

"""simple docstring"""


import argparse
import os

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


if is_torch_available():
			import numpy as np
			import torch

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


logging.set_verbosity_info()

__A						       =      {
    "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			)     ->     Optional[int]:
		if model_type not in MODEL_CLASSES:
				raise ValueError(F"Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()			)}."			)

		__lowerCAmelCase				,			__lowerCAmelCase				,			__lowerCAmelCase				,			__lowerCAmelCase:	Optional[int]   				=      MODEL_CLASSES[model_type]

		# Initialise TF model
		if config_file in aws_config_map:
				__lowerCAmelCase:	Union[str, Any]   				=      cached_file(__lowerCAmelCase ,			__lowerCAmelCase ,			force_download=not use_cached_models			)
		__lowerCAmelCase:	Any   				=      config_class.from_json_file(__lowerCAmelCase			)
		__lowerCAmelCase:	Optional[int]   				=      True
		__lowerCAmelCase:	Any   				=      True
		print(F"Building TensorFlow model from configuration: {config}"			)
		__lowerCAmelCase:	Union[str, Any]   				=      model_class(__lowerCAmelCase			)

		# Load weights from tf checkpoint
		if pytorch_checkpoint_path in aws_config_map.keys():
				__lowerCAmelCase:	Optional[int]   				=      cached_file(
				    __lowerCAmelCase ,			__lowerCAmelCase ,			force_download=not use_cached_models			)
		# Load PyTorch checkpoint in tf2 model:
		__lowerCAmelCase:	Union[str, Any]   				=      load_pytorch_checkpoint_in_tfa_model(__lowerCAmelCase ,			__lowerCAmelCase			)

		if compare_with_pt_model:
				__lowerCAmelCase:	List[str]   				=      tf_model(tf_model.dummy_inputs ,			training=__lowerCAmelCase			)  # build the network

				__lowerCAmelCase:	Union[str, Any]   				=      torch.load(__lowerCAmelCase ,			map_location="cpu"			)
				__lowerCAmelCase:	Optional[int]   				=      pt_model_class.from_pretrained(
				    pretrained_model_name_or_path=__lowerCAmelCase ,			config=__lowerCAmelCase ,			state_dict=__lowerCAmelCase			)

				with torch.no_grad():
						__lowerCAmelCase:	Any   				=      pt_model(**pt_model.dummy_inputs			)

				__lowerCAmelCase:	List[str]   				=      pto[0].numpy()
				__lowerCAmelCase:	Tuple   				=      tfo[0].numpy()
				__lowerCAmelCase:	Optional[Any]   				=      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(__lowerCAmelCase ,			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 ,			)     ->     Union[str, Any]:
		if args_model_type is None:
				__lowerCAmelCase:	Optional[int]   				=      list(MODEL_CLASSES.keys()			)
		else:
				__lowerCAmelCase:	Dict   				=      [args_model_type]

		for j, model_type in enumerate(__lowerCAmelCase ,			start=1			):
				print("=" * 1_0_0			)
				print(F" Converting model type {j}/{len(__lowerCAmelCase			)}: {model_type}"			)
				print("=" * 1_0_0			)
				if model_type not in MODEL_CLASSES:
						raise ValueError(F"Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()			)}."			)

				__lowerCAmelCase				,			__lowerCAmelCase				,			__lowerCAmelCase				,			__lowerCAmelCase				,			__lowerCAmelCase:	Any   				=      MODEL_CLASSES[model_type]

				if model_shortcut_names_or_path is None:
						__lowerCAmelCase:	Union[str, Any]   				=      list(aws_model_maps.keys()			)
				if config_shortcut_names_or_path is None:
						__lowerCAmelCase:	List[Any]   				=      model_shortcut_names_or_path

				for i, (model_shortcut_name, config_shortcut_name) in enumerate(
				    zip(__lowerCAmelCase ,			__lowerCAmelCase			) ,			start=1			):
						print("-" * 1_0_0			)
						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
								__lowerCAmelCase:	Union[str, Any]   				=      model_shortcut_name
						elif only_convert_finetuned_models:
								print(F"    Skipping not finetuned checkpoint {model_shortcut_name}"			)
								continue
						print(
						    F"    Converting checkpoint {i}/{len(__lowerCAmelCase			)}: {model_shortcut_name} - model_type {model_type}"			)
						print("-" * 1_0_0			)

						if config_shortcut_name in aws_config_map:
								__lowerCAmelCase:	List[str]   				=      cached_file(__lowerCAmelCase ,			__lowerCAmelCase ,			force_download=not use_cached_models			)
						else:
								__lowerCAmelCase:	Union[str, Any]   				=      config_shortcut_name

						if model_shortcut_name in aws_model_maps:
								__lowerCAmelCase:	str   				=      cached_file(__lowerCAmelCase ,			__lowerCAmelCase ,			force_download=not use_cached_models			)
						else:
								__lowerCAmelCase:	List[str]   				=      model_shortcut_name

						if os.path.isfile(__lowerCAmelCase			):
								__lowerCAmelCase:	Tuple   				=      "converted_model"

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


if __name__ == "__main__":
			__A						       =      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.")
			__A						       =      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,
			)





"""simple docstring"""


import collections
import importlib.util
import os
import re
from pathlib import Path


__A						       =      "src/transformers"


# Matches is_xxx_available()
__A						       =      re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
__A						       =      re.compile(r"^_import_structure\s+=\s+\{([^\}]+)\}")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
__A						       =      re.compile(r"\s+\"\S*\":\s+\[([^\]]*)\]")
# Catches a line if not is_foo_available
__A						       =      re.compile(r"^\s*if\s+not\s+is\_[a-z_]*\_available\(\)")
# Catches a line _import_struct["bla"].append("foo")
__A						       =      re.compile(r"^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
__A						       =      re.compile(r"^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]")
# Catches a line with an object between quotes and a comma:     "MyModel",
__A						       =      re.compile("^\s+\"([^\"]+)\",")
# Catches a line with objects between brackets only:    ["foo", "bar"],
__A						       =      re.compile("^\s+\[([^\]]+)\]")
# Catches a line with from foo import bar, bla, boo
__A						       =      re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
# Catches a line with try:
__A						       =      re.compile(r"^\s*try:")
# Catches a line with else:
__A						       =      re.compile(r"^\s*else:")






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Any:
		if _re_test_backend.search(__SCREAMING_SNAKE_CASE			) is None:
				return None
		__lowerCAmelCase:	Union[str, Any]   				=      [b[0] for b in _re_backend.findall(__SCREAMING_SNAKE_CASE			)]
		backends.sort()
		return "_and_".join(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     int:
		with open(__SCREAMING_SNAKE_CASE ,			"r" ,			encoding="utf-8" ,			newline="\n"			) as f:
				__lowerCAmelCase:	Optional[int]   				=      f.readlines()

		__lowerCAmelCase:	Dict   				=      0
		while line_index < len(__SCREAMING_SNAKE_CASE			) and not lines[line_index].startswith("_import_structure = {"			):
				line_index += 1

		# If this is a traditional init, just return.
		if line_index >= len(__SCREAMING_SNAKE_CASE			):
				return None

		# First grab the objects without a specific backend in _import_structure
		__lowerCAmelCase:	Optional[Any]   				=      []
		while not lines[line_index].startswith("if TYPE_CHECKING"			) and find_backend(lines[line_index]			) is None:
				__lowerCAmelCase:	Optional[int]   				=      lines[line_index]
				# If we have everything on a single line, let's deal with it.
				if _re_one_line_import_struct.search(__SCREAMING_SNAKE_CASE			):
						__lowerCAmelCase:	List[Any]   				=      _re_one_line_import_struct.search(__SCREAMING_SNAKE_CASE			).groups()[0]
						__lowerCAmelCase:	List[Any]   				=      re.findall("\[([^\]]+)\]" ,			__SCREAMING_SNAKE_CASE			)
						for imp in imports:
								objects.extend([obj[1:-1] for obj in imp.split(", "			)]			)
						line_index += 1
						continue
				__lowerCAmelCase:	str   				=      _re_import_struct_key_value.search(__SCREAMING_SNAKE_CASE			)
				if single_line_import_search is not None:
						__lowerCAmelCase:	str   				=      [obj[1:-1] for obj in single_line_import_search.groups()[0].split(", "			) if len(__SCREAMING_SNAKE_CASE			) > 0]
						objects.extend(__SCREAMING_SNAKE_CASE			)
				elif line.startswith(" " * 8 + "\""			):
						objects.append(line[9:-3]			)
				line_index += 1

		__lowerCAmelCase:	Tuple   				=      {"none": objects}
		# Let's continue with backend-specific objects in _import_structure
		while not lines[line_index].startswith("if TYPE_CHECKING"			):
				# If the line is an if not is_backend_available, we grab all objects associated.
				__lowerCAmelCase:	Optional[int]   				=      find_backend(lines[line_index]			)
				# Check if the backend declaration is inside a try block:
				if _re_try.search(lines[line_index - 1]			) is None:
						__lowerCAmelCase:	Optional[int]   				=      None

				if backend is not None:
						line_index += 1

						# Scroll until we hit the else block of try-except-else
						while _re_else.search(lines[line_index]			) is None:
								line_index += 1

						line_index += 1

						__lowerCAmelCase:	Optional[int]   				=      []
						# Until we unindent, add backend objects to the list
						while len(lines[line_index]			) <= 1 or lines[line_index].startswith(" " * 4			):
								__lowerCAmelCase:	Optional[Any]   				=      lines[line_index]
								if _re_import_struct_add_one.search(__SCREAMING_SNAKE_CASE			) is not None:
										objects.append(_re_import_struct_add_one.search(__SCREAMING_SNAKE_CASE			).groups()[0]			)
								elif _re_import_struct_add_many.search(__SCREAMING_SNAKE_CASE			) is not None:
										__lowerCAmelCase:	Union[str, Any]   				=      _re_import_struct_add_many.search(__SCREAMING_SNAKE_CASE			).groups()[0].split(", "			)
										__lowerCAmelCase:	int   				=      [obj[1:-1] for obj in imports if len(__SCREAMING_SNAKE_CASE			) > 0]
										objects.extend(__SCREAMING_SNAKE_CASE			)
								elif _re_between_brackets.search(__SCREAMING_SNAKE_CASE			) is not None:
										__lowerCAmelCase:	Tuple   				=      _re_between_brackets.search(__SCREAMING_SNAKE_CASE			).groups()[0].split(", "			)
										__lowerCAmelCase:	Tuple   				=      [obj[1:-1] for obj in imports if len(__SCREAMING_SNAKE_CASE			) > 0]
										objects.extend(__SCREAMING_SNAKE_CASE			)
								elif _re_quote_object.search(__SCREAMING_SNAKE_CASE			) is not None:
										objects.append(_re_quote_object.search(__SCREAMING_SNAKE_CASE			).groups()[0]			)
								elif line.startswith(" " * 8 + "\""			):
										objects.append(line[9:-3]			)
								elif line.startswith(" " * 1_2 + "\""			):
										objects.append(line[1_3:-3]			)
								line_index += 1

						__lowerCAmelCase:	Union[str, Any]   				=      objects
				else:
						line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
		__lowerCAmelCase:	str   				=      []
		while (
		    line_index < len(__SCREAMING_SNAKE_CASE			)
		    and find_backend(lines[line_index]			) is None
		    and not lines[line_index].startswith("else"			)
		):
				__lowerCAmelCase:	List[Any]   				=      lines[line_index]
				__lowerCAmelCase:	Tuple   				=      _re_import.search(__SCREAMING_SNAKE_CASE			)
				if single_line_import_search is not None:
						objects.extend(single_line_import_search.groups()[0].split(", "			)			)
				elif line.startswith(" " * 8			):
						objects.append(line[8:-2]			)
				line_index += 1

		__lowerCAmelCase:	Any   				=      {"none": objects}
		# Let's continue with backend-specific objects
		while line_index < len(__SCREAMING_SNAKE_CASE			):
				# If the line is an if is_backend_available, we grab all objects associated.
				__lowerCAmelCase:	Optional[Any]   				=      find_backend(lines[line_index]			)
				# Check if the backend declaration is inside a try block:
				if _re_try.search(lines[line_index - 1]			) is None:
						__lowerCAmelCase:	Any   				=      None

				if backend is not None:
						line_index += 1

						# Scroll until we hit the else block of try-except-else
						while _re_else.search(lines[line_index]			) is None:
								line_index += 1

						line_index += 1

						__lowerCAmelCase:	List[Any]   				=      []
						# Until we unindent, add backend objects to the list
						while len(lines[line_index]			) <= 1 or lines[line_index].startswith(" " * 8			):
								__lowerCAmelCase:	Optional[int]   				=      lines[line_index]
								__lowerCAmelCase:	Any   				=      _re_import.search(__SCREAMING_SNAKE_CASE			)
								if single_line_import_search is not None:
										objects.extend(single_line_import_search.groups()[0].split(", "			)			)
								elif line.startswith(" " * 1_2			):
										objects.append(line[1_2:-2]			)
								line_index += 1

						__lowerCAmelCase:	Union[str, Any]   				=      objects
				else:
						line_index += 1

		return import_dict_objects, type_hint_objects






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Tuple:

		def find_duplicates(__SCREAMING_SNAKE_CASE			):
				return [k for k, v in collections.Counter(__SCREAMING_SNAKE_CASE			).items() if v > 1]

		if list(import_dict_objects.keys()			) != list(type_hint_objects.keys()			):
				return ["Both sides of the init do not have the same backends!"]

		__lowerCAmelCase:	Optional[int]   				=      []
		for key in import_dict_objects.keys():
				__lowerCAmelCase:	Union[str, Any]   				=      find_duplicates(import_dict_objects[key]			)
				if duplicate_imports:
						errors.append(F"Duplicate _import_structure definitions for: {duplicate_imports}"			)
				__lowerCAmelCase:	Optional[Any]   				=      find_duplicates(type_hint_objects[key]			)
				if duplicate_type_hints:
						errors.append(F"Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}"			)

				if sorted(set(import_dict_objects[key]			)			) != sorted(set(type_hint_objects[key]			)			):
						__lowerCAmelCase:	Union[str, Any]   				=      "base imports" if key == "none" else F"{key} backend"
						errors.append(F"Differences for {name}:"			)
						for a in type_hint_objects[key]:
								if a not in import_dict_objects[key]:
										errors.append(F"  {a} in TYPE_HINT but not in _import_structure."			)
						for a in import_dict_objects[key]:
								if a not in type_hint_objects[key]:
										errors.append(F"  {a} in _import_structure but not in TYPE_HINT."			)
		return errors






def    a__   (   )     ->     Tuple:
		__lowerCAmelCase:	Optional[Any]   				=      []
		for root, _, files in os.walk(__SCREAMING_SNAKE_CASE			):
				if "__init__.py" in files:
						__lowerCAmelCase:	List[str]   				=      os.path.join(__SCREAMING_SNAKE_CASE ,			"__init__.py"			)
						__lowerCAmelCase:	int   				=      parse_init(__SCREAMING_SNAKE_CASE			)
						if objects is not None:
								__lowerCAmelCase:	Optional[Any]   				=      analyze_results(*__SCREAMING_SNAKE_CASE			)
								if len(__SCREAMING_SNAKE_CASE			) > 0:
										__lowerCAmelCase:	Union[str, Any]   				=      F"Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"
										failures.append("\n".join(__SCREAMING_SNAKE_CASE			)			)
		if len(__SCREAMING_SNAKE_CASE			) > 0:
				raise ValueError("\n\n".join(__SCREAMING_SNAKE_CASE			)			)






def    a__   (   )     ->     Any:
		__lowerCAmelCase:	Optional[int]   				=      []
		for path, directories, files in os.walk(__SCREAMING_SNAKE_CASE			):
				for folder in directories:
						# Ignore private modules
						if folder.startswith("_"			):
								directories.remove(__SCREAMING_SNAKE_CASE			)
								continue
						# Ignore leftovers from branches (empty folders apart from pycache)
						if len(list((Path(__SCREAMING_SNAKE_CASE			) / folder).glob("*.py"			)			)			) == 0:
								continue
						__lowerCAmelCase:	Optional[int]   				=      str((Path(__SCREAMING_SNAKE_CASE			) / folder).relative_to(__SCREAMING_SNAKE_CASE			)			)
						__lowerCAmelCase:	Tuple   				=      short_path.replace(os.path.sep ,			"."			)
						submodules.append(__SCREAMING_SNAKE_CASE			)
				for fname in files:
						if fname == "__init__.py":
								continue
						__lowerCAmelCase:	Dict   				=      str((Path(__SCREAMING_SNAKE_CASE			) / fname).relative_to(__SCREAMING_SNAKE_CASE			)			)
						__lowerCAmelCase:	Dict   				=      short_path.replace(".py" ,			""			).replace(os.path.sep ,			"."			)
						if len(submodule.split("."			)			) == 1:
								submodules.append(__SCREAMING_SNAKE_CASE			)
		return submodules


__A						       =      [
    "convert_pytorch_checkpoint_to_tf2",
    "modeling_flax_pytorch_utils",
]






def    a__   (   )     ->     Optional[int]:
		# This is to make sure the transformers module imported is the one in the repo.
		__lowerCAmelCase:	Optional[Any]   				=      importlib.util.spec_from_file_location(
		    "transformers" ,			os.path.join(__SCREAMING_SNAKE_CASE ,			"__init__.py"			) ,			submodule_search_locations=[PATH_TO_TRANSFORMERS] ,			)
		__lowerCAmelCase:	Optional[int]   				=      spec.loader.load_module()

		__lowerCAmelCase:	str   				=      [
		    module
		    for module in get_transformers_submodules()
		    if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
		]
		if len(__SCREAMING_SNAKE_CASE			) > 0:
				__lowerCAmelCase:	Optional[int]   				=      "\n".join(F"- {module}" for module in module_not_registered			)
				raise ValueError(
				    "The following submodules are not properly registered in the main init of Transformers:\n"
				    F"{list_of_modules}\n"
				    "Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value."			)


if __name__ == "__main__":
			check_all_inits()
			check_submodules()


