from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers.models.layoutlm.modeling_tf_layoutlm import (
      TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLayoutLMForMaskedLM,
      TFLayoutLMForQuestionAnswering,
      TFLayoutLMForSequenceClassification,
      TFLayoutLMForTokenClassification,
      TFLayoutLMModel,
  )




class 			__magic_name__	:





   """simple docstring"""




   def __init__(						self       :	Any		,	_lowercase       :	Dict		,	_lowercase       :	Union[str, Any]=13		,	_lowercase       :	Any=7		,	_lowercase       :	Tuple=True		,	_lowercase       :	Union[str, Any]=True		,	_lowercase       :	List[str]=True		,	_lowercase       :	Optional[int]=True		,	_lowercase       :	Dict=99		,	_lowercase       :	Tuple=32		,	_lowercase       :	Optional[Any]=2		,	_lowercase       :	Dict=4		,	_lowercase       :	Union[str, Any]=37		,	_lowercase       :	Any="gelu"		,	_lowercase       :	Optional[int]=0.1		,	_lowercase       :	str=0.1		,	_lowercase       :	str=512		,	_lowercase       :	Optional[Any]=16		,	_lowercase       :	Union[str, Any]=2		,	_lowercase       :	Dict=0.02		,	_lowercase       :	Dict=3		,	_lowercase       :	Tuple=4		,	_lowercase       :	Tuple=None		,	_lowercase       :	List[Any]=1_000		,	):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			parent
    _UpperCamelCase:  str    						=			batch_size
    _UpperCamelCase:  Union[str, Any]    						=			seq_length
    _UpperCamelCase:  str    						=			is_training
    _UpperCamelCase:  Tuple    						=			use_input_mask
    _UpperCamelCase:  Optional[int]    						=			use_token_type_ids
    _UpperCamelCase:  List[Any]    						=			use_labels
    _UpperCamelCase:  Tuple    						=			vocab_size
    _UpperCamelCase:  Any    						=			hidden_size
    _UpperCamelCase:  Tuple    						=			num_hidden_layers
    _UpperCamelCase:  Optional[Any]    						=			num_attention_heads
    _UpperCamelCase:  Any    						=			intermediate_size
    _UpperCamelCase:  List[Any]    						=			hidden_act
    _UpperCamelCase:  Dict    						=			hidden_dropout_prob
    _UpperCamelCase:  Optional[Any]    						=			attention_probs_dropout_prob
    _UpperCamelCase:  Any    						=			max_position_embeddings
    _UpperCamelCase:  List[str]    						=			type_vocab_size
    _UpperCamelCase:  Optional[Any]    						=			type_sequence_label_size
    _UpperCamelCase:  Tuple    						=			initializer_range
    _UpperCamelCase:  Optional[Any]    						=			num_labels
    _UpperCamelCase:  List[str]    						=			num_choices
    _UpperCamelCase:  Dict    						=			scope
    _UpperCamelCase:  List[Any]    						=			range_bbox



   def 	lowerCAmelCase    (						self       :	List[str]				):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size				)

    # convert bbox to numpy since TF does not support item assignment
    _UpperCamelCase:  Optional[Any]    						=			ids_tensor([self.batch_size, self.seq_length, 4]		,	self.range_bbox				).numpy()
    # Ensure that bbox is legal
    for i in range(bbox.shape[0]				):
     for j in range(bbox.shape[1]				):
      if bbox[i, j, 3] < bbox[i, j, 1]:
       _UpperCamelCase:  int    						=			bbox[i, j, 3]
       _UpperCamelCase:  List[Any]    						=			bbox[i, j, 1]
       _UpperCamelCase:  str    						=			t
      if bbox[i, j, 2] < bbox[i, j, 0]:
       _UpperCamelCase:  Any    						=			bbox[i, j, 2]
       _UpperCamelCase:  Tuple    						=			bbox[i, j, 0]
       _UpperCamelCase:  Optional[Any]    						=			t
    _UpperCamelCase:  int    						=			tf.convert_to_tensor(_snake_case				)

    _UpperCamelCase:  Tuple    						=			None
    if self.use_input_mask:
     _UpperCamelCase:  List[str]    						=			random_attention_mask([self.batch_size, self.seq_length]				)

    _UpperCamelCase:  List[Any]    						=			None
    if self.use_token_type_ids:
     _UpperCamelCase:  Optional[int]    						=			ids_tensor([self.batch_size, self.seq_length]		,	self.type_vocab_size				)

    _UpperCamelCase:  Tuple    						=			None
    _UpperCamelCase:  Optional[int]    						=			None
    _UpperCamelCase:  int    						=			None
    if self.use_labels:
     _UpperCamelCase:  Dict    						=			ids_tensor([self.batch_size]		,	self.type_sequence_label_size				)
     _UpperCamelCase:  List[Any]    						=			ids_tensor([self.batch_size, self.seq_length]		,	self.num_labels				)
     _UpperCamelCase:  Union[str, Any]    						=			ids_tensor([self.batch_size]		,	self.num_choices				)

    _UpperCamelCase:  Optional[int]    						=			LayoutLMConfig(
        vocab_size=self.vocab_size		,	hidden_size=self.hidden_size		,	num_hidden_layers=self.num_hidden_layers		,	num_attention_heads=self.num_attention_heads		,	intermediate_size=self.intermediate_size		,	hidden_act=self.hidden_act		,	hidden_dropout_prob=self.hidden_dropout_prob		,	attention_probs_dropout_prob=self.attention_probs_dropout_prob		,	max_position_embeddings=self.max_position_embeddings		,	type_vocab_size=self.type_vocab_size		,	initializer_range=self.initializer_range		,	)

    return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



   def 	lowerCAmelCase    (						self       :	Union[str, Any]		,	_lowercase       :	Dict		,	_lowercase       :	Union[str, Any]		,	_lowercase       :	int		,	_lowercase       :	Optional[Any]		,	_lowercase       :	Union[str, Any]		,	_lowercase       :	Tuple		,	_lowercase       :	int		,	_lowercase       :	Optional[int]				):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			TFLayoutLMModel(config=_snake_case				)

    _UpperCamelCase:  Dict    						=			model(_snake_case		,	_snake_case		,	attention_mask=_snake_case		,	token_type_ids=_snake_case				)
    _UpperCamelCase:  List[str]    						=			model(_snake_case		,	_snake_case		,	token_type_ids=_snake_case				)
    _UpperCamelCase:  Union[str, Any]    						=			model(_snake_case		,	_snake_case				)
    self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, self.seq_length, self.hidden_size)				)
    self.parent.assertEqual(result.pooler_output.shape		,	(self.batch_size, self.hidden_size)				)



   def 	lowerCAmelCase    (						self       :	Union[str, Any]		,	_lowercase       :	List[Any]		,	_lowercase       :	Tuple		,	_lowercase       :	int		,	_lowercase       :	Tuple		,	_lowercase       :	str		,	_lowercase       :	Tuple		,	_lowercase       :	List[str]		,	_lowercase       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			TFLayoutLMForMaskedLM(config=_snake_case				)

    _UpperCamelCase:  Tuple    						=			model(_snake_case		,	_snake_case		,	attention_mask=_snake_case		,	token_type_ids=_snake_case		,	labels=_snake_case				)
    self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.seq_length, self.vocab_size)				)



   def 	lowerCAmelCase    (						self       :	Any		,	_lowercase       :	Union[str, Any]		,	_lowercase       :	int		,	_lowercase       :	Union[str, Any]		,	_lowercase       :	List[str]		,	_lowercase       :	int		,	_lowercase       :	Union[str, Any]		,	_lowercase       :	List[str]		,	_lowercase       :	List[Any]				):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			self.num_labels
    _UpperCamelCase:  str    						=			TFLayoutLMForSequenceClassification(config=_snake_case				)

    _UpperCamelCase:  Dict    						=			model(_snake_case		,	_snake_case		,	attention_mask=_snake_case		,	token_type_ids=_snake_case				)
    self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_labels)				)



   def 	lowerCAmelCase    (						self       :	str		,	_lowercase       :	Optional[int]		,	_lowercase       :	str		,	_lowercase       :	List[Any]		,	_lowercase       :	Union[str, Any]		,	_lowercase       :	Tuple		,	_lowercase       :	Optional[Any]		,	_lowercase       :	Union[str, Any]		,	_lowercase       :	int				):



    """simple docstring"""



    _UpperCamelCase:  Tuple    						=			self.num_labels
    _UpperCamelCase:  Any    						=			TFLayoutLMForTokenClassification(config=_snake_case				)

    _UpperCamelCase:  List[str]    						=			model(_snake_case		,	_snake_case		,	attention_mask=_snake_case		,	token_type_ids=_snake_case		,	labels=_snake_case				)
    self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.seq_length, self.num_labels)				)



   def 	lowerCAmelCase    (						self       :	List[str]		,	_lowercase       :	Optional[Any]		,	_lowercase       :	Union[str, Any]		,	_lowercase       :	List[str]		,	_lowercase       :	Union[str, Any]		,	_lowercase       :	int		,	_lowercase       :	int		,	_lowercase       :	List[str]		,	_lowercase       :	Union[str, Any]				):



    """simple docstring"""



    _UpperCamelCase:  Tuple    						=			TFLayoutLMForQuestionAnswering(config=_snake_case				)

    _UpperCamelCase:  Any    						=			model(_snake_case		,	_snake_case		,	attention_mask=_snake_case		,	token_type_ids=_snake_case				)
    self.parent.assertEqual(result.start_logits.shape		,	(self.batch_size, self.seq_length)				)
    self.parent.assertEqual(result.end_logits.shape		,	(self.batch_size, self.seq_length)				)



   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			self.prepare_config_and_inputs()
    (
        (
        _UpperCamelCase     
    )						,				(
        _UpperCamelCase     
    )						,				(
        _UpperCamelCase     
    )						,				(
        _UpperCamelCase     
    )						,				(
        _UpperCamelCase     
    )						,				(
        _UpperCamelCase     
    )						,				(
        _UpperCamelCase     
    )						,				(
        _UpperCamelCase     
    )						,				     
    ):  Tuple    						=			config_and_inputs
    _UpperCamelCase:  List[Any]    						=			{
        '''input_ids''': input_ids,
        '''bbox''': bbox,
        '''token_type_ids''': token_type_ids,
        '''attention_mask''': input_mask,
    }
    return config, inputs_dict




@require_tf
class 			__magic_name__	(		_lowercase   ,						_lowercase   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     Union[str, Any]	      = (
       (
           TFLayoutLMModel,
           TFLayoutLMForMaskedLM,
           TFLayoutLMForTokenClassification,
           TFLayoutLMForSequenceClassification,
           TFLayoutLMForQuestionAnswering,
       )
       if is_tf_available()
       else ()
   )
   lowerCAmelCase   :     List[str]	      = (
       {
           '''feature-extraction''': TFLayoutLMModel,
           '''fill-mask''': TFLayoutLMForMaskedLM,
           '''text-classification''': TFLayoutLMForSequenceClassification,
           '''token-classification''': TFLayoutLMForTokenClassification,
           '''zero-shot''': TFLayoutLMForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   lowerCAmelCase   :     List[Any]	      = False
   lowerCAmelCase   :     List[str]	      = True
   lowerCAmelCase   :     List[str]	      = 1_0



   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			TFLayoutLMModelTester(self				)
    _UpperCamelCase:  List[str]    						=			ConfigTester(self		,	config_class=_snake_case		,	hidden_size=37				)



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    self.config_tester.run_common_tests()



   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    _UpperCamelCase:  Union[str, Any]    						=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_snake_case				)



   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*_snake_case				)



   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*_snake_case				)



   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*_snake_case				)



   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*_snake_case				)



   @slow
   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _UpperCamelCase:  int    						=			TFLayoutLMModel.from_pretrained(_snake_case				)
     self.assertIsNotNone(_snake_case				)



   @unittest.skip('''Onnx compliancy broke with TF 2.10'''				)
   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    pass




def    lowerCAmelCase_  (		)     ->					Tuple:







 '''simple docstring'''
 _UpperCamelCase:  int    						=			tf.convert_to_tensor([[101,1_019,1_014,1_016,1_037,12_849,4_747,1_004,14_246,2_278,5_439,4_524,5_002,2_930,2_193,2_930,4_341,3_208,1_005,1_055,2_171,2_848,11_300,3_531,102],[101,4_070,4_034,7_020,1_024,3_058,1_015,1_013,2_861,1_013,6_070,19_274,2_772,6_205,27_814,16_147,16_147,4_343,2_047,10_283,10_969,14_389,1_012,2_338,102]]		)  # noqa: E231
 _UpperCamelCase:  List[Any]    						=			tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]		)  # noqa: E231
 _UpperCamelCase:  str    						=			tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1_000,1_000,1_000,1_000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1_000,1_000,1_000,1_000]]]		)  # noqa: E231
 _UpperCamelCase:  int    						=			tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]		)  # noqa: E231
 # these are sequence labels (i.e. at the token level)
 _UpperCamelCase:  Tuple    						=			tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]		)  # noqa: E231
 # fmt: on

 return input_ids, attention_mask, bbox, token_type_ids, labels





@require_tf
class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   @slow
   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			TFLayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''				)

    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Optional[Any]    						=			prepare_layoutlm_batch_inputs()

    # forward pass
    _UpperCamelCase:  List[str]    						=			model(input_ids=_snake_case		,	bbox=_snake_case		,	attention_mask=_snake_case		,	token_type_ids=_snake_case				)

    # test the sequence output on [0, :3, :3]
    _UpperCamelCase:  Tuple    						=			tf.convert_to_tensor(
        [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]		,	)

    self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]		,	_snake_case		,	atol=1E-3				)				)

    # test the pooled output on [1, :3]
    _UpperCamelCase:  str    						=			tf.convert_to_tensor([-0.6580, -0.0214, 0.8552]				)

    self.assertTrue(np.allclose(outputs.pooler_output[1, :3]		,	_snake_case		,	atol=1E-3				)				)



   @slow
   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			TFLayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''		,	num_labels=2				)

    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Optional[int]    						=			prepare_layoutlm_batch_inputs()

    # forward pass
    _UpperCamelCase:  str    						=			model(
        input_ids=_snake_case		,	bbox=_snake_case		,	attention_mask=_snake_case		,	token_type_ids=_snake_case		,	labels=tf.convert_to_tensor([1, 1]				)		,	)

    # test whether we get a loss as a scalar
    _UpperCamelCase:  Optional[int]    						=			outputs.loss
    _UpperCamelCase:  Tuple    						=			(2,)
    self.assertEqual(loss.shape		,	_snake_case				)

    # test the shape of the logits
    _UpperCamelCase:  Tuple    						=			outputs.logits
    _UpperCamelCase:  Optional[Any]    						=			(2, 2)
    self.assertEqual(logits.shape		,	_snake_case				)



   @slow
   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			TFLayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''		,	num_labels=13				)

    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  int    						=			prepare_layoutlm_batch_inputs()

    # forward pass
    _UpperCamelCase:  Optional[Any]    						=			model(
        input_ids=_snake_case		,	bbox=_snake_case		,	attention_mask=_snake_case		,	token_type_ids=_snake_case		,	labels=_snake_case				)

    # test the shape of the logits
    _UpperCamelCase:  Optional[Any]    						=			outputs.logits
    _UpperCamelCase:  Union[str, Any]    						=			tf.convert_to_tensor((2, 25, 13)				)
    self.assertEqual(logits.shape		,	_snake_case				)



   @slow
   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    _UpperCamelCase:  Union[str, Any]    						=			TFLayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased'''				)

    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Union[str, Any]    						=			prepare_layoutlm_batch_inputs()

    # forward pass
    _UpperCamelCase:  Optional[int]    						=			model(input_ids=_snake_case		,	bbox=_snake_case		,	attention_mask=_snake_case		,	token_type_ids=_snake_case				)

    # test the shape of the logits
    _UpperCamelCase:  Dict    						=			tf.convert_to_tensor((2, 25)				)
    self.assertEqual(outputs.start_logits.shape		,	_snake_case				)
    self.assertEqual(outputs.end_logits.shape		,	_snake_case				)

"""simple docstring"""





import argparse
import os
import re


UpperCamelCase_       :    Any						      =			'''src/transformers/models/auto'''


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
UpperCamelCase_       :    Optional[int]						      =			re.compile(R'''[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict''')
# re pattern that matches identifiers in mappings
UpperCamelCase_       :    Tuple						      =			re.compile(R'''\s*\(\s*"(\S[^"]+)"''')
def 				A_  (__a					, __a = False					):

					'''simple docstring'''





					with open(__a					, "r"					, encoding="utf-8"					) as f:
										A_								=    f.read()

					A_								=    content.split("\n"					)
					A_								=    []
					A_								=    0
					while line_idx < len(__a					):
										if _re_intro_mapping.search(lines[line_idx]					) is not None:
															A_								=    len(re.search(R"^(\s*)\S"					, lines[line_idx]					).groups()[0]					) + 8
															# Start of a new mapping!
															while not lines[line_idx].startswith(" " * indent + "("					):
																				new_lines.append(lines[line_idx]					)
																				line_idx += 1

															A_								=    []
															while lines[line_idx].strip() != "]":
																				# Blocks either fit in one line or not
																				if lines[line_idx].strip() == "(":
																									A_								=    line_idx
																									while not lines[line_idx].startswith(" " * indent + ")"					):
																														line_idx += 1
																									blocks.append("\n".join(lines[start_idx : line_idx + 1]					)					)
																				else:
																									blocks.append(lines[line_idx]					)
																				line_idx += 1

															# Sort blocks by their identifiers
															A_								=    sorted(__a					, key=lambda __a					: _re_identifier.search(__a					).groups()[0]					)
															new_lines += blocks
										else:
															new_lines.append(lines[line_idx]					)
															line_idx += 1

					if overwrite:
										with open(__a					, "w"					, encoding="utf-8"					) as f:
															f.write("\n".join(__a					)					)
					elif "\n".join(__a					) != content:
										return True
def 				A_  (__a = False					):

					'''simple docstring'''





					A_								=    [os.path.join(__a					, __a					) for f in os.listdir(__a					) if f.endswith(".py"					)]
					A_								=    [sort_auto_mapping(__a					, overwrite=__a					) for fname in fnames]

					if not overwrite and any(__a					):
										A_								=    [f for f, d in zip(__a					, __a					) if d]
										raise ValueError(
										    f'The following files have auto mappings that need sorting: {", ".join(__a					)}. Run `make style` to fix'
										    " this."					)


if __name__ == "__main__":
		UpperCamelCase_       :    str						      =			argparse.ArgumentParser()
		parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
		UpperCamelCase_       :    List[Any]						      =			parser.parse_args()

		sort_all_auto_mappings(not args.check_only)




