





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	,							A_	,							A_=13	,							A_=7	,							A_=True	,							A_=True	,							A_=True	,							A_=True	,							A_=99	,							A_=32	,							A_=2	,							A_=4	,							A_=37	,							A_="gelu"	,							A_=0.1	,							A_=0.1	,							A_=512	,							A_=16	,							A_=2	,							A_=0.02	,							A_=3	,							A_=4	,							A_=None	,							A_=1000	,							):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size       )

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

       UpperCamelCase     : Optional[Any]	     =       None
       if self.use_input_mask:
        UpperCamelCase     : List[Any]	     =       random_attention_mask([self.batch_size, self.seq_length]       )

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

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

       UpperCamelCase     : str	     =       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	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : str	     =       TFLayoutLMModel(config=A_       )

       UpperCamelCase     : Any	     =       model(A_	,							A_	,							attention_mask=A_	,							token_type_ids=A_       )
       UpperCamelCase     : Union[str, Any]	     =       model(A_	,							A_	,							token_type_ids=A_       )
       UpperCamelCase     : str	     =       model(A_	,							A_       )
       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	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       TFLayoutLMForMaskedLM(config=A_       )

       UpperCamelCase     : Any	     =       model(A_	,							A_	,							attention_mask=A_	,							token_type_ids=A_	,							labels=A_       )
       self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.seq_length, self.vocab_size)       )





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       self.num_labels
       UpperCamelCase     : Optional[int]	     =       TFLayoutLMForSequenceClassification(config=A_       )

       UpperCamelCase     : Optional[int]	     =       model(A_	,							A_	,							attention_mask=A_	,							token_type_ids=A_       )
       self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.num_labels)       )





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : str	     =       self.num_labels
       UpperCamelCase     : List[str]	     =       TFLayoutLMForTokenClassification(config=A_       )

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





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       TFLayoutLMForQuestionAnswering(config=A_       )

       UpperCamelCase     : Union[str, Any]	     =       model(A_	,							A_	,							attention_mask=A_	,							token_type_ids=A_       )
       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'''




       UpperCamelCase     : Union[str, Any]	     =       self.prepare_config_and_inputs()
       (
           (
           UpperCamelCase
       )     ,   (
           UpperCamelCase
       )     ,   (
           UpperCamelCase
       )     ,   (
           UpperCamelCase
       )     ,   (
           UpperCamelCase
       )     ,   (
           UpperCamelCase
       )     ,   (
           UpperCamelCase
       )     ,   (
           UpperCamelCase
       )     ,   
       )     : List[Any]	     =       config_and_inputs
       UpperCamelCase     : 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  A__       (	__snake_case	,       __snake_case	,       unittest.TestCase      ):
      _UpperCAmelCase     :Optional[Any]						   =       (
          (
              TFLayoutLMModel,
              TFLayoutLMForMaskedLM,
              TFLayoutLMForTokenClassification,
              TFLayoutLMForSequenceClassification,
              TFLayoutLMForQuestionAnswering,
          )
          if is_tf_available()
          else ()
      )
      _UpperCAmelCase     :Union[str, 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     :Any						   =       True
      _UpperCAmelCase     :Any						   =       1_0





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       self.config_tester.run_common_tests()





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[Any]	     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*A_       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*A_       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : List[Any]	     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*A_       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : List[Any]	     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*A_       )





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCamelCase     : Optional[Any]	     =       TFLayoutLMModel.from_pretrained(A_       )
        self.assertIsNotNone(A_       )





      @unittest.skip("Onnx compliancy broke with TF 2.10"       )
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       pass



def 		A_    (					)						->			Any:
 # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
 # fmt: off
 UpperCamelCase     : Union[str, Any]	     =       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
 UpperCamelCase     : List[Any]	     =       tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]					)  # noqa: E231
 UpperCamelCase     : List[str]	     =       tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[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
 UpperCamelCase     : List[str]	     =       tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]					)  # noqa: E231
 # these are sequence labels (i.e. at the token level)
 UpperCamelCase     : Optional[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  A__       (	unittest.TestCase      ):





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




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

       UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     : List[str]	     =       prepare_layoutlm_batch_inputs()

       # forward pass
       UpperCamelCase     : Any	     =       model(input_ids=A_	,							bbox=A_	,							attention_mask=A_	,							token_type_ids=A_       )

       # test the sequence output on [0, :3, :3]
       UpperCamelCase     : Optional[Any]	     =       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]	,							A_	,							atol=1e-3       )       )

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

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





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"	,							num_labels=2       )

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

       # forward pass
       UpperCamelCase     : Tuple	     =       model(
           input_ids=A_	,							bbox=A_	,							attention_mask=A_	,							token_type_ids=A_	,							labels=tf.convert_to_tensor([1, 1]       )	,							)

       # test whether we get a loss as a scalar
       UpperCamelCase     : Union[str, Any]	     =       outputs.loss
       UpperCamelCase     : Union[str, Any]	     =       (2,)
       self.assertEqual(loss.shape	,							A_       )

       # test the shape of the logits
       UpperCamelCase     : Optional[Any]	     =       outputs.logits
       UpperCamelCase     : Optional[int]	     =       (2, 2)
       self.assertEqual(logits.shape	,							A_       )





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"	,							num_labels=13       )

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

       # forward pass
       UpperCamelCase     : Union[str, Any]	     =       model(
           input_ids=A_	,							bbox=A_	,							attention_mask=A_	,							token_type_ids=A_	,							labels=A_       )

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





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




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

       UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     : Any	     =       prepare_layoutlm_batch_inputs()

       # forward pass
       UpperCamelCase     : Tuple	     =       model(input_ids=A_	,							bbox=A_	,							attention_mask=A_	,							token_type_ids=A_       )

       # test the shape of the logits
       UpperCamelCase     : Tuple	     =       tf.convert_to_tensor((2, 25)       )
       self.assertEqual(outputs.start_logits.shape	,							A_       )
       self.assertEqual(outputs.end_logits.shape	,							A_       )







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import tensorflow as tf

    from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



@require_tf
@require_sentencepiece
@require_tokenizers
class  A__       (	unittest.TestCase      ):





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : List[str]	     =       TFAutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"       )
       UpperCamelCase     : Tuple	     =       AutoTokenizer.from_pretrained("google/mt5-small"       )

       UpperCamelCase     : Dict	     =       tokenizer("Hello there"	,							return_tensors="tf"       ).input_ids
       UpperCamelCase     : int	     =       tokenizer("Hi I am"	,							return_tensors="tf"       ).input_ids

       UpperCamelCase     : Union[str, Any]	     =       model(A_	,							labels=A_       ).loss
       UpperCamelCase     : List[str]	     =       -tf.math.reduce_mean(A_       ).numpy()

       UpperCamelCase     : Union[str, Any]	     =       -21.22_81_68
       self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 2e-4       )
