


import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


UpperCAmelCase   :    Optional[int]     =		logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

UpperCAmelCase   :    Dict     =		{
    """base""": AutoModel,
    """sequence-classification""": AutoModelForSequenceClassification,
    """question-answering""": AutoModelForQuestionAnswering,
    """pretraining""": AutoModelForPreTraining,
    """token-classification""": AutoModelForTokenClassification,
    """language-modeling""": AutoModelWithLMHead,
    """summarization""": AutoModelForSeqaSeqLM,
    """translation""": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
UpperCAmelCase   :    int     =		{
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
UpperCAmelCase   :    Union[str, Any]     =		sorted(arg_to_scheduler.keys())
UpperCAmelCase   :    Optional[int]     =		"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""







class        __lowercase       (							pl.LightningModule  ):



		"""simple docstring"""

		def __init__( self		,      A		,      A=None		,      A="base"		,      A=None		,      A=None		,      A=None		,      **A		,      )  -> Optional[Any]:







					'''simple docstring'''







					super().__init__()
					# TODO: move to self.save_hyperparameters()
					# self.save_hyperparameters()
					# can also expand arguments into trainer signature for easier reading

					self.save_hyperparameters(a_	)
					lowerCamelCase    		=  0
					lowerCamelCase    		=  Path(self.hparams.output_dir	)
					lowerCamelCase    		=  self.hparams.cache_dir if self.hparams.cache_dir else None
					if config is None:
								lowerCamelCase    		=  AutoConfig.from_pretrained(
								    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path		,      **({"""num_labels""": num_labels} if num_labels is not None else {})		,      cache_dir=a_		,      **a_		,      )
					else:
								lowerCamelCase    		=  config

					lowerCamelCase    		=  ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
					for p in extra_model_params:
								if getattr(self.hparams		,      a_		,      a_	):
											assert hasattr(self.config		,      a_	), F'model config doesn\'t have a `{p}` attribute'
											setattr(self.config		,      a_		,      getattr(self.hparams		,      a_	)	)

					if tokenizer is None:
								lowerCamelCase    		=  AutoTokenizer.from_pretrained(
								    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path		,      cache_dir=a_		,      )
					else:
								lowerCamelCase    		=  tokenizer
					lowerCamelCase    		=  MODEL_MODES[mode]
					if model is None:
								lowerCamelCase    		=  self.model_type.from_pretrained(
								    self.hparams.model_name_or_path		,      from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path	)		,      config=self.config		,      cache_dir=a_		,      )
					else:
								lowerCamelCase    		=  model







		def 					__A   ( self		,      *A		,      **A	)  -> Optional[int]:







					'''simple docstring'''







					lowerCamelCase    		=  self.model_type.from_pretrained(*a_		,      **a_	)







		def 					__A   ( self	)  -> Tuple:







					'''simple docstring'''







					lowerCamelCase    		=  arg_to_scheduler[self.hparams.lr_scheduler]
					lowerCamelCase    		=  get_schedule_func(
					    self.opt		,      num_warmup_steps=self.hparams.warmup_steps		,      num_training_steps=self.total_steps()	)
					lowerCamelCase    		=  {"scheduler": scheduler, "interval": "step", "frequency": 1}
					return scheduler







		def 					__A   ( self	)  -> Any:







					'''simple docstring'''







					lowerCamelCase    		=  self.model
					lowerCamelCase    		=  ["bias", "LayerNorm.weight"]
					lowerCamelCase    		=  [
					    {
					        "params": [
					            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay	)
					        ],  # check this named paramters
					        "weight_decay": self.hparams.weight_decay,
					    },
					    {
					        "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay	)],
					        "weight_decay": 0.0,
					    },
					]
					if self.hparams.adafactor:
								lowerCamelCase    		=  Adafactor(
								    a_		,      lr=self.hparams.learning_rate		,      scale_parameter=a_		,      relative_step=a_	)

					else:
								lowerCamelCase    		=  AdamW(
								    a_		,      lr=self.hparams.learning_rate		,      eps=self.hparams.adam_epsilon	)
					lowerCamelCase    		=  optimizer

					lowerCamelCase    		=  self.get_lr_scheduler()

					return [optimizer], [scheduler]







		def 					__A   ( self		,      A		,      A	)  -> Optional[int]:







					'''simple docstring'''







					return self.validation_step(a_		,      a_	)







		def 					__A   ( self		,      A	)  -> List[str]:







					'''simple docstring'''







					return self.validation_end(a_	)







		def 					__A   ( self	)  -> int:







					'''simple docstring'''







					lowerCamelCase    		=  max(1		,      self.hparams.gpus	)  # TODO: consider num_tpu_cores
					lowerCamelCase    		=  self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
					return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs







		def 					__A   ( self		,      A	)  -> Dict:







					'''simple docstring'''







					if stage == "test":
								lowerCamelCase    		=  len(self.test_dataloader().dataset	)
					else:
								lowerCamelCase    		=  self.get_dataloader("""train"""		,      self.hparams.train_batch_size		,      shuffle=a_	)
								lowerCamelCase    		=  len(self.train_dataloader().dataset	)







		def 					__A   ( self		,      A		,      A		,      A = False	)  -> Dict:







					'''simple docstring'''







					raise NotImplementedError("""You must implement this for your task"""	)







		def 					__A   ( self	)  -> Optional[Any]:







					'''simple docstring'''







					return self.train_loader







		def 					__A   ( self	)  -> Dict:







					'''simple docstring'''







					return self.get_dataloader("""dev"""		,      self.hparams.eval_batch_size		,      shuffle=a_	)







		def 					__A   ( self	)  -> Optional[Any]:







					'''simple docstring'''







					return self.get_dataloader("""test"""		,      self.hparams.eval_batch_size		,      shuffle=a_	)







		def 					__A   ( self		,      A	)  -> Union[str, Any]:







					'''simple docstring'''







					return os.path.join(
					    self.hparams.data_dir		,      """cached_{}_{}_{}""".format(
					        a_		,      list(filter(a_		,      self.hparams.model_name_or_path.split("""/"""	)	)	).pop()		,      str(self.hparams.max_seq_length	)		,      )		,      )







		@pl.utilities.rank_zero_only
		def 					__A   ( self		,      A	)  -> None:







					'''simple docstring'''







					lowerCamelCase    		=  self.output_dir.joinpath("""best_tfmr"""	)
					lowerCamelCase    		=  self.step_count
					self.model.save_pretrained(a_	)
					self.tokenizer.save_pretrained(a_	)







		@staticmethod
		def 					__A   ( A		,      A	)  -> Union[str, Any]:







					'''simple docstring'''







					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(
					    """--config_name"""		,      default=""""""		,      type=a_		,      help="""Pretrained config name or path if not the same as model_name"""	)
					parser.add_argument(
					    """--tokenizer_name"""		,      default=a_		,      type=a_		,      help="""Pretrained tokenizer name or path if not the same as model_name"""		,      )
					parser.add_argument(
					    """--cache_dir"""		,      default=str(Path(a_	).parent / """test_run""" / """cache"""	)		,      type=a_		,      help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""		,      )
					parser.add_argument(
					    """--encoder_layerdrop"""		,      type=a_		,      help="""Encoder layer dropout probability (Optional). Goes into model.config"""		,      )
					parser.add_argument(
					    """--decoder_layerdrop"""		,      type=a_		,      help="""Decoder layer dropout probability (Optional). Goes into model.config"""		,      )
					parser.add_argument(
					    """--dropout"""		,      type=a_		,      help="""Dropout probability (Optional). Goes into model.config"""		,      )
					parser.add_argument(
					    """--attention_dropout"""		,      type=a_		,      help="""Attention dropout probability (Optional). Goes into model.config"""		,      )
					parser.add_argument("""--learning_rate"""		,      default=5e-5		,      type=a_		,      help="""The initial learning rate for Adam."""	)
					parser.add_argument(
					    """--lr_scheduler"""		,      default="""linear"""		,      choices=a_		,      metavar=a_		,      type=a_		,      help="""Learning rate scheduler"""		,      )
					parser.add_argument("""--weight_decay"""		,      default=0.0		,      type=a_		,      help="""Weight decay if we apply some."""	)
					parser.add_argument("""--adam_epsilon"""		,      default=1e-8		,      type=a_		,      help="""Epsilon for Adam optimizer."""	)
					parser.add_argument("""--warmup_steps"""		,      default=0		,      type=a_		,      help="""Linear warmup over warmup_steps."""	)
					parser.add_argument("""--num_workers"""		,      default=4		,      type=a_		,      help="""kwarg passed to DataLoader"""	)
					parser.add_argument("""--num_train_epochs"""		,      dest="""max_epochs"""		,      default=3		,      type=a_	)
					parser.add_argument("""--train_batch_size"""		,      default=32		,      type=a_	)
					parser.add_argument("""--eval_batch_size"""		,      default=32		,      type=a_	)
					parser.add_argument("""--adafactor"""		,      action="""store_true"""	)







class        __lowercase       (							pl.Callback  ):



		"""simple docstring"""

		def 					__A   ( self		,      A		,      A	)  -> Union[str, Any]:







					'''simple docstring'''







					if (
					    trainer.is_global_zero and trainer.global_rank == 0
					):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
								pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.







class        __lowercase       (							pl.Callback  ):



		"""simple docstring"""

		def 					__A   ( self		,      A		,      A	)  -> int:







					'''simple docstring'''







					for name, param in pl_module.model.rag.named_parameters():
								if param.grad is None:
											print(a_	)







class        __lowercase       (							pl.Callback  ):



		"""simple docstring"""

		def 					__A   ( self		,      A		,      A	)  -> Dict:







					'''simple docstring'''







					lowerCamelCase    		=  trainer.lr_schedulers[0]["scheduler"]
					lowerCamelCase    		=  {F'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()	)}
					pl_module.logger.log_metrics(a_	)







		def 					__A   ( self		,      A		,      A	)  -> int:







					'''simple docstring'''







					rank_zero_info("""***** Validation results *****"""	)
					lowerCamelCase    		=  trainer.callback_metrics
					# Log results
					for key in sorted(a_	):
								if key not in ["log", "progress_bar"]:
											rank_zero_info("""{} = {}\n""".format(a_		,      str(metrics[key]	)	)	)







		def 					__A   ( self		,      A		,      A	)  -> Optional[Any]:







					'''simple docstring'''







					rank_zero_info("""***** Test results *****"""	)
					lowerCamelCase    		=  trainer.callback_metrics
					# Log and save results to file
					lowerCamelCase    		=  os.path.join(pl_module.hparams.output_dir		,      """test_results.txt"""	)
					with open(a_		,      """w"""	) as writer:
								for key in sorted(a_	):
											if key not in ["log", "progress_bar"]:
														rank_zero_info("""{} = {}\n""".format(a_		,      str(metrics[key]	)	)	)
														writer.write("""{} = {}\n""".format(a_		,      str(metrics[key]	)	)	)






def 	__lowerCamelCase						(							lowerCamelCase__		:						int  ,							lowerCamelCase__		:						str      ):





			'''simple docstring'''




			parser.add_argument(
			    """--output_dir"""  ,							default=str(Path(lowerCamelCase__      ).parent / """test_run""" / """model_checkpoints"""      )  ,							type=lowerCamelCase__  ,							help="""The output directory where the model predictions and checkpoints will be written."""  ,							)
			parser.add_argument(
			    """--fp16"""  ,							action="""store_true"""  ,							help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""  ,							)

			parser.add_argument(
			    """--fp16_opt_level"""  ,							type=lowerCamelCase__  ,							default="""O2"""  ,							help=(
			        """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
			        """See details at https://nvidia.github.io/apex/amp.html"""
			    )  ,							)
			parser.add_argument("""--n_tpu_cores"""  ,							dest="""tpu_cores"""  ,							type=lowerCamelCase__      )
			parser.add_argument("""--max_grad_norm"""  ,							dest="""gradient_clip_val"""  ,							default=1.0  ,							type=lowerCamelCase__  ,							help="""Max gradient norm"""      )
			parser.add_argument("""--do_train"""  ,							action="""store_true"""  ,							help="""Whether to run training."""      )
			parser.add_argument("""--do_predict"""  ,							action="""store_true"""  ,							help="""Whether to run predictions on the test set."""      )
			parser.add_argument(
			    """--gradient_accumulation_steps"""  ,							dest="""accumulate_grad_batches"""  ,							type=lowerCamelCase__  ,							default=1  ,							help="""Number of updates steps to accumulate before performing a backward/update pass."""  ,							)
			parser.add_argument("""--seed"""  ,							type=lowerCamelCase__  ,							default=42  ,							help="""random seed for initialization"""      )
			parser.add_argument(
			    """--data_dir"""  ,							default=str(Path(lowerCamelCase__      ).parent / """test_run""" / """dummy-train-data"""      )  ,							type=lowerCamelCase__  ,							help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""  ,							)






def 	__lowerCamelCase						(							lowerCamelCase__		:						List[str]  ,							lowerCamelCase__		:						int  ,							lowerCamelCase__		:						Tuple=None  ,							lowerCamelCase__		:						List[str]=True  ,							lowerCamelCase__		:						str=[]  ,							lowerCamelCase__		:						Union[str, Any]=None  ,							lowerCamelCase__		:						str=None  ,							**lowerCamelCase__		:						int  ,							):





			'''simple docstring'''




			pl.seed_everything(args.seed      )

			# init model
			lowerCamelCase    		=  Path(model.hparams.output_dir      )
			odir.mkdir(exist_ok=lowerCamelCase__      )

			# add custom checkpoints
			if checkpoint_callback is None:
						lowerCamelCase    		=  pl.callbacks.ModelCheckpoint(
						    filepath=args.output_dir  ,							prefix="""checkpoint"""  ,							monitor="""val_loss"""  ,							mode="""min"""  ,							save_top_k=1      )
			if early_stopping_callback:
						extra_callbacks.append(lowerCamelCase__      )
			if logging_callback is None:
						lowerCamelCase    		=  LoggingCallback()

			lowerCamelCase    		=  {}

			if args.fpaa:
						lowerCamelCase    		=  16

			if args.gpus > 1:
						lowerCamelCase    		=  "auto"
						lowerCamelCase    		=  "ddp"

			lowerCamelCase    		=  args.accumulate_grad_batches
			lowerCamelCase    		=  None
			lowerCamelCase    		=  "auto"

			lowerCamelCase    		=  pl.Trainer.from_argparse_args(
			    lowerCamelCase__  ,							weights_summary=lowerCamelCase__  ,							callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]  ,							logger=lowerCamelCase__  ,							val_check_interval=1  ,							num_sanity_val_steps=2  ,							**lowerCamelCase__  ,							)

			if args.do_train:
						trainer.fit(lowerCamelCase__      )

			else:
						print("""RAG modeling tests with new set functions successfuly executed!"""      )
			return trainer





'''simple docstring'''
import collections
import importlib.util
import os
import re
from pathlib import Path


lowerCAmelCase   : List[str]             =				"""src/transformers"""


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




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




def 	_A  (       A							)  ->				Optional[Any]:
	with open(A							,"r"							,encoding="utf-8"							,newline="\n"							) as f:
		lowercase						:		Tuple   					=		f.readlines()

	lowercase						:		List[str]   					=		0
	while line_index < len(A							) and not lines[line_index].startswith("_import_structure = {"							):
		line_index += 1

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

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

	lowercase						:		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.
		lowercase						:		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:
			lowercase						:		str   					=		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

			lowercase						:		Any   					=		[]
			# Until we unindent, add backend objects to the list
			while len(lines[line_index]							) <= 1 or lines[line_index].startswith(" " * 4							):
				lowercase						:		List[Any]   					=		lines[line_index]
				if _re_import_struct_add_one.search(A							) is not None:
					objects.append(_re_import_struct_add_one.search(A							).groups()[0]							)
				elif _re_import_struct_add_many.search(A							) is not None:
					lowercase						:		Dict   					=		_re_import_struct_add_many.search(A							).groups()[0].split(", "							)
					lowercase						:		Any   					=		[obj[1:-1] for obj in imports if len(A							) > 0]
					objects.extend(A							)
				elif _re_between_brackets.search(A							) is not None:
					lowercase						:		Optional[Any]   					=		_re_between_brackets.search(A							).groups()[0].split(", "							)
					lowercase						:		Tuple   					=		[obj[1:-1] for obj in imports if len(A							) > 0]
					objects.extend(A							)
				elif _re_quote_object.search(A							) is not None:
					objects.append(_re_quote_object.search(A							).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

			lowercase						:		Optional[int]   					=		objects
		else:
			line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
	lowercase						:		str   					=		[]
	while (
	    line_index < len(A							)
	    and find_backend(lines[line_index]							) is None
	    and not lines[line_index].startswith("else"							)
	):
		lowercase						:		int   					=		lines[line_index]
		lowercase						:		Any   					=		_re_import.search(A							)
		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

	lowercase						:		Optional[Any]   					=		{"none": objects}
	# Let's continue with backend-specific objects
	while line_index < len(A							):
		# If the line is an if is_backend_available, we grab all objects associated.
		lowercase						:		List[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:
			lowercase						:		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

			lowercase						:		Optional[Any]   					=		[]
			# Until we unindent, add backend objects to the list
			while len(lines[line_index]							) <= 1 or lines[line_index].startswith(" " * 8							):
				lowercase						:		List[Any]   					=		lines[line_index]
				lowercase						:		Tuple   					=		_re_import.search(A							)
				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

			lowercase						:		Dict   					=		objects
		else:
			line_index += 1

	return import_dict_objects, type_hint_objects




def 	_A  (       A							,A							)  ->				List[str]:

	def find_duplicates(A							):
		return [k for k, v in collections.Counter(A							).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!"]

	lowercase						:		str   					=		[]
	for key in import_dict_objects.keys():
		lowercase						:		List[str]   					=		find_duplicates(import_dict_objects[key]							)
		if duplicate_imports:
			errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}'''							)
		lowercase						:		List[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]							)							):
			lowercase						:		Optional[int]   					=		"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  (       )  ->				List[Any]:
	lowercase						:		Any   					=		[]
	for root, _, files in os.walk(A							):
		if "__init__.py" in files:
			lowercase						:		Optional[Any]   					=		os.path.join(A							,"__init__.py"							)
			lowercase						:		Tuple   					=		parse_init(A							)
			if objects is not None:
				lowercase						:		Tuple   					=		analyze_results(*A							)
				if len(A							) > 0:
					lowercase						:		Tuple   					=		F'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
					failures.append("\n".join(A							)							)
	if len(A							) > 0:
		raise ValueError("\n\n".join(A							)							)




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


lowerCAmelCase   : Dict             =				[
    """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.
	lowercase						:		int   					=		importlib.util.spec_from_file_location(
	    "transformers"							,os.path.join(A							,"__init__.py"							)							,submodule_search_locations=[PATH_TO_TRANSFORMERS]							,)
	lowercase						:		Optional[int]   					=		spec.loader.load_module()

	lowercase						:		Dict   					=		[
	    module
	    for module in get_transformers_submodules()
	    if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
	]
	if len(A							) > 0:
		lowercase						:		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()


