


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        snake_case_   :
					def __init__(      self   :Union[str, Any]			,__snake_case   :str			,__snake_case   :Dict=13			,__snake_case   :List[Any]=7			,__snake_case   :Optional[Any]=True			,__snake_case   :Dict=True			,__snake_case   :int=True			,__snake_case   :Union[str, Any]=True			,__snake_case   :List[str]=99			,__snake_case   :List[str]=32			,__snake_case   :str=2			,__snake_case   :Optional[Any]=4			,__snake_case   :List[Any]=37			,__snake_case   :Optional[int]="gelu"			,__snake_case   :Any=0.1			,__snake_case   :Tuple=0.1			,__snake_case   :Optional[int]=5_12			,__snake_case   :Optional[int]=16			,__snake_case   :List[Any]=2			,__snake_case   :Any=0.02			,__snake_case   :List[str]=3			,__snake_case   :Any=4			,__snake_case   :Dict=None			,__snake_case   :List[str]=10_00			,)					->    List[str]:
												a__             =     parent
												a__             =     batch_size
												a__             =     seq_length
												a__             =     is_training
												a__             =     use_input_mask
												a__             =     use_token_type_ids
												a__             =     use_labels
												a__             =     vocab_size
												a__             =     hidden_size
												a__             =     num_hidden_layers
												a__             =     num_attention_heads
												a__             =     intermediate_size
												a__             =     hidden_act
												a__             =     hidden_dropout_prob
												a__             =     attention_probs_dropout_prob
												a__             =     max_position_embeddings
												a__             =     type_vocab_size
												a__             =     type_sequence_label_size
												a__             =     initializer_range
												a__             =     num_labels
												a__             =     num_choices
												a__             =     scope
												a__             =     range_bbox
					def  lowerCamelCase__(      self   :List[Any]      )					->    List[Any]:
												a__             =     ids_tensor([self.batch_size, self.seq_length]			,self.vocab_size      )

												# convert bbox to numpy since TF does not support item assignment
												a__             =     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]:
																																	a__             =     bbox[i, j, 3]
																																	a__             =     bbox[i, j, 1]
																																	a__             =     t
																										if bbox[i, j, 2] < bbox[i, j, 0]:
																																	a__             =     bbox[i, j, 2]
																																	a__             =     bbox[i, j, 0]
																																	a__             =     t
												a__             =     tf.convert_to_tensor(__snake_case      )

												a__             =     None
												if self.use_input_mask:
																			a__             =     random_attention_mask([self.batch_size, self.seq_length]      )

												a__             =     None
												if self.use_token_type_ids:
																			a__             =     ids_tensor([self.batch_size, self.seq_length]			,self.type_vocab_size      )

												a__             =     None
												a__             =     None
												a__             =     None
												if self.use_labels:
																			a__             =     ids_tensor([self.batch_size]			,self.type_sequence_label_size      )
																			a__             =     ids_tensor([self.batch_size, self.seq_length]			,self.num_labels      )
																			a__             =     ids_tensor([self.batch_size]			,self.num_choices      )

												a__             =     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   :Tuple			,__snake_case   :Any			,__snake_case   :Optional[int]			,__snake_case   :str			,__snake_case   :str			,__snake_case   :Optional[int]			,__snake_case   :Tuple			,__snake_case   :List[Any]			,__snake_case   :str      )					->    str:
												a__             =     TFLayoutLMModel(config=__snake_case      )

												a__             =     model(__snake_case			,__snake_case			,attention_mask=__snake_case			,token_type_ids=__snake_case      )
												a__             =     model(__snake_case			,__snake_case			,token_type_ids=__snake_case      )
												a__             =     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]			,__snake_case   :Optional[int]			,__snake_case   :List[str]			,__snake_case   :Tuple			,__snake_case   :int			,__snake_case   :int			,__snake_case   :Any			,__snake_case   :Dict			,__snake_case   :Union[str, Any]      )					->    Optional[int]:
												a__             =     TFLayoutLMForMaskedLM(config=__snake_case      )

												a__             =     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   :List[Any]			,__snake_case   :Optional[Any]			,__snake_case   :Dict			,__snake_case   :Optional[int]			,__snake_case   :Tuple			,__snake_case   :Dict			,__snake_case   :str			,__snake_case   :Union[str, Any]			,__snake_case   :Optional[int]      )					->    Optional[Any]:
												a__             =     self.num_labels
												a__             =     TFLayoutLMForSequenceClassification(config=__snake_case      )

												a__             =     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   :Tuple			,__snake_case   :int			,__snake_case   :str			,__snake_case   :int			,__snake_case   :List[str]			,__snake_case   :Any			,__snake_case   :Union[str, Any]			,__snake_case   :Dict			,__snake_case   :Dict      )					->    Any:
												a__             =     self.num_labels
												a__             =     TFLayoutLMForTokenClassification(config=__snake_case      )

												a__             =     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   :Optional[int]			,__snake_case   :Any			,__snake_case   :Dict			,__snake_case   :Dict			,__snake_case   :Dict			,__snake_case   :Any			,__snake_case   :Dict			,__snake_case   :Optional[int]			,__snake_case   :List[Any]      )					->    Union[str, Any]:
												a__             =     TFLayoutLMForQuestionAnswering(config=__snake_case      )

												a__             =     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      )					->    Optional[Any]:
												a__             =     self.prepare_config_and_inputs()
												(
												    (
												    a__
												)      ,					(
												    a__
												)      ,					(
												    a__
												)      ,					(
												    a__
												)      ,					(
												    a__
												)      ,					(
												    a__
												)      ,					(
												    a__
												)      ,					(
												    a__
												)      ,					
												)             =     config_and_inputs
												a__             =     {
												    'input_ids': input_ids,
												    'bbox': bbox,
												    'token_type_ids': token_type_ids,
												    'attention_mask': input_mask,
												}
												return config, inputs_dict







@require_tf
class        snake_case_   (lowerCamelCase_	,						lowerCamelCase_	,						unittest.TestCase      ):
					UpperCAmelCase__					:    Optional[int]          =			(
					    (
					        TFLayoutLMModel,
					        TFLayoutLMForMaskedLM,
					        TFLayoutLMForTokenClassification,
					        TFLayoutLMForSequenceClassification,
					        TFLayoutLMForQuestionAnswering,
					    )
					    if is_tf_available()
					    else ()
					)
					UpperCAmelCase__					:    List[Any]          =			(
					    {
					        '''feature-extraction''': TFLayoutLMModel,
					        '''fill-mask''': TFLayoutLMForMaskedLM,
					        '''text-classification''': TFLayoutLMForSequenceClassification,
					        '''token-classification''': TFLayoutLMForTokenClassification,
					        '''zero-shot''': TFLayoutLMForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					UpperCAmelCase__					:    Dict          =			False
					UpperCAmelCase__					:    List[Any]          =			True
					UpperCAmelCase__					:    Tuple          =			1_0
					def  lowerCamelCase__(      self   :Any      )					->    List[Any]:
												a__             =     TFLayoutLMModelTester(self      )
												a__             =     ConfigTester(self			,config_class=__snake_case			,hidden_size=37      )
					def  lowerCamelCase__(      self   :Tuple      )					->    Any:
												self.config_tester.run_common_tests()
					def  lowerCamelCase__(      self   :Any      )					->    Dict:
												a__             =     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__snake_case      )
					def  lowerCamelCase__(      self   :Dict      )					->    List[Any]:
												a__             =     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_lm(*__snake_case      )
					def  lowerCamelCase__(      self   :Dict      )					->    Union[str, Any]:
												a__             =     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*__snake_case      )
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    int:
												a__             =     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*__snake_case      )
					def  lowerCamelCase__(      self   :Tuple      )					->    Any:
												a__             =     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*__snake_case      )
					@slow
					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    List[Any]:
												for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			a__             =     TFLayoutLMModel.from_pretrained(__snake_case      )
																			self.assertIsNotNone(__snake_case      )



					@unittest.skip('Onnx compliancy broke with TF 2.10'      )
					def  lowerCamelCase__(      self   :int      )					->    Optional[Any]:
												pass






def 							__lowercase   ( ):
							# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
							# fmt: off
							a__             =     tf.convert_to_tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]]   )  # noqa: E231
							a__             =     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
							a__             =     tf.convert_to_tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]]   )  # noqa: E231
							a__             =     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)
							a__             =     tf.convert_to_tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]]   )  # noqa: E231
							# fmt: on

							return input_ids, attention_mask, bbox, token_type_ids, labels







@require_tf
class        snake_case_   (unittest.TestCase      ):
					@slow
					def  lowerCamelCase__(      self   :List[str]      )					->    Any:
												a__             =     TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'      )

												a__      ,					a__      ,					a__      ,					a__      ,					a__             =     prepare_layoutlm_batch_inputs()

												# forward pass
												a__             =     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]
												a__             =     tf.convert_to_tensor(
												    [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]			,)

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

												# test the pooled output on [1, :3]
												a__             =     tf.convert_to_tensor([-0.65_80, -0.02_14, 0.85_52]      )

												self.assertTrue(np.allclose(outputs.pooler_output[1, :3]			,__snake_case			,atol=1E-3      )      )
					@slow
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    int:
												# initialize model with randomly initialized sequence classification head
												a__             =     TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'			,num_labels=2      )

												a__      ,					a__      ,					a__      ,					a__      ,					a__             =     prepare_layoutlm_batch_inputs()

												# forward pass
												a__             =     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
												a__             =     outputs.loss
												a__             =     (2,)
												self.assertEqual(loss.shape			,__snake_case      )

												# test the shape of the logits
												a__             =     outputs.logits
												a__             =     (2, 2)
												self.assertEqual(logits.shape			,__snake_case      )
					@slow
					def  lowerCamelCase__(      self   :Tuple      )					->    Dict:
												# initialize model with randomly initialized token classification head
												a__             =     TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'			,num_labels=13      )

												a__      ,					a__      ,					a__      ,					a__      ,					a__             =     prepare_layoutlm_batch_inputs()

												# forward pass
												a__             =     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
												a__             =     outputs.logits
												a__             =     tf.convert_to_tensor((2, 25, 13)      )
												self.assertEqual(logits.shape			,__snake_case      )



					@slow
					def  lowerCamelCase__(      self   :int      )					->    Union[str, Any]:
												# initialize model with randomly initialized token classification head
												a__             =     TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'      )

												a__      ,					a__      ,					a__      ,					a__      ,					a__             =     prepare_layoutlm_batch_inputs()

												# forward pass
												a__             =     model(input_ids=__snake_case			,bbox=__snake_case			,attention_mask=__snake_case			,token_type_ids=__snake_case      )

												# test the shape of the logits
												a__             =     tf.convert_to_tensor((2, 25)      )
												self.assertEqual(outputs.start_logits.shape			,__snake_case      )
												self.assertEqual(outputs.end_logits.shape			,__snake_case      )








import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def 							__lowercase   ( __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				Any					,    __lowerCAmelCase							:				int   ):
							# Construct model
							if gpta_config_file == "":
														a__             =     GPTaConfig()
							else:
														a__             =     GPTaConfig.from_json_file(__lowerCAmelCase   )
							a__             =     GPTaModel(__lowerCAmelCase   )

							# Load weights from numpy
							load_tf_weights_in_gpta(__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase   )

							# Save pytorch-model
							a__             =     pytorch_dump_folder_path + '/' + WEIGHTS_NAME
							a__             =     pytorch_dump_folder_path + '/' + CONFIG_NAME
							print(F'Save PyTorch model to {pytorch_weights_dump_path}'   )
							torch.save(model.state_dict()					,    __lowerCAmelCase   )
							print(F'Save configuration file to {pytorch_config_dump_path}'   )
							with open(__lowerCAmelCase					,    'w'					,    encoding='utf-8'   ) as f:
														f.write(config.to_json_string()   )


if __name__ == "__main__":
						snake_case							:       List[Any]     			=      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--gpt2_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
						)
						parser.add_argument(
						    '''--gpt2_config_file''',
						    default='''''',
						    type=str,
						    help=(
						        '''An optional config json file corresponding to the pre-trained OpenAI model. \n'''
						        '''This specifies the model architecture.'''
						    ),
						)
						snake_case							:       Any     			=      parser.parse_args()
						convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)




