optional[int] = storage.field("""path""" )
else:
_UpperCAmelCase : int = pa.array([None] * len(lowerCAmelCase__ ) , type=pa.string() )
_UpperCAmelCase : int = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
_UpperCAmelCase : str = pa.array(
[encode_np_array(np.array(lowerCAmelCase__ ) )["""bytes"""] if arr is not None else None for arr in storage.to_pylist()] , type=pa.binary() , )
_UpperCAmelCase : Union[str, Any] = pa.array([None] * len(lowerCAmelCase__ ) , type=pa.string() )
_UpperCAmelCase : Any = pa.StructArray.from_arrays(
[bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(lowerCAmelCase__ , self.pa_type )
def snake_case_ (self , lowerCAmelCase__ ):
@no_op_if_value_is_null
def path_to_bytes(lowerCAmelCase__ ):
with xopen(lowerCAmelCase__ , """rb""" ) as f:
_UpperCAmelCase : Dict = f.read()
return bytes_
_UpperCAmelCase : List[str] = pa.array(
[
(path_to_bytes(x["""path"""] ) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
_UpperCAmelCase : Any = pa.array(
[os.path.basename(lowerCAmelCase__ ) if path is not None else None for path in storage.field("""path""" ).to_pylist()] , type=pa.string() , )
_UpperCAmelCase : Any = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(lowerCAmelCase__ , self.pa_type )
def __A ( ):
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
global _IMAGE_COMPRESSION_FORMATS
if _IMAGE_COMPRESSION_FORMATS is None:
PIL.Image.init()
_UpperCAmelCase : Dict = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def __A ( lowerCAmelCase_ ):
_UpperCAmelCase : str = BytesIO()
if image.format in list_image_compression_formats():
_UpperCAmelCase : int = image.format
else:
_UpperCAmelCase : Optional[int] = """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
image.save(lowerCAmelCase_ , format=lowerCAmelCase_ )
return buffer.getvalue()
def __A ( lowerCAmelCase_ ):
if hasattr(lowerCAmelCase_ , """filename""" ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(lowerCAmelCase_ )}
def __A ( lowerCAmelCase_ ):
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
_UpperCAmelCase : List[Any] = array.dtype
_UpperCAmelCase : Any = dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
_UpperCAmelCase : Optional[int] = dtype.kind
_UpperCAmelCase : Tuple = dtype.itemsize
_UpperCAmelCase : Any = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
_UpperCAmelCase : List[Any] = np.dtype("""|u1""" )
if dtype_kind not in ["u", "i"]:
raise TypeError(
f"Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays." )
if dtype is not dest_dtype:
warnings.warn(f"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'" )
# Exact match
elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
_UpperCAmelCase : Optional[int] = dtype
else: # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
while dtype_itemsize >= 1:
_UpperCAmelCase : Tuple = dtype_byteorder + dtype_kind + str(lowerCAmelCase_ )
_UpperCAmelCase : Union[str, Any] = np.dtype(lowerCAmelCase_ )
if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
warnings.warn(f"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'" )
break
else:
dtype_itemsize //= 2
if dest_dtype is None:
raise TypeError(
f"Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}" )
_UpperCAmelCase : Dict = PIL.Image.fromarray(array.astype(lowerCAmelCase_ ) )
return {"path": None, "bytes": image_to_bytes(lowerCAmelCase_ )}
def __A ( lowerCAmelCase_ ):
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if objs:
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = first_non_null_value(lowerCAmelCase_ )
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(lowerCAmelCase_ , np.ndarray ):
_UpperCAmelCase : Optional[int] = no_op_if_value_is_null(lowerCAmelCase_ )
return [obj_to_image_dict_func(lowerCAmelCase_ ) for obj in objs]
elif isinstance(lowerCAmelCase_ , PIL.Image.Image ):
_UpperCAmelCase : List[Any] = no_op_if_value_is_null(lowerCAmelCase_ )
return [obj_to_image_dict_func(lowerCAmelCase_ ) for obj in objs]
else:
return objs
else:
return objs
| 170 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __lowerCAmelCase ( __a ):
def __init__(self , lowerCAmelCase__ , lowerCAmelCase__ ):
super().__init__()
# make sure scheduler can always be converted to DDIM
_UpperCAmelCase : Tuple = DDIMScheduler.from_config(scheduler.config )
self.register_modules(unet=lowerCAmelCase__ , scheduler=lowerCAmelCase__ )
@torch.no_grad()
def __call__(self , lowerCAmelCase__ = 1 , lowerCAmelCase__ = None , lowerCAmelCase__ = 0.0 , lowerCAmelCase__ = 5_0 , lowerCAmelCase__ = None , lowerCAmelCase__ = "pil" , lowerCAmelCase__ = True , ):
# Sample gaussian noise to begin loop
if isinstance(self.unet.config.sample_size , lowerCAmelCase__ ):
_UpperCAmelCase : str = (
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size,
self.unet.config.sample_size,
)
else:
_UpperCAmelCase : int = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and len(lowerCAmelCase__ ) != batch_size:
raise ValueError(
F"You have passed a list of generators of length {len(lowerCAmelCase__ )}, but requested an effective batch"
F" size of {batch_size}. Make sure the batch size matches the length of the generators." )
_UpperCAmelCase : Optional[Any] = randn_tensor(lowerCAmelCase__ , generator=lowerCAmelCase__ , device=self.device , dtype=self.unet.dtype )
# set step values
self.scheduler.set_timesteps(lowerCAmelCase__ )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
_UpperCAmelCase : str = self.unet(lowerCAmelCase__ , lowerCAmelCase__ ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
_UpperCAmelCase : List[str] = self.scheduler.step(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , eta=lowerCAmelCase__ , use_clipped_model_output=lowerCAmelCase__ , generator=lowerCAmelCase__ ).prev_sample
_UpperCAmelCase : Optional[int] = (image / 2 + 0.5).clamp(0 , 1 )
_UpperCAmelCase : str = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_UpperCAmelCase : str = self.numpy_to_pil(lowerCAmelCase__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowerCAmelCase__ )
| 170 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ = {
'''configuration_bigbird_pegasus''': [
'''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BigBirdPegasusConfig''',
'''BigBirdPegasusOnnxConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
'''BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BigBirdPegasusForCausalLM''',
'''BigBirdPegasusForConditionalGeneration''',
'''BigBirdPegasusForQuestionAnswering''',
'''BigBirdPegasusForSequenceClassification''',
'''BigBirdPegasusModel''',
'''BigBirdPegasusPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
BigBirdPegasusConfig,
BigBirdPegasusOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
BigBirdPegasusForCausalLM,
BigBirdPegasusForConditionalGeneration,
BigBirdPegasusForQuestionAnswering,
BigBirdPegasusForSequenceClassification,
BigBirdPegasusModel,
BigBirdPegasusPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 301 |
"""simple docstring"""
import os
from distutils.util import strtobool
def lowercase (_lowerCAmelCase , _lowerCAmelCase ):
for e in env_keys:
__lowerCAmelCase = int(os.environ.get(_lowerCAmelCase , -1 ) )
if val >= 0:
return val
return default
def lowercase (_lowerCAmelCase , _lowerCAmelCase=False ):
__lowerCAmelCase = os.environ.get(_lowerCAmelCase , str(_lowerCAmelCase ) )
return strtobool(_lowerCAmelCase ) == 1 # As its name indicates `strtobool` actually returns an int...
def lowercase (_lowerCAmelCase , _lowerCAmelCase="no" ):
__lowerCAmelCase = os.environ.get(_lowerCAmelCase , str(_lowerCAmelCase ) )
return value
| 301 | 1 |
"""simple docstring"""
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch
if is_faiss_available():
import faiss
@require_faiss
class __SCREAMING_SNAKE_CASE ( UpperCamelCase ):
def _UpperCamelCase ( self : Union[str, Any] ):
'''simple docstring'''
A__ : Tuple = tempfile.mkdtemp()
A__ : List[Any] = 8
# DPR tok
A__ : str = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
A__ : Union[str, Any] = os.path.join(self.tmpdirname , """dpr_tokenizer""" )
os.makedirs(snake_case , exist_ok=snake_case )
A__ : str = os.path.join(snake_case , DPR_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] ) )
# BART tok
A__ : Optional[int] = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""<unk>""",
]
A__ : Tuple = dict(zip(snake_case , range(len(snake_case ) ) ) )
A__ : Optional[Any] = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
A__ : Any = {"""unk_token""": """<unk>"""}
A__ : List[Any] = os.path.join(self.tmpdirname , """bart_tokenizer""" )
os.makedirs(snake_case , exist_ok=snake_case )
A__ : List[Any] = os.path.join(snake_case , BART_VOCAB_FILES_NAMES["""vocab_file"""] )
A__ : List[Any] = os.path.join(snake_case , BART_VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(snake_case ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(snake_case ) )
def _UpperCamelCase ( self : Dict ):
'''simple docstring'''
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , """dpr_tokenizer""" ) )
def _UpperCamelCase ( self : List[str] ):
'''simple docstring'''
return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , """dpr_tokenizer""" ) )
def _UpperCamelCase ( self : int ):
'''simple docstring'''
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , """bart_tokenizer""" ) )
def _UpperCamelCase ( self : int ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def _UpperCamelCase ( self : Dict ):
'''simple docstring'''
A__ : List[str] = Dataset.from_dict(
{
"""id""": ["""0""", """1"""],
"""text""": ["""foo""", """bar"""],
"""title""": ["""Foo""", """Bar"""],
"""embeddings""": [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
} )
dataset.add_faiss_index("""embeddings""" , string_factory="""Flat""" , metric_type=faiss.METRIC_INNER_PRODUCT )
return dataset
def _UpperCamelCase ( self : Dict ):
'''simple docstring'''
A__ : Any = self.get_dummy_dataset()
A__ : int = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , )
with patch("""transformers.models.rag.retrieval_rag.load_dataset""" ) as mock_load_dataset:
A__ : Any = dataset
A__ : Any = RagRetriever(
snake_case , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
return retriever
def _UpperCamelCase ( self : List[str] , snake_case : bool ):
'''simple docstring'''
A__ : Dict = self.get_dummy_dataset()
A__ : Union[str, Any] = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name="""custom""" , )
if from_disk:
A__ : Any = os.path.join(self.tmpdirname , """dataset""" )
A__ : Tuple = os.path.join(self.tmpdirname , """index.faiss""" )
dataset.get_index("""embeddings""" ).save(os.path.join(self.tmpdirname , """index.faiss""" ) )
dataset.drop_index("""embeddings""" )
dataset.save_to_disk(os.path.join(self.tmpdirname , """dataset""" ) )
del dataset
A__ : Tuple = RagRetriever(
snake_case , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
else:
A__ : int = RagRetriever(
snake_case , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , index=CustomHFIndex(config.retrieval_vector_size , snake_case ) , )
return retriever
def _UpperCamelCase ( self : str ):
'''simple docstring'''
A__ : Optional[Any] = Dataset.from_dict(
{
"""id""": ["""0""", """1"""],
"""text""": ["""foo""", """bar"""],
"""title""": ["""Foo""", """Bar"""],
"""embeddings""": [np.ones(self.retrieval_vector_size + 1 ), 2 * np.ones(self.retrieval_vector_size + 1 )],
} )
dataset.add_faiss_index("""embeddings""" , string_factory="""Flat""" , metric_type=faiss.METRIC_INNER_PRODUCT )
A__ : List[str] = os.path.join(self.tmpdirname , """hf_bert_base.hnswSQ8_correct_phi_128.c_index""" )
dataset.save_faiss_index("""embeddings""" , index_file_name + """.index.dpr""" )
pickle.dump(dataset["""id"""] , open(index_file_name + """.index_meta.dpr""" , """wb""" ) )
A__ : int = os.path.join(self.tmpdirname , """psgs_w100.tsv.pkl""" )
A__ : Dict = {sample["""id"""]: [sample["""text"""], sample["""title"""]] for sample in dataset}
pickle.dump(snake_case , open(snake_case , """wb""" ) )
A__ : Optional[int] = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name="""legacy""" , index_path=self.tmpdirname , )
A__ : List[Any] = RagRetriever(
snake_case , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() )
return retriever
def _UpperCamelCase ( self : Union[str, Any] ):
'''simple docstring'''
A__ : Dict = 1
A__ : str = self.get_dummy_canonical_hf_index_retriever()
A__ : Tuple = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
A__ : Optional[Any] = retriever.retrieve(snake_case , n_docs=snake_case )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(snake_case ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ["""embeddings""", """id""", """text""", """title"""] )
self.assertEqual(len(doc_dicts[0]["""id"""] ) , snake_case )
self.assertEqual(doc_dicts[0]["""id"""][0] , """1""" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["""id"""][0] , """0""" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def _UpperCamelCase ( self : Optional[Any] ):
'''simple docstring'''
A__ : List[str] = self.get_dummy_canonical_hf_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
with patch("""transformers.models.rag.retrieval_rag.load_dataset""" ) as mock_load_dataset:
A__ : Dict = self.get_dummy_dataset()
retriever.save_pretrained(snake_case )
A__ : List[str] = RagRetriever.from_pretrained(snake_case )
self.assertIsInstance(snake_case , snake_case )
A__ : Optional[int] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
A__ : Union[str, Any] = retriever.retrieve(snake_case , n_docs=1 )
self.assertTrue(out is not None )
def _UpperCamelCase ( self : Optional[int] ):
'''simple docstring'''
A__ : Union[str, Any] = 1
A__ : int = self.get_dummy_custom_hf_index_retriever(from_disk=snake_case )
A__ : List[Any] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
A__ : Optional[int] = retriever.retrieve(snake_case , n_docs=snake_case )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(snake_case ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ["""embeddings""", """id""", """text""", """title"""] )
self.assertEqual(len(doc_dicts[0]["""id"""] ) , snake_case )
self.assertEqual(doc_dicts[0]["""id"""][0] , """1""" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["""id"""][0] , """0""" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def _UpperCamelCase ( self : Dict ):
'''simple docstring'''
A__ : Union[str, Any] = self.get_dummy_custom_hf_index_retriever(from_disk=snake_case )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(snake_case )
A__ : Dict = RagRetriever.from_pretrained(snake_case )
self.assertIsInstance(snake_case , snake_case )
A__ : List[Any] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
A__ : List[Any] = retriever.retrieve(snake_case , n_docs=1 )
self.assertTrue(out is not None )
def _UpperCamelCase ( self : Any ):
'''simple docstring'''
A__ : Tuple = 1
A__ : Dict = self.get_dummy_custom_hf_index_retriever(from_disk=snake_case )
A__ : Any = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
A__ : List[Any] = retriever.retrieve(snake_case , n_docs=snake_case )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(snake_case ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ["""embeddings""", """id""", """text""", """title"""] )
self.assertEqual(len(doc_dicts[0]["""id"""] ) , snake_case )
self.assertEqual(doc_dicts[0]["""id"""][0] , """1""" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["""id"""][0] , """0""" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def _UpperCamelCase ( self : Union[str, Any] ):
'''simple docstring'''
A__ : int = self.get_dummy_custom_hf_index_retriever(from_disk=snake_case )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(snake_case )
A__ : Optional[int] = RagRetriever.from_pretrained(snake_case )
self.assertIsInstance(snake_case , snake_case )
A__ : Tuple = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
A__ : Any = retriever.retrieve(snake_case , n_docs=1 )
self.assertTrue(out is not None )
def _UpperCamelCase ( self : List[Any] ):
'''simple docstring'''
A__ : List[str] = 1
A__ : Tuple = self.get_dummy_legacy_index_retriever()
A__ : Optional[int] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
A__ : int = retriever.retrieve(snake_case , n_docs=snake_case )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(snake_case ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ["""text""", """title"""] )
self.assertEqual(len(doc_dicts[0]["""text"""] ) , snake_case )
self.assertEqual(doc_dicts[0]["""text"""][0] , """bar""" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["""text"""][0] , """foo""" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def _UpperCamelCase ( self : int ):
'''simple docstring'''
A__ : List[str] = self.get_dummy_legacy_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(snake_case )
A__ : Union[str, Any] = RagRetriever.from_pretrained(snake_case )
self.assertIsInstance(snake_case , snake_case )
A__ : Dict = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
A__ : List[str] = retriever.retrieve(snake_case , n_docs=1 )
self.assertTrue(out is not None )
@require_torch
@require_tokenizers
@require_sentencepiece
def _UpperCamelCase ( self : Union[str, Any] ):
'''simple docstring'''
import torch
A__ : Tuple = 1
A__ : Union[str, Any] = self.get_dummy_canonical_hf_index_retriever()
A__ : Union[str, Any] = [[5, 7], [10, 11]]
A__ : Optional[Any] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
A__ : Optional[Any] = retriever(snake_case , snake_case , prefix=retriever.config.generator.prefix , n_docs=snake_case )
A__ : List[str] = (
out["""context_input_ids"""],
out["""context_attention_mask"""],
out["""retrieved_doc_embeds"""],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(snake_case , snake_case )
self.assertIsInstance(snake_case , snake_case )
self.assertIsInstance(snake_case , np.ndarray )
A__ : Union[str, Any] = retriever(
snake_case , snake_case , prefix=retriever.config.generator.prefix , n_docs=snake_case , return_tensors="""pt""" , )
A__ : List[Any] = ( # noqa: F841
out["""context_input_ids"""],
out["""context_attention_mask"""],
out["""retrieved_doc_embeds"""],
out["""doc_ids"""],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(snake_case , torch.Tensor )
self.assertIsInstance(snake_case , torch.Tensor )
self.assertIsInstance(snake_case , torch.Tensor )
@require_torch
@require_tokenizers
@require_sentencepiece
def _UpperCamelCase ( self : Dict ):
'''simple docstring'''
A__ : Any = self.get_dpr_ctx_encoder_tokenizer()
A__ : str = 1
A__ : Union[str, Any] = self.get_dummy_custom_hf_index_retriever(from_disk=snake_case )
retriever.set_ctx_encoder_tokenizer(snake_case )
A__ : List[str] = [[5, 7], [10, 11]]
A__ : Dict = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
A__ : Dict = retriever(snake_case , snake_case , prefix=retriever.config.generator.prefix , n_docs=snake_case )
self.assertEqual(
len(snake_case ) , 6 ) # check whether the retriever output consist of 6 attributes including tokenized docs
self.assertEqual(
all(k in out for k in ("""tokenized_doc_ids""", """tokenized_doc_attention_mask""") ) , snake_case ) # check for doc token related keys in dictionary.
| 369 |
"""simple docstring"""
import numpy as np
class __SCREAMING_SNAKE_CASE :
def __init__( self : List[str] ):
'''simple docstring'''
A__ : Optional[int] = (0, 0)
A__ : Dict = None
A__ : int = 0
A__ : str = 0
A__ : Optional[Any] = 0
def __eq__( self : str , snake_case : Optional[int] ):
'''simple docstring'''
return self.position == cell.position
def _UpperCamelCase ( self : List[str] ):
'''simple docstring'''
print(self.position )
class __SCREAMING_SNAKE_CASE :
def __init__( self : int , snake_case : Any=(5, 5) ):
'''simple docstring'''
A__ : Optional[int] = np.zeros(snake_case )
A__ : List[Any] = world_size[0]
A__ : Dict = world_size[1]
def _UpperCamelCase ( self : Any ):
'''simple docstring'''
print(self.w )
def _UpperCamelCase ( self : Optional[int] , snake_case : List[Any] ):
'''simple docstring'''
A__ : int = [
(-1, -1),
(-1, 0),
(-1, 1),
(0, -1),
(0, 1),
(1, -1),
(1, 0),
(1, 1),
]
A__ : int = cell.position[0]
A__ : str = cell.position[1]
A__ : Any = []
for n in neughbour_cord:
A__ : List[Any] = current_x + n[0]
A__ : Tuple = current_y + n[1]
if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
A__ : List[Any] = Cell()
A__ : str = (x, y)
A__ : Optional[Any] = cell
neighbours.append(snake_case )
return neighbours
def _lowerCAmelCase ( UpperCAmelCase__ : List[str], UpperCAmelCase__ : Optional[Any], UpperCAmelCase__ : Dict ) ->Dict:
A__ : Union[str, Any] = []
A__ : Optional[int] = []
_open.append(UpperCAmelCase__ )
while _open:
A__ : List[Any] = np.argmin([n.f for n in _open] )
A__ : Union[str, Any] = _open[min_f]
_closed.append(_open.pop(UpperCAmelCase__ ) )
if current == goal:
break
for n in world.get_neigbours(UpperCAmelCase__ ):
for c in _closed:
if c == n:
continue
A__ : Dict = current.g + 1
A__ , A__ : int = n.position
A__ , A__ : Optional[int] = goal.position
A__ : Union[str, Any] = (ya - ya) ** 2 + (xa - xa) ** 2
A__ : Optional[int] = n.h + n.g
for c in _open:
if c == n and c.f < n.f:
continue
_open.append(UpperCAmelCase__ )
A__ : List[str] = []
while current.parent is not None:
path.append(current.position )
A__ : Union[str, Any] = current.parent
path.append(current.position )
return path[::-1]
if __name__ == "__main__":
A_ = Gridworld()
# Start position and goal
A_ = Cell()
A_ = (0, 0)
A_ = Cell()
A_ = (4, 4)
print(F'path from {start.position} to {goal.position}')
A_ = astar(world, start, goal)
# Just for visual reasons.
for i in s:
A_ = 1
print(world.w)
| 296 | 0 |
from __future__ import annotations
from collections.abc import Iterator
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__(self : Tuple , UpperCAmelCase_ : int) ->Optional[Any]:
'''simple docstring'''
lowerCamelCase__: List[str] =value
lowerCamelCase__: Node | None =None
lowerCamelCase__: Node | None =None
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__(self : Optional[int] , UpperCAmelCase_ : Node) ->Any:
'''simple docstring'''
lowerCamelCase__: List[str] =tree
def SCREAMING_SNAKE_CASE_ (self : Optional[int] , UpperCAmelCase_ : Node | None) ->int:
'''simple docstring'''
if node is None:
return 0
return node.value + (
self.depth_first_search(node.left) + self.depth_first_search(node.right)
)
def __iter__(self : int) ->List[str]:
'''simple docstring'''
yield self.depth_first_search(self.tree)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 10 |
"""simple docstring"""
from __future__ import annotations
def snake_case_ ( A_ : str ):
'''simple docstring'''
return [ord(A_ ) - 96 for elem in plain]
def snake_case_ ( A_ : list[int] ):
'''simple docstring'''
return "".join(chr(elem + 96 ) for elem in encoded )
def snake_case_ ( ):
'''simple docstring'''
_lowerCamelCase : Dict = encode(input('''-> ''' ).strip().lower() )
print('''Encoded: ''', A_ )
print('''Decoded:''', decode(A_ ) )
if __name__ == "__main__":
main()
| 72 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
_lowerCAmelCase : str = {
"microsoft/unispeech-sat-base-100h-libri-ft": (
"https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json"
),
# See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = 'unispeech-sat'
def __init__( self , __snake_case=32 , __snake_case=768 , __snake_case=12 , __snake_case=12 , __snake_case=3072 , __snake_case="gelu" , __snake_case=0.1 , __snake_case=0.1 , __snake_case=0.1 , __snake_case=0.0 , __snake_case=0.0 , __snake_case=0.1 , __snake_case=0.1 , __snake_case=0.02 , __snake_case=1e-5 , __snake_case="group" , __snake_case="gelu" , __snake_case=(512, 512, 512, 512, 512, 512, 512) , __snake_case=(5, 2, 2, 2, 2, 2, 2) , __snake_case=(10, 3, 3, 3, 3, 2, 2) , __snake_case=False , __snake_case=128 , __snake_case=16 , __snake_case=False , __snake_case=True , __snake_case=0.05 , __snake_case=10 , __snake_case=2 , __snake_case=0.0 , __snake_case=10 , __snake_case=0 , __snake_case=320 , __snake_case=2 , __snake_case=0.1 , __snake_case=100 , __snake_case=256 , __snake_case=256 , __snake_case=0.1 , __snake_case="mean" , __snake_case=False , __snake_case=False , __snake_case=256 , __snake_case=(512, 512, 512, 512, 1500) , __snake_case=(5, 3, 3, 1, 1) , __snake_case=(1, 2, 3, 1, 1) , __snake_case=512 , __snake_case=0 , __snake_case=1 , __snake_case=2 , __snake_case=504 , **__snake_case , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(**__snake_case , pad_token_id=__snake_case , bos_token_id=__snake_case , eos_token_id=__snake_case )
__a =hidden_size
__a =feat_extract_norm
__a =feat_extract_activation
__a =list(__snake_case )
__a =list(__snake_case )
__a =list(__snake_case )
__a =conv_bias
__a =num_conv_pos_embeddings
__a =num_conv_pos_embedding_groups
__a =len(self.conv_dim )
__a =num_hidden_layers
__a =intermediate_size
__a =hidden_act
__a =num_attention_heads
__a =hidden_dropout
__a =attention_dropout
__a =activation_dropout
__a =feat_proj_dropout
__a =final_dropout
__a =layerdrop
__a =layer_norm_eps
__a =initializer_range
__a =vocab_size
__a =num_clusters
__a =do_stable_layer_norm
__a =use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
f' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'
f' `len(config.conv_kernel) = {len(self.conv_kernel )}`.' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__a =apply_spec_augment
__a =mask_time_prob
__a =mask_time_length
__a =mask_time_min_masks
__a =mask_feature_prob
__a =mask_feature_length
__a =mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
__a =num_codevectors_per_group
__a =num_codevector_groups
__a =contrastive_logits_temperature
__a =feat_quantizer_dropout
__a =num_negatives
__a =codevector_dim
__a =proj_codevector_dim
__a =diversity_loss_weight
# ctc loss
__a =ctc_loss_reduction
__a =ctc_zero_infinity
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
__a =classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
__a =list(__snake_case )
__a =list(__snake_case )
__a =list(__snake_case )
__a =xvector_output_dim
@property
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 308 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowerCAmelCase : int = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : Dict = ["BartphoTokenizer"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
_lowerCAmelCase : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 308 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class A ( UpperCAmelCase_ ):
__UpperCAmelCase : List[str] = (
'This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'
'It takes two arguments named `image` which should be the original image, and `label` which should be a text '
'describing the elements what should be identified in the segmentation mask. The tool returns the mask.'
)
__UpperCAmelCase : Tuple = 'CIDAS/clipseg-rd64-refined'
__UpperCAmelCase : str = 'image_segmenter'
__UpperCAmelCase : Any = CLIPSegForImageSegmentation
__UpperCAmelCase : str = ['image', 'text']
__UpperCAmelCase : Tuple = ['image']
def __init__(self : Union[str, Any] , *__UpperCAmelCase : int , **__UpperCAmelCase : Dict ) -> Optional[int]:
"""simple docstring"""
requires_backends(self , ["vision"] )
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase )
def lowercase_ (self : List[str] , __UpperCAmelCase : "Image" , __UpperCAmelCase : str ) -> Tuple:
"""simple docstring"""
return self.pre_processor(text=[label] , images=[image] , padding=__UpperCAmelCase , return_tensors="pt" )
def lowercase_ (self : Optional[int] , __UpperCAmelCase : str ) -> int:
"""simple docstring"""
with torch.no_grad():
UpperCAmelCase__ = self.model(**__UpperCAmelCase ).logits
return logits
def lowercase_ (self : Union[str, Any] , __UpperCAmelCase : int ) -> int:
"""simple docstring"""
UpperCAmelCase__ = outputs.cpu().detach().numpy()
UpperCAmelCase__ = 0
UpperCAmelCase__ = 1
return Image.fromarray((array * 2_5_5).astype(np.uinta ) )
| 65 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items() )
def lowercase ( _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
_UpperCAmelCase = {}
for key, value in state_dict.items():
if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
continue
_UpperCAmelCase = key.replace('''heads.cmd.mim_head.cls.predictions''' , '''mmm_image_head''' )
_UpperCAmelCase = key.replace('''heads.cmd.mlm_head.cls.predictions''' , '''mmm_text_head''' )
_UpperCAmelCase = key.replace('''heads.cmd.itm_head.cls''' , '''itm_head''' )
_UpperCAmelCase = key.replace('''heads.cmd.itm_head.pooler''' , '''itm_head.pooler''' )
_UpperCAmelCase = key.replace('''heads.cmd.clip_head.logit_scale''' , '''flava.logit_scale''' )
_UpperCAmelCase = key.replace('''heads.fairseq_mlm.cls.predictions''' , '''mlm_head''' )
_UpperCAmelCase = key.replace('''heads.imagenet.mim_head.cls.predictions''' , '''mim_head''' )
_UpperCAmelCase = key.replace('''mm_text_projection''' , '''flava.text_to_mm_projection''' )
_UpperCAmelCase = key.replace('''mm_image_projection''' , '''flava.image_to_mm_projection''' )
_UpperCAmelCase = key.replace('''image_encoder.module''' , '''flava.image_model''' )
_UpperCAmelCase = key.replace('''text_encoder.module''' , '''flava.text_model''' )
_UpperCAmelCase = key.replace('''mm_encoder.module.encoder.cls_token''' , '''flava.multimodal_model.cls_token''' )
_UpperCAmelCase = key.replace('''mm_encoder.module''' , '''flava.multimodal_model''' )
_UpperCAmelCase = key.replace('''text_projection''' , '''flava.text_projection''' )
_UpperCAmelCase = key.replace('''image_projection''' , '''flava.image_projection''' )
_UpperCAmelCase = value.float()
for key, value in codebook_state_dict.items():
_UpperCAmelCase = value
return upgrade
@torch.no_grad()
def lowercase ( _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Optional[int]=None ):
'''simple docstring'''
if config_path is not None:
_UpperCAmelCase = FlavaConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
else:
_UpperCAmelCase = FlavaConfig()
_UpperCAmelCase = FlavaForPreTraining(_SCREAMING_SNAKE_CASE ).eval()
_UpperCAmelCase = convert_dalle_checkpoint(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , save_checkpoint=_SCREAMING_SNAKE_CASE )
if os.path.exists(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = torch.load(_SCREAMING_SNAKE_CASE , map_location='''cpu''' )
else:
_UpperCAmelCase = torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE , map_location='''cpu''' )
_UpperCAmelCase = upgrade_state_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
hf_model.load_state_dict(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = hf_model.state_dict()
_UpperCAmelCase = count_parameters(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = count_parameters(_SCREAMING_SNAKE_CASE ) + count_parameters(_SCREAMING_SNAKE_CASE )
assert torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1E-3 )
hf_model.save_pretrained(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__A : Dict = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
parser.add_argument("--codebook_path", default=None, type=str, help="Path to flava codebook checkpoint")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
__A : Optional[Any] = parser.parse_args()
convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
| 260 | 0 |
import math
class _SCREAMING_SNAKE_CASE :
def __init__( self : Tuple , __lowerCamelCase : int=0 ): # a graph with Node 0,1,...,N-1
UpperCamelCase :Optional[int] = n
UpperCamelCase :Any = [
[math.inf for j in range(0 , __lowerCamelCase )] for i in range(0 , __lowerCamelCase )
] # adjacency matrix for weight
UpperCamelCase :Optional[int] = [
[math.inf for j in range(0 , __lowerCamelCase )] for i in range(0 , __lowerCamelCase )
] # dp[i][j] stores minimum distance from i to j
def _A ( self : str , __lowerCamelCase : Dict , __lowerCamelCase : List[str] , __lowerCamelCase : Tuple ):
UpperCamelCase :int = w
def _A ( self : Tuple ):
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
UpperCamelCase :Tuple = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def _A ( self : List[Any] , __lowerCamelCase : Optional[int] , __lowerCamelCase : str ):
return self.dp[u][v]
if __name__ == "__main__":
UpperCAmelCase_ : List[str] = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 62 |
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class _SCREAMING_SNAKE_CASE ( _a ):
snake_case__ : Union[str, Any] = """char"""
snake_case__ : Optional[int] = """bpe"""
snake_case__ : Dict = """wp"""
UpperCAmelCase_ : List[Any] = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class _SCREAMING_SNAKE_CASE ( _a ):
snake_case__ : List[Any] = ["""image_processor""", """char_tokenizer"""]
snake_case__ : Dict = """ViTImageProcessor"""
snake_case__ : List[str] = """MgpstrTokenizer"""
def __init__( self : Optional[int] , __lowerCamelCase : Optional[int]=None , __lowerCamelCase : Dict=None , **__lowerCamelCase : Any ):
UpperCamelCase :Optional[Any] = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , __lowerCamelCase , )
UpperCamelCase :Optional[int] = kwargs.pop("""feature_extractor""" )
UpperCamelCase :List[str] = 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`.""" )
UpperCamelCase :Optional[int] = tokenizer
UpperCamelCase :int = AutoTokenizer.from_pretrained("""gpt2""" )
UpperCamelCase :int = AutoTokenizer.from_pretrained("""bert-base-uncased""" )
super().__init__(__lowerCamelCase , __lowerCamelCase )
def __call__( self : str , __lowerCamelCase : Optional[int]=None , __lowerCamelCase : Dict=None , __lowerCamelCase : str=None , **__lowerCamelCase : Dict ):
if images is None and text is None:
raise ValueError("""You need to specify either an `images` or `text` input to process.""" )
if images is not None:
UpperCamelCase :Tuple = self.image_processor(__lowerCamelCase , return_tensors=__lowerCamelCase , **__lowerCamelCase )
if text is not None:
UpperCamelCase :Any = self.char_tokenizer(__lowerCamelCase , return_tensors=__lowerCamelCase , **__lowerCamelCase )
if text is None:
return inputs
elif images is None:
return encodings
else:
UpperCamelCase :Dict = encodings["""input_ids"""]
return inputs
def _A ( self : Tuple , __lowerCamelCase : str ):
UpperCamelCase , UpperCamelCase , UpperCamelCase :int = sequences
UpperCamelCase :Tuple = char_preds.size(0 )
UpperCamelCase , UpperCamelCase :str = self._decode_helper(__lowerCamelCase , """char""" )
UpperCamelCase , UpperCamelCase :List[Any] = self._decode_helper(__lowerCamelCase , """bpe""" )
UpperCamelCase , UpperCamelCase :List[Any] = self._decode_helper(__lowerCamelCase , """wp""" )
UpperCamelCase :Any = []
UpperCamelCase :str = []
for i in range(__lowerCamelCase ):
UpperCamelCase :Union[str, Any] = [char_scores[i], bpe_scores[i], wp_scores[i]]
UpperCamelCase :Any = [char_strs[i], bpe_strs[i], wp_strs[i]]
UpperCamelCase :str = scores.index(max(__lowerCamelCase ) )
final_strs.append(strs[max_score_index] )
final_scores.append(scores[max_score_index] )
UpperCamelCase :Optional[Any] = {}
UpperCamelCase :Dict = final_strs
UpperCamelCase :Union[str, Any] = final_scores
UpperCamelCase :List[str] = char_strs
UpperCamelCase :Tuple = bpe_strs
UpperCamelCase :Optional[Any] = wp_strs
return out
def _A ( self : int , __lowerCamelCase : List[Any] , __lowerCamelCase : List[str] ):
if format == DecodeType.CHARACTER:
UpperCamelCase :List[str] = self.char_decode
UpperCamelCase :Union[str, Any] = 1
UpperCamelCase :Optional[Any] = """[s]"""
elif format == DecodeType.BPE:
UpperCamelCase :Union[str, Any] = self.bpe_decode
UpperCamelCase :str = 2
UpperCamelCase :int = """#"""
elif format == DecodeType.WORDPIECE:
UpperCamelCase :int = self.wp_decode
UpperCamelCase :Any = 102
UpperCamelCase :int = """[SEP]"""
else:
raise ValueError(F"""Format {format} is not supported.""" )
UpperCamelCase , UpperCamelCase :int = [], []
UpperCamelCase :Any = pred_logits.size(0 )
UpperCamelCase :List[Any] = pred_logits.size(1 )
UpperCamelCase , UpperCamelCase :Optional[int] = pred_logits.topk(1 , dim=-1 , largest=__lowerCamelCase , sorted=__lowerCamelCase )
UpperCamelCase :Optional[Any] = preds_index.view(-1 , __lowerCamelCase )[:, 1:]
UpperCamelCase :int = decoder(__lowerCamelCase )
UpperCamelCase , UpperCamelCase :Optional[int] = torch.nn.functional.softmax(__lowerCamelCase , dim=2 ).max(dim=2 )
UpperCamelCase :Tuple = preds_max_prob[:, 1:]
for index in range(__lowerCamelCase ):
UpperCamelCase :Tuple = preds_str[index].find(__lowerCamelCase )
UpperCamelCase :List[Any] = preds_str[index][:pred_eos]
UpperCamelCase :List[Any] = preds_index[index].cpu().tolist()
UpperCamelCase :Optional[Any] = pred_index.index(__lowerCamelCase ) if eos_token in pred_index else -1
UpperCamelCase :List[str] = preds_max_prob[index][: pred_eos_index + 1]
UpperCamelCase :List[str] = pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0
dec_strs.append(__lowerCamelCase )
conf_scores.append(__lowerCamelCase )
return dec_strs, conf_scores
def _A ( self : Optional[Any] , __lowerCamelCase : str ):
UpperCamelCase :Dict = [seq.replace(""" """ , """""" ) for seq in self.char_tokenizer.batch_decode(__lowerCamelCase )]
return decode_strs
def _A ( self : Union[str, Any] , __lowerCamelCase : str ):
return self.bpe_tokenizer.batch_decode(__lowerCamelCase )
def _A ( self : int , __lowerCamelCase : Optional[int] ):
UpperCamelCase :Any = [seq.replace(""" """ , """""" ) for seq in self.wp_tokenizer.batch_decode(__lowerCamelCase )]
return decode_strs
| 62 | 1 |
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import BertGenerationDecoder, BertGenerationEncoder
class snake_case__ :
"""simple docstring"""
def __init__( self , __lowercase , __lowercase=1_3 , __lowercase=7 , __lowercase=True , __lowercase=True , __lowercase=9_9 , __lowercase=3_2 , __lowercase=5 , __lowercase=4 , __lowercase=3_7 , __lowercase="gelu" , __lowercase=0.1 , __lowercase=0.1 , __lowercase=5_0 , __lowercase=0.0_2 , __lowercase=True , __lowercase=None , ) -> Any:
"""simple docstring"""
a__ : List[Any] = parent
a__ : Optional[int] = batch_size
a__ : Dict = seq_length
a__ : Dict = is_training
a__ : Union[str, Any] = use_input_mask
a__ : List[Any] = vocab_size
a__ : str = hidden_size
a__ : List[Any] = num_hidden_layers
a__ : Optional[int] = num_attention_heads
a__ : int = intermediate_size
a__ : int = hidden_act
a__ : List[str] = hidden_dropout_prob
a__ : Dict = attention_probs_dropout_prob
a__ : int = max_position_embeddings
a__ : Optional[Any] = initializer_range
a__ : List[Any] = use_labels
a__ : Optional[int] = scope
def SCREAMING_SNAKE_CASE__( self ) -> List[str]:
"""simple docstring"""
a__ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a__ : Any = None
if self.use_input_mask:
a__ : int = random_attention_mask([self.batch_size, self.seq_length] )
if self.use_labels:
a__ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a__ : Dict = self.get_config()
return config, input_ids, input_mask, token_labels
def SCREAMING_SNAKE_CASE__( self ) -> Optional[Any]:
"""simple docstring"""
return BertGenerationConfig(
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 , is_decoder=__lowercase , initializer_range=self.initializer_range , )
def SCREAMING_SNAKE_CASE__( self ) -> Any:
"""simple docstring"""
(
(
a__
) , (
a__
) , (
a__
) , (
a__
) ,
) : int = self.prepare_config_and_inputs()
a__ : str = True
a__ : Dict = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
a__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
input_mask,
token_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase , __lowercase , __lowercase , **__lowercase , ) -> List[Any]:
"""simple docstring"""
a__ : List[Any] = BertGenerationEncoder(config=__lowercase )
model.to(__lowercase )
model.eval()
a__ : List[Any] = model(__lowercase , attention_mask=__lowercase )
a__ : Dict = model(__lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , **__lowercase , ) -> List[Any]:
"""simple docstring"""
a__ : int = True
a__ : str = BertGenerationEncoder(config=__lowercase )
model.to(__lowercase )
model.eval()
a__ : Any = model(
__lowercase , attention_mask=__lowercase , encoder_hidden_states=__lowercase , encoder_attention_mask=__lowercase , )
a__ : Optional[Any] = model(
__lowercase , attention_mask=__lowercase , encoder_hidden_states=__lowercase , )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , **__lowercase , ) -> Optional[Any]:
"""simple docstring"""
a__ : Optional[Any] = True
a__ : Optional[Any] = True
a__ : List[Any] = BertGenerationDecoder(config=__lowercase ).to(__lowercase ).eval()
# first forward pass
a__ : Any = model(
__lowercase , attention_mask=__lowercase , encoder_hidden_states=__lowercase , encoder_attention_mask=__lowercase , use_cache=__lowercase , )
a__ : List[Any] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
a__ : str = ids_tensor((self.batch_size, 3) , config.vocab_size )
a__ : Dict = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
a__ : Tuple = torch.cat([input_ids, next_tokens] , dim=-1 )
a__ : List[str] = torch.cat([input_mask, next_mask] , dim=-1 )
a__ : Dict = model(
__lowercase , attention_mask=__lowercase , encoder_hidden_states=__lowercase , encoder_attention_mask=__lowercase , output_hidden_states=__lowercase , )["""hidden_states"""][0]
a__ : Union[str, Any] = model(
__lowercase , attention_mask=__lowercase , encoder_hidden_states=__lowercase , encoder_attention_mask=__lowercase , past_key_values=__lowercase , output_hidden_states=__lowercase , )["""hidden_states"""][0]
# select random slice
a__ : Any = ids_tensor((1,) , output_from_past.shape[-1] ).item()
a__ : Union[str, Any] = output_from_no_past[:, -3:, random_slice_idx].detach()
a__ : int = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(__lowercase , __lowercase , atol=1E-3 ) )
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase , __lowercase , __lowercase , *__lowercase , ) -> Dict:
"""simple docstring"""
a__ : str = BertGenerationDecoder(__lowercase )
model.to(__lowercase )
model.eval()
a__ : int = model(__lowercase , attention_mask=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__( self ) -> Dict:
"""simple docstring"""
a__ , a__ , a__ , a__ : Optional[Any] = self.prepare_config_and_inputs()
a__ : Dict = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class snake_case__ (A__ , A__ , A__ , unittest.TestCase ):
"""simple docstring"""
__lowerCAmelCase :List[Any] = (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
__lowerCAmelCase :str = (BertGenerationDecoder,) if is_torch_available() else ()
__lowerCAmelCase :int = (
{"feature-extraction": BertGenerationEncoder, "text-generation": BertGenerationDecoder}
if is_torch_available()
else {}
)
def SCREAMING_SNAKE_CASE__( self ) -> Optional[int]:
"""simple docstring"""
a__ : int = BertGenerationEncoderTester(self )
a__ : int = ConfigTester(self , config_class=__lowercase , hidden_size=3_7 )
def SCREAMING_SNAKE_CASE__( self ) -> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__( self ) -> List[str]:
"""simple docstring"""
a__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowercase )
def SCREAMING_SNAKE_CASE__( self ) -> Optional[int]:
"""simple docstring"""
a__ , a__ , a__ , a__ : Dict = self.model_tester.prepare_config_and_inputs()
a__ : List[str] = """bert"""
self.model_tester.create_and_check_model(__lowercase , __lowercase , __lowercase , __lowercase )
def SCREAMING_SNAKE_CASE__( self ) -> Dict:
"""simple docstring"""
a__ : Dict = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*__lowercase )
def SCREAMING_SNAKE_CASE__( self ) -> List[Any]:
"""simple docstring"""
a__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*__lowercase )
def SCREAMING_SNAKE_CASE__( self ) -> Any:
"""simple docstring"""
(
(
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) ,
) : Dict = self.model_tester.prepare_config_and_inputs_for_decoder()
a__ : Tuple = None
self.model_tester.create_and_check_model_as_decoder(
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , )
def SCREAMING_SNAKE_CASE__( self ) -> int:
"""simple docstring"""
a__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_for_causal_lm(*__lowercase )
@slow
def SCREAMING_SNAKE_CASE__( self ) -> List[Any]:
"""simple docstring"""
a__ : Union[str, Any] = BertGenerationEncoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder""" )
self.assertIsNotNone(__lowercase )
@require_torch
class snake_case__ (unittest.TestCase ):
"""simple docstring"""
@slow
def SCREAMING_SNAKE_CASE__( self ) -> Union[str, Any]:
"""simple docstring"""
a__ : str = BertGenerationEncoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder""" )
a__ : List[str] = torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]] )
with torch.no_grad():
a__ : Dict = model(__lowercase )[0]
a__ : Optional[Any] = torch.Size([1, 8, 1_0_2_4] )
self.assertEqual(output.shape , __lowercase )
a__ : List[str] = torch.tensor(
[[[0.1_7_7_5, 0.0_0_8_3, -0.0_3_2_1], [1.6_0_0_2, 0.1_2_8_7, 0.3_9_1_2], [2.1_4_7_3, 0.5_7_9_1, 0.6_0_6_6]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __lowercase , atol=1E-4 ) )
@require_torch
class snake_case__ (unittest.TestCase ):
"""simple docstring"""
@slow
def SCREAMING_SNAKE_CASE__( self ) -> Tuple:
"""simple docstring"""
a__ : Tuple = BertGenerationDecoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder""" )
a__ : int = torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]] )
with torch.no_grad():
a__ : Optional[int] = model(__lowercase )[0]
a__ : str = torch.Size([1, 8, 5_0_3_5_8] )
self.assertEqual(output.shape , __lowercase )
a__ : List[str] = torch.tensor(
[[[-0.5_7_8_8, -2.5_9_9_4, -3.7_0_5_4], [0.0_4_3_8, 4.7_9_9_7, 1.8_7_9_5], [1.5_8_6_2, 6.6_4_0_9, 4.4_6_3_8]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __lowercase , atol=1E-4 ) )
| 170 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthez import BarthezTokenizer
else:
_lowercase : List[str] =None
_lowercase : Union[str, Any] =logging.get_logger(__name__)
_lowercase : Optional[int] ={"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
_lowercase : Dict ={
"vocab_file": {
"moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
"moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
"moussaKam/barthez-orangesum-title": (
"https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
),
},
"tokenizer_file": {
"moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json",
"moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json",
"moussaKam/barthez-orangesum-title": (
"https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"
),
},
}
_lowercase : str ={
"moussaKam/mbarthez": 1024,
"moussaKam/barthez": 1024,
"moussaKam/barthez-orangesum-title": 1024,
}
_lowercase : Dict ="▁"
class snake_case__ (A__ ):
"""simple docstring"""
__lowerCAmelCase :Union[str, Any] = VOCAB_FILES_NAMES
__lowerCAmelCase :Optional[int] = PRETRAINED_VOCAB_FILES_MAP
__lowerCAmelCase :int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCAmelCase :Any = ["input_ids", "attention_mask"]
__lowerCAmelCase :Any = BarthezTokenizer
def __init__( self , __lowercase=None , __lowercase=None , __lowercase="<s>" , __lowercase="</s>" , __lowercase="</s>" , __lowercase="<s>" , __lowercase="<unk>" , __lowercase="<pad>" , __lowercase="<mask>" , **__lowercase , ) -> str:
"""simple docstring"""
a__ : int = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else mask_token
super().__init__(
__lowercase , tokenizer_file=__lowercase , bos_token=__lowercase , eos_token=__lowercase , unk_token=__lowercase , sep_token=__lowercase , cls_token=__lowercase , pad_token=__lowercase , mask_token=__lowercase , **__lowercase , )
a__ : List[str] = vocab_file
a__ : List[Any] = False if not self.vocab_file else True
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
a__ : Tuple = [self.cls_token_id]
a__ : List[str] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase = None ) -> List[int]:
"""simple docstring"""
a__ : List[Any] = [self.sep_token_id]
a__ : str = [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 SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(__lowercase ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
a__ : Tuple = os.path.join(
__lowercase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowercase ):
copyfile(self.vocab_file , __lowercase )
return (out_vocab_file,)
| 170 | 1 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase_ = logging.get_logger(__name__)
def snake_case ( A__ ,A__ ,A__ ,A__ ):
UpperCAmelCase_ : Optional[Any] = original_name.split("." )[0]
UpperCAmelCase_ : Optional[Any] = key.split("." )
UpperCAmelCase_ : str = int(key_list[key_list.index(A__ ) - 2] )
UpperCAmelCase_ : str = int(key_list[key_list.index(A__ ) - 1] )
UpperCAmelCase_ : List[Any] = orig_block_num - offset
UpperCAmelCase_ : List[str] = key.replace(F"""{orig_block_num}.{layer_num}.{original_name}""" ,F"""block.{new_block_num}.{layer_num}.{new_name}""" )
return key
def snake_case ( A__ ):
UpperCAmelCase_ : List[str] = OrderedDict()
UpperCAmelCase_ , UpperCAmelCase_ : Optional[Any] = 0, 0
for key, value in state_dict.items():
if key.startswith("network" ):
UpperCAmelCase_ : Tuple = key.replace("network" ,"poolformer.encoder" )
if "proj" in key:
# Works for the first embedding as well as the internal embedding layers
if key.endswith("bias" ) and "patch_embed" not in key:
patch_emb_offset += 1
UpperCAmelCase_ : List[str] = key[: key.find("proj" )]
UpperCAmelCase_ : Optional[Any] = key.replace(A__ ,F"""patch_embeddings.{total_embed_found}.""" )
UpperCAmelCase_ : Union[str, Any] = key.replace("proj" ,"projection" )
if key.endswith("bias" ):
total_embed_found += 1
if "patch_embeddings" in key:
UpperCAmelCase_ : Dict = "poolformer.encoder." + key
if "mlp.fc1" in key:
UpperCAmelCase_ : List[Any] = replace_key_with_offset(A__ ,A__ ,"mlp.fc1" ,"output.conv1" )
if "mlp.fc2" in key:
UpperCAmelCase_ : List[Any] = replace_key_with_offset(A__ ,A__ ,"mlp.fc2" ,"output.conv2" )
if "norm1" in key:
UpperCAmelCase_ : List[Any] = replace_key_with_offset(A__ ,A__ ,"norm1" ,"before_norm" )
if "norm2" in key:
UpperCAmelCase_ : str = replace_key_with_offset(A__ ,A__ ,"norm2" ,"after_norm" )
if "layer_scale_1" in key:
UpperCAmelCase_ : Optional[Any] = replace_key_with_offset(A__ ,A__ ,"layer_scale_1" ,"layer_scale_1" )
if "layer_scale_2" in key:
UpperCAmelCase_ : str = replace_key_with_offset(A__ ,A__ ,"layer_scale_2" ,"layer_scale_2" )
if "head" in key:
UpperCAmelCase_ : List[str] = key.replace("head" ,"classifier" )
UpperCAmelCase_ : Tuple = value
return new_state_dict
def snake_case ( ):
UpperCAmelCase_ : List[Any] = "http://images.cocodataset.org/val2017/000000039769.jpg"
UpperCAmelCase_ : Any = Image.open(requests.get(A__ ,stream=A__ ).raw )
return image
@torch.no_grad()
def snake_case ( A__ ,A__ ,A__ ):
UpperCAmelCase_ : List[Any] = PoolFormerConfig()
# set attributes based on model_name
UpperCAmelCase_ : Union[str, Any] = "huggingface/label-files"
UpperCAmelCase_ : Dict = model_name[-3:]
UpperCAmelCase_ : List[Any] = 10_00
UpperCAmelCase_ : List[str] = "imagenet-1k-id2label.json"
UpperCAmelCase_ : Union[str, Any] = (1, 10_00)
# set config attributes
UpperCAmelCase_ : List[str] = json.load(open(hf_hub_download(A__ ,A__ ,repo_type="dataset" ) ,"r" ) )
UpperCAmelCase_ : str = {int(A__ ): v for k, v in idalabel.items()}
UpperCAmelCase_ : Dict = idalabel
UpperCAmelCase_ : Union[str, Any] = {v: k for k, v in idalabel.items()}
if size == "s12":
UpperCAmelCase_ : Any = [2, 2, 6, 2]
UpperCAmelCase_ : Union[str, Any] = [64, 1_28, 3_20, 5_12]
UpperCAmelCase_ : int = 4.0
UpperCAmelCase_ : Tuple = 0.9
elif size == "s24":
UpperCAmelCase_ : List[Any] = [4, 4, 12, 4]
UpperCAmelCase_ : Tuple = [64, 1_28, 3_20, 5_12]
UpperCAmelCase_ : List[Any] = 4.0
UpperCAmelCase_ : Dict = 0.9
elif size == "s36":
UpperCAmelCase_ : Optional[int] = [6, 6, 18, 6]
UpperCAmelCase_ : Tuple = [64, 1_28, 3_20, 5_12]
UpperCAmelCase_ : List[Any] = 4.0
UpperCAmelCase_ : int = 1e-6
UpperCAmelCase_ : Union[str, Any] = 0.9
elif size == "m36":
UpperCAmelCase_ : List[Any] = [6, 6, 18, 6]
UpperCAmelCase_ : List[str] = [96, 1_92, 3_84, 7_68]
UpperCAmelCase_ : Union[str, Any] = 4.0
UpperCAmelCase_ : List[str] = 1e-6
UpperCAmelCase_ : List[str] = 0.95
elif size == "m48":
UpperCAmelCase_ : Tuple = [8, 8, 24, 8]
UpperCAmelCase_ : List[Any] = [96, 1_92, 3_84, 7_68]
UpperCAmelCase_ : List[Any] = 4.0
UpperCAmelCase_ : str = 1e-6
UpperCAmelCase_ : Union[str, Any] = 0.95
else:
raise ValueError(F"""Size {size} not supported""" )
# load image processor
UpperCAmelCase_ : Optional[Any] = PoolFormerImageProcessor(crop_pct=A__ )
# Prepare image
UpperCAmelCase_ : Union[str, Any] = prepare_img()
UpperCAmelCase_ : Any = image_processor(images=A__ ,return_tensors="pt" ).pixel_values
logger.info(F"""Converting model {model_name}...""" )
# load original state dict
UpperCAmelCase_ : Dict = torch.load(A__ ,map_location=torch.device("cpu" ) )
# rename keys
UpperCAmelCase_ : Dict = rename_keys(A__ )
# create HuggingFace model and load state dict
UpperCAmelCase_ : List[Any] = PoolFormerForImageClassification(A__ )
model.load_state_dict(A__ )
model.eval()
# Define image processor
UpperCAmelCase_ : Union[str, Any] = PoolFormerImageProcessor(crop_pct=A__ )
UpperCAmelCase_ : Optional[int] = image_processor(images=prepare_img() ,return_tensors="pt" ).pixel_values
# forward pass
UpperCAmelCase_ : Any = model(A__ )
UpperCAmelCase_ : int = outputs.logits
# define expected logit slices for different models
if size == "s12":
UpperCAmelCase_ : List[Any] = torch.tensor([-0.3045, -0.6758, -0.4869] )
elif size == "s24":
UpperCAmelCase_ : Union[str, Any] = torch.tensor([0.4402, -0.1374, -0.8045] )
elif size == "s36":
UpperCAmelCase_ : Union[str, Any] = torch.tensor([-0.6080, -0.5133, -0.5898] )
elif size == "m36":
UpperCAmelCase_ : List[Any] = torch.tensor([0.3952, 0.2263, -1.2668] )
elif size == "m48":
UpperCAmelCase_ : Union[str, Any] = torch.tensor([0.1167, -0.0656, -0.3423] )
else:
raise ValueError(F"""Size {size} not supported""" )
# verify logits
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3] ,A__ ,atol=1e-2 )
# finally, save model and image processor
logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
Path(A__ ).mkdir(exist_ok=A__ )
model.save_pretrained(A__ )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(A__ )
if __name__ == "__main__":
lowerCamelCase_ = argparse.ArgumentParser()
parser.add_argument(
'''--model_name''',
default='''poolformer_s12''',
type=str,
help='''Name of the model you\'d like to convert.''',
)
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, help='''Path 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.'''
)
lowerCamelCase_ = parser.parse_args()
convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 253 |
"""simple docstring"""
def snake_case ( A__ ):
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(A__ ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__('''doctest''').testmod()
| 253 | 1 |
"""simple docstring"""
def a_ ( _lowerCAmelCase : str = 1000 ):
'''simple docstring'''
lowercase__ : Optional[Any] = 2**power
lowercase__ : str = str(_SCREAMING_SNAKE_CASE )
lowercase__ : str = list(_SCREAMING_SNAKE_CASE )
lowercase__ : int = 0
for i in list_num:
sum_of_num += int(_SCREAMING_SNAKE_CASE )
return sum_of_num
if __name__ == "__main__":
_UpperCamelCase : Any = int(input("Enter the power of 2: ").strip())
print("2 ^ ", power, " = ", 2**power)
_UpperCamelCase : Tuple = solution(power)
print("Sum of the digits is: ", result)
| 77 |
import os
from distutils.util import strtobool
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple:
'''simple docstring'''
for e in env_keys:
SCREAMING_SNAKE_CASE = int(os.environ.get(_SCREAMING_SNAKE_CASE , -1 ) )
if val >= 0:
return val
return default
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = os.environ.get(_SCREAMING_SNAKE_CASE , str(_SCREAMING_SNAKE_CASE ) )
return strtobool(_SCREAMING_SNAKE_CASE ) == 1 # As its name indicates `strtobool` actually returns an int...
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE="no" ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = os.environ.get(_SCREAMING_SNAKE_CASE , str(_SCREAMING_SNAKE_CASE ) )
return value
| 296 | 0 |
'''simple docstring'''
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
return EnvironmentCommand()
class UpperCAmelCase ( snake_case_ ):
@staticmethod
def lowercase__ ( __snake_case : ArgumentParser ) -> Optional[int]:
_lowerCAmelCase = parser.add_parser("""env""" )
download_parser.set_defaults(func=__snake_case )
def lowercase__ ( self : Optional[int] ) -> List[str]:
_lowerCAmelCase = huggingface_hub.__version__
_lowerCAmelCase = """not installed"""
_lowerCAmelCase = """NA"""
if is_torch_available():
import torch
_lowerCAmelCase = torch.__version__
_lowerCAmelCase = torch.cuda.is_available()
_lowerCAmelCase = """not installed"""
if is_transformers_available():
import transformers
_lowerCAmelCase = transformers.__version__
_lowerCAmelCase = """not installed"""
if is_accelerate_available():
import accelerate
_lowerCAmelCase = accelerate.__version__
_lowerCAmelCase = """not installed"""
if is_xformers_available():
import xformers
_lowerCAmelCase = xformers.__version__
_lowerCAmelCase = {
"""`diffusers` version""": version,
"""Platform""": platform.platform(),
"""Python version""": platform.python_version(),
"""PyTorch version (GPU?)""": f"{pt_version} ({pt_cuda_available})",
"""Huggingface_hub version""": hub_version,
"""Transformers version""": transformers_version,
"""Accelerate version""": accelerate_version,
"""xFormers version""": xformers_version,
"""Using GPU in script?""": """<fill in>""",
"""Using distributed or parallel set-up in script?""": """<fill in>""",
}
print("""\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n""" )
print(self.format_dict(__snake_case ) )
return info
@staticmethod
def lowercase__ ( __snake_case : Any ) -> Union[str, Any]:
return "\n".join([f"- {prop}: {val}" for prop, val in d.items()] ) + "\n"
| 220 |
'''simple docstring'''
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def UpperCamelCase__ ( lowerCAmelCase=None , lowerCAmelCase=None ):
"""simple docstring"""
return field(default_factory=lambda: default , metadata=lowerCAmelCase )
@dataclass
class UpperCAmelCase :
_lowercase: str = field(
metadata={'''help''': '''The csv file to plot.'''} , )
_lowercase: bool = field(
default=snake_case_ , metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''} , )
_lowercase: bool = field(
default=snake_case_ , metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''} , )
_lowercase: bool = field(
default=snake_case_ , metadata={'''help''': '''Disable logarithmic scale when plotting'''} , )
_lowercase: bool = field(
default=snake_case_ , metadata={
'''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
} , )
_lowercase: Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''} , )
_lowercase: Optional[List[str]] = list_field(
default=snake_case_ , metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''} )
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
try:
int(lowerCAmelCase )
return True
except ValueError:
return False
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
try:
float(lowerCAmelCase )
return True
except ValueError:
return False
class UpperCAmelCase :
def __init__( self : List[str] , __snake_case : Union[str, Any] ) -> int:
_lowerCAmelCase = args
_lowerCAmelCase = defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}} )
with open(self.args.csv_file , newline="""""" ) as csv_file:
_lowerCAmelCase = csv.DictReader(__snake_case )
for row in reader:
_lowerCAmelCase = row["""model"""]
self.result_dict[model_name]["bsz"].append(int(row["""batch_size"""] ) )
self.result_dict[model_name]["seq_len"].append(int(row["""sequence_length"""] ) )
if can_convert_to_int(row["""result"""] ):
# value is not None
_lowerCAmelCase = int(row["""result"""] )
elif can_convert_to_float(row["""result"""] ):
# value is not None
_lowerCAmelCase = float(row["""result"""] )
def lowercase__ ( self : Dict ) -> str:
_lowerCAmelCase , _lowerCAmelCase = plt.subplots()
_lowerCAmelCase = """Time usage""" if self.args.is_time else """Memory usage"""
_lowerCAmelCase = title_str + """ for training""" if self.args.is_train else title_str + """ for inference"""
if not self.args.no_log_scale:
# set logarithm scales
ax.set_xscale("""log""" )
ax.set_yscale("""log""" )
for axis in [ax.xaxis, ax.yaxis]:
axis.set_major_formatter(ScalarFormatter() )
for model_name_idx, model_name in enumerate(self.result_dict.keys() ):
_lowerCAmelCase = sorted(set(self.result_dict[model_name]["""bsz"""] ) )
_lowerCAmelCase = sorted(set(self.result_dict[model_name]["""seq_len"""] ) )
_lowerCAmelCase = self.result_dict[model_name]["""result"""]
((_lowerCAmelCase) , (_lowerCAmelCase)) = (
(batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
)
_lowerCAmelCase = (
model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
)
for inner_loop_value in inner_loop_array:
if self.args.plot_along_batch:
_lowerCAmelCase = np.asarray(
[results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] , dtype=__snake_case , )
else:
_lowerCAmelCase = np.asarray(
[results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] , dtype=np.floataa , )
((_lowerCAmelCase) , (_lowerCAmelCase)) = (
("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
)
_lowerCAmelCase = np.asarray(__snake_case , __snake_case )[: len(__snake_case )]
plt.scatter(
__snake_case , __snake_case , label=f"{label_model_name} - {inner_loop_label}: {inner_loop_value}" )
plt.plot(__snake_case , __snake_case , """--""" )
title_str += f" {label_model_name} vs."
_lowerCAmelCase = title_str[:-4]
_lowerCAmelCase = """Time in s""" if self.args.is_time else """Memory in MB"""
# plot
plt.title(__snake_case )
plt.xlabel(__snake_case )
plt.ylabel(__snake_case )
plt.legend()
if self.args.figure_png_file is not None:
plt.savefig(self.args.figure_png_file )
else:
plt.show()
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = HfArgumentParser(lowerCAmelCase )
_lowerCAmelCase = parser.parse_args_into_dataclasses()[0]
_lowerCAmelCase = Plot(args=lowerCAmelCase )
plot.plot()
if __name__ == "__main__":
main()
| 220 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'microsoft/unispeech-sat-base-100h-libri-ft': (
'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'
),
# See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}
class _A ( _lowerCamelCase ):
_UpperCamelCase : Optional[int] = '''unispeech-sat'''
def __init__( self : Tuple , _A : List[str]=32 , _A : str=768 , _A : Optional[int]=12 , _A : List[Any]=12 , _A : int=3_072 , _A : Dict="gelu" , _A : int=0.1 , _A : Optional[int]=0.1 , _A : str=0.1 , _A : str=0.0 , _A : Tuple=0.0 , _A : Optional[Any]=0.1 , _A : Optional[int]=0.1 , _A : int=0.02 , _A : List[str]=1E-5 , _A : Tuple="group" , _A : Optional[int]="gelu" , _A : List[Any]=(512, 512, 512, 512, 512, 512, 512) , _A : Dict=(5, 2, 2, 2, 2, 2, 2) , _A : Union[str, Any]=(10, 3, 3, 3, 3, 2, 2) , _A : List[str]=False , _A : Optional[int]=128 , _A : int=16 , _A : List[Any]=False , _A : Optional[int]=True , _A : int=0.05 , _A : Any=10 , _A : int=2 , _A : List[str]=0.0 , _A : str=10 , _A : Optional[int]=0 , _A : str=320 , _A : Union[str, Any]=2 , _A : Any=0.1 , _A : Tuple=100 , _A : Any=256 , _A : List[Any]=256 , _A : Dict=0.1 , _A : Any="mean" , _A : List[str]=False , _A : Union[str, Any]=False , _A : Optional[int]=256 , _A : Optional[Any]=(512, 512, 512, 512, 1_500) , _A : List[str]=(5, 3, 3, 1, 1) , _A : Union[str, Any]=(1, 2, 3, 1, 1) , _A : List[str]=512 , _A : str=0 , _A : Optional[int]=1 , _A : Dict=2 , _A : str=504 , **_A : Union[str, Any] , ) -> int:
"""simple docstring"""
super().__init__(**_A , pad_token_id=_A , bos_token_id=_A , eos_token_id=_A )
lowercase : List[Any] = hidden_size
lowercase : int = feat_extract_norm
lowercase : Union[str, Any] = feat_extract_activation
lowercase : List[Any] = list(_A )
lowercase : str = list(_A )
lowercase : Optional[int] = list(_A )
lowercase : Optional[Any] = conv_bias
lowercase : str = num_conv_pos_embeddings
lowercase : Any = num_conv_pos_embedding_groups
lowercase : Optional[Any] = len(self.conv_dim )
lowercase : Optional[int] = num_hidden_layers
lowercase : List[Any] = intermediate_size
lowercase : Any = hidden_act
lowercase : Any = num_attention_heads
lowercase : int = hidden_dropout
lowercase : Tuple = attention_dropout
lowercase : Optional[int] = activation_dropout
lowercase : List[str] = feat_proj_dropout
lowercase : Tuple = final_dropout
lowercase : Union[str, Any] = layerdrop
lowercase : int = layer_norm_eps
lowercase : List[str] = initializer_range
lowercase : List[str] = vocab_size
lowercase : int = num_clusters
lowercase : int = do_stable_layer_norm
lowercase : Tuple = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
f""" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"""
f""" `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowercase : List[Any] = apply_spec_augment
lowercase : Optional[Any] = mask_time_prob
lowercase : Any = mask_time_length
lowercase : Union[str, Any] = mask_time_min_masks
lowercase : Union[str, Any] = mask_feature_prob
lowercase : Tuple = mask_feature_length
lowercase : Dict = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
lowercase : Any = num_codevectors_per_group
lowercase : Any = num_codevector_groups
lowercase : List[str] = contrastive_logits_temperature
lowercase : str = feat_quantizer_dropout
lowercase : Optional[int] = num_negatives
lowercase : Tuple = codevector_dim
lowercase : List[str] = proj_codevector_dim
lowercase : Optional[Any] = diversity_loss_weight
# ctc loss
lowercase : Optional[int] = ctc_loss_reduction
lowercase : str = ctc_zero_infinity
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
lowercase : Any = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
lowercase : List[Any] = list(_A )
lowercase : Dict = list(_A )
lowercase : Optional[Any] = list(_A )
lowercase : str = xvector_output_dim
@property
def __a ( self : List[str] ) -> List[Any]:
"""simple docstring"""
return functools.reduce(operator.mul , self.conv_stride , 1 ) | 308 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def snake_case( __magic_name__ , __magic_name__ , __magic_name__ ) -> Optional[Any]:
'''simple docstring'''
lowercase : int = 1.5
lowercase : int = int(factor * num_class_images )
lowercase : Any = ClipClient(
url='''https://knn.laion.ai/knn-service''' , indice_name='''laion_400m''' , num_images=__magic_name__ , aesthetic_weight=0.1 )
os.makedirs(F"""{class_data_dir}/images""" , exist_ok=__magic_name__ )
if len(list(Path(F"""{class_data_dir}/images""" ).iterdir() ) ) >= num_class_images:
return
while True:
lowercase : str = client.query(text=__magic_name__ )
if len(__magic_name__ ) >= factor * num_class_images or num_images > 1e4:
break
else:
lowercase : List[str] = int(factor * num_images )
lowercase : List[str] = ClipClient(
url='''https://knn.laion.ai/knn-service''' , indice_name='''laion_400m''' , num_images=__magic_name__ , aesthetic_weight=0.1 , )
lowercase : Dict = 0
lowercase : Optional[Any] = 0
lowercase : List[Any] = tqdm(desc='''downloading real regularization images''' , total=__magic_name__ )
with open(F"""{class_data_dir}/caption.txt""" , '''w''' ) as fa, open(F"""{class_data_dir}/urls.txt""" , '''w''' ) as fa, open(
F"""{class_data_dir}/images.txt""" , '''w''' ) as fa:
while total < num_class_images:
lowercase : int = class_images[count]
count += 1
try:
lowercase : int = requests.get(images['''url'''] )
if img.status_code == 2_00:
lowercase : List[Any] = Image.open(BytesIO(img.content ) )
with open(F"""{class_data_dir}/images/{total}.jpg""" , '''wb''' ) as f:
f.write(img.content )
fa.write(images['''caption'''] + '''\n''' )
fa.write(images['''url'''] + '''\n''' )
fa.write(F"""{class_data_dir}/images/{total}.jpg""" + '''\n''' )
total += 1
pbar.update(1 )
else:
continue
except Exception:
continue
return
def snake_case( ) -> Optional[int]:
'''simple docstring'''
lowercase : List[str] = argparse.ArgumentParser('''''' , add_help=__magic_name__ )
parser.add_argument('''--class_prompt''' , help='''text prompt to retrieve images''' , required=__magic_name__ , type=__magic_name__ )
parser.add_argument('''--class_data_dir''' , help='''path to save images''' , required=__magic_name__ , type=__magic_name__ )
parser.add_argument('''--num_class_images''' , help='''number of images to download''' , default=2_00 , type=__magic_name__ )
return parser.parse_args()
if __name__ == "__main__":
lowerCAmelCase_ = parse_args()
retrieve(args.class_prompt, args.class_data_dir, args.num_class_images) | 308 | 1 |
def __lowerCamelCase ( lowerCamelCase__ = 200 ):
"""simple docstring"""
lowercase__ : Tuple = [1, 2, 5, 10, 20, 50, 100, 200]
lowercase__ : List[str] = [0] * (pence + 1)
lowercase__ : Tuple = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(_A , pence + 1 , 1 ):
number_of_ways[i] += number_of_ways[i - coin]
return number_of_ways[pence]
if __name__ == "__main__":
assert solution(2_0_0) == 7_3_6_8_2
| 353 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and not is_torch_available():
raise RuntimeError('''At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training''')
# TF training parameters
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def __lowerCamelCase ( lowerCamelCase__ ):
"""simple docstring"""
return TrainCommand(lowerCamelCase__ )
class snake_case__(_UpperCamelCase ):
"""simple docstring"""
@staticmethod
def snake_case ( SCREAMING_SNAKE_CASE : ArgumentParser ):
lowercase__ : Optional[int] = parser.add_parser("train" , help="CLI tool to train a model on a task." )
train_parser.add_argument(
"--train_data" , type=SCREAMING_SNAKE_CASE , required=SCREAMING_SNAKE_CASE , help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences." , )
train_parser.add_argument(
"--column_label" , type=SCREAMING_SNAKE_CASE , default=0 , help="Column of the dataset csv file with example labels." )
train_parser.add_argument(
"--column_text" , type=SCREAMING_SNAKE_CASE , default=1 , help="Column of the dataset csv file with example texts." )
train_parser.add_argument(
"--column_id" , type=SCREAMING_SNAKE_CASE , default=2 , help="Column of the dataset csv file with example ids." )
train_parser.add_argument(
"--skip_first_row" , action="store_true" , help="Skip the first row of the csv file (headers)." )
train_parser.add_argument("--validation_data" , type=SCREAMING_SNAKE_CASE , default="" , help="path to validation dataset." )
train_parser.add_argument(
"--validation_split" , type=SCREAMING_SNAKE_CASE , default=0.1 , help="if validation dataset is not provided, fraction of train dataset to use as validation dataset." , )
train_parser.add_argument("--output" , type=SCREAMING_SNAKE_CASE , default="./" , help="path to saved the trained model." )
train_parser.add_argument(
"--task" , type=SCREAMING_SNAKE_CASE , default="text_classification" , help="Task to train the model on." )
train_parser.add_argument(
"--model" , type=SCREAMING_SNAKE_CASE , default="bert-base-uncased" , help="Model's name or path to stored model." )
train_parser.add_argument("--train_batch_size" , type=SCREAMING_SNAKE_CASE , default=32 , help="Batch size for training." )
train_parser.add_argument("--valid_batch_size" , type=SCREAMING_SNAKE_CASE , default=64 , help="Batch size for validation." )
train_parser.add_argument("--learning_rate" , type=SCREAMING_SNAKE_CASE , default=3E-5 , help="Learning rate." )
train_parser.add_argument("--adam_epsilon" , type=SCREAMING_SNAKE_CASE , default=1E-0_8 , help="Epsilon for Adam optimizer." )
train_parser.set_defaults(func=SCREAMING_SNAKE_CASE )
def __init__( self : int , SCREAMING_SNAKE_CASE : Namespace ):
lowercase__ : int = logging.get_logger("transformers-cli/training" )
lowercase__ : List[Any] = "tf" if is_tf_available() else "torch"
os.makedirs(args.output , exist_ok=SCREAMING_SNAKE_CASE )
lowercase__ : Optional[Any] = args.output
lowercase__ : Union[str, Any] = args.column_label
lowercase__ : Optional[int] = args.column_text
lowercase__ : Optional[int] = args.column_id
self.logger.info(f"""Loading {args.task} pipeline for {args.model}""" )
if args.task == "text_classification":
lowercase__ : int = TextClassificationPipeline.from_pretrained(args.model )
elif args.task == "token_classification":
raise NotImplementedError
elif args.task == "question_answering":
raise NotImplementedError
self.logger.info(f"""Loading dataset from {args.train_data}""" )
lowercase__ : List[str] = Processor.create_from_csv(
args.train_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
lowercase__ : Union[str, Any] = None
if args.validation_data:
self.logger.info(f"""Loading validation dataset from {args.validation_data}""" )
lowercase__ : Optional[int] = Processor.create_from_csv(
args.validation_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
lowercase__ : Dict = args.validation_split
lowercase__ : List[str] = args.train_batch_size
lowercase__ : Any = args.valid_batch_size
lowercase__ : Optional[int] = args.learning_rate
lowercase__ : int = args.adam_epsilon
def snake_case ( self : Dict ):
if self.framework == "tf":
return self.run_tf()
return self.run_torch()
def snake_case ( self : Union[str, Any] ):
raise NotImplementedError
def snake_case ( self : Union[str, Any] ):
self.pipeline.fit(
self.train_dataset , validation_data=self.valid_dataset , validation_split=self.validation_split , learning_rate=self.learning_rate , adam_epsilon=self.adam_epsilon , train_batch_size=self.train_batch_size , valid_batch_size=self.valid_batch_size , )
# Save trained pipeline
self.pipeline.save_pretrained(self.output )
| 121 | 0 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
_A = logging.getLogger(__name__)
class UpperCAmelCase__ ( A_ ):
"""simple docstring"""
def __init__( self , A_ , A_ , A_ , A_=None ) -> List[str]:
super().__init__(
A_ , question_encoder_tokenizer=A_ , generator_tokenizer=A_ , index=A_ , init_retrieval=A_ , )
__UpperCamelCase =None
def _a ( self , A_ ) -> Any:
logger.info('initializing retrieval' )
# initializing a separate process group for retrieval as the default
# nccl backend doesn't support gather/scatter operations while gloo
# is too slow to replace nccl for the core gpu communication
if dist.is_initialized():
logger.info('dist initialized' )
# needs to be set manually
__UpperCamelCase =self._infer_socket_ifname()
# avoid clash with the NCCL port
__UpperCamelCase =str(distributed_port + 1 )
__UpperCamelCase =dist.new_group(ranks=A_ , backend='gloo' )
# initialize retriever only on the main worker
if not dist.is_initialized() or self._is_main():
logger.info('dist not initialized / main' )
self.index.init_index()
# all processes wait untill the retriever is initialized by the main process
if dist.is_initialized():
torch.distributed.barrier(group=self.process_group )
def _a ( self ) -> List[str]:
return dist.get_rank(group=self.process_group ) == 0
def _a ( self , A_ , A_ , A_=torch.floataa ) -> Tuple:
__UpperCamelCase =torch.empty(A_ , dtype=A_ )
dist.scatter(A_ , src=0 , scatter_list=A_ , group=self.process_group )
return target_tensor
def _a ( self ) -> Dict:
__UpperCamelCase =psutil.net_if_addrs()
# a hacky way to deal with varying network interface names
__UpperCamelCase =next((addr for addr in addrs if addr.startswith('e' )) , A_ )
return ifname
def _a ( self , A_ , A_ ) -> Tuple[np.ndarray, List[dict]]:
# single GPU training
if not dist.is_initialized():
__UpperCamelCase , __UpperCamelCase =self._main_retrieve(A_ , A_ )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(A_ )
# distributed training
__UpperCamelCase =dist.get_world_size(group=self.process_group )
# gather logic
__UpperCamelCase =None
if self._is_main():
__UpperCamelCase =[torch.empty(question_hidden_states.shape , dtype=torch.floataa ) for _ in range(A_ )]
dist.gather(torch.tensor(A_ ) , dst=0 , gather_list=A_ , group=self.process_group )
# scatter logic
__UpperCamelCase =question_hidden_states.shape[0]
__UpperCamelCase =[]
__UpperCamelCase =[]
if self._is_main():
assert len(A_ ) == world_size
__UpperCamelCase , __UpperCamelCase =self._main_retrieve(torch.cat(A_ ).numpy() , A_ )
__UpperCamelCase , __UpperCamelCase =torch.tensor(A_ ), torch.tensor(A_ )
__UpperCamelCase =self._chunk_tensor(A_ , A_ )
__UpperCamelCase =self._chunk_tensor(A_ , A_ )
__UpperCamelCase =self._scattered(A_ , [n_queries, n_docs] , target_type=torch.intaa )
__UpperCamelCase =self._scattered(A_ , [n_queries, n_docs, question_hidden_states.shape[1]] )
return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(A_ )
| 62 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self , A_ = None ) -> None:
if components is None:
__UpperCamelCase =[]
__UpperCamelCase =list(A_ )
def __len__( self ) -> int:
return len(self.__components )
def __str__( self ) -> str:
return "(" + ",".join(map(A_ , self.__components ) ) + ")"
def __add__( self , A_ ) -> Vector:
__UpperCamelCase =len(self )
if size == len(A_ ):
__UpperCamelCase =[self.__components[i] + other.component(A_ ) for i in range(A_ )]
return Vector(A_ )
else:
raise Exception('must have the same size' )
def __sub__( self , A_ ) -> Vector:
__UpperCamelCase =len(self )
if size == len(A_ ):
__UpperCamelCase =[self.__components[i] - other.component(A_ ) for i in range(A_ )]
return Vector(A_ )
else: # error case
raise Exception('must have the same size' )
@overload
def __mul__( self , A_ ) -> Vector:
...
@overload
def __mul__( self , A_ ) -> float:
...
def __mul__( self , A_ ) -> float | Vector:
if isinstance(A_ , (float, int) ):
__UpperCamelCase =[c * other for c in self.__components]
return Vector(A_ )
elif isinstance(A_ , A_ ) and len(self ) == len(A_ ):
__UpperCamelCase =len(self )
__UpperCamelCase =[self.__components[i] * other.component(A_ ) for i in range(A_ )]
return sum(A_ )
else: # error case
raise Exception('invalid operand!' )
def _a ( self ) -> Vector:
return Vector(self.__components )
def _a ( self , A_ ) -> float:
if isinstance(A_ , A_ ) and -len(self.__components ) <= i < len(self.__components ):
return self.__components[i]
else:
raise Exception('index out of range' )
def _a ( self , A_ , A_ ) -> None:
assert -len(self.__components ) <= pos < len(self.__components )
__UpperCamelCase =value
def _a ( self ) -> float:
if len(self.__components ) == 0:
raise Exception('Vector is empty' )
__UpperCamelCase =[c**2 for c in self.__components]
return math.sqrt(sum(A_ ) )
def _a ( self , A_ , A_ = False ) -> float:
__UpperCamelCase =self * other
__UpperCamelCase =self.euclidean_length() * other.euclidean_length()
if deg:
return math.degrees(math.acos(num / den ) )
else:
return math.acos(num / den )
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : int ):
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return Vector([0] * dimension )
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ):
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and (isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ))
__UpperCamelCase =[0] * dimension
__UpperCamelCase =1
return Vector(SCREAMING_SNAKE_CASE__ )
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : Vector , SCREAMING_SNAKE_CASE__ : Vector ):
assert (
isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
and isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
and (isinstance(SCREAMING_SNAKE_CASE__ , (int, float) ))
)
return x * scalar + y
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ):
random.seed(SCREAMING_SNAKE_CASE__ )
__UpperCamelCase =[random.randint(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) for _ in range(SCREAMING_SNAKE_CASE__ )]
return Vector(SCREAMING_SNAKE_CASE__ )
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self , A_ , A_ , A_ ) -> None:
__UpperCamelCase =matrix
__UpperCamelCase =w
__UpperCamelCase =h
def __str__( self ) -> str:
__UpperCamelCase =''
for i in range(self.__height ):
ans += "|"
for j in range(self.__width ):
if j < self.__width - 1:
ans += str(self.__matrix[i][j] ) + ","
else:
ans += str(self.__matrix[i][j] ) + "|\n"
return ans
def __add__( self , A_ ) -> Matrix:
if self.__width == other.width() and self.__height == other.height():
__UpperCamelCase =[]
for i in range(self.__height ):
__UpperCamelCase =[
self.__matrix[i][j] + other.component(A_ , A_ )
for j in range(self.__width )
]
matrix.append(A_ )
return Matrix(A_ , self.__width , self.__height )
else:
raise Exception('matrix must have the same dimension!' )
def __sub__( self , A_ ) -> Matrix:
if self.__width == other.width() and self.__height == other.height():
__UpperCamelCase =[]
for i in range(self.__height ):
__UpperCamelCase =[
self.__matrix[i][j] - other.component(A_ , A_ )
for j in range(self.__width )
]
matrix.append(A_ )
return Matrix(A_ , self.__width , self.__height )
else:
raise Exception('matrices must have the same dimension!' )
@overload
def __mul__( self , A_ ) -> Matrix:
...
@overload
def __mul__( self , A_ ) -> Vector:
...
def __mul__( self , A_ ) -> Vector | Matrix:
if isinstance(A_ , A_ ): # matrix-vector
if len(A_ ) == self.__width:
__UpperCamelCase =zero_vector(self.__height )
for i in range(self.__height ):
__UpperCamelCase =[
self.__matrix[i][j] * other.component(A_ )
for j in range(self.__width )
]
ans.change_component(A_ , sum(A_ ) )
return ans
else:
raise Exception(
'vector must have the same size as the '
'number of columns of the matrix!' )
elif isinstance(A_ , (int, float) ): # matrix-scalar
__UpperCamelCase =[
[self.__matrix[i][j] * other for j in range(self.__width )]
for i in range(self.__height )
]
return Matrix(A_ , self.__width , self.__height )
return None
def _a ( self ) -> int:
return self.__height
def _a ( self ) -> int:
return self.__width
def _a ( self , A_ , A_ ) -> float:
if 0 <= x < self.__height and 0 <= y < self.__width:
return self.__matrix[x][y]
else:
raise Exception('change_component: indices out of bounds' )
def _a ( self , A_ , A_ , A_ ) -> None:
if 0 <= x < self.__height and 0 <= y < self.__width:
__UpperCamelCase =value
else:
raise Exception('change_component: indices out of bounds' )
def _a ( self , A_ , A_ ) -> float:
if self.__height != self.__width:
raise Exception('Matrix is not square' )
__UpperCamelCase =self.__matrix[:x] + self.__matrix[x + 1 :]
for i in range(len(A_ ) ):
__UpperCamelCase =minor[i][:y] + minor[i][y + 1 :]
return Matrix(A_ , self.__width - 1 , self.__height - 1 ).determinant()
def _a ( self , A_ , A_ ) -> float:
if self.__height != self.__width:
raise Exception('Matrix is not square' )
if 0 <= x < self.__height and 0 <= y < self.__width:
return (-1) ** (x + y) * self.minor(A_ , A_ )
else:
raise Exception('Indices out of bounds' )
def _a ( self ) -> float:
if self.__height != self.__width:
raise Exception('Matrix is not square' )
if self.__height < 1:
raise Exception('Matrix has no element' )
elif self.__height == 1:
return self.__matrix[0][0]
elif self.__height == 2:
return (
self.__matrix[0][0] * self.__matrix[1][1]
- self.__matrix[0][1] * self.__matrix[1][0]
)
else:
__UpperCamelCase =[
self.__matrix[0][y] * self.cofactor(0 , A_ ) for y in range(self.__width )
]
return sum(A_ )
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : int ):
__UpperCamelCase =[[0] * n for _ in range(SCREAMING_SNAKE_CASE__ )]
return Matrix(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ):
random.seed(SCREAMING_SNAKE_CASE__ )
__UpperCamelCase =[
[random.randint(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) for _ in range(SCREAMING_SNAKE_CASE__ )] for _ in range(SCREAMING_SNAKE_CASE__ )
]
return Matrix(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
| 62 | 1 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class _UpperCAmelCase ( _lowerCAmelCase ):
def __init__( self : List[str] , _lowercase : Tuple , _lowercase : Any ):
__UpperCAmelCase = params
__UpperCAmelCase = np.array(_A )
__UpperCAmelCase = 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 : Optional[Any] , _lowercase : Union[str, Any] ):
return (self.token_ids[index], self.lengths[index])
def __len__( self : List[str] ):
return len(self.lengths )
def a ( self : Optional[Any] ):
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 a ( self : Dict ):
__UpperCAmelCase = self.params.max_model_input_size
__UpperCAmelCase = self.lengths > max_len
logger.info(F'''Splitting {sum(_A )} too long sequences.''' )
def divide_chunks(_lowercase : str , _lowercase : List[Any] ):
return [l[i : i + n] for i in range(0 , len(_A ) , _A )]
__UpperCAmelCase = []
__UpperCAmelCase = []
if self.params.mlm:
__UpperCAmelCase = self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
else:
__UpperCAmelCase = 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 = []
for sub_s in divide_chunks(seq_ , max_len - 2 ):
if sub_s[0] != cls_id:
__UpperCAmelCase = np.insert(_A , 0 , _A )
if sub_s[-1] != sep_id:
__UpperCAmelCase = 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 = np.array(_A )
__UpperCAmelCase = np.array(_A )
def a ( self : Union[str, Any] ):
__UpperCAmelCase = len(self )
__UpperCAmelCase = self.lengths > 11
__UpperCAmelCase = self.token_ids[indices]
__UpperCAmelCase = self.lengths[indices]
__UpperCAmelCase = len(self )
logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.''' )
def a ( self : int ):
if "unk_token" not in self.params.special_tok_ids:
return
else:
__UpperCAmelCase = self.params.special_tok_ids['unk_token']
__UpperCAmelCase = len(self )
__UpperCAmelCase = np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
__UpperCAmelCase = (unk_occs / self.lengths) < 0.5
__UpperCAmelCase = self.token_ids[indices]
__UpperCAmelCase = self.lengths[indices]
__UpperCAmelCase = len(self )
logger.info(F'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).''' )
def a ( self : Any ):
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 a ( self : Dict , _lowercase : Optional[Any] ):
__UpperCAmelCase = [t[0] for t in batch]
__UpperCAmelCase = [t[1] for t in batch]
assert len(_A ) == len(_A )
# Max for paddings
__UpperCAmelCase = max(_A )
# Pad token ids
if self.params.mlm:
__UpperCAmelCase = self.params.special_tok_ids['pad_token']
else:
__UpperCAmelCase = self.params.special_tok_ids['unk_token']
__UpperCAmelCase = [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 = torch.tensor(tk_ ) # (bs, max_seq_len_)
__UpperCAmelCase = torch.tensor(_A ) # (bs)
return tk_t, lg_t
| 356 |
"""simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MaskFormerSwinBackbone
from transformers.models.maskformer import MaskFormerSwinModel
class _UpperCAmelCase :
def __init__( self : Dict , _lowercase : int , _lowercase : List[str]=13 , _lowercase : Dict=32 , _lowercase : Any=2 , _lowercase : Optional[int]=3 , _lowercase : Optional[Any]=16 , _lowercase : Optional[int]=[1, 2, 1] , _lowercase : int=[2, 2, 4] , _lowercase : Optional[Any]=2 , _lowercase : Union[str, Any]=2.0 , _lowercase : Any=True , _lowercase : Optional[Any]=0.0 , _lowercase : Dict=0.0 , _lowercase : Dict=0.1 , _lowercase : str="gelu" , _lowercase : List[Any]=False , _lowercase : List[Any]=True , _lowercase : Optional[Any]=0.02 , _lowercase : str=1E-5 , _lowercase : str=True , _lowercase : Any=None , _lowercase : Tuple=True , _lowercase : Any=10 , _lowercase : int=8 , _lowercase : Optional[Any]=["stage1", "stage2", "stage3"] , _lowercase : Optional[Any]=[1, 2, 3] , ):
__UpperCAmelCase = parent
__UpperCAmelCase = batch_size
__UpperCAmelCase = image_size
__UpperCAmelCase = patch_size
__UpperCAmelCase = num_channels
__UpperCAmelCase = embed_dim
__UpperCAmelCase = depths
__UpperCAmelCase = num_heads
__UpperCAmelCase = window_size
__UpperCAmelCase = mlp_ratio
__UpperCAmelCase = qkv_bias
__UpperCAmelCase = hidden_dropout_prob
__UpperCAmelCase = attention_probs_dropout_prob
__UpperCAmelCase = drop_path_rate
__UpperCAmelCase = hidden_act
__UpperCAmelCase = use_absolute_embeddings
__UpperCAmelCase = patch_norm
__UpperCAmelCase = layer_norm_eps
__UpperCAmelCase = initializer_range
__UpperCAmelCase = is_training
__UpperCAmelCase = scope
__UpperCAmelCase = use_labels
__UpperCAmelCase = type_sequence_label_size
__UpperCAmelCase = encoder_stride
__UpperCAmelCase = out_features
__UpperCAmelCase = out_indices
def a ( self : int ):
__UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__UpperCAmelCase = None
if self.use_labels:
__UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def a ( self : Dict ):
return MaskFormerSwinConfig(
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 , out_features=self.out_features , out_indices=self.out_indices , )
def a ( self : List[Any] , _lowercase : Union[str, Any] , _lowercase : str , _lowercase : int ):
__UpperCAmelCase = MaskFormerSwinModel(config=_lowercase )
model.to(_lowercase )
model.eval()
__UpperCAmelCase = model(_lowercase )
__UpperCAmelCase = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
__UpperCAmelCase = 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 a ( self : int , _lowercase : Optional[Any] , _lowercase : Any , _lowercase : Dict ):
__UpperCAmelCase = MaskFormerSwinBackbone(config=_lowercase )
model.to(_lowercase )
model.eval()
__UpperCAmelCase = model(_lowercase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [13, 16, 16, 16] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , [16, 32, 64] )
# verify ValueError
with self.parent.assertRaises(_lowercase ):
__UpperCAmelCase = ['''stem''']
__UpperCAmelCase = MaskFormerSwinBackbone(config=_lowercase )
def a ( self : Optional[int] ):
__UpperCAmelCase = self.prepare_config_and_inputs()
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = config_and_inputs
__UpperCAmelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class _UpperCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
a__ : List[Any] = (
(
MaskFormerSwinModel,
MaskFormerSwinBackbone,
)
if is_torch_available()
else ()
)
a__ : Optional[int] = {"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
a__ : List[str] = False
a__ : int = False
a__ : str = False
a__ : str = False
a__ : Any = False
def a ( self : Optional[Any] ):
__UpperCAmelCase = MaskFormerSwinModelTester(self )
__UpperCAmelCase = ConfigTester(self , config_class=_lowercase , embed_dim=37 )
@require_torch_multi_gpu
@unittest.skip(
reason=(
'''`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'''
''' `nn.DataParallel`'''
) )
def a ( self : int ):
pass
def a ( self : Dict ):
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 a ( self : str ):
return
def a ( self : Optional[Any] ):
__UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowercase )
def a ( self : Optional[int] ):
__UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_lowercase )
@unittest.skip('''Swin does not use inputs_embeds''' )
def a ( self : List[Any] ):
pass
@unittest.skip('''Swin does not support feedforward chunking''' )
def a ( self : str ):
pass
def a ( self : Union[str, Any] ):
__UpperCAmelCase , __UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase = model_class(_lowercase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__UpperCAmelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowercase , nn.Linear ) )
def a ( self : Union[str, Any] ):
__UpperCAmelCase , __UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase = model_class(_lowercase )
__UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__UpperCAmelCase = [*signature.parameters.keys()]
__UpperCAmelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _lowercase )
@unittest.skip(reason='''MaskFormerSwin is only used as backbone and doesn\'t support output_attentions''' )
def a ( self : Optional[Any] ):
pass
@unittest.skip(reason='''MaskFormerSwin is only used as an internal backbone''' )
def a ( self : Optional[Any] ):
pass
def a ( self : List[Any] , _lowercase : Union[str, Any] , _lowercase : List[str] , _lowercase : Dict , _lowercase : Tuple ):
__UpperCAmelCase = model_class(_lowercase )
model.to(_lowercase )
model.eval()
with torch.no_grad():
__UpperCAmelCase = model(**self._prepare_for_class(_lowercase , _lowercase ) )
__UpperCAmelCase = outputs.hidden_states
__UpperCAmelCase = getattr(
self.model_tester , '''expected_num_hidden_layers''' , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(_lowercase ) , _lowercase )
# Swin has a different seq_length
__UpperCAmelCase = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__UpperCAmelCase = (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] , )
def a ( self : str ):
__UpperCAmelCase , __UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase = (
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:
__UpperCAmelCase = True
self.check_hidden_states_output(_lowercase , _lowercase , _lowercase , _lowercase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__UpperCAmelCase = True
self.check_hidden_states_output(_lowercase , _lowercase , _lowercase , _lowercase )
def a ( self : Optional[Any] ):
__UpperCAmelCase , __UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase = 3
__UpperCAmelCase = (
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)
)
__UpperCAmelCase = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__UpperCAmelCase = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
__UpperCAmelCase = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
__UpperCAmelCase = True
self.check_hidden_states_output(_lowercase , _lowercase , _lowercase , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__UpperCAmelCase = True
self.check_hidden_states_output(_lowercase , _lowercase , _lowercase , (padded_height, padded_width) )
@unittest.skip(reason='''MaskFormerSwin doesn\'t have pretrained checkpoints''' )
def a ( self : Any ):
pass
@unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin''' )
def a ( self : str ):
pass
@unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin''' )
def a ( self : Tuple ):
pass
def a ( self : Tuple ):
__UpperCAmelCase , __UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(_lowercase : List[str] ):
__UpperCAmelCase = 0
return t
def check_equivalence(_lowercase : List[Any] , _lowercase : Any , _lowercase : str , _lowercase : List[str]={} ):
with torch.no_grad():
__UpperCAmelCase = model(**_lowercase , return_dict=_lowercase , **_lowercase )
__UpperCAmelCase = model(**_lowercase , return_dict=_lowercase , **_lowercase ).to_tuple()
def recursive_check(_lowercase : Dict , _lowercase : Optional[Any] ):
if isinstance(_lowercase , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(_lowercase , _lowercase ):
recursive_check(_lowercase , _lowercase )
elif isinstance(_lowercase , _lowercase ):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values() , dict_object.values() ):
recursive_check(_lowercase , _lowercase )
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(_lowercase ) , set_nan_tensor_to_zero(_lowercase ) , atol=1E-5 ) , msg=(
'''Tuple and dict output are not equal. Difference:'''
F''' {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:'''
F''' {torch.isnan(_lowercase ).any()} and `inf`: {torch.isinf(_lowercase )}. Dict has'''
F''' `nan`: {torch.isnan(_lowercase ).any()} and `inf`: {torch.isinf(_lowercase )}.'''
) , )
recursive_check(_lowercase , _lowercase )
for model_class in self.all_model_classes:
__UpperCAmelCase = model_class(_lowercase )
model.to(_lowercase )
model.eval()
__UpperCAmelCase = self._prepare_for_class(_lowercase , _lowercase )
__UpperCAmelCase = self._prepare_for_class(_lowercase , _lowercase )
check_equivalence(_lowercase , _lowercase , _lowercase )
__UpperCAmelCase = self._prepare_for_class(_lowercase , _lowercase , return_labels=_lowercase )
__UpperCAmelCase = self._prepare_for_class(_lowercase , _lowercase , return_labels=_lowercase )
check_equivalence(_lowercase , _lowercase , _lowercase )
__UpperCAmelCase = self._prepare_for_class(_lowercase , _lowercase )
__UpperCAmelCase = self._prepare_for_class(_lowercase , _lowercase )
check_equivalence(_lowercase , _lowercase , _lowercase , {'''output_hidden_states''': True} )
__UpperCAmelCase = self._prepare_for_class(_lowercase , _lowercase , return_labels=_lowercase )
__UpperCAmelCase = self._prepare_for_class(_lowercase , _lowercase , return_labels=_lowercase )
check_equivalence(_lowercase , _lowercase , _lowercase , {'''output_hidden_states''': True} )
@require_torch
class _UpperCAmelCase ( unittest.TestCase , _lowerCAmelCase ):
a__ : Optional[Any] = (MaskFormerSwinBackbone,) if is_torch_available() else ()
a__ : List[str] = MaskFormerSwinConfig
def a ( self : List[str] ):
__UpperCAmelCase = MaskFormerSwinModelTester(self )
def a ( self : List[Any] ):
__UpperCAmelCase , __UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase = inputs_dict['''pixel_values'''].shape[0]
for backbone_class in self.all_model_classes:
__UpperCAmelCase = backbone_class(_lowercase )
backbone.to(_lowercase )
backbone.eval()
__UpperCAmelCase = backbone(**_lowercase )
# Test default outputs and verify feature maps
self.assertIsInstance(outputs.feature_maps , _lowercase )
self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
for feature_map, n_channels in zip(outputs.feature_maps , backbone.channels ):
self.assertTrue(feature_map.shape[:2] , (batch_size, n_channels) )
self.assertIsNone(outputs.hidden_states )
self.assertIsNone(outputs.attentions )
# Test output_hidden_states=True
__UpperCAmelCase = backbone(**_lowercase , output_hidden_states=_lowercase )
self.assertIsNotNone(outputs.hidden_states )
self.assertTrue(len(outputs.hidden_states ) , len(backbone.stage_names ) )
# We skip the stem layer
for hidden_states, n_channels in zip(outputs.hidden_states[1:] , backbone.channels ):
for hidden_state in hidden_states:
# Hidden states are in the format (batch_size, (height * width), n_channels)
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = hidden_state.shape
self.assertTrue((h_batch_size, h_n_channels) , (batch_size, n_channels) )
# Test output_attentions=True
if self.has_attentions:
__UpperCAmelCase = backbone(**_lowercase , output_attentions=_lowercase )
self.assertIsNotNone(outputs.attentions )
| 86 | 0 |
import math
def A_ ( a , a = 0 , a = 0 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = end or len(a )
for i in range(a , a ):
SCREAMING_SNAKE_CASE_ : List[Any] = i
SCREAMING_SNAKE_CASE_ : Optional[Any] = array[i]
while temp_index != start and temp_index_value < array[temp_index - 1]:
SCREAMING_SNAKE_CASE_ : Tuple = array[temp_index - 1]
temp_index -= 1
SCREAMING_SNAKE_CASE_ : str = temp_index_value
return array
def A_ ( a , a , a ): # Max Heap
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = index
SCREAMING_SNAKE_CASE_ : str = 2 * index + 1 # Left Node
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 2 * index + 2 # Right Node
if left_index < heap_size and array[largest] < array[left_index]:
SCREAMING_SNAKE_CASE_ : Dict = left_index
if right_index < heap_size and array[largest] < array[right_index]:
SCREAMING_SNAKE_CASE_ : Any = right_index
if largest != index:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Tuple = array[largest], array[index]
heapify(a , a , a )
def A_ ( a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = len(a )
for i in range(n // 2 , -1 , -1 ):
heapify(a , a , a )
for i in range(n - 1 , 0 , -1 ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Tuple = array[0], array[i]
heapify(a , 0 , a )
return array
def A_ ( a , a , a , a ):
"""simple docstring"""
if (array[first_index] > array[middle_index]) != (
array[first_index] > array[last_index]
):
return array[first_index]
elif (array[middle_index] > array[first_index]) != (
array[middle_index] > array[last_index]
):
return array[middle_index]
else:
return array[last_index]
def A_ ( a , a , a , a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = low
SCREAMING_SNAKE_CASE_ : Tuple = high
while True:
while array[i] < pivot:
i += 1
j -= 1
while pivot < array[j]:
j -= 1
if i >= j:
return i
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Tuple = array[j], array[i]
i += 1
def A_ ( a ):
"""simple docstring"""
if len(a ) == 0:
return array
SCREAMING_SNAKE_CASE_ : Any = 2 * math.ceil(math.loga(len(a ) ) )
SCREAMING_SNAKE_CASE_ : int = 1_6
return intro_sort(a , 0 , len(a ) , a , a )
def A_ ( a , a , a , a , a ):
"""simple docstring"""
while end - start > size_threshold:
if max_depth == 0:
return heap_sort(a )
max_depth -= 1
SCREAMING_SNAKE_CASE_ : Optional[int] = median_of_a(a , a , start + ((end - start) // 2) + 1 , end - 1 )
SCREAMING_SNAKE_CASE_ : Dict = partition(a , a , a , a )
intro_sort(a , a , a , a , a )
SCREAMING_SNAKE_CASE_ : List[Any] = p
return insertion_sort(a , a , a )
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCAmelCase : List[str] = input('Enter numbers separated by a comma : ').strip()
lowerCAmelCase : Optional[Any] = [float(item) for item in user_input.split(',')]
print(sort(unsorted))
| 253 |
import argparse
from collections import defaultdict
import yaml
lowerCAmelCase : Dict = 'docs/source/en/_toctree.yml'
def A_ ( a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = defaultdict(a )
for doc in model_doc:
counts[doc["local"]] += 1
SCREAMING_SNAKE_CASE_ : Tuple = [key for key, value in counts.items() if value > 1]
SCREAMING_SNAKE_CASE_ : int = []
for duplicate_key in duplicates:
SCREAMING_SNAKE_CASE_ : List[Any] = list({doc['title'] for doc in model_doc if doc['local'] == duplicate_key} )
if len(a ) > 1:
raise ValueError(
f"{duplicate_key} is present several times in the documentation table of content at "
'`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
'others.' )
# Only add this once
new_doc.append({'local': duplicate_key, 'title': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in model_doc if counts[doc['local']] == 1] )
# Sort
return sorted(a , key=lambda a : s["title"].lower() )
def A_ ( a=False ):
"""simple docstring"""
with open(a , encoding='utf-8' ) as f:
SCREAMING_SNAKE_CASE_ : str = yaml.safe_load(f.read() )
# Get to the API doc
SCREAMING_SNAKE_CASE_ : List[str] = 0
while content[api_idx]["title"] != "API":
api_idx += 1
SCREAMING_SNAKE_CASE_ : List[str] = content[api_idx]['sections']
# Then to the model doc
SCREAMING_SNAKE_CASE_ : List[str] = 0
while api_doc[model_idx]["title"] != "Models":
model_idx += 1
SCREAMING_SNAKE_CASE_ : Optional[Any] = api_doc[model_idx]['sections']
SCREAMING_SNAKE_CASE_ : List[str] = [(idx, section) for idx, section in enumerate(a ) if 'sections' in section]
SCREAMING_SNAKE_CASE_ : List[Any] = False
for idx, modality_doc in modalities_docs:
SCREAMING_SNAKE_CASE_ : Tuple = modality_doc['sections']
SCREAMING_SNAKE_CASE_ : int = clean_model_doc_toc(a )
if old_modality_doc != new_modality_doc:
SCREAMING_SNAKE_CASE_ : List[str] = True
if overwrite:
SCREAMING_SNAKE_CASE_ : Optional[int] = new_modality_doc
if diff:
if overwrite:
SCREAMING_SNAKE_CASE_ : List[Any] = model_doc
SCREAMING_SNAKE_CASE_ : List[Any] = api_doc
with open(a , 'w' , encoding='utf-8' ) as f:
f.write(yaml.dump(a , allow_unicode=a ) )
else:
raise ValueError(
'The model doc part of the table of content is not properly sorted, run `make style` to fix this.' )
if __name__ == "__main__":
lowerCAmelCase : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
lowerCAmelCase : List[str] = parser.parse_args()
check_model_doc(args.fix_and_overwrite)
| 253 | 1 |
"""simple docstring"""
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment
def snake_case ( ):
UpperCAmelCase_ : Any = torch.nn.Linear(2 ,4 )
UpperCAmelCase_ : Optional[int] = torch.optim.AdamW(model.parameters() ,lr=1.0 )
UpperCAmelCase_ : Dict = torch.optim.lr_scheduler.OneCycleLR(A__ ,max_lr=0.01 ,steps_per_epoch=2 ,epochs=1 )
UpperCAmelCase_ : Optional[Any] = DataLoader(TensorDataset(torch.tensor([1, 2, 3] ) ) )
UpperCAmelCase_ : Union[str, Any] = DataLoader(TensorDataset(torch.tensor([4, 5, 6] ) ) )
return model, optimizer, scheduler, train_dl, valid_dl
def snake_case ( A__ ):
return (model.weight.abs().sum() + model.bias.abs().sum()).item()
def snake_case ( A__ ):
UpperCAmelCase_ : List[Any] = torch.nn.Linear(*tuple(model.weight.T.shape ) ).state_dict()
model.load_state_dict(A__ )
class UpperCamelCase_ (__lowerCamelCase ):
@require_cuda
def _SCREAMING_SNAKE_CASE ( self : Any ) -> Optional[int]:
UpperCAmelCase_ : Dict = Accelerator()
assert PartialState._shared_state["_cpu"] is False
assert PartialState._shared_state["device"].type == "cuda"
with self.assertRaises(UpperCamelCase_ ):
UpperCAmelCase_ : str = Accelerator(cpu=UpperCamelCase_ )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Any:
UpperCAmelCase_ : Union[str, Any] = Accelerator()
UpperCAmelCase_ : List[str] = GradientState()
assert state.num_steps == 1
UpperCAmelCase_ : List[Any] = 4
assert state.num_steps == 4
assert state.sync_gradients is True
UpperCAmelCase_ : Union[str, Any] = False
assert state.sync_gradients is False
GradientState._reset_state()
def _SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Union[str, Any]:
UpperCAmelCase_ : Tuple = Accelerator()
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Optional[int] = create_components()
(
(
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) ,
) : int = accelerator.prepare(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
self.assertTrue(prepared_model in accelerator._models )
self.assertTrue(prepared_optimizer in accelerator._optimizers )
self.assertTrue(prepared_scheduler in accelerator._schedulers )
self.assertTrue(prepared_train_dl in accelerator._dataloaders )
self.assertTrue(prepared_valid_dl in accelerator._dataloaders )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> int:
UpperCAmelCase_ : Union[str, Any] = Accelerator()
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Tuple = create_components()
accelerator.prepare(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
accelerator.free_memory()
self.assertTrue(len(accelerator._models ) == 0 )
self.assertTrue(len(accelerator._optimizers ) == 0 )
self.assertTrue(len(accelerator._schedulers ) == 0 )
self.assertTrue(len(accelerator._dataloaders ) == 0 )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Dict:
PartialState._reset_state()
# Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
def noop(*lowerCAmelCase_ : List[Any] , **lowerCAmelCase_ : int ):
pass
with patch("torch.cuda.set_device" , UpperCamelCase_ ), patch_environment(ACCELERATE_TORCH_DEVICE="cuda:64" ):
UpperCAmelCase_ : List[Any] = Accelerator()
self.assertEqual(str(accelerator.state.device ) , "cuda:64" )
def _SCREAMING_SNAKE_CASE ( self : int ) -> Optional[Any]:
UpperCAmelCase_ : int = Accelerator()
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Optional[Any] = create_components()
accelerator.prepare(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
UpperCAmelCase_ : List[Any] = get_signature(UpperCamelCase_ )
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(UpperCamelCase_ )
# make sure random weights don't match
load_random_weights(UpperCamelCase_ )
self.assertTrue(abs(model_signature - get_signature(UpperCamelCase_ ) ) > 1e-3 )
# make sure loaded weights match
accelerator.load_state(UpperCamelCase_ )
self.assertTrue(abs(model_signature - get_signature(UpperCamelCase_ ) ) < 1e-3 )
def _SCREAMING_SNAKE_CASE ( self : str ) -> Dict:
UpperCAmelCase_ : Union[str, Any] = Accelerator()
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : int = create_components()
accelerator.prepare(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
UpperCAmelCase_ : Optional[Any] = get_signature(UpperCamelCase_ )
# saving hook
def save_config(lowerCAmelCase_ : Any , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : List[str] ):
UpperCAmelCase_ : Any = {"class_name": models[0].__class__.__name__}
with open(os.path.join(UpperCamelCase_ , "data.json" ) , "w" ) as f:
json.dump(UpperCamelCase_ , UpperCamelCase_ )
# loading hook
def load_config(lowerCAmelCase_ : Dict , lowerCAmelCase_ : List[Any] ):
with open(os.path.join(UpperCamelCase_ , "data.json" ) , "r" ) as f:
UpperCAmelCase_ : Dict = json.load(UpperCamelCase_ )
UpperCAmelCase_ : Dict = config["class_name"]
UpperCAmelCase_ : int = accelerator.register_save_state_pre_hook(UpperCamelCase_ )
UpperCAmelCase_ : Tuple = accelerator.register_load_state_pre_hook(UpperCamelCase_ )
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(UpperCamelCase_ )
# make sure random weights don't match with hooks
load_random_weights(UpperCamelCase_ )
self.assertTrue(abs(model_signature - get_signature(UpperCamelCase_ ) ) > 1e-3 )
# random class name to verify correct one is loaded
UpperCAmelCase_ : Union[str, Any] = "random"
# make sure loaded weights match with hooks
accelerator.load_state(UpperCamelCase_ )
self.assertTrue(abs(model_signature - get_signature(UpperCamelCase_ ) ) < 1e-3 )
# mode.class_name is loaded from config
self.assertTrue(model.class_name == model.__class__.__name__ )
# remove hooks
save_hook.remove()
load_hook.remove()
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(UpperCamelCase_ )
# make sure random weights don't match with hooks removed
load_random_weights(UpperCamelCase_ )
self.assertTrue(abs(model_signature - get_signature(UpperCamelCase_ ) ) > 1e-3 )
# random class name to verify correct one is loaded
UpperCAmelCase_ : int = "random"
# make sure loaded weights match with hooks removed
accelerator.load_state(UpperCamelCase_ )
self.assertTrue(abs(model_signature - get_signature(UpperCamelCase_ ) ) < 1e-3 )
# mode.class_name is NOT loaded from config
self.assertTrue(model.class_name != model.__class__.__name__ )
def _SCREAMING_SNAKE_CASE ( self : List[Any] ) -> List[str]:
UpperCAmelCase_ : Any = Accelerator()
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Optional[Any] = create_components()
UpperCAmelCase_ : List[Any] = None
# This should work
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : List[str] = accelerator.prepare(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
self.assertTrue(dummy_obj is None )
def _SCREAMING_SNAKE_CASE ( self : Any ) -> Optional[Any]:
UpperCAmelCase_ : Optional[int] = Accelerator()
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : List[Any] = create_components()
UpperCAmelCase_ : Optional[Any] = [1, 2, 3]
# This should work
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Tuple = accelerator.prepare(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
self.assertEqual(
getattr(UpperCamelCase_ , "_is_accelerate_prepared" , UpperCamelCase_ ) , UpperCamelCase_ , "Dummy object should have `_is_accelerate_prepared` set to `True`" , )
self.assertEqual(
getattr(UpperCamelCase_ , "_is_accelerate_prepared" , UpperCamelCase_ ) , UpperCamelCase_ , "Model is missing `_is_accelerator_prepared` or is set to `False`" , )
self.assertEqual(
getattr(UpperCamelCase_ , "_is_accelerate_prepared" , UpperCamelCase_ ) , UpperCamelCase_ , "Optimizer is missing `_is_accelerator_prepared` or is set to `False`" , )
self.assertEqual(
getattr(UpperCamelCase_ , "_is_accelerate_prepared" , UpperCamelCase_ ) , UpperCamelCase_ , "Scheduler is missing `_is_accelerator_prepared` or is set to `False`" , )
self.assertEqual(
getattr(UpperCamelCase_ , "_is_accelerate_prepared" , UpperCamelCase_ ) , UpperCamelCase_ , "Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`" , )
self.assertEqual(
getattr(UpperCamelCase_ , "_is_accelerate_prepared" , UpperCamelCase_ ) , UpperCamelCase_ , "Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`" , )
@slow
@require_bnb
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> List[str]:
from transformers import AutoModelForCausalLM
UpperCAmelCase_ : Union[str, Any] = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , load_in_abit=UpperCamelCase_ , device_map={"": 0} , )
UpperCAmelCase_ : Tuple = Accelerator()
# This should work
UpperCAmelCase_ : List[Any] = accelerator.prepare(UpperCamelCase_ )
@slow
@require_bnb
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> List[str]:
from transformers import AutoModelForCausalLM
UpperCAmelCase_ : Dict = Accelerator()
with init_empty_weights():
UpperCAmelCase_ : Any = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , )
model.tie_weights()
UpperCAmelCase_ : Dict = infer_auto_device_map(UpperCamelCase_ )
UpperCAmelCase_ : Union[str, Any] = "cpu"
UpperCAmelCase_ : Tuple = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , device_map=UpperCamelCase_ , load_in_abit=UpperCamelCase_ , llm_inta_enable_fpaa_cpu_offload=UpperCamelCase_ )
# This should not work and get value error
with self.assertRaises(UpperCamelCase_ ):
UpperCAmelCase_ : Any = accelerator.prepare(UpperCamelCase_ )
@slow
@require_bnb
@require_multi_gpu
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Union[str, Any]:
from transformers import AutoModelForCausalLM
UpperCAmelCase_ : Union[str, Any] = {"distributed_type": DistributedType.MULTI_GPU}
with init_empty_weights():
UpperCAmelCase_ : Tuple = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , )
model.tie_weights()
UpperCAmelCase_ : Dict = infer_auto_device_map(UpperCamelCase_ )
UpperCAmelCase_ : str = 1
UpperCAmelCase_ : Union[str, Any] = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , load_in_abit=UpperCamelCase_ , device_map=UpperCamelCase_ , )
UpperCAmelCase_ : int = Accelerator()
# This should not work and get value error
with self.assertRaises(UpperCamelCase_ ):
UpperCAmelCase_ : int = accelerator.prepare(UpperCamelCase_ )
PartialState._reset_state()
@slow
@require_bnb
@require_multi_gpu
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> int:
from transformers import AutoModelForCausalLM
with init_empty_weights():
UpperCAmelCase_ : Optional[int] = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , )
UpperCAmelCase_ : int = infer_auto_device_map(UpperCamelCase_ )
UpperCAmelCase_ : str = 1
UpperCAmelCase_ : Dict = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , load_in_abit=UpperCamelCase_ , device_map=UpperCamelCase_ , )
UpperCAmelCase_ : List[str] = Accelerator()
# This should work
UpperCAmelCase_ : str = accelerator.prepare(UpperCamelCase_ )
@require_cuda
def _SCREAMING_SNAKE_CASE ( self : List[Any] ) -> int:
UpperCAmelCase_ : Optional[Any] = torch.nn.Linear(10 , 10 )
UpperCAmelCase_ : Optional[Any] = torch.optim.SGD(model.parameters() , lr=0.0_1 )
UpperCAmelCase_ : Union[str, Any] = Accelerator(cpu=UpperCamelCase_ )
UpperCAmelCase_ : Optional[Any] = accelerator.prepare(UpperCamelCase_ )
| 357 |
"""simple docstring"""
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached_file,
get_file_from_repo,
has_file,
)
lowerCamelCase_ = '''hf-internal-testing/tiny-random-bert'''
lowerCamelCase_ = os.path.join(TRANSFORMERS_CACHE, '''models--hf-internal-testing--tiny-random-bert''')
lowerCamelCase_ = '''9b8c223d42b2188cb49d29af482996f9d0f3e5a6'''
class UpperCamelCase_ (unittest.TestCase ):
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> List[str]:
UpperCAmelCase_ : List[Any] = cached_file(lowerCAmelCase_ , lowerCAmelCase_ )
# Should have downloaded the file in here
self.assertTrue(os.path.isdir(lowerCAmelCase_ ) )
# Cache should contain at least those three subfolders:
for subfolder in ["blobs", "refs", "snapshots"]:
self.assertTrue(os.path.isdir(os.path.join(lowerCAmelCase_ , lowerCAmelCase_ ) ) )
with open(os.path.join(lowerCAmelCase_ , "refs" , "main" ) ) as f:
UpperCAmelCase_ : Optional[int] = f.read()
self.assertEqual(lowerCAmelCase_ , os.path.join(lowerCAmelCase_ , "snapshots" , lowerCAmelCase_ , lowerCAmelCase_ ) )
self.assertTrue(os.path.isfile(lowerCAmelCase_ ) )
# File is cached at the same place the second time.
UpperCAmelCase_ : List[str] = cached_file(lowerCAmelCase_ , lowerCAmelCase_ )
self.assertEqual(lowerCAmelCase_ , lowerCAmelCase_ )
# Using a specific revision to test the full commit hash.
UpperCAmelCase_ : int = cached_file(lowerCAmelCase_ , lowerCAmelCase_ , revision="9b8c223" )
self.assertEqual(lowerCAmelCase_ , os.path.join(lowerCAmelCase_ , "snapshots" , lowerCAmelCase_ , lowerCAmelCase_ ) )
def _SCREAMING_SNAKE_CASE ( self : Any ) -> Union[str, Any]:
with self.assertRaisesRegex(lowerCAmelCase_ , "is not a valid model identifier" ):
UpperCAmelCase_ : List[Any] = cached_file("tiny-random-bert" , lowerCAmelCase_ )
with self.assertRaisesRegex(lowerCAmelCase_ , "is not a valid git identifier" ):
UpperCAmelCase_ : Optional[Any] = cached_file(lowerCAmelCase_ , lowerCAmelCase_ , revision="aaaa" )
with self.assertRaisesRegex(lowerCAmelCase_ , "does not appear to have a file named" ):
UpperCAmelCase_ : Union[str, Any] = cached_file(lowerCAmelCase_ , "conf" )
def _SCREAMING_SNAKE_CASE ( self : Any ) -> List[str]:
with self.assertRaisesRegex(lowerCAmelCase_ , "does not appear to have a file named" ):
UpperCAmelCase_ : Any = cached_file(lowerCAmelCase_ , "conf" )
with open(os.path.join(lowerCAmelCase_ , "refs" , "main" ) ) as f:
UpperCAmelCase_ : List[str] = f.read()
self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase_ , ".no_exist" , lowerCAmelCase_ , "conf" ) ) )
UpperCAmelCase_ : str = cached_file(lowerCAmelCase_ , "conf" , _raise_exceptions_for_missing_entries=lowerCAmelCase_ )
self.assertIsNone(lowerCAmelCase_ )
UpperCAmelCase_ : List[Any] = cached_file(lowerCAmelCase_ , "conf" , local_files_only=lowerCAmelCase_ , _raise_exceptions_for_missing_entries=lowerCAmelCase_ )
self.assertIsNone(lowerCAmelCase_ )
UpperCAmelCase_ : Any = mock.Mock()
UpperCAmelCase_ : List[str] = 500
UpperCAmelCase_ : Optional[Any] = {}
UpperCAmelCase_ : List[Any] = HTTPError
UpperCAmelCase_ : List[str] = {}
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("requests.Session.request" , return_value=lowerCAmelCase_ ) as mock_head:
UpperCAmelCase_ : List[Any] = cached_file(lowerCAmelCase_ , "conf" , _raise_exceptions_for_connection_errors=lowerCAmelCase_ )
self.assertIsNone(lowerCAmelCase_ )
# This check we did call the fake head request
mock_head.assert_called()
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> Dict:
self.assertTrue(has_file("hf-internal-testing/tiny-bert-pt-only" , lowerCAmelCase_ ) )
self.assertFalse(has_file("hf-internal-testing/tiny-bert-pt-only" , lowerCAmelCase_ ) )
self.assertFalse(has_file("hf-internal-testing/tiny-bert-pt-only" , lowerCAmelCase_ ) )
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> Union[str, Any]:
# `get_file_from_repo` returns None if the file does not exist
self.assertIsNone(get_file_from_repo("bert-base-cased" , "ahah.txt" ) )
# The function raises if the repository does not exist.
with self.assertRaisesRegex(lowerCAmelCase_ , "is not a valid model identifier" ):
get_file_from_repo("bert-base-case" , lowerCAmelCase_ )
# The function raises if the revision does not exist.
with self.assertRaisesRegex(lowerCAmelCase_ , "is not a valid git identifier" ):
get_file_from_repo("bert-base-cased" , lowerCAmelCase_ , revision="ahaha" )
UpperCAmelCase_ : int = get_file_from_repo("bert-base-cased" , lowerCAmelCase_ )
# The name is the cached name which is not very easy to test, so instead we load the content.
UpperCAmelCase_ : Optional[int] = json.loads(open(lowerCAmelCase_ , "r" ).read() )
self.assertEqual(config["hidden_size"] , 768 )
def _SCREAMING_SNAKE_CASE ( self : Tuple ) -> List[Any]:
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCAmelCase_ : Union[str, Any] = Path(lowerCAmelCase_ ) / "a.txt"
filename.touch()
self.assertEqual(get_file_from_repo(lowerCAmelCase_ , "a.txt" ) , str(lowerCAmelCase_ ) )
self.assertIsNone(get_file_from_repo(lowerCAmelCase_ , "b.txt" ) )
| 253 | 0 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( __snake_case : str ):
'''simple docstring'''
lowercase = 0
# if input_string is "aba" than new_input_string become "a|b|a"
lowercase = ''
lowercase = ''
# append each character + "|" in new_string for range(0, length-1)
for i in input_string[: len(__snake_case ) - 1]:
new_input_string += i + "|"
# append last character
new_input_string += input_string[-1]
# we will store the starting and ending of previous furthest ending palindromic
# substring
lowercase , lowercase = 0, 0
# length[i] shows the length of palindromic substring with center i
lowercase = [1 for i in range(len(__snake_case ) )]
# for each character in new_string find corresponding palindromic string
lowercase = 0
for j in range(len(__snake_case ) ):
lowercase = 1 if j > r else min(length[l + r - j] // 2 , r - j + 1 )
while (
j - k >= 0
and j + k < len(__snake_case )
and new_input_string[k + j] == new_input_string[j - k]
):
k += 1
lowercase = 2 * k - 1
# does this string is ending after the previously explored end (that is r) ?
# if yes the update the new r to the last index of this
if j + k - 1 > r:
lowercase = j - k + 1 # noqa: E741
lowercase = j + k - 1
# update max_length and start position
if max_length < length[j]:
lowercase = length[j]
lowercase = j
# create that string
lowercase = new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
for i in s:
if i != "|":
output_string += i
return output_string
if __name__ == "__main__":
import doctest
doctest.testmod()
| 220 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class a ( unittest.TestCase ):
def UpperCamelCase_ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def UpperCamelCase_ ( self ):
lowercase , lowercase = FlaxStableDiffusionPipeline.from_pretrained(
'stabilityai/stable-diffusion-2' , revision='bf16' , dtype=jnp.bfloataa , )
lowercase = 'A painting of a squirrel eating a burger'
lowercase = jax.device_count()
lowercase = num_samples * [prompt]
lowercase = sd_pipe.prepare_inputs(_lowerCamelCase )
lowercase = replicate(_lowerCamelCase )
lowercase = shard(_lowerCamelCase )
lowercase = jax.random.PRNGKey(0 )
lowercase = jax.random.split(_lowerCamelCase , jax.device_count() )
lowercase = sd_pipe(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , num_inference_steps=2_5 , jit=_lowerCamelCase )[0]
assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)
lowercase = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowercase = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
lowercase = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowercase = jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2] )
print(F'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
def UpperCamelCase_ ( self ):
lowercase = 'stabilityai/stable-diffusion-2'
lowercase , lowercase = FlaxDPMSolverMultistepScheduler.from_pretrained(_lowerCamelCase , subfolder='scheduler' )
lowercase , lowercase = FlaxStableDiffusionPipeline.from_pretrained(
_lowerCamelCase , scheduler=_lowerCamelCase , revision='bf16' , dtype=jnp.bfloataa , )
lowercase = scheduler_params
lowercase = 'A painting of a squirrel eating a burger'
lowercase = jax.device_count()
lowercase = num_samples * [prompt]
lowercase = sd_pipe.prepare_inputs(_lowerCamelCase )
lowercase = replicate(_lowerCamelCase )
lowercase = shard(_lowerCamelCase )
lowercase = jax.random.PRNGKey(0 )
lowercase = jax.random.split(_lowerCamelCase , jax.device_count() )
lowercase = sd_pipe(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , num_inference_steps=2_5 , jit=_lowerCamelCase )[0]
assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)
lowercase = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowercase = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
lowercase = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowercase = jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7] )
print(F'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 220 | 1 |
from __future__ import annotations
def lowerCAmelCase_ ( __a ):
"""simple docstring"""
lowerCamelCase__: str =2
lowerCamelCase__: Tuple =[]
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__a )
if n > 1:
factors.append(__a )
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 358 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
__A = get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( enum.Enum ):
'''simple docstring'''
lowercase_ = "all_checks"
lowercase_ = "basic_checks"
lowercase_ = "no_checks"
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def lowerCAmelCase_ ( __a , __a , __a=None ) -> Optional[int]:
"""simple docstring"""
if expected_checksums is None:
logger.info("Unable to verify checksums." )
return
if len(set(__a ) - set(__a ) ) > 0:
raise ExpectedMoreDownloadedFiles(str(set(__a ) - set(__a ) ) )
if len(set(__a ) - set(__a ) ) > 0:
raise UnexpectedDownloadedFile(str(set(__a ) - set(__a ) ) )
lowerCamelCase__: List[Any] =[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
lowerCamelCase__: Union[str, Any] =" for " + verification_name if verification_name is not None else ""
if len(__a ) > 0:
raise NonMatchingChecksumError(
F"""Checksums didn't match{for_verification_name}:\n"""
F"""{bad_urls}\n"""
"Set `verification_mode='no_checks'` to skip checksums verification and ignore this error" )
logger.info("All the checksums matched successfully" + for_verification_name )
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def lowerCAmelCase_ ( __a , __a ) -> Any:
"""simple docstring"""
if expected_splits is None:
logger.info("Unable to verify splits sizes." )
return
if len(set(__a ) - set(__a ) ) > 0:
raise ExpectedMoreSplits(str(set(__a ) - set(__a ) ) )
if len(set(__a ) - set(__a ) ) > 0:
raise UnexpectedSplits(str(set(__a ) - set(__a ) ) )
lowerCamelCase__: Optional[int] =[
{"expected": expected_splits[name], "recorded": recorded_splits[name]}
for name in expected_splits
if expected_splits[name].num_examples != recorded_splits[name].num_examples
]
if len(__a ) > 0:
raise NonMatchingSplitsSizesError(str(__a ) )
logger.info("All the splits matched successfully." )
def lowerCAmelCase_ ( __a , __a = True ) -> dict:
"""simple docstring"""
if record_checksum:
lowerCamelCase__: str =shaaaa()
with open(__a , "rb" ) as f:
for chunk in iter(lambda: f.read(1 << 20 ) , b"" ):
m.update(__a )
lowerCamelCase__: Dict =m.hexdigest()
else:
lowerCamelCase__: List[str] =None
return {"num_bytes": os.path.getsize(__a ), "checksum": checksum}
def lowerCAmelCase_ ( __a ) -> int:
"""simple docstring"""
if dataset_size and config.IN_MEMORY_MAX_SIZE:
return dataset_size < config.IN_MEMORY_MAX_SIZE
else:
return False
| 273 | 0 |
'''simple docstring'''
import datasets
__SCREAMING_SNAKE_CASE :List[Any] = '''\
@InProceedings{conneau2018xnli,
author = "Conneau, Alexis
and Rinott, Ruty
and Lample, Guillaume
and Williams, Adina
and Bowman, Samuel R.
and Schwenk, Holger
and Stoyanov, Veselin",
title = "XNLI: Evaluating Cross-lingual Sentence Representations",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods
in Natural Language Processing",
year = "2018",
publisher = "Association for Computational Linguistics",
location = "Brussels, Belgium",
}
'''
__SCREAMING_SNAKE_CASE :Any = '''\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
'''
__SCREAMING_SNAKE_CASE :Dict = '''
Computes XNLI score which is just simple accuracy.
Args:
predictions: Predicted labels.
references: Ground truth labels.
Returns:
\'accuracy\': accuracy
Examples:
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> xnli_metric = datasets.load_metric("xnli")
>>> results = xnli_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'accuracy\': 1.0}
'''
def UpperCAmelCase_ ( __lowercase : Tuple , __lowercase : Optional[int] ) -> str:
'''simple docstring'''
return (preds == labels).mean()
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A_ ( datasets.Metric ):
def lowercase ( self : Dict ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("int64" if self.config_name != "sts-b" else "float32" ),
"references": datasets.Value("int64" if self.config_name != "sts-b" else "float32" ),
} ) , codebase_urls=[] , reference_urls=[] , format="numpy" , )
def lowercase ( self : str , snake_case_ : str , snake_case_ : int ):
return {"accuracy": simple_accuracy(snake_case_ , snake_case_ )}
| 22 |
UpperCAmelCase__ : Optional[Any] = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
def lowerCamelCase__ ( a , a , a ) -> list[str]:
_A: Union[str, Any] = set()
# keep track of all the paths to be checked
_A: Union[str, 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
_A: Any = queue.pop(0 )
# get the last node from the path
_A: Union[str, Any] = path[-1]
if node not in explored:
_A: str = graph[node]
# go through all neighbour nodes, construct a new path and
# push it into the queue
for neighbour in neighbours:
_A: Optional[int] = list(a )
new_path.append(a )
queue.append(a )
# return path if neighbour is goal
if neighbour == goal:
return new_path
# mark node as explored
explored.add(a )
# in case there's no path between the 2 nodes
return []
def lowerCamelCase__ ( a , a , a ) -> int:
if not graph or start not in graph or target not in graph:
return -1
if start == target:
return 0
_A: Any = [start]
_A: List[str] = set(a )
# Keep tab on distances from `start` node.
_A: Optional[int] = {start: 0, target: -1}
while queue:
_A: Union[str, Any] = queue.pop(0 )
if node == target:
_A: Dict = (
dist[node] if dist[target] == -1 else min(dist[target] , dist[node] )
)
for adjacent in graph[node]:
if adjacent not in visited:
visited.add(a )
queue.append(a )
_A: List[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
| 121 | 0 |
"""simple docstring"""
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
lowercase = {
"E": 12.70,
"T": 9.06,
"A": 8.17,
"O": 7.51,
"I": 6.97,
"N": 6.75,
"S": 6.33,
"H": 6.09,
"R": 5.99,
"D": 4.25,
"L": 4.03,
"C": 2.78,
"U": 2.76,
"M": 2.41,
"W": 2.36,
"F": 2.23,
"G": 2.02,
"Y": 1.97,
"P": 1.93,
"B": 1.29,
"V": 0.98,
"K": 0.77,
"J": 0.15,
"X": 0.15,
"Q": 0.10,
"Z": 0.07,
}
lowercase = "ETAOINSHRDLCUMWFGYPBVKJXQZ"
lowercase = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> dict[str, int]:
A__ = {letter: 0 for letter in string.ascii_uppercase}
for letter in message.upper():
if letter in LETTERS:
letter_count[letter] += 1
return letter_count
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> str:
return x[0]
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> str:
A__ = get_letter_count(lowercase_ )
A__ = {
freq: [] for letter, freq in letter_to_freq.items()
}
for letter in LETTERS:
freq_to_letter[letter_to_freq[letter]].append(lowercase_ )
A__ = {}
for freq in freq_to_letter:
freq_to_letter[freq].sort(key=ETAOIN.find , reverse=lowercase_ )
A__ = "".join(freq_to_letter[freq] )
A__ = list(freq_to_letter_str.items() )
freq_pairs.sort(key=lowercase_ , reverse=lowercase_ )
A__ = [freq_pair[1] for freq_pair in freq_pairs]
return "".join(lowercase_ )
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
A__ = get_frequency_order(lowercase_ )
A__ = 0
for common_letter in ETAOIN[:6]:
if common_letter in freq_order[:6]:
match_score += 1
for uncommon_letter in ETAOIN[-6:]:
if uncommon_letter in freq_order[-6:]:
match_score += 1
return match_score
if __name__ == "__main__":
import doctest
doctest.testmod()
| 370 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
SCREAMING_SNAKE_CASE = {
"vocab_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"
),
},
"tokenizer_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"
),
},
}
SCREAMING_SNAKE_CASE = {
"facebook/nllb-large-en-ro": 1024,
"facebook/nllb-200-distilled-600M": 1024,
}
# fmt: off
SCREAMING_SNAKE_CASE = ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]
class UpperCAmelCase_ ( A_ ):
lowercase__ = VOCAB_FILES_NAMES
lowercase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ = PRETRAINED_VOCAB_FILES_MAP
lowercase__ = ['''input_ids''', '''attention_mask''']
lowercase__ = NllbTokenizer
lowercase__ = []
lowercase__ = []
def __init__( self : int , snake_case_ : int=None , snake_case_ : Any=None , snake_case_ : int="<s>" , snake_case_ : List[Any]="</s>" , snake_case_ : Optional[int]="</s>" , snake_case_ : int="<s>" , snake_case_ : str="<unk>" , snake_case_ : str="<pad>" , snake_case_ : Optional[int]="<mask>" , snake_case_ : str=None , snake_case_ : List[Any]=None , snake_case_ : Tuple=None , snake_case_ : Optional[int]=False , **snake_case_ : List[str] , ) -> Tuple:
'''simple docstring'''
A__ = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else mask_token
A__ = legacy_behaviour
super().__init__(
vocab_file=snake_case_ , tokenizer_file=snake_case_ , bos_token=snake_case_ , eos_token=snake_case_ , sep_token=snake_case_ , cls_token=snake_case_ , unk_token=snake_case_ , pad_token=snake_case_ , mask_token=snake_case_ , src_lang=snake_case_ , tgt_lang=snake_case_ , additional_special_tokens=snake_case_ , legacy_behaviour=snake_case_ , **snake_case_ , )
A__ = vocab_file
A__ = False if not self.vocab_file else True
A__ = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({"additional_special_tokens": _additional_special_tokens} )
A__ = {
lang_code: self.convert_tokens_to_ids(snake_case_ ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
A__ = src_lang if src_lang is not None else "eng_Latn"
A__ = self.convert_tokens_to_ids(self._src_lang )
A__ = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def __magic_name__ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
return self._src_lang
@src_lang.setter
def __magic_name__ ( self : Optional[int] , snake_case_ : str ) -> None:
'''simple docstring'''
A__ = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def __magic_name__ ( self : Optional[int] , snake_case_ : List[int] , snake_case_ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def __magic_name__ ( self : Any , snake_case_ : List[int] , snake_case_ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
A__ = [self.sep_token_id]
A__ = [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 __magic_name__ ( self : int , snake_case_ : Tuple , snake_case_ : str , snake_case_ : Optional[str] , snake_case_ : Optional[str] , **snake_case_ : Tuple ) -> List[Any]:
'''simple docstring'''
if src_lang is None or tgt_lang is None:
raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model" )
A__ = src_lang
A__ = self(snake_case_ , add_special_tokens=snake_case_ , return_tensors=snake_case_ , **snake_case_ )
A__ = self.convert_tokens_to_ids(snake_case_ )
A__ = tgt_lang_id
return inputs
def __magic_name__ ( self : int , snake_case_ : List[str] , snake_case_ : str = "eng_Latn" , snake_case_ : Optional[List[str]] = None , snake_case_ : str = "fra_Latn" , **snake_case_ : Dict , ) -> BatchEncoding:
'''simple docstring'''
A__ = src_lang
A__ = tgt_lang
return super().prepare_seqaseq_batch(snake_case_ , snake_case_ , **snake_case_ )
def __magic_name__ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
return self.set_src_lang_special_tokens(self.src_lang )
def __magic_name__ ( self : Tuple ) -> Dict:
'''simple docstring'''
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def __magic_name__ ( self : List[Any] , snake_case_ : Dict ) -> None:
'''simple docstring'''
A__ = self.convert_tokens_to_ids(snake_case_ )
if self.legacy_behaviour:
A__ = []
A__ = [self.eos_token_id, self.cur_lang_code]
else:
A__ = [self.cur_lang_code]
A__ = [self.eos_token_id]
A__ = self.convert_ids_to_tokens(self.prefix_tokens )
A__ = self.convert_ids_to_tokens(self.suffix_tokens )
A__ = processors.TemplateProcessing(
single=prefix_tokens_str + ["$A"] + suffix_tokens_str , pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def __magic_name__ ( self : List[Any] , snake_case_ : str ) -> None:
'''simple docstring'''
A__ = self.convert_tokens_to_ids(snake_case_ )
if self.legacy_behaviour:
A__ = []
A__ = [self.eos_token_id, self.cur_lang_code]
else:
A__ = [self.cur_lang_code]
A__ = [self.eos_token_id]
A__ = self.convert_ids_to_tokens(self.prefix_tokens )
A__ = self.convert_ids_to_tokens(self.suffix_tokens )
A__ = processors.TemplateProcessing(
single=prefix_tokens_str + ["$A"] + suffix_tokens_str , pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def __magic_name__ ( self : List[str] , snake_case_ : str , snake_case_ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer." )
if not os.path.isdir(snake_case_ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory.""" )
return
A__ = os.path.join(
snake_case_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case_ ):
copyfile(self.vocab_file , snake_case_ )
return (out_vocab_file,)
| 230 | 0 |
'''simple docstring'''
_lowercase : Optional[int] = {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
_lowercase : str = ["a", "b", "c", "d", "e"]
def snake_case_ ( __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Union[str, Any] ):
"""simple docstring"""
lowercase_ : Optional[int] = start
# add current to visited
visited.append(__SCREAMING_SNAKE_CASE )
lowercase_ : Dict = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
lowercase_ : Dict = topological_sort(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# if all neighbors visited add current to sort
sort.append(__SCREAMING_SNAKE_CASE )
# if all vertices haven't been visited select a new one to visit
if len(__SCREAMING_SNAKE_CASE ) != len(__SCREAMING_SNAKE_CASE ):
for vertice in vertices:
if vertice not in visited:
lowercase_ : List[Any] = topological_sort(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# return sort
return sort
if __name__ == "__main__":
_lowercase : Dict = topological_sort("a", [], [])
print(sort)
| 93 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"""microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
"""microsoft/markuplm-large""": """https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json""",
}
class A__ ( _lowerCamelCase):
A_ : List[Any] = 'markuplm'
def __init__( self , _SCREAMING_SNAKE_CASE=3_05_22 , _SCREAMING_SNAKE_CASE=7_68 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=30_72 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=5_12 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=0.02 , _SCREAMING_SNAKE_CASE=1E-12 , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=2_56 , _SCREAMING_SNAKE_CASE=10_24 , _SCREAMING_SNAKE_CASE=2_16 , _SCREAMING_SNAKE_CASE=10_01 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=50 , _SCREAMING_SNAKE_CASE="absolute" , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE , ):
super().__init__(
pad_token_id=_SCREAMING_SNAKE_CASE , bos_token_id=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
__lowerCAmelCase : Union[str, Any] = vocab_size
__lowerCAmelCase : Any = hidden_size
__lowerCAmelCase : List[Any] = num_hidden_layers
__lowerCAmelCase : Tuple = num_attention_heads
__lowerCAmelCase : Union[str, Any] = hidden_act
__lowerCAmelCase : List[Any] = intermediate_size
__lowerCAmelCase : List[str] = hidden_dropout_prob
__lowerCAmelCase : List[str] = attention_probs_dropout_prob
__lowerCAmelCase : Optional[int] = max_position_embeddings
__lowerCAmelCase : int = type_vocab_size
__lowerCAmelCase : Tuple = initializer_range
__lowerCAmelCase : int = layer_norm_eps
__lowerCAmelCase : List[str] = position_embedding_type
__lowerCAmelCase : List[Any] = use_cache
__lowerCAmelCase : Optional[Any] = classifier_dropout
# additional properties
__lowerCAmelCase : Optional[int] = max_depth
__lowerCAmelCase : List[str] = max_xpath_tag_unit_embeddings
__lowerCAmelCase : Optional[Any] = max_xpath_subs_unit_embeddings
__lowerCAmelCase : Any = tag_pad_id
__lowerCAmelCase : Union[str, Any] = subs_pad_id
__lowerCAmelCase : int = xpath_unit_hidden_size | 86 | 0 |
from collections.abc import Callable
def A ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
UpperCAmelCase_ = a
UpperCAmelCase_ = b
if function(__UpperCAmelCase ) == 0: # one of the a or b is a root for the function
return a
elif function(__UpperCAmelCase ) == 0:
return b
elif (
function(__UpperCAmelCase ) * function(__UpperCAmelCase ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError('''could not find root in given interval.''' )
else:
UpperCAmelCase_ = start + (end - start) / 2.0
while abs(start - mid ) > 10**-7: # until precisely equals to 10^-7
if function(__UpperCAmelCase ) == 0:
return mid
elif function(__UpperCAmelCase ) * function(__UpperCAmelCase ) < 0:
UpperCAmelCase_ = mid
else:
UpperCAmelCase_ = mid
UpperCAmelCase_ = start + (end - start) / 2.0
return mid
def A ( __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1_000))
import doctest
doctest.testmod()
| 356 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {"configuration_opt": ["OPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OPTConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
"OPT_PRETRAINED_MODEL_ARCHIVE_LIST",
"OPTForCausalLM",
"OPTModel",
"OPTPreTrainedModel",
"OPTForSequenceClassification",
"OPTForQuestionAnswering",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = ["TFOPTForCausalLM", "TFOPTModel", "TFOPTPreTrainedModel"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
"FlaxOPTForCausalLM",
"FlaxOPTModel",
"FlaxOPTPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_opt import (
OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OPTForCausalLM,
OPTForQuestionAnswering,
OPTForSequenceClassification,
OPTModel,
OPTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel
else:
import sys
UpperCamelCase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 344 | 0 |
'''simple docstring'''
UpperCamelCase__: List[str] = [0, 2, 4, 6, 8]
UpperCamelCase__: Any = [1, 3, 5, 7, 9]
def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : list[int] , _lowerCAmelCase : int ) -> int:
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
for i in range(length // 2 - 1 , -1 , -1 ):
remainder += digits[i] + digits[length - i - 1]
if remainder % 2 == 0:
return 0
remainder //= 10
return 1
if remaining_length == 1:
if remainder % 2 == 0:
return 0
UpperCAmelCase : Union[str, Any] = 0
for digit in range(10 ):
UpperCAmelCase : Optional[Any] = digit
result += reversible_numbers(
0 , (remainder + 2 * digit) // 10 , _lowerCAmelCase , _lowerCAmelCase )
return result
UpperCAmelCase : str = 0
for digita in range(10 ):
UpperCAmelCase : List[Any] = digita
if (remainder + digita) % 2 == 0:
UpperCAmelCase : int = ODD_DIGITS
else:
UpperCAmelCase : Any = EVEN_DIGITS
for digita in other_parity_digits:
UpperCAmelCase : List[str] = digita
result += reversible_numbers(
remaining_length - 2 , (remainder + digita + digita) // 10 , _lowerCAmelCase , _lowerCAmelCase , )
return result
def snake_case_ ( _lowerCAmelCase : int = 9 ) -> int:
UpperCAmelCase : Union[str, Any] = 0
for length in range(1 , max_power + 1 ):
result += reversible_numbers(_lowerCAmelCase , 0 , [0] * length , _lowerCAmelCase )
return result
if __name__ == "__main__":
print(F"{solution() = }")
| 23 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
lowerCAmelCase : List[Any] = ['\nclass', '\ndef', '\n#', '\n@', '\nprint', '\nif']
class _A ( __magic_name__):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=1 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = tokenizer
SCREAMING_SNAKE_CASE_ : List[str] = dataset
SCREAMING_SNAKE_CASE_ : List[Any] = len(_SCREAMING_SNAKE_CASE ) if n_tasks is None else n_tasks
SCREAMING_SNAKE_CASE_ : Optional[int] = n_copies
def __iter__( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = []
for task in range(self.n_tasks ):
# without strip, the model generate commented codes ...
prompts.append(self.tokenizer.eos_token + self.dataset[task]['prompt'].strip() )
SCREAMING_SNAKE_CASE_ : Tuple = self.tokenizer(_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , return_tensors='pt' )
for task in range(self.n_tasks ):
for _ in range(self.n_copies ):
yield {
"ids": outputs.input_ids[task],
"task_id": task,
"input_len": outputs.attention_mask[task].sum(),
}
class _A ( __magic_name__):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = start_length
SCREAMING_SNAKE_CASE_ : Any = eof_strings
SCREAMING_SNAKE_CASE_ : Tuple = tokenizer
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = self.tokenizer.batch_decode(input_ids[:, self.start_length :] )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = []
for decoded_generation in decoded_generations:
done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings ) )
return all(_SCREAMING_SNAKE_CASE )
def A_ ( a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = re.split('(%s)' % '|'.join(a ) , a )
# last string should be ""
return "".join(string_list[:-2] )
def A_ ( a , a , a , a , a , a=2_0 , **a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = defaultdict(a ) # dict of list of generated tokens
for step, batch in tqdm(enumerate(a ) ):
with torch.no_grad():
SCREAMING_SNAKE_CASE_ : Optional[Any] = batch['ids'].shape[-1]
SCREAMING_SNAKE_CASE_ : str = accelerator.unwrap_model(a ).generate(
input_ids=batch['ids'][:, : batch['input_len']] , num_return_sequences=a , **a )
# each task is generated batch_size times
SCREAMING_SNAKE_CASE_ : Union[str, Any] = batch['task_id'].repeat(a )
SCREAMING_SNAKE_CASE_ : Optional[int] = accelerator.pad_across_processes(
a , dim=1 , pad_index=tokenizer.pad_token_id )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Union[str, Any] = accelerator.gather((generated_tokens, generated_tasks) )
SCREAMING_SNAKE_CASE_ : int = generated_tokens.cpu().numpy()
SCREAMING_SNAKE_CASE_ : List[Any] = generated_tasks.cpu().numpy()
for task, generated_tokens in zip(a , a ):
gen_token_dict[task].append(a )
SCREAMING_SNAKE_CASE_ : str = [[] for _ in range(a )]
for task, generated_tokens in gen_token_dict.items():
for s in generated_tokens:
SCREAMING_SNAKE_CASE_ : Dict = tokenizer.decode(a , skip_special_tokens=a , clean_up_tokenization_spaces=a )
code_gens[task].append(remove_last_block(a ) )
return code_gens
def A_ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = HfArgumentParser(a )
SCREAMING_SNAKE_CASE_ : Tuple = parser.parse_args()
transformers.logging.set_verbosity_error()
# enables code execution in code_eval metric
SCREAMING_SNAKE_CASE_ : Optional[int] = args.HF_ALLOW_CODE_EVAL
# make sure tokenizer plays nice with multiprocessing
SCREAMING_SNAKE_CASE_ : List[str] = 'false'
if args.num_workers is None:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = multiprocessing.cpu_count()
# Use dataset load to feed to accelerate
SCREAMING_SNAKE_CASE_ : Dict = Accelerator()
set_seed(args.seed , device_specific=a )
# Load model and tokenizer
SCREAMING_SNAKE_CASE_ : str = AutoTokenizer.from_pretrained(args.model_ckpt )
SCREAMING_SNAKE_CASE_ : List[Any] = tokenizer.eos_token
SCREAMING_SNAKE_CASE_ : Any = AutoModelForCausalLM.from_pretrained(args.model_ckpt )
# Generation settings
SCREAMING_SNAKE_CASE_ : Any = {
'do_sample': args.do_sample,
'temperature': args.temperature,
'max_new_tokens': args.max_new_tokens,
'top_p': args.top_p,
'top_k': args.top_k,
'stopping_criteria': StoppingCriteriaList([EndOfFunctionCriteria(0 , a , a )] ),
}
# Load evaluation dataset and metric
SCREAMING_SNAKE_CASE_ : List[str] = load_dataset('openai_humaneval' )
SCREAMING_SNAKE_CASE_ : str = load_metric('code_eval' )
SCREAMING_SNAKE_CASE_ : Tuple = args.num_tasks if args.num_tasks is not None else len(human_eval['test'] )
SCREAMING_SNAKE_CASE_ : Any = args.n_samples // args.batch_size
SCREAMING_SNAKE_CASE_ : int = TokenizedDataset(a , human_eval['test'] , n_copies=a , n_tasks=a )
# do not confuse args.batch_size, which is actually the num_return_sequences
SCREAMING_SNAKE_CASE_ : Tuple = DataLoader(a , batch_size=1 )
# Run a quick test to see if code evaluation is enabled
try:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = code_eval_metric.compute(references=[''] , predictions=[['']] )
except ValueError as exception:
print(
'Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'
' flag to enable code evaluation.' )
raise exception
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : List[Any] = accelerator.prepare(a , a )
SCREAMING_SNAKE_CASE_ : List[str] = complete_code(
a , a , a , a , n_tasks=a , batch_size=args.batch_size , **a , )
if accelerator.is_main_process:
SCREAMING_SNAKE_CASE_ : str = []
for task in tqdm(range(a ) ):
SCREAMING_SNAKE_CASE_ : str = human_eval['test'][task]['test']
SCREAMING_SNAKE_CASE_ : int = f"check({human_eval['test'][task]['entry_point']})"
references.append('\n' + test_func + '\n' + entry_point )
# Evaluate completions with "code_eval" metric
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Any = code_eval_metric.compute(
references=a , predictions=a , num_workers=args.num_workers )
print(f"Results: {pass_at_k}" )
# Save results to json file
with open(args.output_file , 'w' ) as fp:
json.dump(a , a )
# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
main()
| 253 | 0 |
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def __UpperCAmelCase ( __a : int ) -> Union[str, Any]:
"""simple docstring"""
_a : Any = SwinConfig(image_size=192 )
if "base" in model_name:
_a : Optional[int] = 6
_a : int = 128
_a : str = (2, 2, 18, 2)
_a : Tuple = (4, 8, 16, 32)
elif "large" in model_name:
_a : Union[str, Any] = 12
_a : List[Any] = 192
_a : str = (2, 2, 18, 2)
_a : Optional[Any] = (6, 12, 24, 48)
else:
raise ValueError('''Model not supported, only supports base and large variants''' )
_a : List[str] = window_size
_a : str = embed_dim
_a : Union[str, Any] = depths
_a : Optional[Any] = num_heads
return config
def __UpperCAmelCase ( __a : Tuple ) -> Dict:
"""simple docstring"""
if "encoder.mask_token" in name:
_a : int = name.replace('''encoder.mask_token''' ,'''embeddings.mask_token''' )
if "encoder.patch_embed.proj" in name:
_a : List[str] = name.replace('''encoder.patch_embed.proj''' ,'''embeddings.patch_embeddings.projection''' )
if "encoder.patch_embed.norm" in name:
_a : Tuple = name.replace('''encoder.patch_embed.norm''' ,'''embeddings.norm''' )
if "attn.proj" in name:
_a : List[str] = name.replace('''attn.proj''' ,'''attention.output.dense''' )
if "attn" in name:
_a : int = name.replace('''attn''' ,'''attention.self''' )
if "norm1" in name:
_a : List[str] = name.replace('''norm1''' ,'''layernorm_before''' )
if "norm2" in name:
_a : int = name.replace('''norm2''' ,'''layernorm_after''' )
if "mlp.fc1" in name:
_a : Optional[Any] = name.replace('''mlp.fc1''' ,'''intermediate.dense''' )
if "mlp.fc2" in name:
_a : int = name.replace('''mlp.fc2''' ,'''output.dense''' )
if name == "encoder.norm.weight":
_a : Dict = '''layernorm.weight'''
if name == "encoder.norm.bias":
_a : Optional[Any] = '''layernorm.bias'''
if "decoder" in name:
pass
else:
_a : List[Any] = '''swin.''' + name
return name
def __UpperCAmelCase ( __a : Dict ,__a : Dict ) -> List[str]:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
_a : Tuple = orig_state_dict.pop(__a )
if "attn_mask" in key:
pass
elif "qkv" in key:
_a : List[str] = key.split('''.''' )
_a : Dict = int(key_split[2] )
_a : Any = int(key_split[4] )
_a : Tuple = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
_a : str = val[:dim, :]
_a : List[Any] = val[
dim : dim * 2, :
]
_a : Tuple = val[-dim:, :]
else:
_a : Optional[Any] = val[
:dim
]
_a : List[Any] = val[
dim : dim * 2
]
_a : str = val[
-dim:
]
else:
_a : Dict = val
return orig_state_dict
def __UpperCAmelCase ( __a : Any ,__a : Optional[Any] ,__a : Union[str, Any] ,__a : Any ) -> List[Any]:
"""simple docstring"""
_a : Any = torch.load(__a ,map_location='''cpu''' )['''model''']
_a : Optional[Any] = get_swin_config(__a )
_a : int = SwinForMaskedImageModeling(__a )
model.eval()
_a : List[str] = convert_state_dict(__a ,__a )
model.load_state_dict(__a )
_a : Tuple = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
_a : Tuple = ViTImageProcessor(size={'''height''': 192, '''width''': 192} )
_a : Any = Image.open(requests.get(__a ,stream=__a ).raw )
_a : str = image_processor(images=__a ,return_tensors='''pt''' )
with torch.no_grad():
_a : List[Any] = model(**__a ).logits
print(outputs.keys() )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
print(F"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(__a )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(__a )
if push_to_hub:
print(F"""Pushing model and image processor for {model_name} to hub""" )
model.push_to_hub(F"""microsoft/{model_name}""" )
image_processor.push_to_hub(F"""microsoft/{model_name}""" )
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''swin-base-simmim-window6-192''',
type=str,
choices=['''swin-base-simmim-window6-192''', '''swin-large-simmim-window12-192'''],
help='''Name of the Swin SimMIM model you\'d like to convert.''',
)
parser.add_argument(
'''--checkpoint_path''',
default='''/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth''',
type=str,
help='''Path to the original PyTorch checkpoint (.pth file).''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
a__ = parser.parse_args()
convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
| 360 |
import numpy as np
def __UpperCAmelCase ( __a : np.ndarray ,__a : np.ndarray ,__a : float = 1E-12 ,__a : int = 100 ,) -> tuple[float, np.ndarray]:
"""simple docstring"""
assert np.shape(__a )[0] == np.shape(__a )[1]
# Ensure proper dimensionality.
assert np.shape(__a )[0] == np.shape(__a )[0]
# Ensure inputs are either both complex or both real
assert np.iscomplexobj(__a ) == np.iscomplexobj(__a )
_a : List[str] = np.iscomplexobj(__a )
if is_complex:
# Ensure complex input_matrix is Hermitian
assert np.array_equal(__a ,input_matrix.conj().T )
# Set convergence to False. Will define convergence when we exceed max_iterations
# or when we have small changes from one iteration to next.
_a : List[str] = False
_a : List[str] = 0
_a : Tuple = 0
_a : str = 1E12
while not convergence:
# Multiple matrix by the vector.
_a : str = np.dot(__a ,__a )
# Normalize the resulting output vector.
_a : List[Any] = w / np.linalg.norm(__a )
# Find rayleigh quotient
# (faster than usual b/c we know vector is normalized already)
_a : Dict = vector.conj().T if is_complex else vector.T
_a : Tuple = np.dot(__a ,np.dot(__a ,__a ) )
# Check convergence.
_a : List[str] = np.abs(lambda_ - lambda_previous ) / lambda_
iterations += 1
if error <= error_tol or iterations >= max_iterations:
_a : Dict = True
_a : str = lambda_
if is_complex:
_a : Tuple = np.real(lambda_ )
return lambda_, vector
def __UpperCAmelCase ( ) -> None:
"""simple docstring"""
_a : List[str] = np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]] )
_a : int = np.array([41, 4, 20] )
_a : Optional[Any] = real_input_matrix.astype(np.complexaaa )
_a : int = np.triu(1j * complex_input_matrix ,1 )
complex_input_matrix += imag_matrix
complex_input_matrix += -1 * imag_matrix.T
_a : Union[str, Any] = np.array([41, 4, 20] ).astype(np.complexaaa )
for problem_type in ["real", "complex"]:
if problem_type == "real":
_a : Optional[int] = real_input_matrix
_a : Union[str, Any] = real_vector
elif problem_type == "complex":
_a : str = complex_input_matrix
_a : str = complex_vector
# Our implementation.
_a , _a : Optional[Any] = power_iteration(__a ,__a )
# Numpy implementation.
# Get eigenvalues and eigenvectors using built-in numpy
# eigh (eigh used for symmetric or hermetian matrices).
_a , _a : List[str] = np.linalg.eigh(__a )
# Last eigenvalue is the maximum one.
_a : Tuple = eigen_values[-1]
# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
_a : List[Any] = eigen_vectors[:, -1]
# Check our implementation and numpy gives close answers.
assert np.abs(eigen_value - eigen_value_max ) <= 1E-6
# Take absolute values element wise of each eigenvector.
# as they are only unique to a minus sign.
assert np.linalg.norm(np.abs(__a ) - np.abs(__a ) ) <= 1E-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_power_iteration()
| 15 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase_ = {
"configuration_perceiver": ["PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP", "PerceiverConfig", "PerceiverOnnxConfig"],
"tokenization_perceiver": ["PerceiverTokenizer"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ["PerceiverFeatureExtractor"]
lowerCAmelCase_ = ["PerceiverImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
"PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST",
"PerceiverForImageClassificationConvProcessing",
"PerceiverForImageClassificationFourier",
"PerceiverForImageClassificationLearned",
"PerceiverForMaskedLM",
"PerceiverForMultimodalAutoencoding",
"PerceiverForOpticalFlow",
"PerceiverForSequenceClassification",
"PerceiverLayer",
"PerceiverModel",
"PerceiverPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
from .tokenization_perceiver import PerceiverTokenizer
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_perceiver import PerceiverFeatureExtractor
from .image_processing_perceiver import PerceiverImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_perceiver import (
PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
PerceiverForImageClassificationConvProcessing,
PerceiverForImageClassificationFourier,
PerceiverForImageClassificationLearned,
PerceiverForMaskedLM,
PerceiverForMultimodalAutoencoding,
PerceiverForOpticalFlow,
PerceiverForSequenceClassification,
PerceiverLayer,
PerceiverModel,
PerceiverPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 16 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class A_ (unittest.TestCase ):
@slow
def _lowercase ( self ):
'''simple docstring'''
UpperCAmelCase = XLMRobertaModel.from_pretrained('''xlm-roberta-base''' )
UpperCAmelCase = torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]] )
# The dog is cute and lives in the garden house
UpperCAmelCase = torch.Size((1, 1_2, 7_6_8) ) # batch_size, sequence_length, embedding_vector_dim
UpperCAmelCase = torch.tensor(
[[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
UpperCAmelCase = model(_A )['''last_hidden_state'''].detach()
self.assertEqual(output.shape , _A )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , _A , atol=1E-3 ) )
@slow
def _lowercase ( self ):
'''simple docstring'''
UpperCAmelCase = XLMRobertaModel.from_pretrained('''xlm-roberta-large''' )
UpperCAmelCase = torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]] )
# The dog is cute and lives in the garden house
UpperCAmelCase = torch.Size((1, 1_2, 1_0_2_4) ) # batch_size, sequence_length, embedding_vector_dim
UpperCAmelCase = torch.tensor(
[[-0.06_99, -0.03_18, 0.07_05, -0.12_41, 0.09_99, -0.05_20, 0.10_04, -0.18_38, -0.47_04, 0.14_37, 0.08_21, 0.01_26]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
UpperCAmelCase = model(_A )['''last_hidden_state'''].detach()
self.assertEqual(output.shape , _A )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , _A , atol=1E-3 ) )
| 273 | 0 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class __A ( a ):
def _snake_case ( self , UpperCAmelCase_ ):
with open(UpperCAmelCase_ , encoding="""utf-8""" ) as input_file:
lowerCamelCase =re.compile(r"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)""" )
lowerCamelCase =input_file.read()
lowerCamelCase =regexp.search(UpperCAmelCase_ )
return match
def _snake_case ( self , UpperCAmelCase_ ):
with open(UpperCAmelCase_ , encoding="""utf-8""" ) as input_file:
lowerCamelCase =re.compile(r"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()""" , re.DOTALL )
lowerCamelCase =input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
lowerCamelCase =regexp.finditer(UpperCAmelCase_ )
lowerCamelCase =[match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def _snake_case ( self ):
lowerCamelCase =Path("""./datasets""" )
lowerCamelCase =list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(UpperCAmelCase_ ) ):
raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}""" )
def _snake_case ( self ):
lowerCamelCase =Path("""./datasets""" )
lowerCamelCase =list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_print_statements(str(UpperCAmelCase_ ) ):
raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead.""" )
| 262 |
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 _lowercase ( _UpperCAmelCase ) -> str:
lowerCamelCase =[]
for line in lines:
lowerCamelCase =re.sub(r"""#.*""" , """""" , _UpperCAmelCase ) # remove comments
if line:
filtered_lines.append(_UpperCAmelCase )
lowerCamelCase ="""\n""".join(_UpperCAmelCase )
# Make a hash from all this code
lowerCamelCase =full_str.encode("""utf-8""" )
return shaaaa(_UpperCAmelCase ).hexdigest()
# get importable module names and hash for caching
UpperCAmelCase__ : 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
UpperCAmelCase__ : Tuple ={
'''.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})
UpperCAmelCase__ : Optional[Any] ={'''imagefolder''', '''audiofolder'''}
# Used to filter data files based on extensions given a module name
UpperCAmelCase__ : 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''')
| 262 | 1 |
"""simple docstring"""
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper
import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname
import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary
from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
_a : Union[str, Any] = 2
# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
_a : Union[str, Any] = {
# fairseq:
'wmt19-ru-en': {'length_penalty': 1.1},
'wmt19-en-ru': {'length_penalty': 1.15},
'wmt19-en-de': {'length_penalty': 1.0},
'wmt19-de-en': {'length_penalty': 1.1},
# allenai:
'wmt16-en-de-dist-12-1': {'length_penalty': 0.6},
'wmt16-en-de-dist-6-1': {'length_penalty': 0.6},
'wmt16-en-de-12-1': {'length_penalty': 0.8},
'wmt19-de-en-6-6-base': {'length_penalty': 0.6},
'wmt19-de-en-6-6-big': {'length_penalty': 0.6},
}
# this remaps the different models to their organization names
_a : Tuple = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
_a : Union[str, Any] = 'facebook'
for m in [
"wmt16-en-de-dist-12-1",
"wmt16-en-de-dist-6-1",
"wmt16-en-de-12-1",
"wmt19-de-en-6-6-base",
"wmt19-de-en-6-6-big",
]:
_a : Union[str, Any] = 'allenai'
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optional[int] ) -> Dict:
_lowerCAmelCase : List[Any] = dict((re.sub(r"""@@$""" ,"""""" ,__lowerCAmelCase ), v) if k.endswith("""@@""" ) else (re.sub(r"""$""" ,"""</w>""" ,__lowerCAmelCase ), v) for k, v in d.items() )
_lowerCAmelCase : List[str] = '''<s> <pad> </s> <unk>'''.split()
# restore the special tokens
for k in keep_keys:
del da[f"{k}</w>"]
_lowerCAmelCase : List[str] = d[k] # restore
return da
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Tuple ,_lowerCamelCase : Any ) -> Optional[int]:
assert os.path.exists(__lowerCAmelCase )
os.makedirs(__lowerCAmelCase ,exist_ok=__lowerCAmelCase )
print(f"Writing results to {pytorch_dump_folder_path}" )
# handle various types of models
_lowerCAmelCase : int = basename(__lowerCAmelCase )
_lowerCAmelCase : List[str] = dirname(__lowerCAmelCase )
_lowerCAmelCase : Optional[int] = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
_lowerCAmelCase : Tuple = cls.hub_models()
_lowerCAmelCase : Dict = {'''bpe''': '''fastbpe''', '''tokenizer''': '''moses'''}
_lowerCAmelCase : List[Any] = '''.'''
# note: since the model dump is old, fairseq has upgraded its model some
# time later, and it does a whole lot of rewrites and splits on the saved
# weights, therefore we can't use torch.load() directly on the model file.
# see: upgrade_state_dict(state_dict) in fairseq_model.py
print(f"using checkpoint {checkpoint_file}" )
_lowerCAmelCase : Dict = hub_utils.from_pretrained(
__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase ,archive_map=__lowerCAmelCase ,**__lowerCAmelCase )
_lowerCAmelCase : Tuple = vars(chkpt["""args"""]["""model"""] )
_lowerCAmelCase : Union[str, Any] = args['''source_lang''']
_lowerCAmelCase : Optional[int] = args['''target_lang''']
_lowerCAmelCase : Optional[Any] = dirname(__lowerCAmelCase )
_lowerCAmelCase : int = basename(__lowerCAmelCase )
# dicts
_lowerCAmelCase : List[str] = os.path.join(__lowerCAmelCase ,f"dict.{src_lang}.txt" )
_lowerCAmelCase : Tuple = os.path.join(__lowerCAmelCase ,f"dict.{tgt_lang}.txt" )
_lowerCAmelCase : Union[str, Any] = Dictionary.load(__lowerCAmelCase )
_lowerCAmelCase : List[str] = rewrite_dict_keys(src_dict.indices )
_lowerCAmelCase : List[Any] = len(__lowerCAmelCase )
_lowerCAmelCase : List[str] = os.path.join(__lowerCAmelCase ,"""vocab-src.json""" )
print(f"Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records" )
with open(__lowerCAmelCase ,"""w""" ,encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase ,ensure_ascii=__lowerCAmelCase ,indent=__lowerCAmelCase ) )
# detect whether this is a do_lower_case situation, which can be derived by checking whether we
# have at least one uppercase letter in the source vocab
_lowerCAmelCase : List[Any] = True
for k in src_vocab.keys():
if not k.islower():
_lowerCAmelCase : Any = False
break
_lowerCAmelCase : Optional[Any] = Dictionary.load(__lowerCAmelCase )
_lowerCAmelCase : int = rewrite_dict_keys(tgt_dict.indices )
_lowerCAmelCase : Any = len(__lowerCAmelCase )
_lowerCAmelCase : Dict = os.path.join(__lowerCAmelCase ,"""vocab-tgt.json""" )
print(f"Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records" )
with open(__lowerCAmelCase ,"""w""" ,encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase ,ensure_ascii=__lowerCAmelCase ,indent=__lowerCAmelCase ) )
# merges_file (bpecodes)
_lowerCAmelCase : Tuple = os.path.join(__lowerCAmelCase ,VOCAB_FILES_NAMES["""merges_file"""] )
for fn in ["bpecodes", "code"]: # older fairseq called the merges file "code"
_lowerCAmelCase : Dict = os.path.join(__lowerCAmelCase ,__lowerCAmelCase )
if os.path.exists(__lowerCAmelCase ):
break
with open(__lowerCAmelCase ,encoding="""utf-8""" ) as fin:
_lowerCAmelCase : Union[str, Any] = fin.read()
_lowerCAmelCase : str = re.sub(r""" \d+$""" ,"""""" ,__lowerCAmelCase ,0 ,re.M ) # remove frequency number
print(f"Generating {merges_file}" )
with open(__lowerCAmelCase ,"""w""" ,encoding="""utf-8""" ) as fout:
fout.write(__lowerCAmelCase )
# model config
_lowerCAmelCase : List[Any] = os.path.join(__lowerCAmelCase ,"""config.json""" )
# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
# may have to modify the tokenizer if a different type is used by a future model
assert args["bpe"] == "fastbpe", f"need to extend tokenizer to support bpe={args['bpe']}"
assert args["tokenizer"] == "moses", f"need to extend tokenizer to support bpe={args['tokenizer']}"
_lowerCAmelCase : Tuple = {
'''architectures''': ['''FSMTForConditionalGeneration'''],
'''model_type''': '''fsmt''',
'''activation_dropout''': args['''activation_dropout'''],
'''activation_function''': '''relu''',
'''attention_dropout''': args['''attention_dropout'''],
'''d_model''': args['''decoder_embed_dim'''],
'''dropout''': args['''dropout'''],
'''init_std''': 0.02,
'''max_position_embeddings''': args['''max_source_positions'''],
'''num_hidden_layers''': args['''encoder_layers'''],
'''src_vocab_size''': src_vocab_size,
'''tgt_vocab_size''': tgt_vocab_size,
'''langs''': [src_lang, tgt_lang],
'''encoder_attention_heads''': args['''encoder_attention_heads'''],
'''encoder_ffn_dim''': args['''encoder_ffn_embed_dim'''],
'''encoder_layerdrop''': args['''encoder_layerdrop'''],
'''encoder_layers''': args['''encoder_layers'''],
'''decoder_attention_heads''': args['''decoder_attention_heads'''],
'''decoder_ffn_dim''': args['''decoder_ffn_embed_dim'''],
'''decoder_layerdrop''': args['''decoder_layerdrop'''],
'''decoder_layers''': args['''decoder_layers'''],
'''bos_token_id''': 0,
'''pad_token_id''': 1,
'''eos_token_id''': 2,
'''is_encoder_decoder''': True,
'''scale_embedding''': not args['''no_scale_embedding'''],
'''tie_word_embeddings''': args['''share_all_embeddings'''],
}
# good hparam defaults to start with
_lowerCAmelCase : Any = 5
_lowerCAmelCase : Optional[Any] = False
if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
_lowerCAmelCase : Optional[Any] = best_score_hparams[model_dir]['''length_penalty''']
else:
_lowerCAmelCase : Optional[Any] = 1.0
print(f"Generating {fsmt_model_config_file}" )
with open(__lowerCAmelCase ,"""w""" ,encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase ,ensure_ascii=__lowerCAmelCase ,indent=__lowerCAmelCase ) )
# tokenizer config
_lowerCAmelCase : Tuple = os.path.join(__lowerCAmelCase ,__lowerCAmelCase )
_lowerCAmelCase : Optional[Any] = {
'''langs''': [src_lang, tgt_lang],
'''model_max_length''': 1024,
'''do_lower_case''': do_lower_case,
}
print(f"Generating {fsmt_tokenizer_config_file}" )
with open(__lowerCAmelCase ,"""w""" ,encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase ,ensure_ascii=__lowerCAmelCase ,indent=__lowerCAmelCase ) )
# model
_lowerCAmelCase : Dict = chkpt['''models'''][0]
_lowerCAmelCase : int = model.state_dict()
# rename keys to start with 'model.'
_lowerCAmelCase : str = OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items() )
# remove unneeded keys
_lowerCAmelCase : List[str] = [
'''model.model''',
'''model.encoder.version''',
'''model.decoder.version''',
'''model.encoder_embed_tokens.weight''',
'''model.decoder_embed_tokens.weight''',
'''model.encoder.embed_positions._float_tensor''',
'''model.decoder.embed_positions._float_tensor''',
]
for k in ignore_keys:
model_state_dict.pop(__lowerCAmelCase ,__lowerCAmelCase )
_lowerCAmelCase : str = FSMTConfig.from_pretrained(__lowerCAmelCase )
_lowerCAmelCase : List[Any] = FSMTForConditionalGeneration(__lowerCAmelCase )
# check that it loads ok
model_new.load_state_dict(__lowerCAmelCase ,strict=__lowerCAmelCase )
# save
_lowerCAmelCase : Optional[Any] = os.path.join(__lowerCAmelCase ,__lowerCAmelCase )
print(f"Generating {pytorch_weights_dump_path}" )
torch.save(__lowerCAmelCase ,__lowerCAmelCase )
print("""Conversion is done!""" )
print("""\nLast step is to upload the files to s3""" )
print(f"cd {data_root}" )
print(f"transformers-cli upload {model_dir}" )
if __name__ == "__main__":
_a : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--fsmt_checkpoint_path',
default=None,
type=str,
required=True,
help=(
'Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'
' bpecodes, etc.'
),
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
_a : Union[str, Any] = parser.parse_args()
convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
| 44 |
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class a ( __lowerCamelCase , unittest.TestCase ):
__lowerCAmelCase : int = BertTokenizer
__lowerCAmelCase : int = BertTokenizerFast
__lowerCAmelCase : int = True
__lowerCAmelCase : List[Any] = True
__lowerCAmelCase : Any = filter_non_english
def __lowerCamelCase ( self :str ):
super().setUp()
snake_case__ : str = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
snake_case__ : str = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file ,'''w''' ,encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def __lowerCamelCase ( self :List[str] ,__lowercase :str ):
snake_case__ : List[Any] = '''UNwant\u00E9d,running'''
snake_case__ : str = '''unwanted, running'''
return input_text, output_text
def __lowerCamelCase ( self :Optional[int] ):
snake_case__ : str = self.tokenizer_class(self.vocab_file )
snake_case__ : str = tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(__lowercase ,['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase ) ,[9, 6, 7, 1_2, 1_0, 1_1] )
def __lowerCamelCase ( self :List[str] ):
if not self.test_rust_tokenizer:
return
snake_case__ : str = self.get_tokenizer()
snake_case__ : Dict = self.get_rust_tokenizer()
snake_case__ : List[Any] = '''UNwant\u00E9d,running'''
snake_case__ : Dict = tokenizer.tokenize(__lowercase )
snake_case__ : int = rust_tokenizer.tokenize(__lowercase )
self.assertListEqual(__lowercase ,__lowercase )
snake_case__ : List[str] = tokenizer.encode(__lowercase ,add_special_tokens=__lowercase )
snake_case__ : List[str] = rust_tokenizer.encode(__lowercase ,add_special_tokens=__lowercase )
self.assertListEqual(__lowercase ,__lowercase )
snake_case__ : Optional[Any] = self.get_rust_tokenizer()
snake_case__ : List[Any] = tokenizer.encode(__lowercase )
snake_case__ : Dict = rust_tokenizer.encode(__lowercase )
self.assertListEqual(__lowercase ,__lowercase )
# With lower casing
snake_case__ : List[Any] = self.get_tokenizer(do_lower_case=__lowercase )
snake_case__ : Optional[int] = self.get_rust_tokenizer(do_lower_case=__lowercase )
snake_case__ : int = '''UNwant\u00E9d,running'''
snake_case__ : List[str] = tokenizer.tokenize(__lowercase )
snake_case__ : Union[str, Any] = rust_tokenizer.tokenize(__lowercase )
self.assertListEqual(__lowercase ,__lowercase )
snake_case__ : int = tokenizer.encode(__lowercase ,add_special_tokens=__lowercase )
snake_case__ : List[str] = rust_tokenizer.encode(__lowercase ,add_special_tokens=__lowercase )
self.assertListEqual(__lowercase ,__lowercase )
snake_case__ : Tuple = self.get_rust_tokenizer()
snake_case__ : int = tokenizer.encode(__lowercase )
snake_case__ : Dict = rust_tokenizer.encode(__lowercase )
self.assertListEqual(__lowercase ,__lowercase )
def __lowerCamelCase ( self :Tuple ):
snake_case__ : Optional[Any] = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize('''ah\u535A\u63A8zz''' ) ,['''ah''', '''\u535A''', '''\u63A8''', '''zz'''] )
def __lowerCamelCase ( self :Any ):
snake_case__ : Dict = BasicTokenizer(do_lower_case=__lowercase )
self.assertListEqual(
tokenizer.tokenize(''' \tHeLLo!how \n Are yoU? ''' ) ,['''hello''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) ,['''hello'''] )
def __lowerCamelCase ( self :List[str] ):
snake_case__ : Optional[int] = BasicTokenizer(do_lower_case=__lowercase ,strip_accents=__lowercase )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) ,['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) ,['''h\u00E9llo'''] )
def __lowerCamelCase ( self :str ):
snake_case__ : Union[str, Any] = BasicTokenizer(do_lower_case=__lowercase ,strip_accents=__lowercase )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) ,['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) ,['''hello'''] )
def __lowerCamelCase ( self :Optional[Any] ):
snake_case__ : Union[str, Any] = BasicTokenizer(do_lower_case=__lowercase )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) ,['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) ,['''hello'''] )
def __lowerCamelCase ( self :Optional[Any] ):
snake_case__ : str = BasicTokenizer(do_lower_case=__lowercase )
self.assertListEqual(
tokenizer.tokenize(''' \tHeLLo!how \n Are yoU? ''' ) ,['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
def __lowerCamelCase ( self :Tuple ):
snake_case__ : Any = BasicTokenizer(do_lower_case=__lowercase ,strip_accents=__lowercase )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) ,['''HäLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
def __lowerCamelCase ( self :int ):
snake_case__ : str = BasicTokenizer(do_lower_case=__lowercase ,strip_accents=__lowercase )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) ,['''HaLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
def __lowerCamelCase ( self :Tuple ):
snake_case__ : List[Any] = BasicTokenizer(do_lower_case=__lowercase ,never_split=['''[UNK]'''] )
self.assertListEqual(
tokenizer.tokenize(''' \tHeLLo!how \n Are yoU? [UNK]''' ) ,['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''', '''[UNK]'''] )
def __lowerCamelCase ( self :Dict ):
snake_case__ : List[str] = BasicTokenizer()
snake_case__ : Any = '''a\n\'ll !!to?\'d of, can\'t.'''
snake_case__ : Optional[int] = ['''a''', '''\'''', '''ll''', '''!''', '''!''', '''to''', '''?''', '''\'''', '''d''', '''of''', ''',''', '''can''', '''\'''', '''t''', '''.''']
self.assertListEqual(tokenizer.tokenize(__lowercase ) ,__lowercase )
def __lowerCamelCase ( self :List[str] ):
snake_case__ : Any = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']
snake_case__ : Optional[Any] = {}
for i, token in enumerate(__lowercase ):
snake_case__ : Dict = i
snake_case__ : int = WordpieceTokenizer(vocab=__lowercase ,unk_token='''[UNK]''' )
self.assertListEqual(tokenizer.tokenize('''''' ) ,[] )
self.assertListEqual(tokenizer.tokenize('''unwanted running''' ) ,['''un''', '''##want''', '''##ed''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.tokenize('''unwantedX running''' ) ,['''[UNK]''', '''runn''', '''##ing'''] )
def __lowerCamelCase ( self :List[Any] ):
self.assertTrue(_is_whitespace(''' ''' ) )
self.assertTrue(_is_whitespace('''\t''' ) )
self.assertTrue(_is_whitespace('''\r''' ) )
self.assertTrue(_is_whitespace('''\n''' ) )
self.assertTrue(_is_whitespace('''\u00A0''' ) )
self.assertFalse(_is_whitespace('''A''' ) )
self.assertFalse(_is_whitespace('''-''' ) )
def __lowerCamelCase ( self :Union[str, Any] ):
self.assertTrue(_is_control('''\u0005''' ) )
self.assertFalse(_is_control('''A''' ) )
self.assertFalse(_is_control(''' ''' ) )
self.assertFalse(_is_control('''\t''' ) )
self.assertFalse(_is_control('''\r''' ) )
def __lowerCamelCase ( self :str ):
self.assertTrue(_is_punctuation('''-''' ) )
self.assertTrue(_is_punctuation('''$''' ) )
self.assertTrue(_is_punctuation('''`''' ) )
self.assertTrue(_is_punctuation('''.''' ) )
self.assertFalse(_is_punctuation('''A''' ) )
self.assertFalse(_is_punctuation(''' ''' ) )
def __lowerCamelCase ( self :List[str] ):
snake_case__ : str = self.get_tokenizer()
snake_case__ : List[str] = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(__lowercase ) for t in ['''Test''', '''\xad''', '''test''']] ,[['''[UNK]'''], [], ['''[UNK]''']] )
self.assertListEqual(
[rust_tokenizer.tokenize(__lowercase ) for t in ['''Test''', '''\xad''', '''test''']] ,[['''[UNK]'''], [], ['''[UNK]''']] )
@slow
def __lowerCamelCase ( self :List[str] ):
snake_case__ : List[str] = self.tokenizer_class.from_pretrained('''bert-base-uncased''' )
snake_case__ : Union[str, Any] = tokenizer.encode('''sequence builders''' ,add_special_tokens=__lowercase )
snake_case__ : str = tokenizer.encode('''multi-sequence build''' ,add_special_tokens=__lowercase )
snake_case__ : Tuple = tokenizer.build_inputs_with_special_tokens(__lowercase )
snake_case__ : int = tokenizer.build_inputs_with_special_tokens(__lowercase ,__lowercase )
assert encoded_sentence == [1_0_1] + text + [1_0_2]
assert encoded_pair == [1_0_1] + text + [1_0_2] + text_a + [1_0_2]
def __lowerCamelCase ( self :List[Any] ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
snake_case__ : str = self.rust_tokenizer_class.from_pretrained(__lowercase ,**__lowercase )
snake_case__ : List[str] = F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
snake_case__ : List[Any] = tokenizer_r.encode_plus(
__lowercase ,return_attention_mask=__lowercase ,return_token_type_ids=__lowercase ,return_offsets_mapping=__lowercase ,add_special_tokens=__lowercase ,)
snake_case__ : List[str] = tokenizer_r.do_lower_case if hasattr(__lowercase ,'''do_lower_case''' ) else False
snake_case__ : Optional[int] = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), '''A'''),
((1, 2), ''','''),
((3, 5), '''na'''),
((5, 6), '''##ï'''),
((6, 8), '''##ve'''),
((9, 1_5), tokenizer_r.mask_token),
((1_6, 2_1), '''Allen'''),
((2_1, 2_3), '''##NL'''),
((2_3, 2_4), '''##P'''),
((2_5, 3_3), '''sentence'''),
((3_3, 3_4), '''.'''),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), '''a'''),
((1, 2), ''','''),
((3, 8), '''naive'''),
((9, 1_5), tokenizer_r.mask_token),
((1_6, 2_1), '''allen'''),
((2_1, 2_3), '''##nl'''),
((2_3, 2_4), '''##p'''),
((2_5, 3_3), '''sentence'''),
((3_3, 3_4), '''.'''),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] ,tokenizer_r.convert_ids_to_tokens(tokens['''input_ids'''] ) )
self.assertEqual([e[0] for e in expected_results] ,tokens['''offset_mapping'''] )
def __lowerCamelCase ( self :List[str] ):
snake_case__ : str = ['''的''', '''人''', '''有''']
snake_case__ : Optional[int] = ''''''.join(__lowercase )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
snake_case__ : Optional[Any] = True
snake_case__ : int = self.tokenizer_class.from_pretrained(__lowercase ,**__lowercase )
snake_case__ : Tuple = self.rust_tokenizer_class.from_pretrained(__lowercase ,**__lowercase )
snake_case__ : Union[str, Any] = tokenizer_p.encode(__lowercase ,add_special_tokens=__lowercase )
snake_case__ : List[str] = tokenizer_r.encode(__lowercase ,add_special_tokens=__lowercase )
snake_case__ : List[str] = tokenizer_r.convert_ids_to_tokens(__lowercase )
snake_case__ : Optional[Any] = tokenizer_p.convert_ids_to_tokens(__lowercase )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(__lowercase ,__lowercase )
self.assertListEqual(__lowercase ,__lowercase )
snake_case__ : str = False
snake_case__ : Dict = self.rust_tokenizer_class.from_pretrained(__lowercase ,**__lowercase )
snake_case__ : int = self.tokenizer_class.from_pretrained(__lowercase ,**__lowercase )
snake_case__ : int = tokenizer_r.encode(__lowercase ,add_special_tokens=__lowercase )
snake_case__ : Tuple = tokenizer_p.encode(__lowercase ,add_special_tokens=__lowercase )
snake_case__ : List[Any] = tokenizer_r.convert_ids_to_tokens(__lowercase )
snake_case__ : Union[str, Any] = tokenizer_p.convert_ids_to_tokens(__lowercase )
# it is expected that only the first Chinese character is not preceded by "##".
snake_case__ : Optional[int] = [
F"""##{token}""" if idx != 0 else token for idx, token in enumerate(__lowercase )
]
self.assertListEqual(__lowercase ,__lowercase )
self.assertListEqual(__lowercase ,__lowercase )
| 230 | 0 |
"""simple docstring"""
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class snake_case ( _UpperCamelCase ):
def __init__( self : List[Any] , UpperCamelCase__ : str = "▁" , UpperCamelCase__ : bool = True , UpperCamelCase__ : Union[str, AddedToken] = "<unk>" , UpperCamelCase__ : Union[str, AddedToken] = "</s>" , UpperCamelCase__ : Union[str, AddedToken] = "<pad>" , )-> int:
'''simple docstring'''
__lowerCAmelCase: Union[str, Any] = {
'pad': {'id': 0, 'token': pad_token},
'eos': {'id': 1, 'token': eos_token},
'unk': {'id': 2, 'token': unk_token},
}
__lowerCAmelCase: Optional[Any] = [None] * len(self.special_tokens)
for token_dict in self.special_tokens.values():
__lowerCAmelCase: Optional[Any] = token_dict['token']
__lowerCAmelCase: Optional[int] = Tokenizer(Unigram())
__lowerCAmelCase: List[Any] = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(" {2,}") , " "),
normalizers.Lowercase(),
])
__lowerCAmelCase: Optional[int] = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=_UpperCAmelCase , add_prefix_space=_UpperCAmelCase),
pre_tokenizers.Digits(individual_digits=_UpperCAmelCase),
pre_tokenizers.Punctuation(),
])
__lowerCAmelCase: int = decoders.Metaspace(replacement=_UpperCAmelCase , add_prefix_space=_UpperCAmelCase)
__lowerCAmelCase: Optional[Any] = TemplateProcessing(
single=f"$A {self.special_tokens['eos']['token']}" , special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])] , )
__lowerCAmelCase: str = {
'model': 'SentencePieceUnigram',
'replacement': replacement,
'add_prefix_space': add_prefix_space,
}
super().__init__(_UpperCAmelCase , _UpperCAmelCase)
def lowercase_ ( self : str , UpperCamelCase__ : Union[str, List[str]] , UpperCamelCase__ : int = 8_0_0_0 , UpperCamelCase__ : bool = True , )-> int:
'''simple docstring'''
__lowerCAmelCase: int = trainers.UnigramTrainer(
vocab_size=_UpperCAmelCase , special_tokens=self.special_tokens_list , show_progress=_UpperCAmelCase , )
if isinstance(_UpperCAmelCase , _UpperCAmelCase):
__lowerCAmelCase: Union[str, Any] = [files]
self._tokenizer.train(_UpperCAmelCase , trainer=_UpperCAmelCase)
self.add_unk_id()
def lowercase_ ( self : Optional[Any] , UpperCamelCase__ : Union[Iterator[str], Iterator[Iterator[str]]] , UpperCamelCase__ : int = 8_0_0_0 , UpperCamelCase__ : bool = True , )-> Tuple:
'''simple docstring'''
__lowerCAmelCase: Tuple = trainers.UnigramTrainer(
vocab_size=_UpperCAmelCase , special_tokens=self.special_tokens_list , show_progress=_UpperCAmelCase , )
self._tokenizer.train_from_iterator(_UpperCAmelCase , trainer=_UpperCAmelCase)
self.add_unk_id()
def lowercase_ ( self : Any)-> Optional[Any]:
'''simple docstring'''
__lowerCAmelCase: Optional[Any] = json.loads(self._tokenizer.to_str())
__lowerCAmelCase: Any = self.special_tokens['unk']['id']
__lowerCAmelCase: int = Tokenizer.from_str(json.dumps(_UpperCAmelCase))
| 369 |
"""simple docstring"""
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin
enable_full_determinism()
class snake_case ( __snake_case, unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Tuple = PriorTransformer
SCREAMING_SNAKE_CASE_ : List[str] = """hidden_states"""
@property
def lowercase_ ( self : Dict)-> str:
'''simple docstring'''
__lowerCAmelCase: str = 4
__lowerCAmelCase: int = 8
__lowerCAmelCase: int = 7
__lowerCAmelCase: str = floats_tensor((batch_size, embedding_dim)).to(UpperCamelCase__)
__lowerCAmelCase: Optional[Any] = floats_tensor((batch_size, embedding_dim)).to(UpperCamelCase__)
__lowerCAmelCase: Any = floats_tensor((batch_size, num_embeddings, embedding_dim)).to(UpperCamelCase__)
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
def lowercase_ ( self : Optional[int] , UpperCamelCase__ : str=0)-> str:
'''simple docstring'''
torch.manual_seed(UpperCamelCase__)
__lowerCAmelCase: List[Any] = 4
__lowerCAmelCase: Dict = 8
__lowerCAmelCase: int = 7
__lowerCAmelCase: List[str] = torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)
__lowerCAmelCase: Tuple = torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)
__lowerCAmelCase: List[Any] = torch.randn((batch_size, num_embeddings, embedding_dim)).to(UpperCamelCase__)
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
@property
def lowercase_ ( self : Dict)-> List[Any]:
'''simple docstring'''
return (4, 8)
@property
def lowercase_ ( self : Optional[int])-> int:
'''simple docstring'''
return (4, 8)
def lowercase_ ( self : Optional[int])-> Tuple:
'''simple docstring'''
__lowerCAmelCase: str = {
"num_attention_heads": 2,
"attention_head_dim": 4,
"num_layers": 2,
"embedding_dim": 8,
"num_embeddings": 7,
"additional_embeddings": 4,
}
__lowerCAmelCase: Any = self.dummy_input
return init_dict, inputs_dict
def lowercase_ ( self : List[Any])-> int:
'''simple docstring'''
__lowerCAmelCase , __lowerCAmelCase: Optional[int] = PriorTransformer.from_pretrained(
"hf-internal-testing/prior-dummy" , output_loading_info=UpperCamelCase__)
self.assertIsNotNone(UpperCamelCase__)
self.assertEqual(len(loading_info["missing_keys"]) , 0)
model.to(UpperCamelCase__)
__lowerCAmelCase: Dict = model(**self.dummy_input)[0]
assert hidden_states is not None, "Make sure output is not None"
def lowercase_ ( self : List[str])-> Tuple:
'''simple docstring'''
__lowerCAmelCase , __lowerCAmelCase: Optional[Any] = self.prepare_init_args_and_inputs_for_common()
__lowerCAmelCase: Tuple = self.model_class(**UpperCamelCase__)
__lowerCAmelCase: List[str] = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCAmelCase: List[Any] = [*signature.parameters.keys()]
__lowerCAmelCase: Any = ["hidden_states", "timestep"]
self.assertListEqual(arg_names[:2] , UpperCamelCase__)
def lowercase_ ( self : Optional[int])-> List[str]:
'''simple docstring'''
__lowerCAmelCase: int = PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy")
__lowerCAmelCase: Union[str, Any] = model.to(UpperCamelCase__)
if hasattr(UpperCamelCase__ , "set_default_attn_processor"):
model.set_default_attn_processor()
__lowerCAmelCase: str = self.get_dummy_seed_input()
with torch.no_grad():
__lowerCAmelCase: Dict = model(**UpperCamelCase__)[0]
__lowerCAmelCase: Dict = output[0, :5].flatten().cpu()
print(UpperCamelCase__)
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
__lowerCAmelCase: List[str] = torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239])
self.assertTrue(torch_all_close(UpperCamelCase__ , UpperCamelCase__ , rtol=1e-2))
@slow
class snake_case ( unittest.TestCase ):
def lowercase_ ( self : int , UpperCamelCase__ : Dict=1 , UpperCamelCase__ : str=7_6_8 , UpperCamelCase__ : int=7_7 , UpperCamelCase__ : Any=0)-> Union[str, Any]:
'''simple docstring'''
torch.manual_seed(UpperCamelCase__)
__lowerCAmelCase: List[Any] = batch_size
__lowerCAmelCase: Any = embedding_dim
__lowerCAmelCase: Dict = num_embeddings
__lowerCAmelCase: Dict = torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)
__lowerCAmelCase: str = torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)
__lowerCAmelCase: int = torch.randn((batch_size, num_embeddings, embedding_dim)).to(UpperCamelCase__)
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
def lowercase_ ( self : List[Any])-> Union[str, Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@parameterized.expand(
[
# fmt: off
[1_3, [-0.5861, 0.1283, -0.0931, 0.0882, 0.4476, 0.1329, -0.0498, 0.0640]],
[3_7, [-0.4913, 0.0110, -0.0483, 0.0541, 0.4954, -0.0170, 0.0354, 0.1651]],
# fmt: on
])
def lowercase_ ( self : Optional[int] , UpperCamelCase__ : List[str] , UpperCamelCase__ : int)-> List[Any]:
'''simple docstring'''
__lowerCAmelCase: List[str] = PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior" , subfolder="prior")
model.to(UpperCamelCase__)
__lowerCAmelCase: Dict = self.get_dummy_seed_input(seed=UpperCamelCase__)
with torch.no_grad():
__lowerCAmelCase: Optional[Any] = model(**UpperCamelCase__)[0]
assert list(sample.shape) == [1, 7_6_8]
__lowerCAmelCase: Dict = sample[0, :8].flatten().cpu()
print(UpperCamelCase__)
__lowerCAmelCase: Union[str, Any] = torch.tensor(UpperCamelCase__)
assert torch_all_close(UpperCamelCase__ , UpperCamelCase__ , atol=1e-3)
| 108 | 0 |
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
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 transformers import OneFormerImageProcessor
from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput
if is_vision_available():
from PIL import Image
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase="shi-labs/oneformer_demo" ) -> List[Any]:
with open(hf_hub_download(_UpperCAmelCase , _UpperCAmelCase , repo_type='dataset' ) , 'r' ) as f:
lowerCamelCase__ : str = json.load(_UpperCAmelCase )
lowerCamelCase__ : Tuple = {}
lowerCamelCase__ : List[Any] = []
lowerCamelCase__ : str = []
for key, info in class_info.items():
lowerCamelCase__ : Union[str, Any] = info['name']
class_names.append(info['name'] )
if info["isthing"]:
thing_ids.append(int(_UpperCAmelCase ) )
lowerCamelCase__ : Optional[int] = thing_ids
lowerCamelCase__ : Union[str, Any] = class_names
return metadata
class lowerCAmelCase ( unittest.TestCase ):
def __init__( self : List[str] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[int]=7 , UpperCAmelCase : Union[str, Any]=3 , UpperCAmelCase : int=30 , UpperCAmelCase : List[str]=400 , UpperCAmelCase : List[Any]=None , UpperCAmelCase : Any=True , UpperCAmelCase : List[Any]=True , UpperCAmelCase : Dict=[0.5, 0.5, 0.5] , UpperCAmelCase : List[str]=[0.5, 0.5, 0.5] , UpperCAmelCase : Optional[Any]=10 , UpperCAmelCase : Tuple=False , UpperCAmelCase : Optional[int]=255 , UpperCAmelCase : Any="shi-labs/oneformer_demo" , UpperCAmelCase : Any="ade20k_panoptic.json" , UpperCAmelCase : List[Any]=10 , ) -> Union[str, Any]:
lowerCamelCase__ : Tuple = parent
lowerCamelCase__ : Tuple = batch_size
lowerCamelCase__ : str = num_channels
lowerCamelCase__ : Union[str, Any] = min_resolution
lowerCamelCase__ : int = max_resolution
lowerCamelCase__ : Dict = do_resize
lowerCamelCase__ : Optional[int] = {'shortest_edge': 32, 'longest_edge': 1333} if size is None else size
lowerCamelCase__ : Dict = do_normalize
lowerCamelCase__ : Tuple = image_mean
lowerCamelCase__ : List[str] = image_std
lowerCamelCase__ : Any = class_info_file
lowerCamelCase__ : Any = prepare_metadata(UpperCAmelCase , UpperCAmelCase )
lowerCamelCase__ : Optional[int] = num_text
lowerCamelCase__ : List[str] = repo_path
# for the post_process_functions
lowerCamelCase__ : Any = 2
lowerCamelCase__ : str = 10
lowerCamelCase__ : str = 10
lowerCamelCase__ : Any = 3
lowerCamelCase__ : Union[str, Any] = 4
lowerCamelCase__ : Any = num_labels
lowerCamelCase__ : str = do_reduce_labels
lowerCamelCase__ : str = ignore_index
def A_ ( self : Union[str, Any] ) -> Any:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"num_labels": self.num_labels,
"do_reduce_labels": self.do_reduce_labels,
"ignore_index": self.ignore_index,
"class_info_file": self.class_info_file,
"metadata": self.metadata,
"num_text": self.num_text,
}
def A_ ( self : Optional[int] , UpperCAmelCase : List[str] , UpperCAmelCase : List[Any]=False ) -> int:
if not batched:
lowerCamelCase__ : List[str] = image_inputs[0]
if isinstance(UpperCAmelCase , Image.Image ):
lowerCamelCase__ , lowerCamelCase__ : Tuple = image.size
else:
lowerCamelCase__ , lowerCamelCase__ : Dict = image.shape[1], image.shape[2]
if w < h:
lowerCamelCase__ : Dict = int(self.size['shortest_edge'] * h / w )
lowerCamelCase__ : List[Any] = self.size['shortest_edge']
elif w > h:
lowerCamelCase__ : Optional[Any] = self.size['shortest_edge']
lowerCamelCase__ : str = int(self.size['shortest_edge'] * w / h )
else:
lowerCamelCase__ : str = self.size['shortest_edge']
lowerCamelCase__ : Union[str, Any] = self.size['shortest_edge']
else:
lowerCamelCase__ : Any = []
for image in image_inputs:
lowerCamelCase__ , lowerCamelCase__ : List[str] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
lowerCamelCase__ : Optional[Any] = max(UpperCAmelCase , key=lambda UpperCAmelCase : item[0] )[0]
lowerCamelCase__ : str = max(UpperCAmelCase , key=lambda UpperCAmelCase : item[1] )[1]
return expected_height, expected_width
def A_ ( self : Tuple ) -> Tuple:
return OneFormerForUniversalSegmentationOutput(
# +1 for null class
class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1) ) , masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width) ) , )
@require_torch
@require_vision
class lowerCAmelCase ( __UpperCamelCase, unittest.TestCase ):
UpperCAmelCase__ = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
# only for test_image_processing_common.test_image_proc_to_json_string
UpperCAmelCase__ = image_processing_class
def A_ ( self : Any ) -> int:
lowerCamelCase__ : Union[str, Any] = OneFormerImageProcessorTester(self )
@property
def A_ ( self : str ) -> int:
return self.image_processing_tester.prepare_image_processor_dict()
def A_ ( self : int ) -> Any:
lowerCamelCase__ : Optional[Any] = 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' ) )
self.assertTrue(hasattr(UpperCAmelCase , 'ignore_index' ) )
self.assertTrue(hasattr(UpperCAmelCase , 'class_info_file' ) )
self.assertTrue(hasattr(UpperCAmelCase , 'num_text' ) )
self.assertTrue(hasattr(UpperCAmelCase , 'repo_path' ) )
self.assertTrue(hasattr(UpperCAmelCase , 'metadata' ) )
self.assertTrue(hasattr(UpperCAmelCase , 'do_reduce_labels' ) )
def A_ ( self : str ) -> List[Any]:
pass
def A_ ( self : Tuple ) -> Union[str, Any]:
# Initialize image_processor
lowerCamelCase__ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCamelCase__ : int = prepare_image_inputs(self.image_processing_tester , equal_resolution=UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase , Image.Image )
# Test not batched input
lowerCamelCase__ : List[str] = image_processor(image_inputs[0] , ['semantic'] , return_tensors='pt' ).pixel_values
lowerCamelCase__ , lowerCamelCase__ : str = self.image_processing_tester.get_expected_values(UpperCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowerCamelCase__ , lowerCamelCase__ : Tuple = self.image_processing_tester.get_expected_values(UpperCAmelCase , batched=UpperCAmelCase )
lowerCamelCase__ : List[str] = image_processor(
UpperCAmelCase , ['semantic'] * len(UpperCAmelCase ) , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def A_ ( self : Tuple ) -> str:
# Initialize image_processor
lowerCamelCase__ : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCamelCase__ : Union[str, Any] = prepare_image_inputs(self.image_processing_tester , equal_resolution=UpperCAmelCase , numpify=UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase , np.ndarray )
# Test not batched input
lowerCamelCase__ : List[str] = image_processor(image_inputs[0] , ['semantic'] , return_tensors='pt' ).pixel_values
lowerCamelCase__ , lowerCamelCase__ : List[str] = self.image_processing_tester.get_expected_values(UpperCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowerCamelCase__ , lowerCamelCase__ : List[str] = self.image_processing_tester.get_expected_values(UpperCAmelCase , batched=UpperCAmelCase )
lowerCamelCase__ : str = image_processor(
UpperCAmelCase , ['semantic'] * len(UpperCAmelCase ) , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def A_ ( self : Optional[int] ) -> Union[str, Any]:
# Initialize image_processor
lowerCamelCase__ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCamelCase__ : Union[str, Any] = prepare_image_inputs(self.image_processing_tester , equal_resolution=UpperCAmelCase , torchify=UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase , torch.Tensor )
# Test not batched input
lowerCamelCase__ : Union[str, Any] = image_processor(image_inputs[0] , ['semantic'] , return_tensors='pt' ).pixel_values
lowerCamelCase__ , lowerCamelCase__ : str = self.image_processing_tester.get_expected_values(UpperCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowerCamelCase__ , lowerCamelCase__ : int = self.image_processing_tester.get_expected_values(UpperCAmelCase , batched=UpperCAmelCase )
lowerCamelCase__ : int = image_processor(
UpperCAmelCase , ['semantic'] * len(UpperCAmelCase ) , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def A_ ( self : int , UpperCAmelCase : List[str]=False , UpperCAmelCase : List[Any]=False , UpperCAmelCase : Union[str, Any]="np" ) -> str:
lowerCamelCase__ : List[str] = self.image_processing_class(**self.image_processor_dict )
# prepare image and target
lowerCamelCase__ : Dict = self.image_processing_tester.num_labels
lowerCamelCase__ : List[str] = None
lowerCamelCase__ : Optional[int] = None
lowerCamelCase__ : str = prepare_image_inputs(self.image_processing_tester , equal_resolution=UpperCAmelCase )
if with_segmentation_maps:
lowerCamelCase__ : Tuple = num_labels
if is_instance_map:
lowerCamelCase__ : Dict = list(range(UpperCAmelCase ) ) * 2
lowerCamelCase__ : Optional[int] = dict(enumerate(UpperCAmelCase ) )
lowerCamelCase__ : int = [
np.random.randint(0 , high * 2 , (img.size[1], img.size[0]) ).astype(np.uinta ) for img in image_inputs
]
if segmentation_type == "pil":
lowerCamelCase__ : Optional[int] = [Image.fromarray(UpperCAmelCase ) for annotation in annotations]
lowerCamelCase__ : List[str] = image_processor(
UpperCAmelCase , ['semantic'] * len(UpperCAmelCase ) , UpperCAmelCase , return_tensors='pt' , instance_id_to_semantic_id=UpperCAmelCase , pad_and_return_pixel_mask=UpperCAmelCase , )
return inputs
def A_ ( self : str ) -> Any:
pass
def A_ ( self : Tuple ) -> List[Any]:
def common(UpperCAmelCase : Union[str, Any]=False , UpperCAmelCase : Optional[Any]=None ):
lowerCamelCase__ : Any = self.comm_get_image_processor_inputs(
with_segmentation_maps=UpperCAmelCase , is_instance_map=UpperCAmelCase , segmentation_type=UpperCAmelCase )
lowerCamelCase__ : Tuple = inputs['mask_labels']
lowerCamelCase__ : Union[str, Any] = inputs['class_labels']
lowerCamelCase__ : Optional[Any] = inputs['pixel_values']
lowerCamelCase__ : List[Any] = inputs['text_inputs']
# check the batch_size
for mask_label, class_label, text_input in zip(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
self.assertEqual(mask_label.shape[0] , class_label.shape[0] )
# this ensure padding has happened
self.assertEqual(mask_label.shape[1:] , pixel_values.shape[2:] )
self.assertEqual(len(UpperCAmelCase ) , self.image_processing_tester.num_text )
common()
common(is_instance_map=UpperCAmelCase )
common(is_instance_map=UpperCAmelCase , segmentation_type='pil' )
common(is_instance_map=UpperCAmelCase , segmentation_type='pil' )
def A_ ( self : Optional[int] ) -> Any:
lowerCamelCase__ : Dict = np.zeros((20, 50) )
lowerCamelCase__ : List[Any] = 1
lowerCamelCase__ : Dict = 1
lowerCamelCase__ : Optional[int] = 1
lowerCamelCase__ : Union[str, Any] = binary_mask_to_rle(UpperCAmelCase )
self.assertEqual(len(UpperCAmelCase ) , 4 )
self.assertEqual(rle[0] , 21 )
self.assertEqual(rle[1] , 45 )
def A_ ( self : Union[str, Any] ) -> str:
lowerCamelCase__ : str = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file='ade20k_panoptic.json' , num_text=self.image_processing_tester.num_text , repo_path='shi-labs/oneformer_demo' , )
lowerCamelCase__ : Tuple = self.image_processing_tester.get_fake_oneformer_outputs()
lowerCamelCase__ : Any = fature_extractor.post_process_semantic_segmentation(UpperCAmelCase )
self.assertEqual(len(UpperCAmelCase ) , self.image_processing_tester.batch_size )
self.assertEqual(
segmentation[0].shape , (
self.image_processing_tester.height,
self.image_processing_tester.width,
) , )
lowerCamelCase__ : Any = [(1, 4) for i in range(self.image_processing_tester.batch_size )]
lowerCamelCase__ : Dict = fature_extractor.post_process_semantic_segmentation(UpperCAmelCase , target_sizes=UpperCAmelCase )
self.assertEqual(segmentation[0].shape , target_sizes[0] )
def A_ ( self : List[str] ) -> List[str]:
lowerCamelCase__ : Tuple = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file='ade20k_panoptic.json' , num_text=self.image_processing_tester.num_text , repo_path='shi-labs/oneformer_demo' , )
lowerCamelCase__ : Union[str, Any] = self.image_processing_tester.get_fake_oneformer_outputs()
lowerCamelCase__ : str = image_processor.post_process_instance_segmentation(UpperCAmelCase , threshold=0 )
self.assertTrue(len(UpperCAmelCase ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue('segmentation' in el )
self.assertTrue('segments_info' in el )
self.assertEqual(type(el['segments_info'] ) , UpperCAmelCase )
self.assertEqual(
el['segmentation'].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
def A_ ( self : Any ) -> Union[str, Any]:
lowerCamelCase__ : int = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file='ade20k_panoptic.json' , num_text=self.image_processing_tester.num_text , repo_path='shi-labs/oneformer_demo' , )
lowerCamelCase__ : int = self.image_processing_tester.get_fake_oneformer_outputs()
lowerCamelCase__ : Tuple = image_processor.post_process_panoptic_segmentation(UpperCAmelCase , threshold=0 )
self.assertTrue(len(UpperCAmelCase ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue('segmentation' in el )
self.assertTrue('segments_info' in el )
self.assertEqual(type(el['segments_info'] ) , UpperCAmelCase )
self.assertEqual(
el['segmentation'].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
| 50 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase__ : Optional[int] = {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : int = ['YolosFeatureExtractor']
UpperCamelCase__ : int = ['YolosImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Dict = [
'YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST',
'YolosForObjectDetection',
'YolosModel',
'YolosPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_yolos import YolosFeatureExtractor
from .image_processing_yolos import YolosImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_yolos import (
YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
YolosForObjectDetection,
YolosModel,
YolosPreTrainedModel,
)
else:
import sys
UpperCamelCase__ : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 344 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class A__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = StableDiffusionInpaintPipeline
SCREAMING_SNAKE_CASE = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
SCREAMING_SNAKE_CASE = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
SCREAMING_SNAKE_CASE = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
SCREAMING_SNAKE_CASE = frozenset([] )
def _SCREAMING_SNAKE_CASE ( self: Any) -> Optional[int]:
"""simple docstring"""
torch.manual_seed(0)
__lowerCAmelCase : List[Any] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=9 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=_SCREAMING_SNAKE_CASE , )
__lowerCAmelCase : Optional[Any] = PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE)
torch.manual_seed(0)
__lowerCAmelCase : Dict = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0)
__lowerCAmelCase : Tuple = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act="gelu" , projection_dim=512 , )
__lowerCAmelCase : List[Any] = CLIPTextModel(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Any = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
__lowerCAmelCase : Tuple = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def _SCREAMING_SNAKE_CASE ( self: str , _SCREAMING_SNAKE_CASE: List[Any] , _SCREAMING_SNAKE_CASE: Union[str, Any]=0) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase : str = floats_tensor((1, 3, 32, 32) , rng=random.Random(_SCREAMING_SNAKE_CASE)).to(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Any = image.cpu().permute(0 , 2 , 3 , 1)[0]
__lowerCAmelCase : List[Any] = Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE)).convert("RGB").resize((64, 64))
__lowerCAmelCase : Union[str, Any] = Image.fromarray(np.uinta(image + 4)).convert("RGB").resize((64, 64))
if str(_SCREAMING_SNAKE_CASE).startswith("mps"):
__lowerCAmelCase : Dict = torch.manual_seed(_SCREAMING_SNAKE_CASE)
else:
__lowerCAmelCase : Tuple = torch.Generator(device=_SCREAMING_SNAKE_CASE).manual_seed(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : List[Any] = {
"prompt": "A painting of a squirrel eating a burger",
"image": init_image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def _SCREAMING_SNAKE_CASE ( self: Dict) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : Dict = "cpu" # ensure determinism for the device-dependent torch.Generator
__lowerCAmelCase : Optional[Any] = self.get_dummy_components()
__lowerCAmelCase : str = StableDiffusionInpaintPipeline(**_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Dict = sd_pipe.to(_SCREAMING_SNAKE_CASE)
sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : List[str] = self.get_dummy_inputs(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : int = sd_pipe(**_SCREAMING_SNAKE_CASE).images
__lowerCAmelCase : str = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__lowerCAmelCase : str = np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def _SCREAMING_SNAKE_CASE ( self: Optional[int]) -> Dict:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
@slow
@require_torch_gpu
class A__ ( unittest.TestCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self: Union[str, Any]) -> Dict:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _SCREAMING_SNAKE_CASE ( self: Optional[int]) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-inpaint/init_image.png")
__lowerCAmelCase : List[str] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
__lowerCAmelCase : Dict = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
"/yellow_cat_sitting_on_a_park_bench.npy")
__lowerCAmelCase : Optional[int] = "stabilityai/stable-diffusion-2-inpainting"
__lowerCAmelCase : str = StableDiffusionInpaintPipeline.from_pretrained(_SCREAMING_SNAKE_CASE , safety_checker=_SCREAMING_SNAKE_CASE)
pipe.to(_SCREAMING_SNAKE_CASE)
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)
pipe.enable_attention_slicing()
__lowerCAmelCase : Any = "Face of a yellow cat, high resolution, sitting on a park bench"
__lowerCAmelCase : Dict = torch.manual_seed(0)
__lowerCAmelCase : Any = pipe(
prompt=_SCREAMING_SNAKE_CASE , image=_SCREAMING_SNAKE_CASE , mask_image=_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , output_type="np" , )
__lowerCAmelCase : Union[str, Any] = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image).max() < 9e-3
def _SCREAMING_SNAKE_CASE ( self: Tuple) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : int = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-inpaint/init_image.png")
__lowerCAmelCase : Dict = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
__lowerCAmelCase : Optional[int] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
"/yellow_cat_sitting_on_a_park_bench_fp16.npy")
__lowerCAmelCase : List[str] = "stabilityai/stable-diffusion-2-inpainting"
__lowerCAmelCase : List[Any] = StableDiffusionInpaintPipeline.from_pretrained(
_SCREAMING_SNAKE_CASE , torch_dtype=torch.floataa , safety_checker=_SCREAMING_SNAKE_CASE , )
pipe.to(_SCREAMING_SNAKE_CASE)
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)
pipe.enable_attention_slicing()
__lowerCAmelCase : Dict = "Face of a yellow cat, high resolution, sitting on a park bench"
__lowerCAmelCase : List[Any] = torch.manual_seed(0)
__lowerCAmelCase : str = pipe(
prompt=_SCREAMING_SNAKE_CASE , image=_SCREAMING_SNAKE_CASE , mask_image=_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , output_type="np" , )
__lowerCAmelCase : Optional[int] = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image).max() < 5e-1
def _SCREAMING_SNAKE_CASE ( self: List[Any]) -> List[str]:
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__lowerCAmelCase : Any = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-inpaint/init_image.png")
__lowerCAmelCase : List[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
__lowerCAmelCase : Union[str, Any] = "stabilityai/stable-diffusion-2-inpainting"
__lowerCAmelCase : int = PNDMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE , subfolder="scheduler")
__lowerCAmelCase : Optional[int] = StableDiffusionInpaintPipeline.from_pretrained(
_SCREAMING_SNAKE_CASE , safety_checker=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE , torch_dtype=torch.floataa , )
pipe.to(_SCREAMING_SNAKE_CASE)
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)
pipe.enable_attention_slicing(1)
pipe.enable_sequential_cpu_offload()
__lowerCAmelCase : str = "Face of a yellow cat, high resolution, sitting on a park bench"
__lowerCAmelCase : Union[str, Any] = torch.manual_seed(0)
__lowerCAmelCase : int = pipe(
prompt=_SCREAMING_SNAKE_CASE , image=_SCREAMING_SNAKE_CASE , mask_image=_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , num_inference_steps=2 , output_type="np" , )
__lowerCAmelCase : Any = torch.cuda.max_memory_allocated()
# make sure that less than 2.65 GB is allocated
assert mem_bytes < 2.65 * 10**9 | 58 |
"""simple docstring"""
def _lowercase ( __snake_case ) -> int:
if not isinstance(__snake_case ,__snake_case ):
raise ValueError("Input must be an integer" )
if input_num <= 0:
raise ValueError("Input must be positive" )
return sum(
divisor for divisor in range(1 ,input_num // 2 + 1 ) if input_num % divisor == 0 )
if __name__ == "__main__":
import doctest
doctest.testmod() | 58 | 1 |
"""simple docstring"""
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
A_ : List[Any] = {}
A_ : Union[str, Any] = job['started_at']
A_ : int = job['completed_at']
A_ : Optional[Any] = date_parser.parse(a_ )
A_ : Union[str, Any] = date_parser.parse(a_ )
A_ : List[str] = round((end_datetime - start_datetime).total_seconds() / 60.0 )
A_ : Dict = start
A_ : Union[str, Any] = end
A_ : Union[str, Any] = duration_in_min
return job_info
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase=None ):
"""simple docstring"""
A_ : List[str] = None
if token is not None:
A_ : List[str] = {'Accept': 'application/vnd.github+json', 'Authorization': f"""Bearer {token}"""}
A_ : Optional[int] = f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
A_ : str = requests.get(a_ , headers=a_ ).json()
A_ : Optional[Any] = {}
try:
job_time.update({job['name']: extract_time_from_single_job(a_ ) for job in result['jobs']} )
A_ : Any = math.ceil((result['total_count'] - 100) / 100 )
for i in range(a_ ):
A_ : int = requests.get(url + f"""&page={i + 2}""" , headers=a_ ).json()
job_time.update({job['name']: extract_time_from_single_job(a_ ) for job in result['jobs']} )
return job_time
except Exception:
print(f"""Unknown error, could not fetch links:\n{traceback.format_exc()}""" )
return {}
if __name__ == "__main__":
lowerCamelCase_ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
lowerCamelCase_ : Optional[int] = parser.parse_args()
lowerCamelCase_ : Union[str, Any] = get_job_time(args.workflow_run_id)
lowerCamelCase_ : Optional[int] = dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))
for k, v in job_time.items():
print(F"{k}: {v['duration']}") | 286 |
from typing import Dict, Optional
import numpy as np
import datasets
SCREAMING_SNAKE_CASE :List[Any] = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n'
SCREAMING_SNAKE_CASE :List[str] = '\nArgs:\n predictions (`List[ndarray]`):\n List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n references (`List[ndarray]`):\n List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n num_labels (`int`):\n Number of classes (categories).\n ignore_index (`int`):\n Index that will be ignored during evaluation.\n nan_to_num (`int`, *optional*):\n If specified, NaN values will be replaced by the number defined by the user.\n label_map (`dict`, *optional*):\n If specified, dictionary mapping old label indices to new label indices.\n reduce_labels (`bool`, *optional*, defaults to `False`):\n Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n `Dict[str, float | ndarray]` comprising various elements:\n - *mean_iou* (`float`):\n Mean Intersection-over-Union (IoU averaged over all categories).\n - *mean_accuracy* (`float`):\n Mean accuracy (averaged over all categories).\n - *overall_accuracy* (`float`):\n Overall accuracy on all images.\n - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n Per category accuracy.\n - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n Per category IoU.\n\nExamples:\n\n >>> import numpy as np\n\n >>> mean_iou = datasets.load_metric("mean_iou")\n\n >>> # suppose one has 3 different segmentation maps predicted\n >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n >>> predicted = [predicted_1, predicted_2, predicted_3]\n >>> ground_truth = [actual_1, actual_2, actual_3]\n\n >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0. , 0. , 0.375, 0.4 , 0.5 , 0. , 0.5 , 1. , 1. , 1. ]), \'per_category_accuracy\': array([0. , 0. , 0.75 , 0.66666667, 1. , 0. , 0.5 , 1. , 1. , 1. ])}\n'
SCREAMING_SNAKE_CASE :str = '\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}'
def UpperCAmelCase ( a_ , a_ , a_ , a_ , a_ = None , a_ = False , ) -> Tuple:
"""simple docstring"""
if label_map is not None:
for old_id, new_id in label_map.items():
__A = new_id
# turn into Numpy arrays
__A = np.array(a_ )
__A = np.array(a_ )
if reduce_labels:
__A = 2_5_5
__A = label - 1
__A = 2_5_5
__A = label != ignore_index
__A = np.not_equal(a_ , a_ )
__A = pred_label[mask]
__A = np.array(a_ )[mask]
__A = pred_label[pred_label == label]
__A = np.histogram(a_ , bins=a_ , range=(0, num_labels - 1) )[0]
__A = np.histogram(a_ , bins=a_ , range=(0, num_labels - 1) )[0]
__A = np.histogram(a_ , bins=a_ , range=(0, num_labels - 1) )[0]
__A = area_pred_label + area_label - area_intersect
return area_intersect, area_union, area_pred_label, area_label
def UpperCAmelCase ( a_ , a_ , a_ , a_ , a_ = None , a_ = False , ) -> Union[str, Any]:
"""simple docstring"""
__A = np.zeros((num_labels,) , dtype=np.floataa )
__A = np.zeros((num_labels,) , dtype=np.floataa )
__A = np.zeros((num_labels,) , dtype=np.floataa )
__A = np.zeros((num_labels,) , dtype=np.floataa )
for result, gt_seg_map in zip(a_ , a_ ):
__A , __A , __A , __A = intersect_and_union(
a_ , a_ , a_ , a_ , a_ , a_ )
total_area_intersect += area_intersect
total_area_union += area_union
total_area_pred_label += area_pred_label
total_area_label += area_label
return total_area_intersect, total_area_union, total_area_pred_label, total_area_label
def UpperCAmelCase ( a_ , a_ , a_ , a_ , a_ = None , a_ = None , a_ = False , ) -> str:
"""simple docstring"""
__A , __A , __A , __A = total_intersect_and_union(
a_ , a_ , a_ , a_ , a_ , a_ )
# compute metrics
__A = {}
__A = total_area_intersect.sum() / total_area_label.sum()
__A = total_area_intersect / total_area_union
__A = total_area_intersect / total_area_label
__A = np.nanmean(a_ )
__A = np.nanmean(a_ )
__A = all_acc
__A = iou
__A = acc
if nan_to_num is not None:
__A = {metric: np.nan_to_num(a_ , nan=a_ ) for metric, metric_value in metrics.items()}
return metrics
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase ( datasets.Metric ):
'''simple docstring'''
def UpperCamelCase_ ( self : List[Any] ):
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
# 1st Seq - height dim, 2nd - width dim
{
"predictions": datasets.Sequence(datasets.Sequence(datasets.Value("uint16" ) ) ),
"references": datasets.Sequence(datasets.Sequence(datasets.Value("uint16" ) ) ),
} ) ,reference_urls=[
"https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py"
] ,)
def UpperCamelCase_ ( self : int ,A : Optional[Any] ,A : Optional[Any] ,A : int ,A : bool ,A : Optional[int] = None ,A : Optional[Dict[int, int]] = None ,A : bool = False ,):
__A = mean_iou(
results=A ,gt_seg_maps=A ,num_labels=A ,ignore_index=A ,nan_to_num=A ,label_map=A ,reduce_labels=A ,)
return iou_result
| 15 | 0 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class _snake_case ( unittest.TestCase ):
'''simple docstring'''
A__ : Tuple = inspect.getfile(accelerate.test_utils )
A__ : Dict = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["scripts", "test_cli.py"] )
A__ : List[str] = ["accelerate", "launch"]
A__ : List[Any] = Path.home() / ".cache/huggingface/accelerate"
A__ : Tuple = "default_config.yaml"
A__ : Tuple = config_folder / config_file
A__ : int = config_folder / "_default_config.yaml"
A__ : Optional[Any] = Path("tests/test_configs" )
@classmethod
def A__ ( cls: int ) -> Optional[Any]:
if cls.config_path.is_file():
cls.config_path.rename(cls.changed_path )
@classmethod
def A__ ( cls: Any ) -> Optional[Any]:
if cls.changed_path.is_file():
cls.changed_path.rename(cls.config_path )
def A__ ( self: Tuple ) -> str:
UpperCAmelCase_ : str = self.base_cmd
if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
cmd += ["--multi_gpu"]
execute_subprocess_async(cmd + [self.test_file_path] ,env=os.environ.copy() )
def A__ ( self: List[Any] ) -> str:
for config in sorted(self.test_config_path.glob("""**/*.yaml""" ) ):
with self.subTest(config_file=lowerCamelCase_ ):
execute_subprocess_async(
self.base_cmd + ["""--config_file""", str(lowerCamelCase_ ), self.test_file_path] ,env=os.environ.copy() )
def A__ ( self: Union[str, Any] ) -> Tuple:
execute_subprocess_async(["""accelerate""", """test"""] ,env=os.environ.copy() )
class _snake_case ( unittest.TestCase ):
'''simple docstring'''
A__ : Optional[int] = "test-tpu"
A__ : Optional[int] = "us-central1-a"
A__ : Dict = "ls"
A__ : Optional[int] = ["accelerate", "tpu-config"]
A__ : Optional[Any] = "cd /usr/share"
A__ : Optional[Any] = "tests/test_samples/test_command_file.sh"
A__ : int = "Running gcloud compute tpus tpu-vm ssh"
def A__ ( self: Dict ) -> List[Any]:
UpperCAmelCase_ : Optional[Any] = run_command(
self.cmd
+ ["""--command""", self.command, """--tpu_zone""", self.tpu_zone, """--tpu_name""", self.tpu_name, """--debug"""] ,return_stdout=lowerCamelCase_ ,)
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' ,lowerCamelCase_ ,)
def A__ ( self: Optional[int] ) -> Optional[int]:
UpperCAmelCase_ : int = run_command(
self.cmd
+ [
"""--config_file""",
"""tests/test_configs/0_12_0.yaml""",
"""--command""",
self.command,
"""--tpu_zone""",
self.tpu_zone,
"""--tpu_name""",
self.tpu_name,
"""--debug""",
] ,return_stdout=lowerCamelCase_ ,)
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' ,lowerCamelCase_ ,)
def A__ ( self: Dict ) -> Tuple:
UpperCAmelCase_ : Union[str, Any] = run_command(
self.cmd + ["""--config_file""", """tests/test_configs/latest.yaml""", """--debug"""] ,return_stdout=lowerCamelCase_ )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' ,lowerCamelCase_ ,)
def A__ ( self: Optional[int] ) -> List[str]:
UpperCAmelCase_ : Dict = run_command(
self.cmd + ["""--config_file""", """tests/test_configs/latest.yaml""", """--command""", self.command, """--debug"""] ,return_stdout=lowerCamelCase_ ,)
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' ,lowerCamelCase_ ,)
def A__ ( self: Union[str, Any] ) -> int:
UpperCAmelCase_ : Union[str, Any] = run_command(
self.cmd
+ [
"""--config_file""",
"""tests/test_configs/latest.yaml""",
"""--command""",
self.command,
"""--command""",
"""echo \"Hello World\"""",
"""--debug""",
] ,return_stdout=lowerCamelCase_ ,)
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo "Hello World" --worker all''' ,lowerCamelCase_ ,)
def A__ ( self: str ) -> Optional[Any]:
UpperCAmelCase_ : int = run_command(
self.cmd
+ ["""--config_file""", """tests/test_configs/latest.yaml""", """--command_file""", self.command_file, """--debug"""] ,return_stdout=lowerCamelCase_ ,)
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' ,lowerCamelCase_ ,)
def A__ ( self: Dict ) -> Union[str, Any]:
UpperCAmelCase_ : Optional[int] = run_command(
self.cmd
+ [
"""--config_file""",
"""tests/test_configs/0_12_0.yaml""",
"""--command_file""",
self.command_file,
"""--tpu_zone""",
self.tpu_zone,
"""--tpu_name""",
self.tpu_name,
"""--debug""",
] ,return_stdout=lowerCamelCase_ ,)
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' ,lowerCamelCase_ ,)
def A__ ( self: Optional[int] ) -> Dict:
UpperCAmelCase_ : List[str] = run_command(
self.cmd + ["""--config_file""", """tests/test_configs/latest.yaml""", """--install_accelerate""", """--debug"""] ,return_stdout=lowerCamelCase_ ,)
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo "hello world"; echo "this is a second command" --worker all''' ,lowerCamelCase_ ,)
def A__ ( self: List[str] ) -> List[str]:
UpperCAmelCase_ : Dict = run_command(
self.cmd
+ [
"""--config_file""",
"""tests/test_configs/latest.yaml""",
"""--install_accelerate""",
"""--accelerate_version""",
"""12.0.0""",
"""--debug""",
] ,return_stdout=lowerCamelCase_ ,)
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo "hello world"; echo "this is a second command" --worker all''' ,lowerCamelCase_ ,)
| 59 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'''shi-labs/dinat-mini-in1k-224''': '''https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json''',
# See all Dinat models at https://huggingface.co/models?filter=dinat
}
class _snake_case ( __snake_case , __snake_case ):
'''simple docstring'''
A__ : Optional[Any] = "dinat"
A__ : Any = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self: Any ,lowerCamelCase_: Any=4 ,lowerCamelCase_: Union[str, Any]=3 ,lowerCamelCase_: Union[str, Any]=64 ,lowerCamelCase_: Optional[int]=[3, 4, 6, 5] ,lowerCamelCase_: int=[2, 4, 8, 16] ,lowerCamelCase_: Optional[int]=7 ,lowerCamelCase_: Dict=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]] ,lowerCamelCase_: Tuple=3.0 ,lowerCamelCase_: Any=True ,lowerCamelCase_: int=0.0 ,lowerCamelCase_: Optional[Any]=0.0 ,lowerCamelCase_: Optional[int]=0.1 ,lowerCamelCase_: Optional[int]="gelu" ,lowerCamelCase_: Optional[Any]=0.0_2 ,lowerCamelCase_: List[Any]=1e-5 ,lowerCamelCase_: int=0.0 ,lowerCamelCase_: int=None ,lowerCamelCase_: str=None ,**lowerCamelCase_: Dict ,) -> Union[str, Any]:
super().__init__(**lowerCamelCase_ )
UpperCAmelCase_ : List[str] = patch_size
UpperCAmelCase_ : Tuple = num_channels
UpperCAmelCase_ : Union[str, Any] = embed_dim
UpperCAmelCase_ : int = depths
UpperCAmelCase_ : List[Any] = len(lowerCamelCase_ )
UpperCAmelCase_ : Union[str, Any] = num_heads
UpperCAmelCase_ : Tuple = kernel_size
UpperCAmelCase_ : int = dilations
UpperCAmelCase_ : Optional[Any] = mlp_ratio
UpperCAmelCase_ : Optional[Any] = qkv_bias
UpperCAmelCase_ : List[Any] = hidden_dropout_prob
UpperCAmelCase_ : List[str] = attention_probs_dropout_prob
UpperCAmelCase_ : Tuple = drop_path_rate
UpperCAmelCase_ : List[str] = hidden_act
UpperCAmelCase_ : Any = layer_norm_eps
UpperCAmelCase_ : List[str] = initializer_range
# we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
UpperCAmelCase_ : List[Any] = int(embed_dim * 2 ** (len(lowerCamelCase_ ) - 1) )
UpperCAmelCase_ : Optional[int] = layer_scale_init_value
UpperCAmelCase_ : List[Any] = ["""stem"""] + [F'''stage{idx}''' for idx in range(1 ,len(lowerCamelCase_ ) + 1 )]
UpperCAmelCase_ , UpperCAmelCase_ : Optional[int] = get_aligned_output_features_output_indices(
out_features=lowerCamelCase_ ,out_indices=lowerCamelCase_ ,stage_names=self.stage_names )
| 59 | 1 |
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