


# 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       =							get_logger(__name__)


class        _a							:
							_lowercase	:			Any        = '''dummy_data'''
							_lowercase	:			List[str]        = '''datasets'''
							_lowercase	:			Optional[int]        = False

							def __init__(							self:	Union[str, Any]    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	Union[Version, str]    ,						UpperCamelCase_:	Optional[str] = None    ,						UpperCamelCase_:	bool = False    ,						UpperCamelCase_:	bool = True    ,						UpperCamelCase_:	Optional[List[Callable]] = None    ,						)			->   Union[str, Any]:





									"""simple docstring"""







									lowercase__            =      0
									lowercase__            =      dataset_name
									lowercase__            =      cache_dir
									lowercase__            =      use_local_dummy_data
									lowercase__            =      config
									# download_callbacks take a single url as input
									lowercase__            =      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
									lowercase__            =      load_existing_dummy_data

									# TODO(PVP, QL) might need to make this more general
									lowercase__            =      str(UpperCamelCase_							)
									# to be downloaded
									lowercase__            =      None
									lowercase__            =      None

							@property
							def       lowerCamelCase_ (							self:	int							)			->   List[str]:





									"""simple docstring"""







									if self._dummy_file is None:
											lowercase__            =      self.download_dummy_data()
									return self._dummy_file

							@property
							def       lowerCamelCase_ (							self:	Any							)			->   Optional[Any]:





									"""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       lowerCamelCase_ (							self:	List[str]							)			->   Union[str, Any]:





									"""simple docstring"""







									return os.path.join(self.dummy_data_folder    ,						'''dummy_data.zip'''							)

							def       lowerCamelCase_ (							self:	Optional[Any]							)			->   Union[str, Any]:





									"""simple docstring"""







									lowercase__            =      (
									    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
									)

									lowercase__            =      cached_path(
									    UpperCamelCase_    ,						cache_dir=self.cache_dir    ,						extract_compressed_file=UpperCamelCase_    ,						force_extract=UpperCamelCase_							)

									return os.path.join(UpperCamelCase_    ,						self.dummy_file_name							)

							@property
							def       lowerCamelCase_ (							self:	Tuple							)			->   Optional[int]:





									"""simple docstring"""







									return os.path.join(self.datasets_scripts_dir    ,						self.dataset_name    ,						self.dummy_zip_file							)

							@property
							def       lowerCamelCase_ (							self:	Dict							)			->   Any:





									"""simple docstring"""







									if self._bucket_url is None:
											lowercase__            =      hf_github_url(self.dataset_name    ,						self.dummy_zip_file.replace(os.sep    ,						'''/'''							)							)
									return self._bucket_url

							@property
							def       lowerCamelCase_ (							self:	Optional[int]							)			->   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       lowerCamelCase_ (							self:	Union[str, Any]    ,						UpperCamelCase_:	Optional[Any]    ,						*UpperCamelCase_:	Any							)			->   List[str]:





									"""simple docstring"""







									if self.load_existing_dummy_data:
											# dummy data is downloaded and tested
											lowercase__            =      self.dummy_file
									else:
											# dummy data cannot be downloaded and only the path to dummy file is returned
											lowercase__            =      self.dummy_file_name

									# special case when data_url is a dict
									if isinstance(UpperCamelCase_    ,						UpperCamelCase_							):
											return self.create_dummy_data_dict(UpperCamelCase_    ,						UpperCamelCase_							)
									elif isinstance(UpperCamelCase_    ,						(list, tuple)							):
											return self.create_dummy_data_list(UpperCamelCase_    ,						UpperCamelCase_							)
									else:
											return self.create_dummy_data_single(UpperCamelCase_    ,						UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	str    ,						UpperCamelCase_:	str    ,						*UpperCamelCase_:	Tuple							)			->   Any:





									"""simple docstring"""







									return self.download_and_extract(UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	Dict    ,						UpperCamelCase_:	List[str]    ,						UpperCamelCase_:	List[Any]							)			->   Any:





									"""simple docstring"""







									return self.download_and_extract(UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	List[Any]    ,						UpperCamelCase_:	List[Any]    ,						*UpperCamelCase_:	Optional[int]    ,						**UpperCamelCase_:	List[Any]							)			->   List[Any]:





									"""simple docstring"""







									return path

							def       lowerCamelCase_ (							self:	Dict							)			->   List[Any]:





									"""simple docstring"""







									return {}

							def       lowerCamelCase_ (							self:	str    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	Union[str, Any]							)			->   List[str]:





									"""simple docstring"""







									lowercase__            =      {}
									for key, single_urls in data_url.items():
											for download_callback in self.download_callbacks:
													if isinstance(UpperCamelCase_    ,						UpperCamelCase_							):
															for single_url in single_urls:
																	download_callback(UpperCamelCase_							)
													else:
															lowercase__            =      single_urls
															download_callback(UpperCamelCase_							)
            # 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(UpperCamelCase_    ,						UpperCamelCase_							):
													lowercase__            =      [os.path.join(UpperCamelCase_    ,						urllib.parse.quote_plus(Path(UpperCamelCase_							).name							)							) for x in single_urls]
											else:
													lowercase__            =      single_urls
													lowercase__            =      os.path.join(UpperCamelCase_    ,						urllib.parse.quote_plus(Path(UpperCamelCase_							).name							)							)
											lowercase__            =      value

									# make sure that values are unique
									if all(isinstance(UpperCamelCase_    ,						UpperCamelCase_							) 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
											lowercase__            =      {key: value + key for key, value in dummy_data_dict.items()}

									return dummy_data_dict

							def       lowerCamelCase_ (							self:	Tuple    ,						UpperCamelCase_:	Optional[int]    ,						UpperCamelCase_:	List[str]							)			->   Dict:





									"""simple docstring"""







									lowercase__            =      []
									# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
									lowercase__            =      all(bool(re.findall('''[0-9]{3,}-of-[0-9]{3,}'''    ,						UpperCamelCase_							)							) for url in data_url							)
									lowercase__            =      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):
											lowercase__            =      [data_url[0]] * len(UpperCamelCase_							)
									for single_url in data_url:
											for download_callback in self.download_callbacks:
													download_callback(UpperCamelCase_							)
											# 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
											lowercase__            =      os.path.join(UpperCamelCase_    ,						urllib.parse.quote_plus(single_url.split('''/'''							)[-1]							)							)
											dummy_data_list.append(UpperCamelCase_							)
									return dummy_data_list

							def       lowerCamelCase_ (							self:	str    ,						UpperCamelCase_:	Dict    ,						UpperCamelCase_:	List[str]							)			->   Dict:





									"""simple docstring"""







									for download_callback in self.download_callbacks:
											download_callback(UpperCamelCase_							)
									# 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
									lowercase__            =      os.path.join(UpperCamelCase_    ,						urllib.parse.quote_plus(data_url.split('''/'''							)[-1]							)							)
									if os.path.exists(UpperCamelCase_							) 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       lowerCamelCase_ (							self:	Tuple							)			->   Optional[Any]:





									"""simple docstring"""







									pass

							def       lowerCamelCase_ (							self:	str							)			->   Optional[int]:





									"""simple docstring"""







									pass

							def       lowerCamelCase_ (							self:	List[Any]    ,						UpperCamelCase_:	int							)			->   int:





									"""simple docstring"""







									def _iter_archive_members(UpperCamelCase_:	Optional[int]							):
											# this preserves the order of the members inside the ZIP archive
											lowercase__            =      Path(self.dummy_file							).parent
											lowercase__            =      path.relative_to(UpperCamelCase_							)
											with ZipFile(self.local_path_to_dummy_data							) as zip_file:
													lowercase__            =      zip_file.namelist()
											for member in members:
													if member.startswith(relative_path.as_posix()							):
															yield dummy_parent_path.joinpath(UpperCamelCase_							)

									lowercase__            =      Path(UpperCamelCase_							)
									lowercase__            =      _iter_archive_members(UpperCamelCase_							) 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(UpperCamelCase_							).as_posix(), file_path.open('''rb'''							)


							def       lowerCamelCase_ (							self:	Union[str, Any]    ,						UpperCamelCase_:	int							)			->   int:





									"""simple docstring"""







									if not isinstance(UpperCamelCase_    ,						UpperCamelCase_							):
											lowercase__            =      [paths]
									for path in paths:
											if os.path.isfile(UpperCamelCase_							):
													if os.path.basename(UpperCamelCase_							).startswith(('''.''', '''__''')							):
															return
													yield path
											else:
													for dirpath, dirnames, filenames in os.walk(UpperCamelCase_							):
															if os.path.basename(UpperCamelCase_							).startswith(('''.''', '''__''')							):
																	continue
															dirnames.sort()
															for filename in sorted(UpperCamelCase_							):
																	if filename.startswith(('''.''', '''__''')							):
																			continue
																	yield os.path.join(UpperCamelCase_    ,						UpperCamelCase_							)






import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
			import numpy as np
			import torch

			from . import (
			    AlbertForPreTraining,
			    BartForConditionalGeneration,
			    BertForPreTraining,
			    BertForQuestionAnswering,
			    BertForSequenceClassification,
			    CamembertForMaskedLM,
			    CTRLLMHeadModel,
			    DistilBertForMaskedLM,
			    DistilBertForQuestionAnswering,
			    DPRContextEncoder,
			    DPRQuestionEncoder,
			    DPRReader,
			    ElectraForPreTraining,
			    FlaubertWithLMHeadModel,
			    GPTaLMHeadModel,
			    LayoutLMForMaskedLM,
			    LxmertForPreTraining,
			    LxmertVisualFeatureEncoder,
			    OpenAIGPTLMHeadModel,
			    RobertaForMaskedLM,
			    RobertaForSequenceClassification,
			    TaForConditionalGeneration,
			    TransfoXLLMHeadModel,
			    XLMRobertaForMaskedLM,
			    XLMWithLMHeadModel,
			    XLNetLMHeadModel,
			)


logging.set_verbosity_info()

lowerCAmelCase       =							{
    'bart': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'bert': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-base-cased-finetuned-mrpc': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'dpr': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'gpt2': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlnet': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm-roberta': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'transfo-xl': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'openai-gpt': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'roberta': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'layoutlm': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'roberta-large-mnli': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'camembert': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'flaubert': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert-base-distilled-squad': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert-visual-feature-encoder': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'ctrl': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'albert': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    't5': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'electra': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'wav2vec2': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE=False						,				SCREAMING_SNAKE_CASE=True     ):


		"""simple docstring"""




		if model_type not in MODEL_CLASSES:
				raise ValueError(f'Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()     )}.'     )

		lowercase__     ,		lowercase__     ,		lowercase__     ,		lowercase__            =      MODEL_CLASSES[model_type]

		# Initialise TF model
		if config_file in aws_config_map:
				lowercase__            =      cached_file(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				force_download=not use_cached_models     )
		lowercase__            =      config_class.from_json_file(SCREAMING_SNAKE_CASE     )
		lowercase__            =      True
		lowercase__            =      True
		print(f'Building TensorFlow model from configuration: {config}'     )
		lowercase__            =      model_class(SCREAMING_SNAKE_CASE     )

		# Load weights from tf checkpoint
		if pytorch_checkpoint_path in aws_config_map.keys():
				lowercase__            =      cached_file(
				    SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				force_download=not use_cached_models     )
		# Load PyTorch checkpoint in tf2 model:
		lowercase__            =      load_pytorch_checkpoint_in_tfa_model(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     )

		if compare_with_pt_model:
				lowercase__            =      tf_model(tf_model.dummy_inputs						,				training=SCREAMING_SNAKE_CASE     )  # build the network

				lowercase__            =      torch.load(SCREAMING_SNAKE_CASE						,				map_location='''cpu'''     )
				lowercase__            =      pt_model_class.from_pretrained(
				    pretrained_model_name_or_path=SCREAMING_SNAKE_CASE						,				config=SCREAMING_SNAKE_CASE						,				state_dict=SCREAMING_SNAKE_CASE     )

				with torch.no_grad():
						lowercase__            =      pt_model(**pt_model.dummy_inputs     )

				lowercase__            =      pto[0].numpy()
				lowercase__            =      tfo[0].numpy()
				lowercase__            =      np.amax(np.abs(np_pt - np_tf     )     )
				print(f'Max absolute difference between models outputs {diff}'     )
				assert diff <= 2E-2, f'Error, model absolute difference is >2e-2: {diff}'

		# Save pytorch-model
		print(f'Save TensorFlow model to {tf_dump_path}'     )
		tf_model.save_weights(SCREAMING_SNAKE_CASE						,				save_format='''h5'''     )
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE=None						,				SCREAMING_SNAKE_CASE=None						,				SCREAMING_SNAKE_CASE=False						,				SCREAMING_SNAKE_CASE=False						,				SCREAMING_SNAKE_CASE=False						,				SCREAMING_SNAKE_CASE=False						,				):


		"""simple docstring"""




		if args_model_type is None:
				lowercase__            =      list(MODEL_CLASSES.keys()     )
		else:
				lowercase__            =      [args_model_type]

		for j, model_type in enumerate(SCREAMING_SNAKE_CASE						,				start=1     ):
				print('''=''' * 1_00     )
				print(f' Converting model type {j}/{len(SCREAMING_SNAKE_CASE     )}: {model_type}'     )
				print('''=''' * 1_00     )
				if model_type not in MODEL_CLASSES:
						raise ValueError(f'Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()     )}.'     )

				lowercase__     ,		lowercase__     ,		lowercase__     ,		lowercase__     ,		lowercase__            =      MODEL_CLASSES[model_type]

				if model_shortcut_names_or_path is None:
						lowercase__            =      list(aws_model_maps.keys()     )
				if config_shortcut_names_or_path is None:
						lowercase__            =      model_shortcut_names_or_path

				for i, (model_shortcut_name, config_shortcut_name) in enumerate(
				    zip(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     )						,				start=1     ):
						print('''-''' * 1_00     )
						if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
								if not only_convert_finetuned_models:
										print(f'    Skipping finetuned checkpoint {model_shortcut_name}'     )
										continue
								lowercase__            =      model_shortcut_name
						elif only_convert_finetuned_models:
								print(f'    Skipping not finetuned checkpoint {model_shortcut_name}'     )
								continue
						print(
						    f'    Converting checkpoint {i}/{len(SCREAMING_SNAKE_CASE     )}: {model_shortcut_name} - model_type {model_type}'     )
						print('''-''' * 1_00     )

						if config_shortcut_name in aws_config_map:
								lowercase__            =      cached_file(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				force_download=not use_cached_models     )
						else:
								lowercase__            =      config_shortcut_name

						if model_shortcut_name in aws_model_maps:
								lowercase__            =      cached_file(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				force_download=not use_cached_models     )
						else:
								lowercase__            =      model_shortcut_name

						if os.path.isfile(SCREAMING_SNAKE_CASE     ):
								lowercase__            =      '''converted_model'''

						convert_pt_checkpoint_to_tf(
						    model_type=SCREAMING_SNAKE_CASE						,				pytorch_checkpoint_path=SCREAMING_SNAKE_CASE						,				config_file=SCREAMING_SNAKE_CASE						,				tf_dump_path=os.path.join(SCREAMING_SNAKE_CASE						,				model_shortcut_name + '''-tf_model.h5'''     )						,				compare_with_pt_model=SCREAMING_SNAKE_CASE						,				)
						if remove_cached_files:
								os.remove(SCREAMING_SNAKE_CASE     )
								os.remove(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
			lowerCAmelCase       =							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--tf_dump_path', default=None, type=str, required=True, help='Path to the output Tensorflow dump file.'
			)
			parser.add_argument(
			    '--model_type',
			    default=None,
			    type=str,
			    help=(
			        f"""Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and """
			        'convert all the models from AWS.'
			    ),
			)
			parser.add_argument(
			    '--pytorch_checkpoint_path',
			    default=None,
			    type=str,
			    help=(
			        'Path to the PyTorch checkpoint path or shortcut name to download from AWS. '
			        'If not given, will download and convert all the checkpoints from AWS.'
			    ),
			)
			parser.add_argument(
			    '--config_file',
			    default=None,
			    type=str,
			    help=(
			        'The config json file corresponding to the pre-trained model. \n'
			        'This specifies the model architecture. If not given and '
			        '--pytorch_checkpoint_path is not given or is a shortcut name '
			        'use the configuration associated to the shortcut name on the AWS'
			    ),
			)
			parser.add_argument(
			    '--compare_with_pt_model', action='store_true', help='Compare Tensorflow and PyTorch model predictions.'
			)
			parser.add_argument(
			    '--use_cached_models',
			    action='store_true',
			    help='Use cached models if possible instead of updating to latest checkpoint versions.',
			)
			parser.add_argument(
			    '--remove_cached_files',
			    action='store_true',
			    help='Remove pytorch models after conversion (save memory when converting in batches).',
			)
			parser.add_argument('--only_convert_finetuned_models', action='store_true', help='Only convert finetuned models.')
			lowerCAmelCase       =							parser.parse_args()

			# if args.pytorch_checkpoint_path is not None:
			#     convert_pt_checkpoint_to_tf(args.model_type.lower(),
			#                                 args.pytorch_checkpoint_path,
			#                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
			#                                 args.tf_dump_path,
			#                                 compare_with_pt_model=args.compare_with_pt_model,
			#                                 use_cached_models=args.use_cached_models)
			# else:
			convert_all_pt_checkpoints_to_tf(
			    args.model_type.lower() if args.model_type is not None else None,
			    args.tf_dump_path,
			    model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
			    if args.pytorch_checkpoint_path is not None
			    else None,
			    config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
			    compare_with_pt_model=args.compare_with_pt_model,
			    use_cached_models=args.use_cached_models,
			    remove_cached_files=args.remove_cached_files,
			    only_convert_finetuned_models=args.only_convert_finetuned_models,
			)


