

'''simple docstring'''





import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    BartForSequenceClassification,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    TapexTokenizer,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.17.0.dev0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/text-classification/requirements.txt""")

lowerCAmelCase__	   = logging.getLogger(__name__)

@dataclass
class 					a__						:
			"""simple docstring"""







			__lowerCamelCase        =       field(
			    default='tab_fact'							,	metadata={'help': 'The name of the dataset to use (via the datasets library).'}			)
			__lowerCamelCase        =       field(
			    default='tab_fact'							,	metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}							,	)
			__lowerCamelCase        =       field(
			    default=1024							,	metadata={
			        'help': (
			            'The maximum total input sequence length after tokenization. Sequences longer '
			            'than this will be truncated, sequences shorter will be padded.'
			        )
			    }							,	)
			__lowerCamelCase        =       field(
			    default=snake_case							,	metadata={'help': 'Overwrite the cached preprocessed datasets or not.'}			)
			__lowerCamelCase        =       field(
			    default=snake_case							,	metadata={
			        'help': (
			            'Whether to pad all samples to `max_seq_length`. '
			            'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
			        )
			    }							,	)
			__lowerCamelCase        =       field(
			    default=snake_case							,	metadata={
			        'help': (
			            'For debugging purposes or quicker training, truncate the number of training examples to this '
			            'value if set.'
			        )
			    }							,	)
			__lowerCamelCase        =       field(
			    default=snake_case							,	metadata={
			        'help': (
			            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
			            'value if set.'
			        )
			    }							,	)
			__lowerCamelCase        =       field(
			    default=snake_case							,	metadata={
			        'help': (
			            'For debugging purposes or quicker training, truncate the number of prediction examples to this '
			            'value if set.'
			        )
			    }							,	)
			__lowerCamelCase        =       field(
			    default=snake_case							,	metadata={'help': 'A csv or a json file containing the training data.'}			)
			__lowerCamelCase        =       field(
			    default=snake_case							,	metadata={'help': 'A csv or a json file containing the validation data.'}			)
			__lowerCamelCase        =       field(default=snake_case							,	metadata={'help': 'A csv or a json file containing the test data.'}			)


			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






							if self.dataset_name is not None:
											pass
							elif self.train_file is None or self.validation_file is None:
											raise ValueError("Need either a GLUE task, a training/validation file or a dataset name."       )
							else:
											A__        =			self.train_file.split("."       )[-1]
											assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
											A__        =			self.validation_file.split("."       )[-1]
											assert (
											    validation_extension == train_extension
											), "`validation_file` should have the same extension (csv or json) as `train_file`."

@dataclass
class 					a__						:
			"""simple docstring"""







			__lowerCamelCase        =       field(
			    default=snake_case							,	metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}			)
			__lowerCamelCase        =       field(
			    default=snake_case							,	metadata={'help': 'Pretrained config name or path if not the same as model_name'}			)
			__lowerCamelCase        =       field(
			    default=snake_case							,	metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}			)
			__lowerCamelCase        =       field(
			    default=snake_case							,	metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}							,	)
			__lowerCamelCase        =       field(
			    default=snake_case							,	metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'}							,	)
			__lowerCamelCase        =       field(
			    default='main'							,	metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}							,	)
			__lowerCamelCase        =       field(
			    default=snake_case							,	metadata={
			        'help': (
			            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
			            'with private models).'
			        )
			    }							,	)



def 					lowerCAmelCase__							(	)			->      Optional[int]:




				'''simple docstring'''





				A__        =			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
				if len(sys.argv      ) == 2 and sys.argv[1].endswith(".json"      ):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								A__    ,				A__    ,				A__        =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
				else:
								A__    ,				A__    ,				A__        =			parser.parse_args_into_dataclasses()

				# Setup logging
				logging.basicConfig(
				    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"	,       datefmt="%m/%d/%Y %H:%M:%S"	,       handlers=[logging.StreamHandler(sys.stdout      )]	,       )

				A__        =			training_args.get_process_log_level()
				logger.setLevel(SCREAMING_SNAKE_CASE_      )
				datasets.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE_      )
				transformers.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE_      )
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

				# Log on each process the small summary:
				logger.warning(
				    F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
				    + F'distributed training: {bool(training_args.local_rank != -1      )}, 16-bits training: {training_args.fpaa}'      )
				logger.info(F'Training/evaluation parameters {training_args}'      )

				# Detecting last checkpoint.
				A__        =			None
				if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
								A__        =			get_last_checkpoint(training_args.output_dir      )
								if last_checkpoint is None and len(os.listdir(training_args.output_dir      )      ) > 0:
												raise ValueError(
												    F'Output directory ({training_args.output_dir}) already exists and is not empty. '
												    "Use --overwrite_output_dir to overcome."      )
								elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
												logger.info(
												    F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
												    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."      )

    # Set seed before initializing model.
				set_seed(training_args.seed      )

				# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
				# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
				#
				# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
				#
				# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
				# single column. You can easily tweak this behavior (see below)
				#
				# In distributed training, the load_dataset function guarantee that only one local process can concurrently
				# download the dataset.
				if data_args.dataset_name is not None:
								# Downloading and loading a dataset from the hub.
								A__        =			load_dataset(
								    data_args.dataset_name	,       data_args.dataset_config_name	,       cache_dir=model_args.cache_dir      )
				else:
								# Loading a dataset from your local files.
								# CSV/JSON training and evaluation files are needed.
								A__        =			{"train": data_args.train_file, "validation": data_args.validation_file}

								# Get the test dataset: you can provide your own CSV/JSON test file (see below)
								# when you use `do_predict` without specifying a GLUE benchmark task.
								if training_args.do_predict:
												if data_args.test_file is not None:
																A__        =			data_args.train_file.split("."      )[-1]
																A__        =			data_args.test_file.split("."      )[-1]
																assert (
																    test_extension == train_extension
																), "`test_file` should have the same extension (csv or json) as `train_file`."
																A__        =			data_args.test_file
												else:
																raise ValueError("Need either a GLUE task or a test file for `do_predict`."      )

								for key in data_files.keys():
												logger.info(F'load a local file for {key}: {data_files[key]}'      )

								if data_args.train_file.endswith(".csv"      ):
												# Loading a dataset from local csv files
												A__        =			load_dataset("csv"	,       data_files=SCREAMING_SNAKE_CASE_	,       cache_dir=model_args.cache_dir      )
								else:
												# Loading a dataset from local json files
												A__        =			load_dataset("json"	,       data_files=SCREAMING_SNAKE_CASE_	,       cache_dir=model_args.cache_dir      )
    # See more about loading any type of standard or custom dataset at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Labels
				A__        =			raw_datasets["train"].features["label"].names
				A__        =			len(SCREAMING_SNAKE_CASE_      )

				# Load pretrained model and tokenizer
				#
				# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.
				A__        =			AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,       num_labels=SCREAMING_SNAKE_CASE_	,       cache_dir=model_args.cache_dir	,       revision=model_args.model_revision	,       use_auth_token=True if model_args.use_auth_token else None	,       )
				# load tapex tokenizer
				A__        =			TapexTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path	,       cache_dir=model_args.cache_dir	,       use_fast=model_args.use_fast_tokenizer	,       revision=model_args.model_revision	,       use_auth_token=True if model_args.use_auth_token else None	,       add_prefix_space=SCREAMING_SNAKE_CASE_	,       )
				A__        =			BartForSequenceClassification.from_pretrained(
				    model_args.model_name_or_path	,       from_tf=bool(".ckpt" in model_args.model_name_or_path      )	,       config=SCREAMING_SNAKE_CASE_	,       cache_dir=model_args.cache_dir	,       revision=model_args.model_revision	,       use_auth_token=True if model_args.use_auth_token else None	,       )

				# Padding strategy
				if data_args.pad_to_max_length:
								A__        =			"max_length"
				else:
								# We will pad later, dynamically at batch creation, to the max sequence length in each batch
								A__        =			False

				# Some models have set the order of the labels to use, so let's make sure we do use it.
				A__        =			{"Refused": 0, "Entailed": 1}
				A__        =			{0: "Refused", 1: "Entailed"}

				if data_args.max_seq_length > tokenizer.model_max_length:
								logger.warning(
								    F'The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'
								    F'model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'      )
				A__        =			min(data_args.max_seq_length	,       tokenizer.model_max_length      )

				def preprocess_tabfact_function(SCREAMING_SNAKE_CASE_:							int      ):
								# Tokenize the texts
								def _convert_table_text_to_pandas(SCREAMING_SNAKE_CASE_:							str      ):
												A__        =			[_table_row.split("#"      ) for _table_row in _table_text.strip("\n"      ).split("\n"      )]
												A__        =			pd.DataFrame.from_records(_table_content[1:]	,       columns=_table_content[0]      )
												return _table_pd

								A__        =			examples["statement"]
								A__        =			list(map(_convert_table_text_to_pandas	,       examples["table_text"]      )      )
								A__        =			tokenizer(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       padding=SCREAMING_SNAKE_CASE_	,       max_length=SCREAMING_SNAKE_CASE_	,       truncation=SCREAMING_SNAKE_CASE_      )

								A__        =			examples["label"]
								return result

				with training_args.main_process_first(desc="dataset map pre-processing"      ):
								A__        =			raw_datasets.map(
								    SCREAMING_SNAKE_CASE_	,       batched=SCREAMING_SNAKE_CASE_	,       load_from_cache_file=not data_args.overwrite_cache	,       desc="Running tokenizer on dataset"	,       )
				if training_args.do_train:
								if "train" not in raw_datasets:
												raise ValueError("--do_train requires a train dataset"      )
								A__        =			raw_datasets["train"]
								if data_args.max_train_samples is not None:
												A__        =			train_dataset.select(range(data_args.max_train_samples      )      )

				if training_args.do_eval:
								if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
												raise ValueError("--do_eval requires a validation dataset"      )
								A__        =			raw_datasets["validation"]
								if data_args.max_eval_samples is not None:
												A__        =			eval_dataset.select(range(data_args.max_eval_samples      )      )

				if training_args.do_predict or data_args.test_file is not None:
								if "test" not in raw_datasets and "test_matched" not in raw_datasets:
												raise ValueError("--do_predict requires a test dataset"      )
								A__        =			raw_datasets["test"]
								if data_args.max_predict_samples is not None:
												A__        =			predict_dataset.select(range(data_args.max_predict_samples      )      )

    # Log a few random samples from the training set:
				if training_args.do_train:
								for index in random.sample(range(len(SCREAMING_SNAKE_CASE_      )      )	,       3      ):
												logger.info(F'Sample {index} of the training set: {train_dataset[index]}.'      )

    # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
    # predictions and label_ids field) and has to return a dictionary string to float.
				def compute_metrics(SCREAMING_SNAKE_CASE_:							EvalPrediction      ):
								A__        =			p.predictions[0] if isinstance(p.predictions	,       SCREAMING_SNAKE_CASE_      ) else p.predictions
								A__        =			np.argmax(SCREAMING_SNAKE_CASE_	,       axis=1      )
								return {"accuracy": (preds == p.label_ids).astype(np.floataa      ).mean().item()}

				# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
				if data_args.pad_to_max_length:
								A__        =			default_data_collator
				elif training_args.fpaa:
								A__        =			DataCollatorWithPadding(SCREAMING_SNAKE_CASE_	,       pad_to_multiple_of=8      )
				else:
								A__        =			None

				# Initialize our Trainer
				A__        =			Trainer(
				    model=SCREAMING_SNAKE_CASE_	,       args=SCREAMING_SNAKE_CASE_	,       train_dataset=train_dataset if training_args.do_train else None	,       eval_dataset=eval_dataset if training_args.do_eval else None	,       compute_metrics=SCREAMING_SNAKE_CASE_	,       tokenizer=SCREAMING_SNAKE_CASE_	,       data_collator=SCREAMING_SNAKE_CASE_	,       )

				# Training
				if training_args.do_train:
								A__        =			None
								if training_args.resume_from_checkpoint is not None:
												A__        =			training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												A__        =			last_checkpoint
								A__        =			trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE_      )
								A__        =			train_result.metrics
								A__        =			(
								    data_args.max_train_samples if data_args.max_train_samples is not None else len(SCREAMING_SNAKE_CASE_      )
								)
								A__        =			min(SCREAMING_SNAKE_CASE_	,       len(SCREAMING_SNAKE_CASE_      )      )

								trainer.save_model()  # Saves the tokenizer too for easy upload

								trainer.log_metrics("train"	,       SCREAMING_SNAKE_CASE_      )
								trainer.save_metrics("train"	,       SCREAMING_SNAKE_CASE_      )
								trainer.save_state()

				# Evaluation
				if training_args.do_eval:
								logger.info("*** Evaluate ***"      )

								A__        =			trainer.evaluate(eval_dataset=SCREAMING_SNAKE_CASE_      )
								A__        =			data_args.max_eval_samples if data_args.max_eval_samples is not None else len(SCREAMING_SNAKE_CASE_      )
								A__        =			min(SCREAMING_SNAKE_CASE_	,       len(SCREAMING_SNAKE_CASE_      )      )

								trainer.log_metrics("eval"	,       SCREAMING_SNAKE_CASE_      )
								trainer.save_metrics("eval"	,       SCREAMING_SNAKE_CASE_      )

				if training_args.do_predict:
								logger.info("*** Predict ***"      )

								# Removing the `label` columns because it contains -1 and Trainer won't like that.
								A__        =			predict_dataset.remove_columns("label"      )
								A__        =			trainer.predict(SCREAMING_SNAKE_CASE_	,       metric_key_prefix="predict"      ).predictions
								A__        =			np.argmax(SCREAMING_SNAKE_CASE_	,       axis=1      )

								A__        =			os.path.join(training_args.output_dir	,       "predict_results_tabfact.txt"      )
								if trainer.is_world_process_zero():
												with open(SCREAMING_SNAKE_CASE_	,       "w"      ) as writer:
																logger.info("***** Predict Results *****"      )
																writer.write("index\tprediction\n"      )
																for index, item in enumerate(SCREAMING_SNAKE_CASE_      ):
																				A__        =			label_list[item]
																				writer.write(F'{index}\t{item}\n'      )

				A__        =			{"finetuned_from": model_args.model_name_or_path, "tasks": "text-classification"}

				if training_args.push_to_hub:
								trainer.push_to_hub(**SCREAMING_SNAKE_CASE_      )
				else:
								trainer.create_model_card(**SCREAMING_SNAKE_CASE_      )



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int      )			->      int:




				'''simple docstring'''





				main()


if __name__ == "__main__":
	main()




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


lowerCAmelCase__	   = """src/transformers"""


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



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Any      )			->      int:




				'''simple docstring'''





				if _re_test_backend.search(SCREAMING_SNAKE_CASE_      ) is None:
								return None
				A__        =			[b[0] for b in _re_backend.findall(SCREAMING_SNAKE_CASE_      )]
				backends.sort()
				return "_and_".join(SCREAMING_SNAKE_CASE_      )



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Union[str, Any]      )			->      Optional[Any]:




				'''simple docstring'''





				with open(SCREAMING_SNAKE_CASE_	,       "r"	,       encoding="utf-8"	,       newline="\n"      ) as f:
								A__        =			f.readlines()

				A__        =			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
				A__        =			[]
				while not lines[line_index].startswith("if TYPE_CHECKING"      ) and find_backend(lines[line_index]      ) is None:
								A__        =			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_      ):
												A__        =			_re_one_line_import_struct.search(SCREAMING_SNAKE_CASE_      ).groups()[0]
												A__        =			re.findall("\[([^\]]+)\]"	,       SCREAMING_SNAKE_CASE_      )
												for imp in imports:
																objects.extend([obj[1:-1] for obj in imp.split(", "      )]      )
												line_index += 1
												continue
								A__        =			_re_import_struct_key_value.search(SCREAMING_SNAKE_CASE_      )
								if single_line_import_search is not None:
												A__        =			[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

				A__        =			{"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.
								A__        =			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:
												A__        =			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

												A__        =			[]
												# Until we unindent, add backend objects to the list
												while len(lines[line_index]      ) <= 1 or lines[line_index].startswith(" " * 4      ):
																A__        =			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:
																				A__        =			_re_import_struct_add_many.search(SCREAMING_SNAKE_CASE_      ).groups()[0].split(", "      )
																				A__        =			[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:
																				A__        =			_re_between_brackets.search(SCREAMING_SNAKE_CASE_      ).groups()[0].split(", "      )
																				A__        =			[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

												A__        =			objects
								else:
												line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
				A__        =			[]
				while (
				    line_index < len(SCREAMING_SNAKE_CASE_      )
				    and find_backend(lines[line_index]      ) is None
				    and not lines[line_index].startswith("else"      )
				):
								A__        =			lines[line_index]
								A__        =			_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

				A__        =			{"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.
								A__        =			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:
												A__        =			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

												A__        =			[]
												# Until we unindent, add backend objects to the list
												while len(lines[line_index]      ) <= 1 or lines[line_index].startswith(" " * 8      ):
																A__        =			lines[line_index]
																A__        =			_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

												A__        =			objects
								else:
												line_index += 1

				return import_dict_objects, type_hint_objects



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Dict	,       SCREAMING_SNAKE_CASE_:							List[Any]      )			->      Optional[int]:




				'''simple docstring'''





				def find_duplicates(SCREAMING_SNAKE_CASE_:							str      ):
								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!"]

				A__        =			[]
				for key in import_dict_objects.keys():
								A__        =			find_duplicates(import_dict_objects[key]      )
								if duplicate_imports:
												errors.append(F'Duplicate _import_structure definitions for: {duplicate_imports}'      )
								A__        =			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]      )      ):
												A__        =			"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 					lowerCAmelCase__							(	)			->      Dict:




				'''simple docstring'''





				A__        =			[]
				for root, _, files in os.walk(SCREAMING_SNAKE_CASE_      ):
								if "__init__.py" in files:
												A__        =			os.path.join(SCREAMING_SNAKE_CASE_	,       "__init__.py"      )
												A__        =			parse_init(SCREAMING_SNAKE_CASE_      )
												if objects is not None:
																A__        =			analyze_results(*SCREAMING_SNAKE_CASE_      )
																if len(SCREAMING_SNAKE_CASE_      ) > 0:
																				A__        =			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 					lowerCAmelCase__							(	)			->      Optional[Any]:




				'''simple docstring'''





				A__        =			[]
				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
												A__        =			str((Path(SCREAMING_SNAKE_CASE_      ) / folder).relative_to(SCREAMING_SNAKE_CASE_      )      )
												A__        =			short_path.replace(os.path.sep	,       "."      )
												submodules.append(SCREAMING_SNAKE_CASE_      )
								for fname in files:
												if fname == "__init__.py":
																continue
												A__        =			str((Path(SCREAMING_SNAKE_CASE_      ) / fname).relative_to(SCREAMING_SNAKE_CASE_      )      )
												A__        =			short_path.replace(".py"	,       ""      ).replace(os.path.sep	,       "."      )
												if len(submodule.split("."      )      ) == 1:
																submodules.append(SCREAMING_SNAKE_CASE_      )
				return submodules


lowerCAmelCase__	   = [
    """convert_pytorch_checkpoint_to_tf2""",
    """modeling_flax_pytorch_utils""",
]



def 					lowerCAmelCase__							(	)			->      Optional[int]:




				'''simple docstring'''





				A__        =			importlib.util.spec_from_file_location(
				    "transformers"	,       os.path.join(SCREAMING_SNAKE_CASE_	,       "__init__.py"      )	,       submodule_search_locations=[PATH_TO_TRANSFORMERS]	,       )
				A__        =			spec.loader.load_module()

				A__        =			[
				    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:
								A__        =			"\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()

