
'''simple docstring'''







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  lowerCAmelCase__   :






					def __init__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=13				,							__SCREAMING_SNAKE_CASE=7				,							__SCREAMING_SNAKE_CASE=True				,							__SCREAMING_SNAKE_CASE=True				,							__SCREAMING_SNAKE_CASE=True				,							__SCREAMING_SNAKE_CASE=True				,							__SCREAMING_SNAKE_CASE=99				,							__SCREAMING_SNAKE_CASE=32				,							__SCREAMING_SNAKE_CASE=2				,							__SCREAMING_SNAKE_CASE=4				,							__SCREAMING_SNAKE_CASE=37				,							__SCREAMING_SNAKE_CASE="gelu"				,							__SCREAMING_SNAKE_CASE=0.1				,							__SCREAMING_SNAKE_CASE=0.1				,							__SCREAMING_SNAKE_CASE=5_12				,							__SCREAMING_SNAKE_CASE=16				,							__SCREAMING_SNAKE_CASE=2				,							__SCREAMING_SNAKE_CASE=0.02				,							__SCREAMING_SNAKE_CASE=3				,							__SCREAMING_SNAKE_CASE=4				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=10_00				,							):


											"""simple docstring"""





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






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Any													=       ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size		)

											# convert bbox to numpy since TF does not support item assignment
											lowercase_	:     Dict													=       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]:
																													lowercase_	:     Any													=       bbox[i, j, 3]
																													lowercase_	:     int													=       bbox[i, j, 1]
																													lowercase_	:     str													=       t
																							if bbox[i, j, 2] < bbox[i, j, 0]:
																													lowercase_	:     List[str]													=       bbox[i, j, 2]
																													lowercase_	:     Union[str, Any]													=       bbox[i, j, 0]
																													lowercase_	:     int													=       t
											lowercase_	:     Tuple													=       tf.convert_to_tensor(__snake_case		)

											lowercase_	:     str													=       None
											if self.use_input_mask:
																	lowercase_	:     Optional[int]													=       random_attention_mask([self.batch_size, self.seq_length]		)

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

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

											lowercase_	:     Dict													=       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 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       TFLayoutLMModel(config=__snake_case		)

											lowercase_	:     List[str]													=       model(__snake_case				,							__snake_case				,							attention_mask=__snake_case				,							token_type_ids=__snake_case		)
											lowercase_	:     List[str]													=       model(__snake_case				,							__snake_case				,							token_type_ids=__snake_case		)
											lowercase_	:     str													=       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 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     List[str]													=       TFLayoutLMForMaskedLM(config=__snake_case		)

											lowercase_	:     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 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     int													=       self.num_labels
											lowercase_	:     Tuple													=       TFLayoutLMForSequenceClassification(config=__snake_case		)

											lowercase_	:     List[Any]													=       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 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     List[str]													=       self.num_labels
											lowercase_	:     List[Any]													=       TFLayoutLMForTokenClassification(config=__snake_case		)

											lowercase_	:     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 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Dict													=       TFLayoutLMForQuestionAnswering(config=__snake_case		)

											lowercase_	:     Union[str, 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 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     int													=       self.prepare_config_and_inputs()
											(
											    (
											    lowercase_					
											)						, (
											    lowercase_					
											)						, (
											    lowercase_					
											)						, (
											    lowercase_					
											)						, (
											    lowercase_					
											)						, (
											    lowercase_					
											)						, (
											    lowercase_					
											)						, (
											    lowercase_					
											)						, 					
											)	:     Optional[int]													=       config_and_inputs
											lowercase_	:     List[str]													=       {
											    '''input_ids''': input_ids,
											    '''bbox''': bbox,
											    '''token_type_ids''': token_type_ids,
											    '''attention_mask''': input_mask,
											}
											return config, inputs_dict







@require_tf
class  lowerCAmelCase__   (  lowerCamelCase_    ,						lowerCamelCase_    ,						unittest.TestCase     ):
					lowerCAmelCase_        =     (
					    (
					        TFLayoutLMModel,
					        TFLayoutLMForMaskedLM,
					        TFLayoutLMForTokenClassification,
					        TFLayoutLMForSequenceClassification,
					        TFLayoutLMForQuestionAnswering,
					    )
					    if is_tf_available()
					    else ()
					)
					lowerCAmelCase_        =     (
					    {
					        '''feature-extraction''': TFLayoutLMModel,
					        '''fill-mask''': TFLayoutLMForMaskedLM,
					        '''text-classification''': TFLayoutLMForSequenceClassification,
					        '''token-classification''': TFLayoutLMForTokenClassification,
					        '''zero-shot''': TFLayoutLMForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					lowerCAmelCase_        =     False
					lowerCAmelCase_        =     True
					lowerCAmelCase_        =     1_0






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Tuple													=       TFLayoutLMModelTester(self		)
											lowercase_	:     Dict													=       ConfigTester(self				,							config_class=__snake_case				,							hidden_size=37		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											self.config_tester.run_common_tests()






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     List[str]													=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__snake_case		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     int													=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*__snake_case		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Dict													=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*__snake_case		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     str													=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*__snake_case		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*__snake_case		)






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





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






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


											"""simple docstring"""





											pass







def 					snake_case_		(				):



						"""simple docstring"""





						lowercase_	:     Optional[int]													=       tf.convert_to_tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]							)  # noqa: E231
						lowercase_	:     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
						lowercase_	:     Union[str, Any]													=       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],[1000,1000,1000,1000]],[[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],[1000,1000,1000,1000]]]							)  # noqa: E231
						lowercase_	:     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)
						lowercase_	:     Any													=       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  lowerCAmelCase__   (  unittest.TestCase     ):






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     List[str]													=       TFLayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''		)

											lowercase_						, lowercase_						, lowercase_						, lowercase_						, lowercase_	:     Tuple													=       prepare_layoutlm_batch_inputs()

											# forward pass
											lowercase_	:     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]
											lowercase_	:     Dict													=       tf.convert_to_tensor(
											    [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]]				,							)

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

											# test the pooled output on [1, :3]
											lowercase_	:     Any													=       tf.convert_to_tensor([-0.6_580, -0.0_214, 0.8_552]		)

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






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Any													=       TFLayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''				,							num_labels=2		)

											lowercase_						, lowercase_						, lowercase_						, lowercase_						, lowercase_	:     Tuple													=       prepare_layoutlm_batch_inputs()

											# forward pass
											lowercase_	:     Optional[Any]													=       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
											lowercase_	:     str													=       outputs.loss
											lowercase_	:     Tuple													=       (2,)
											self.assertEqual(loss.shape				,							__snake_case		)

											# test the shape of the logits
											lowercase_	:     Tuple													=       outputs.logits
											lowercase_	:     List[str]													=       (2, 2)
											self.assertEqual(logits.shape				,							__snake_case		)






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     List[str]													=       TFLayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''				,							num_labels=13		)

											lowercase_						, lowercase_						, lowercase_						, lowercase_						, lowercase_	:     int													=       prepare_layoutlm_batch_inputs()

											# forward pass
											lowercase_	:     List[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
											lowercase_	:     Any													=       outputs.logits
											lowercase_	:     Optional[int]													=       tf.convert_to_tensor((2, 25, 13)		)
											self.assertEqual(logits.shape				,							__snake_case		)






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Dict													=       TFLayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased'''		)

											lowercase_						, lowercase_						, lowercase_						, lowercase_						, lowercase_	:     List[Any]													=       prepare_layoutlm_batch_inputs()

											# forward pass
											lowercase_	:     Any													=       model(input_ids=__snake_case				,							bbox=__snake_case				,							attention_mask=__snake_case				,							token_type_ids=__snake_case		)

											# test the shape of the logits
											lowercase_	:     Any													=       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)




