


'''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    UpperCAmelCase_  :





    """simple docstring"""






    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_=0.0_2			,     SCREAMING_SNAKE_CASE_=3			,     SCREAMING_SNAKE_CASE_=4			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=10_00			,     )				->     Tuple:
         __lowerCamelCase   :	Tuple								=		parent
         __lowerCamelCase   :	Union[str, Any]								=		batch_size
         __lowerCamelCase   :	Dict								=		seq_length
         __lowerCamelCase   :	int								=		is_training
         __lowerCamelCase   :	List[Any]								=		use_input_mask
         __lowerCamelCase   :	List[Any]								=		use_token_type_ids
         __lowerCamelCase   :	str								=		use_labels
         __lowerCamelCase   :	Dict								=		vocab_size
         __lowerCamelCase   :	Tuple								=		hidden_size
         __lowerCamelCase   :	List[Any]								=		num_hidden_layers
         __lowerCamelCase   :	List[str]								=		num_attention_heads
         __lowerCamelCase   :	int								=		intermediate_size
         __lowerCamelCase   :	str								=		hidden_act
         __lowerCamelCase   :	str								=		hidden_dropout_prob
         __lowerCamelCase   :	Dict								=		attention_probs_dropout_prob
         __lowerCamelCase   :	Tuple								=		max_position_embeddings
         __lowerCamelCase   :	int								=		type_vocab_size
         __lowerCamelCase   :	Any								=		type_sequence_label_size
         __lowerCamelCase   :	int								=		initializer_range
         __lowerCamelCase   :	Tuple								=		num_labels
         __lowerCamelCase   :	Optional[Any]								=		num_choices
         __lowerCamelCase   :	Optional[int]								=		scope
         __lowerCamelCase   :	List[Any]								=		range_bbox







    def       lowercase_				(							self	)				->     Dict:
         __lowerCamelCase   :	Dict								=		ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size	)

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

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

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

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

         __lowerCamelCase   :	Union[str, Any]								=		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       lowercase_				(							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_	)				->     List[Any]:
         __lowerCamelCase   :	Any								=		TFLayoutLMModel(config=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	List[Any]								=		model(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     attention_mask=SCREAMING_SNAKE_CASE_			,     token_type_ids=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Dict								=		model(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     token_type_ids=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	List[str]								=		model(SCREAMING_SNAKE_CASE_			,     SCREAMING_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       lowercase_				(							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_	)				->     List[str]:
         __lowerCamelCase   :	Union[str, Any]								=		TFLayoutLMForMaskedLM(config=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	List[str]								=		model(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     attention_mask=SCREAMING_SNAKE_CASE_			,     token_type_ids=SCREAMING_SNAKE_CASE_			,     labels=SCREAMING_SNAKE_CASE_	)
         self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length, self.vocab_size)	)







    def       lowercase_				(							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_	)				->     Dict:
         __lowerCamelCase   :	str								=		self.num_labels
         __lowerCamelCase   :	Dict								=		TFLayoutLMForSequenceClassification(config=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	List[str]								=		model(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     attention_mask=SCREAMING_SNAKE_CASE_			,     token_type_ids=SCREAMING_SNAKE_CASE_	)
         self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_labels)	)







    def       lowercase_				(							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_	)				->     Any:
         __lowerCamelCase   :	Tuple								=		self.num_labels
         __lowerCamelCase   :	Tuple								=		TFLayoutLMForTokenClassification(config=SCREAMING_SNAKE_CASE_	)

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







    def       lowercase_				(							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_	)				->     Any:
         __lowerCamelCase   :	int								=		TFLayoutLMForQuestionAnswering(config=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	List[Any]								=		model(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     attention_mask=SCREAMING_SNAKE_CASE_			,     token_type_ids=SCREAMING_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       lowercase_				(							self	)				->     Optional[int]:
         __lowerCamelCase   :	str								=		self.prepare_config_and_inputs()
         (
             (
             __lowerCamelCase
         )				,       (
             __lowerCamelCase
         )				,       (
             __lowerCamelCase
         )				,       (
             __lowerCamelCase
         )				,       (
             __lowerCamelCase
         )				,       (
             __lowerCamelCase
         )				,       (
             __lowerCamelCase
         )				,       (
             __lowerCamelCase
         )				,       
         )   :	Tuple								=		config_and_inputs
         __lowerCamelCase   :	str								=		{
             'input_ids': input_ids,
             'bbox': bbox,
             'token_type_ids': token_type_ids,
             'attention_mask': input_mask,
         }
         return config, inputs_dict







@require_tf
class    UpperCAmelCase_  (_UpperCAmelCase					,		_UpperCAmelCase					,		unittest.TestCase  ):





    """simple docstring"""






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







    def       lowercase_				(							self	)				->     Any:
         __lowerCamelCase   :	Optional[int]								=		TFLayoutLMModelTester(self	)
         __lowerCamelCase   :	Union[str, Any]								=		ConfigTester(self			,     config_class=SCREAMING_SNAKE_CASE_			,     hidden_size=37	)







    def       lowercase_				(							self	)				->     Tuple:
         self.config_tester.run_common_tests()







    def       lowercase_				(							self	)				->     List[Any]:
         __lowerCamelCase   :	Optional[int]								=		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self	)				->     str:
         __lowerCamelCase   :	Tuple								=		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self	)				->     Optional[Any]:
         __lowerCamelCase   :	List[Any]								=		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self	)				->     Optional[Any]:
         __lowerCamelCase   :	Any								=		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self	)				->     Optional[int]:
         __lowerCamelCase   :	Union[str, Any]								=		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_	)







    @slow
    def       lowercase_				(							self	)				->     Union[str, Any]:
         for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __lowerCamelCase   :	List[str]								=		TFLayoutLMModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
              self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)







    @unittest.skip('Onnx compliancy broke with TF 2.10'	)
    def       lowercase_				(							self	)				->     Dict:
         pass







def 			UpperCAmelCase__					(	)     ->		Any:
     # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
     # fmt: off
     __lowerCamelCase   :	Optional[int]								=		tf.convert_to_tensor([[1_01,10_19,10_14,10_16,10_37,1_28_49,47_47,10_04,1_42_46,22_78,54_39,45_24,50_02,29_30,21_93,29_30,43_41,32_08,10_05,10_55,21_71,28_48,1_13_00,35_31,1_02],[1_01,40_70,40_34,70_20,10_24,30_58,10_15,10_13,28_61,10_13,60_70,1_92_74,27_72,62_05,2_78_14,1_61_47,1_61_47,43_43,20_47,1_02_83,1_09_69,1_43_89,10_12,23_38,1_02]]							)  # noqa: E231
     __lowerCamelCase   :	Union[str, 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
     __lowerCamelCase   :	Any								=		tf.convert_to_tensor([[[0,0,0,0],[4_23,2_37,4_40,2_51],[4_27,2_72,4_41,2_87],[4_19,1_15,4_37,1_29],[9_61,8_85,9_92,9_12],[2_56,38,3_30,58],[2_56,38,3_30,58],[3_36,42,3_53,57],[3_60,39,4_01,56],[3_60,39,4_01,56],[4_11,39,4_71,59],[4_79,41,5_28,59],[5_33,39,6_30,60],[67,1_13,1_34,1_31],[1_41,1_15,2_09,1_32],[68,1_49,1_33,1_66],[1_41,1_49,1_87,1_64],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[2_95,1_48,3_49,1_65],[4_41,1_49,4_92,1_66],[4_97,1_49,5_46,1_64],[64,2_01,1_25,2_18],[10_00,10_00,10_00,10_00]],[[0,0,0,0],[6_62,1_50,7_54,1_66],[6_65,1_99,7_42,2_11],[5_19,2_13,5_54,2_28],[5_19,2_13,5_54,2_28],[1_34,4_33,1_87,4_54],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[3_14,4_69,3_76,4_82],[5_04,6_84,5_82,7_06],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[6_10,7_49,6_52,7_65],[1_30,6_59,1_68,6_72],[1_76,6_57,2_37,6_72],[2_38,6_57,3_12,6_72],[4_43,6_53,6_28,6_72],[4_43,6_53,6_28,6_72],[7_16,3_01,8_25,3_17],[10_00,10_00,10_00,10_00]]]							)  # noqa: E231
     __lowerCamelCase   :	Union[str, Any]								=		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)
     __lowerCamelCase   :	Any								=		tf.convert_to_tensor([[-1_00,10,10,10,9,1,-1_00,7,7,-1_00,7,7,4,2,5,2,8,8,-1_00,-1_00,5,0,3,2,-1_00],[-1_00,12,12,12,-1_00,12,10,-1_00,-1_00,-1_00,-1_00,10,12,9,-1_00,-1_00,-1_00,10,10,10,9,12,-1_00,10,-1_00]]							)  # noqa: E231
     # fmt: on

     return input_ids, attention_mask, bbox, token_type_ids, labels





@require_tf
class    UpperCAmelCase_  (unittest.TestCase  ):





    """simple docstring"""






    @slow
    def       lowercase_				(							self	)				->     List[str]:
         __lowerCamelCase   :	str								=		TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'	)

         __lowerCamelCase				,       __lowerCamelCase				,       __lowerCamelCase				,       __lowerCamelCase				,       __lowerCamelCase   :	str								=		prepare_layoutlm_batch_inputs()

         # forward pass
         __lowerCamelCase   :	str								=		model(input_ids=SCREAMING_SNAKE_CASE_			,     bbox=SCREAMING_SNAKE_CASE_			,     attention_mask=SCREAMING_SNAKE_CASE_			,     token_type_ids=SCREAMING_SNAKE_CASE_	)

         # test the sequence output on [0, :3, :3]
         __lowerCamelCase   :	Tuple								=		tf.convert_to_tensor(
             [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]]			,     )

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

         # test the pooled output on [1, :3]
         __lowerCamelCase   :	List[Any]								=		tf.convert_to_tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2]	)

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







    @slow
    def       lowercase_				(							self	)				->     Optional[int]:
         # initialize model with randomly initialized sequence classification head
         __lowerCamelCase   :	str								=		TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'			,     num_labels=2	)

         __lowerCamelCase				,       __lowerCamelCase				,       __lowerCamelCase				,       __lowerCamelCase				,       __lowerCamelCase   :	int								=		prepare_layoutlm_batch_inputs()

         # forward pass
         __lowerCamelCase   :	Union[str, Any]								=		model(
             input_ids=SCREAMING_SNAKE_CASE_			,     bbox=SCREAMING_SNAKE_CASE_			,     attention_mask=SCREAMING_SNAKE_CASE_			,     token_type_ids=SCREAMING_SNAKE_CASE_			,     labels=tf.convert_to_tensor([1, 1]	)			,     )

         # test whether we get a loss as a scalar
         __lowerCamelCase   :	int								=		outputs.loss
         __lowerCamelCase   :	List[str]								=		(2,)
         self.assertEqual(loss.shape			,     SCREAMING_SNAKE_CASE_	)

         # test the shape of the logits
         __lowerCamelCase   :	Any								=		outputs.logits
         __lowerCamelCase   :	Any								=		(2, 2)
         self.assertEqual(logits.shape			,     SCREAMING_SNAKE_CASE_	)







    @slow
    def       lowercase_				(							self	)				->     Tuple:
         # initialize model with randomly initialized token classification head
         __lowerCamelCase   :	Optional[Any]								=		TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'			,     num_labels=13	)

         __lowerCamelCase				,       __lowerCamelCase				,       __lowerCamelCase				,       __lowerCamelCase				,       __lowerCamelCase   :	Dict								=		prepare_layoutlm_batch_inputs()

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

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







    @slow
    def       lowercase_				(							self	)				->     List[Any]:
         # initialize model with randomly initialized token classification head
         __lowerCamelCase   :	Optional[int]								=		TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'	)

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

         # forward pass
         __lowerCamelCase   :	int								=		model(input_ids=SCREAMING_SNAKE_CASE_			,     bbox=SCREAMING_SNAKE_CASE_			,     attention_mask=SCREAMING_SNAKE_CASE_			,     token_type_ids=SCREAMING_SNAKE_CASE_	)

         # test the shape of the logits
         __lowerCamelCase   :	Optional[int]								=		tf.convert_to_tensor((2, 25)	)
         self.assertEqual(outputs.start_logits.shape			,     SCREAMING_SNAKE_CASE_	)
         self.assertEqual(outputs.end_logits.shape			,     SCREAMING_SNAKE_CASE_	)










'''simple docstring'''



import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


A__		:					str												=  [
    # tf -> hf
    ("""/""", """."""),
    ("""layer_""", """layers."""),
    ("""kernel""", """weight"""),
    ("""beta""", """bias"""),
    ("""gamma""", """weight"""),
    ("""pegasus""", """model"""),
]
A__		:					Dict												=  [
    (""".output.dense""", """.fc2"""),
    ("""intermediate.LayerNorm""", """final_layer_norm"""),
    ("""intermediate.dense""", """fc1"""),
]

A__		:					int												=  (
    INIT_COMMON
    + [
        ("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
        ("""attention.output.dense""", """self_attn.out_proj"""),
        ("""attention.self""", """self_attn"""),
        ("""attention.encdec.LayerNorm""", """encoder_attn_layer_norm"""),
        ("""attention.encdec_output.dense""", """encoder_attn.out_proj"""),
        ("""attention.encdec""", """encoder_attn"""),
        ("""key""", """k_proj"""),
        ("""value""", """v_proj"""),
        ("""query""", """q_proj"""),
        ("""decoder.LayerNorm""", """decoder.layernorm_embedding"""),
    ]
    + END_COMMON
)

A__		:					Tuple												=  (
    INIT_COMMON
    + [
        ("""embeddings.word_embeddings""", """shared.weight"""),
        ("""embeddings.position_embeddings""", """embed_positions.weight"""),
        ("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
        ("""attention.output.dense""", """self_attn.output"""),
        ("""attention.self""", """self_attn.self"""),
        ("""encoder.LayerNorm""", """encoder.layernorm_embedding"""),
    ]
    + END_COMMON
)

A__		:					Tuple												=  [
    """encdec/key/bias""",
    """encdec/query/bias""",
    """encdec/value/bias""",
    """self/key/bias""",
    """self/query/bias""",
    """self/value/bias""",
    """encdec_output/dense/bias""",
    """attention/output/dense/bias""",
]



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]      ,    UpperCAmelCase_    :				Any							)     ->		List[str]:
     for tf_name, hf_name in patterns:
          __lowerCamelCase   :	Optional[int]								=		k.replace(UpperCAmelCase_      ,    UpperCAmelCase_							)
     return k



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				dict      ,    UpperCAmelCase_    :				dict							)     ->		BigBirdPegasusForConditionalGeneration:
     __lowerCamelCase   :	int								=		BigBirdPegasusConfig(**UpperCAmelCase_							)
     __lowerCamelCase   :	Any								=		BigBirdPegasusForConditionalGeneration(UpperCAmelCase_							)
     __lowerCamelCase   :	Union[str, Any]								=		torch_model.state_dict()
     __lowerCamelCase   :	Tuple								=		{}

     # separating decoder weights
     __lowerCamelCase   :	Dict								=		{k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'							)}
     __lowerCamelCase   :	str								=		{k: tf_weights[k] for k in tf_weights if not k.startswith('pegasus/decoder'							)}

     for k, v in tqdm(decoder_weights.items()      ,    'tf -> hf conversion'							):
          __lowerCamelCase   :	Tuple								=		[k.endswith(UpperCAmelCase_							) for ending in KEYS_TO_IGNORE]
          if any(UpperCAmelCase_							):
               continue
          __lowerCamelCase   :	Tuple								=		DECODER_PATTERNS
          __lowerCamelCase   :	Optional[int]								=		rename_state_dict_key(UpperCAmelCase_      ,    UpperCAmelCase_							)
          if new_k not in state_dict:
               raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'							)
          if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']							):
               __lowerCamelCase   :	Union[str, Any]								=		v.T
          __lowerCamelCase   :	str								=		torch.from_numpy(UpperCAmelCase_							)
          assert v.shape == state_dict[new_k].shape, F'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'

     for k, v in tqdm(remaining_weights.items()      ,    'tf -> hf conversion'							):
          __lowerCamelCase   :	Optional[Any]								=		[k.endswith(UpperCAmelCase_							) for ending in KEYS_TO_IGNORE]
          if any(UpperCAmelCase_							):
               continue
          __lowerCamelCase   :	Dict								=		REMAINING_PATTERNS
          __lowerCamelCase   :	List[str]								=		rename_state_dict_key(UpperCAmelCase_      ,    UpperCAmelCase_							)
          if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
               raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'							)
          if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']							):
               __lowerCamelCase   :	List[str]								=		v.T
          __lowerCamelCase   :	List[str]								=		torch.from_numpy(UpperCAmelCase_							)
          if k != "pegasus/embeddings/position_embeddings":
               assert v.shape == state_dict[new_k].shape, F'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'

     __lowerCamelCase   :	Any								=		mapping['model.embed_positions.weight']
     __lowerCamelCase   :	Union[str, Any]								=		mapping.pop('model.embed_positions.weight'							)
     __lowerCamelCase				,       __lowerCamelCase   :	List[str]								=		torch_model.load_state_dict(UpperCAmelCase_      ,    strict=UpperCAmelCase_							)
     __lowerCamelCase   :	Any								=		[
         k
         for k in missing
         if k
         not in [
             'final_logits_bias',
             'model.encoder.embed_tokens.weight',
             'model.decoder.embed_tokens.weight',
             'lm_head.weight',
         ]
     ]
     assert unexpected_missing == [], F'no matches found for the following torch keys {unexpected_missing}'
     assert extra == [], F'no matches found for the following tf keys {extra}'
     return torch_model



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]							)     ->		Dict:
     __lowerCamelCase   :	int								=		tf.train.list_variables(UpperCAmelCase_							)
     __lowerCamelCase   :	List[str]								=		{}
     __lowerCamelCase   :	List[Any]								=		['global_step']
     for name, shape in tqdm(UpperCAmelCase_      ,    desc='converting tf checkpoint to dict'							):
          __lowerCamelCase   :	Any								=		any(pat in name for pat in ignore_name							)
          if skip_key:
               continue
          __lowerCamelCase   :	Dict								=		tf.train.load_variable(UpperCAmelCase_      ,    UpperCAmelCase_							)
          __lowerCamelCase   :	List[Any]								=		array
     return tf_weights



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				str      ,    UpperCAmelCase_    :				str      ,    UpperCAmelCase_    :				dict							)     ->		Union[str, Any]:
     __lowerCamelCase   :	Optional[int]								=		get_tf_weights_as_numpy(UpperCAmelCase_							)
     __lowerCamelCase   :	List[str]								=		convert_bigbird_pegasus(UpperCAmelCase_      ,    UpperCAmelCase_							)
     torch_model.save_pretrained(UpperCAmelCase_							)


if __name__ == "__main__":
      A__		:					Any												=  argparse.ArgumentParser()
      parser.add_argument("""--tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
      parser.add_argument("""--save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
      A__		:					List[str]												=  parser.parse_args()
      A__		:					Optional[int]												=  {}
      convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)






