



'''simple docstring'''




from __future__ import annotations

import os
import tempfile
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow

from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    AutoTokenizer,
						    TFAutoModelForCausalLM,
						    TFAutoModelForSeqaSeqLM,
						    TFAutoModelForSpeechSeqaSeq,
						    TFAutoModelForVisionaSeq,
						    TFBartForConditionalGeneration,
						    TFLogitsProcessorList,
						    TFMinLengthLogitsProcessor,
						    tf_top_k_top_p_filtering,
						)

if is_tensorflow_text_available():
						import tensorflow_text as text







@require_tf
class       _lowerCAmelCase				(     unittest.TestCase					):


	def        _a      (self				):
				A_      :		Optional[int]      =	tf.convert_to_tensor(
				    [
				        [
				            8.2_22_09_91,  # 3rd highest value; idx. 0
				            -0.5_62_00_44,
				            5.23_22_97_52,
				            4.0_38_63_93,
				            -6.8_79_83_78,
				            -0.54_78_58_02,
				            -3.2_01_21_53,
				            2.92_77_71_76,
				            1.88_17_19_53,
				            7.35_34_12_76,  # 5th highest value; idx. 9
				            8.43_20_78_33,  # 2nd highest value; idx. 10
				            -9.85_71_18_36,
				            -5.96_20_92_36,
				            -1.13_03_91_61,
				            -7.1_11_52_94,
				            -0.8_36_96_33,
				            -5.3_18_64_08,
				            7.06_42_74_07,
				            0.81_36_93_44,
				            -0.82_02_38_17,
				            -5.9_17_97_96,
				            0.58_81_34_43,
				            -6.99_77_84_38,
				            4.71_55_11_89,
				            -0.18_77_16_37,
				            7.44_02_07_59,  # 4th highest value; idx. 25
				            9.38_45_09_87,  # 1st highest value; idx. 26
				            2.12_66_29_41,
				            -9.32_56_20_38,
				            2.35_65_25_22,
				        ],  # cummulative prob of 5 highest values <= 0.6
				        [
				            0.58_42_55_18,
				            4.53_13_92_38,
				            -5.57_51_04_64,
				            -6.28_03_06_99,
				            -7.19_52_95_03,
				            -4.02_12_25_51,
				            1.39_33_70_37,
				            -6.06_70_70_57,
				            1.59_48_05_17,
				            -9.64_31_19,
				            0.03_90_77_99,
				            0.67_23_17_62,
				            -8.88_20_67_26,
				            6.27_11_59_22,  # 4th highest value; idx. 13
				            2.28_52_07_23,
				            4.82_76_75_06,
				            4.30_42_13_68,
				            8.8_27_53_13,  # 2nd highest value; idx. 17
				            5.44_02_99_58,  # 5th highest value; idx. 18
				            -4.4_73_57_94,
				            7.38_57_95_36,  # 3rd highest value; idx. 20
				            -2.91_05_16_63,
				            2.61_94_60_77,
				            -2.5_67_47_62,
				            -9.48_95_93_02,
				            -4.02_92_26_45,
				            -1.35_41_69_18,
				            9.67_70_23_23,  # 1st highest value; idx. 27
				            -5.89_47_85_53,
				            1.85_37_04_67,
				        ],  # cummulative prob of 5 highest values <= 0.6
				    ]			, dtype=tf.floataa			, )

				A_      :		Dict      =	tf.convert_to_tensor(
				    [[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]]			, dtype=tf.intaa			, )  # expected non filtered idx as noted above

				A_      :		List[Any]      =	tf.convert_to_tensor(
				    [8.22_20_99, 7.3_53_41_26, 8.43_20_78, 7.4_40_20_75, 9.3_84_51, 6.27_11_59, 8.82_75_31, 5.4_40_29_95, 7.3_85_79_56, 9.67_70_23]			, dtype=tf.floataa			, )  # expected non filtered values as noted above

				A_      :		Dict      =	tf_top_k_top_p_filtering(_snake_case			, top_k=10			, top_p=0.6			, min_tokens_to_keep=4				)

				A_      :		Optional[int]      =	output[output != -float("""inf"""				)]
				A_      :		Tuple      =	tf.cast(
				    tf.where(tf.not_equal(_snake_case			, tf.constant(-float("""inf"""				)			, dtype=tf.floataa				)				)				)			, dtype=tf.intaa			, )

				tf.debugging.assert_near(_snake_case			, _snake_case			, rtol=1E-12				)
				tf.debugging.assert_equal(_snake_case			, _snake_case				)







@require_tf
class       _lowerCAmelCase				(     unittest.TestCase ,     a__					):
	# setting framework_dependent_parameters needs to be gated, just like its contents' imports
	if is_tf_available():
		__SCREAMING_SNAKE_CASE  :						List[Any]						     =   {
		    'AutoModelForCausalLM': TFAutoModelForCausalLM,
		    'AutoModelForSpeechSeq2Seq': TFAutoModelForSpeechSeqaSeq,
		    'AutoModelForSeq2SeqLM': TFAutoModelForSeqaSeqLM,
		    'AutoModelForVision2Seq': TFAutoModelForVisionaSeq,
		    'LogitsProcessorList': TFLogitsProcessorList,
		    'MinLengthLogitsProcessor': TFMinLengthLogitsProcessor,
		    'create_tensor_fn': tf.convert_to_tensor,
		    'floats_tensor': floats_tensor,
		    'return_tensors': 'tf',
		}


	@slow
	def        _a      (self				):
				A_      :		Union[str, Any]      =	TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""				)
				A_      :		List[str]      =	2
				A_      :		List[str]      =	2







				class       _lowerCAmelCase				(     tf.Module					):


					def __init__(self			, lowercase				):
								super(_snake_case			, self				).__init__()
								A_      :		List[Any]      =	model


					@tf.function(
					    input_signature=(
					        tf.TensorSpec((None, input_length)			, tf.intaa			, name="""input_ids"""				),
					        tf.TensorSpec((None, input_length)			, tf.intaa			, name="""attention_mask"""				),
					    )			, jit_compile=_snake_case			, )
					def        _a      (self			, lowercase			, lowercase				):
								A_      :		Any      =	self.model.generate(
								    input_ids=_snake_case			, attention_mask=_snake_case			, max_new_tokens=_snake_case			, return_dict_in_generate=_snake_case			, )
								return {"sequences": outputs["sequences"]}






				A_      :		Tuple      =	[[2, 0], [102, 103]]
				A_      :		Optional[int]      =	[[1, 0], [1, 1]]
				A_      :		Union[str, Any]      =	DummyModel(model=_snake_case				)
				with tempfile.TemporaryDirectory() as tmp_dir:
							tf.saved_model.save(_snake_case			, _snake_case			, signatures={"""serving_default""": dummy_model.serving}				)
							A_      :		str      =	tf.saved_model.load(_snake_case				).signatures["""serving_default"""]
							for batch_size in range(1			, len(_snake_case				) + 1				):
										A_      :		Tuple      =	{
										    """input_ids""": tf.constant(dummy_input_ids[:batch_size]				),
										    """attention_mask""": tf.constant(dummy_attention_masks[:batch_size]				),
										}
										A_      :		int      =	serving_func(**_snake_case				)["""sequences"""]
										A_      :		Optional[int]      =	test_model.generate(**_snake_case			, max_new_tokens=_snake_case				)
										tf.debugging.assert_equal(_snake_case			, _snake_case				)


	@slow
	def        _a      (self				):
				A_      :		Tuple      =	TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""				)
				A_      :		Union[str, Any]      =	1
				A_      :		int      =	2







				class       _lowerCAmelCase				(     tf.Module					):


					def __init__(self			, lowercase				):
								super(_snake_case			, self				).__init__()
								A_      :		Tuple      =	model


					@tf.function(
					    input_signature=(
					        tf.TensorSpec((batch_size, None)			, tf.intaa			, name="""input_ids"""				),
					        tf.TensorSpec((batch_size, None)			, tf.intaa			, name="""attention_mask"""				),
					    )			, jit_compile=_snake_case			, )
					def        _a      (self			, lowercase			, lowercase				):
								A_      :		Dict      =	self.model.generate(
								    input_ids=_snake_case			, attention_mask=_snake_case			, max_new_tokens=_snake_case			, return_dict_in_generate=_snake_case			, )
								return {"sequences": outputs["sequences"]}






				A_      :		List[str]      =	[[2], [102, 103]]
				A_      :		Any      =	[[1], [1, 1]]
				A_      :		List[Any]      =	DummyModel(model=_snake_case				)
				with tempfile.TemporaryDirectory() as tmp_dir:
							tf.saved_model.save(_snake_case			, _snake_case			, signatures={"""serving_default""": dummy_model.serving}				)
							A_      :		Dict      =	tf.saved_model.load(_snake_case				).signatures["""serving_default"""]
							for input_row in range(len(_snake_case				)				):
										A_      :		Union[str, Any]      =	{
										    """input_ids""": tf.constant([dummy_input_ids[input_row]]				),
										    """attention_mask""": tf.constant([dummy_attention_masks[input_row]]				),
										}
										A_      :		str      =	serving_func(**_snake_case				)["""sequences"""]
										A_      :		List[str]      =	test_model.generate(**_snake_case			, max_new_tokens=_snake_case				)
										tf.debugging.assert_equal(_snake_case			, _snake_case				)


	@slow
	@require_tensorflow_text
	def        _a      (self				):






				with tempfile.TemporaryDirectory() as tmp_dir:
							# file needed to load the TF tokenizer
							hf_hub_download(repo_id="""google/flan-t5-small"""			, filename="""spiece.model"""			, local_dir=_snake_case				)







							class       _lowerCAmelCase				(     tf.keras.layers.Layer					):


								def __init__(self				):
											super().__init__()
											A_      :		Optional[int]      =	text.SentencepieceTokenizer(
											    model=tf.io.gfile.GFile(os.path.join(_snake_case			, """spiece.model"""				)			, """rb"""				).read()				)
											A_      :		str      =	TFAutoModelForSeqaSeqLM.from_pretrained("""hf-internal-testing/tiny-random-t5"""				)


								def        _a      (self			, lowercase			, *lowercase			, **lowercase				):
											A_      :		Optional[int]      =	self.tokenizer.tokenize(_snake_case				)
											A_,       A_      :		Optional[Any]      =	text.pad_model_inputs(
											    _snake_case			, max_seq_length=64			, pad_value=self.model.config.pad_token_id				)
											A_      :		int      =	self.model.generate(input_ids=_snake_case			, attention_mask=_snake_case				)
											return self.tokenizer.detokenize(_snake_case				)






							A_      :		List[str]      =	CompleteSentenceTransformer()
							A_      :		Tuple      =	tf.keras.layers.Input(shape=(1,)			, dtype=tf.string			, name="""inputs"""				)
							A_      :		Union[str, Any]      =	complete_model(_snake_case				)
							A_      :		List[Any]      =	tf.keras.Model(_snake_case			, _snake_case				)
							keras_model.save(_snake_case				)


	def        _a      (self				):
				A_      :		List[str]      =	{
				    """do_sample""": True,
				    """num_beams""": 1,
				    """top_p""": 0.7,
				    """top_k""": 10,
				    """temperature""": 0.7,
				}
				A_      :		List[Any]      =	14

				A_      :		Union[str, Any]      =	AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""				)
				A_      :		Tuple      =	"""Hello, my dog is cute and"""
				A_      :		Any      =	tokenizer(_snake_case			, return_tensors="""tf"""				)
				A_      :		Optional[Any]      =	TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""				)

				A_      :		Dict      =	638
				# forces the generation to happen on CPU, to avoid GPU-related quirks
				with tf.device(""":/CPU:0"""				):
							tf.random.set_seed(0				)
							A_      :		Dict      =	model.generate(**_snake_case			, eos_token_id=_snake_case			, **_snake_case				)
				self.assertTrue(expectation == len(generated_tokens[0]				)				)

				A_      :		str      =	[638, 198]
				with tf.device(""":/CPU:0"""				):
							tf.random.set_seed(0				)
							A_      :		Any      =	model.generate(**_snake_case			, eos_token_id=_snake_case			, **_snake_case				)
				self.assertTrue(expectation == len(generated_tokens[0]				)				)


	def        _a      (self				):
				A_      :		int      =	AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bart"""				)
				A_      :		int      =	"""Hugging Face is a technology company based in New York and Paris."""
				A_      :		List[str]      =	bart_tokenizer(_snake_case			, return_tensors="""tf"""				).input_ids
				A_      :		List[str]      =	TFBartForConditionalGeneration.from_pretrained("""hf-internal-testing/tiny-random-bart"""				)
				A_      :		Optional[int]      =	bart_model.generate(_snake_case				).numpy()







				class       _lowerCAmelCase				(     a__					):


					def        _a      (self			, lowercase			, lowercase=None			, **lowercase				):
								return super().call(_snake_case			, **_snake_case				)






				A_      :		Optional[int]      =	FakeBart.from_pretrained("""hf-internal-testing/tiny-random-bart"""				)
				A_      :		List[str]      =	bart_model.generate(_snake_case			, foo="""bar"""				).numpy()
				self.assertTrue(np.array_equal(_snake_case			, _snake_case				)				)







				class       _lowerCAmelCase				(     bart_model.model.encoder.__class__					):


					def        _a      (self			, lowercase			, **lowercase				):
								return super().call(_snake_case			, **_snake_case				)






				A_      :		str      =	FakeEncoder(bart_model.config			, bart_model.model.shared				)
				A_      :		Any      =	fake_encoder

				# Normal generation still works (the output will be different because the encoder weights are different)
				A_      :		int      =	bart_model.generate(_snake_case				).numpy()
				with self.assertRaises(_snake_case				):
							# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
							bart_model.generate(_snake_case			, foo="""bar"""				)






"""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       a							:



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







  """simple docstring"""






  lowerCAmelCase    				= parent
  lowerCAmelCase    				= batch_size
  lowerCAmelCase    				= seq_length
  lowerCAmelCase    				= is_training
  lowerCAmelCase    				= use_input_mask
  lowerCAmelCase    				= use_token_type_ids
  lowerCAmelCase    				= use_labels
  lowerCAmelCase    				= vocab_size
  lowerCAmelCase    				= hidden_size
  lowerCAmelCase    				= num_hidden_layers
  lowerCAmelCase    				= num_attention_heads
  lowerCAmelCase    				= intermediate_size
  lowerCAmelCase    				= hidden_act
  lowerCAmelCase    				= hidden_dropout_prob
  lowerCAmelCase    				= attention_probs_dropout_prob
  lowerCAmelCase    				= max_position_embeddings
  lowerCAmelCase    				= type_vocab_size
  lowerCAmelCase    				= type_sequence_label_size
  lowerCAmelCase    				= initializer_range
  lowerCAmelCase    				= num_labels
  lowerCAmelCase    				= num_choices
  lowerCAmelCase    				= scope
  lowerCAmelCase    				= range_bbox



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size     )

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

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

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

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

  lowerCAmelCase    				= 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    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMModel(config=_snake_case     )

  lowerCAmelCase    				= model(_snake_case				,       _snake_case				,       attention_mask=_snake_case				,       token_type_ids=_snake_case     )
  lowerCAmelCase    				= model(_snake_case				,       _snake_case				,       token_type_ids=_snake_case     )
  lowerCAmelCase    				= 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    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMForMaskedLM(config=_snake_case     )

  lowerCAmelCase    				= 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    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= self.num_labels
  lowerCAmelCase    				= TFLayoutLMForSequenceClassification(config=_snake_case     )

  lowerCAmelCase    				= 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    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= self.num_labels
  lowerCAmelCase    				= TFLayoutLMForTokenClassification(config=_snake_case     )

  lowerCAmelCase    				= 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    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMForQuestionAnswering(config=_snake_case     )

  lowerCAmelCase    				= 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    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.prepare_config_and_inputs()
  (
      (
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,
  )    				= config_and_inputs
  lowerCAmelCase    				= {
      'input_ids': input_ids,
      'bbox': bbox,
      'token_type_ids': token_type_ids,
      'attention_mask': input_mask,
  }
  return config, inputs_dict







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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMModelTester(self     )
  lowerCAmelCase    				= ConfigTester(self				,       config_class=_snake_case				,       hidden_size=37     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self.config_tester.run_common_tests()



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_masked_lm(*_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_sequence_classification(*_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_token_classification(*_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_question_answering(*_snake_case     )



 @slow
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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



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







  """simple docstring"""






  pass


def 		_SCREAMING_SNAKE_CASE			():
 # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
 # fmt: off
 lowerCAmelCase    				= tf.convert_to_tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]]			)  # noqa: E231
 lowerCAmelCase    				= 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    				= 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
 lowerCAmelCase    				= 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    				= 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       a							(   unittest.TestCase  ):



 @slow
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'     )

  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= prepare_layoutlm_batch_inputs()

  # forward pass
  lowerCAmelCase    				= 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]
  lowerCAmelCase    				= 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]
  lowerCAmelCase    				= 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    UpperCamelCase__  (     self     ):







  """simple docstring"""






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

  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= prepare_layoutlm_batch_inputs()

  # forward pass
  lowerCAmelCase    				= 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
  lowerCAmelCase    				= outputs.loss
  lowerCAmelCase    				= (2,)
  self.assertEqual(loss.shape				,       _snake_case     )

  # test the shape of the logits
  lowerCAmelCase    				= outputs.logits
  lowerCAmelCase    				= (2, 2)
  self.assertEqual(logits.shape				,       _snake_case     )



 @slow
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'				,       num_labels=13     )

  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= prepare_layoutlm_batch_inputs()

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



 @slow
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'     )

  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= prepare_layoutlm_batch_inputs()

  # forward pass
  lowerCAmelCase    				= model(input_ids=_snake_case				,       bbox=_snake_case				,       attention_mask=_snake_case				,       token_type_ids=_snake_case     )

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





