



import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowercase_								=     '''src/diffusers'''
lowercase_								=     '''.'''


# This is to make sure the diffusers module imported is the one in the repo.
lowercase_								=     importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(DIFFUSERS_PATH, """__init__.py"""),
    submodule_search_locations=[DIFFUSERS_PATH],
)
lowercase_								=     spec.loader.load_module()



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			return line.startswith(lowerCAmelCase__  ) or len(lowerCAmelCase__  ) <= 1 or re.search(r"^\s*\)(\s*->.*:|:)\s*$"						,  lowerCAmelCase__  ) is not None



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				object_name.split("."  )
			lowercase__					  =				0

			# First let's find the module where our object lives.
			lowercase__					  =				parts[i]
			while i < len(lowerCAmelCase__  ) and not os.path.isfile(os.path.join(lowerCAmelCase__						,  f'''{module}.py'''  )  ):
						i += 1
						if i < len(lowerCAmelCase__  ):
									lowercase__					  =				os.path.join(lowerCAmelCase__						,  parts[i]  )
			if i >= len(lowerCAmelCase__  ):
						raise ValueError(f'''`object_name` should begin with the name of a module of diffusers but got {object_name}.'''  )

			with open(os.path.join(lowerCAmelCase__						,  f'''{module}.py'''  )						,  "r"						,  encoding="utf-8"						,  newline="\n"  ) as f:
						lowercase__					  =				f.readlines()

			# Now let's find the class / func in the code!
			lowercase__					  =				""
			lowercase__					  =				0
			for name in parts[i + 1 :]:
						while (
						    line_index < len(lowerCAmelCase__  ) and re.search(rf'''^{indent}(class|def)\s+{name}(\(|\:)'''						,  lines[line_index]  ) is None
						):
									line_index += 1
						indent += "    "
						line_index += 1

			if line_index >= len(lowerCAmelCase__  ):
						raise ValueError(f''' {object_name} does not match any function or class in {module}.'''  )

			# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
			lowercase__					  =				line_index
			while line_index < len(lowerCAmelCase__  ) and _should_continue(lines[line_index]						,  lowerCAmelCase__  ):
						line_index += 1
			# Clean up empty lines at the end (if any).
			while len(lines[line_index - 1]  ) <= 1:
						line_index -= 1

			lowercase__					  =				lines[start_index:line_index]
			return "".join(lowerCAmelCase__  )


lowercase_								=     re.compile(r"""^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)""")
lowercase_								=     re.compile(r"""^\s*(\S+)->(\S+)(\s+.*|$)""")
lowercase_								=     re.compile(r"""<FILL\s+[^>]*>""")



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				code.split("\n"  )
			lowercase__					  =				0
			while idx < len(lowerCAmelCase__  ) and len(lines[idx]  ) == 0:
						idx += 1
			if idx < len(lowerCAmelCase__  ):
						return re.search(r"^(\s*)\S"						,  lines[idx]  ).groups()[0]
			return ""



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				len(get_indent(lowerCAmelCase__  )  ) > 0
			if has_indent:
						lowercase__					  =				f'''class Bla:\n{code}'''
			lowercase__					  =				black.Mode(target_versions={black.TargetVersion.PYaa}						,  line_length=119						,  preview=lowerCAmelCase__  )
			lowercase__					  =				black.format_str(lowerCAmelCase__						,  mode=lowerCAmelCase__  )
			lowercase__  ,       lowercase__					  =				style_docstrings_in_code(lowerCAmelCase__  )
			return result[len("class Bla:\n"  ) :] if has_indent else result



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_=False  ):
			with open(lowerCAmelCase__						,  "r"						,  encoding="utf-8"						,  newline="\n"  ) as f:
						lowercase__					  =				f.readlines()
			lowercase__					  =				[]
			lowercase__					  =				0
			# Not a for loop cause `lines` is going to change (if `overwrite=True`).
			while line_index < len(lowerCAmelCase__  ):
						lowercase__					  =				_re_copy_warning.search(lines[line_index]  )
						if search is None:
									line_index += 1
									continue

						# There is some copied code here, let's retrieve the original.
						lowercase__  ,       lowercase__  ,       lowercase__					  =				search.groups()
						lowercase__					  =				find_code_in_diffusers(lowerCAmelCase__  )
						lowercase__					  =				get_indent(lowerCAmelCase__  )

						lowercase__					  =				line_index + 1 if indent == theoretical_indent else line_index + 2
						lowercase__					  =				theoretical_indent
						lowercase__					  =				start_index

						# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
						lowercase__					  =				True
						while line_index < len(lowerCAmelCase__  ) and should_continue:
									line_index += 1
									if line_index >= len(lowerCAmelCase__  ):
												break
									lowercase__					  =				lines[line_index]
									lowercase__					  =				_should_continue(lowerCAmelCase__						,  lowerCAmelCase__  ) and re.search(f'''^{indent}# End copy'''						,  lowerCAmelCase__  ) is None
						# Clean up empty lines at the end (if any).
						while len(lines[line_index - 1]  ) <= 1:
									line_index -= 1

						lowercase__					  =				lines[start_index:line_index]
						lowercase__					  =				"".join(lowerCAmelCase__  )

						# Remove any nested `Copied from` comments to avoid circular copies
						lowercase__					  =				[line for line in theoretical_code.split("\n"  ) if _re_copy_warning.search(lowerCAmelCase__  ) is None]
						lowercase__					  =				"\n".join(lowerCAmelCase__  )

						# Before comparing, use the `replace_pattern` on the original code.
						if len(lowerCAmelCase__  ) > 0:
									lowercase__					  =				replace_pattern.replace("with"						,  ""  ).split(","  )
									lowercase__					  =				[_re_replace_pattern.search(lowerCAmelCase__  ) for p in patterns]
									for pattern in patterns:
												if pattern is None:
															continue
												lowercase__  ,       lowercase__  ,       lowercase__					  =				pattern.groups()
												lowercase__					  =				re.sub(lowerCAmelCase__						,  lowerCAmelCase__						,  lowerCAmelCase__  )
												if option.strip() == "all-casing":
															lowercase__					  =				re.sub(obja.lower()						,  obja.lower()						,  lowerCAmelCase__  )
															lowercase__					  =				re.sub(obja.upper()						,  obja.upper()						,  lowerCAmelCase__  )

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
									lowercase__					  =				blackify(lines[start_index - 1] + theoretical_code  )
									lowercase__					  =				theoretical_code[len(lines[start_index - 1]  ) :]

						# Test for a diff and act accordingly.
						if observed_code != theoretical_code:
									diffs.append([object_name, start_index]  )
									if overwrite:
												lowercase__					  =				lines[:start_index] + [theoretical_code] + lines[line_index:]
												lowercase__					  =				start_index + 1

			if overwrite and len(lowerCAmelCase__  ) > 0:
						# Warn the user a file has been modified.
						print(f'''Detected changes, rewriting {filename}.'''  )
						with open(lowerCAmelCase__						,  "w"						,  encoding="utf-8"						,  newline="\n"  ) as f:
									f.writelines(lowerCAmelCase__  )
			return diffs



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_ = False  ):
			lowercase__					  =				glob.glob(os.path.join(lowerCAmelCase__						,  "**/*.py"  )						,  recursive=lowerCAmelCase__  )
			lowercase__					  =				[]
			for filename in all_files:
						lowercase__					  =				is_copy_consistent(lowerCAmelCase__						,  lowerCAmelCase__  )
						diffs += [f'''- {filename}: copy does not match {d[0]} at line {d[1]}''' for d in new_diffs]
			if not overwrite and len(lowerCAmelCase__  ) > 0:
						lowercase__					  =				"\n".join(lowerCAmelCase__  )
						raise Exception(
						    "Found the following copy inconsistencies:\n"
						    + diff
						    + "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."  )


if __name__ == "__main__":
						lowercase_								=     argparse.ArgumentParser()
						parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
						lowercase_								=     parser.parse_args()

						check_copies(args.fix_and_overwrite)









import argparse
import os

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


if is_torch_available():
							import numpy as np
							import torch

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


logging.set_verbosity_info()

lowercase_								=     {
    """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 __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_=False						,  SCREAMING_SNAKE_CASE_=True  ):
			if model_type not in MODEL_CLASSES:
						raise ValueError(f'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()  )}.'''  )

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

			# Initialise TF model
			if config_file in aws_config_map:
						lowercase__					  =				cached_file(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 __lowerCAmelCase (     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						,  ):
			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("=" * 100  )
						print(f''' Converting model type {j}/{len(SCREAMING_SNAKE_CASE_  )}: {model_type}'''  )
						print("=" * 100  )
						if model_type not in MODEL_CLASSES:
									raise ValueError(f'''Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()  )}.'''  )

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

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

						for i, (model_shortcut_name, config_shortcut_name) in enumerate(
						    zip(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )						,  start=1  ):
									print("-" * 100  )
									if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
												if not only_convert_finetuned_models:
															print(f'''    Skipping finetuned checkpoint {model_shortcut_name}'''  )
															continue
												lowercase__					  =				model_shortcut_name
									elif only_convert_finetuned_models:
												print(f'''    Skipping not finetuned checkpoint {model_shortcut_name}'''  )
												continue
									print(
									    f'''    Converting checkpoint {i}/{len(SCREAMING_SNAKE_CASE_  )}: {model_shortcut_name} - model_type {model_type}'''  )
									print("-" * 100  )

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




