

from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import (
         TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
         TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
         TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
         TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
         TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
         LayoutLMvaConfig,
         TFLayoutLMvaForQuestionAnswering,
         TFLayoutLMvaForSequenceClassification,
         TFLayoutLMvaForTokenClassification,
         TFLayoutLMvaModel,
     )

if is_vision_available():
     from PIL import Image

     from transformers import LayoutLMvaImageProcessor



class 						_A     :





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_=2   ,SCREAMING_SNAKE_CASE_=3   ,SCREAMING_SNAKE_CASE_=4   ,SCREAMING_SNAKE_CASE_=2   ,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_=36   ,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_=512   ,SCREAMING_SNAKE_CASE_=16   ,SCREAMING_SNAKE_CASE_=2   ,SCREAMING_SNAKE_CASE_=0.02   ,SCREAMING_SNAKE_CASE_=6   ,SCREAMING_SNAKE_CASE_=6   ,SCREAMING_SNAKE_CASE_=3   ,SCREAMING_SNAKE_CASE_=4   ,SCREAMING_SNAKE_CASE_=None   ,SCREAMING_SNAKE_CASE_=1000   ,):

      '''simple docstring'''
      snake_case							:		int        =		parent
      snake_case							:		Optional[Any]        =		batch_size
      snake_case							:		List[Any]        =		num_channels
      snake_case							:		Optional[Any]        =		image_size
      snake_case							:		List[Any]        =		patch_size
      snake_case							:		List[str]        =		is_training
      snake_case							:		Union[str, Any]        =		use_input_mask
      snake_case							:		Tuple        =		use_token_type_ids
      snake_case							:		str        =		use_labels
      snake_case							:		int        =		vocab_size
      snake_case							:		Optional[Any]        =		hidden_size
      snake_case							:		int        =		num_hidden_layers
      snake_case							:		Dict        =		num_attention_heads
      snake_case							:		Any        =		intermediate_size
      snake_case							:		int        =		hidden_act
      snake_case							:		Any        =		hidden_dropout_prob
      snake_case							:		int        =		attention_probs_dropout_prob
      snake_case							:		List[str]        =		max_position_embeddings
      snake_case							:		List[str]        =		type_vocab_size
      snake_case							:		List[Any]        =		type_sequence_label_size
      snake_case							:		Tuple        =		initializer_range
      snake_case							:		Optional[Any]        =		coordinate_size
      snake_case							:		Optional[Any]        =		shape_size
      snake_case							:		Dict        =		num_labels
      snake_case							:		Tuple        =		num_choices
      snake_case							:		str        =		scope
      snake_case							:		Optional[Any]        =		range_bbox

      # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
      snake_case							:		Dict        =		text_seq_length
      snake_case							:		Optional[Any]        =		(image_size // patch_size) ** 2 + 1
      snake_case							:		Union[str, Any]        =		self.text_seq_length + self.image_seq_length





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Dict        =		ids_tensor([self.batch_size, self.text_seq_length]   ,self.vocab_size    )

      snake_case							:		Tuple        =		ids_tensor([self.batch_size, self.text_seq_length, 4]   ,self.range_bbox    )
      snake_case							:		Optional[int]        =		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]:
                  snake_case							:		Dict        =		bbox[i, j, 3]
                  snake_case							:		Dict        =		bbox[i, j, 1]
                  snake_case							:		Optional[int]        =		tmp_coordinate
              if bbox[i, j, 2] < bbox[i, j, 0]:
                  snake_case							:		Dict        =		bbox[i, j, 2]
                  snake_case							:		Optional[Any]        =		bbox[i, j, 0]
                  snake_case							:		Optional[int]        =		tmp_coordinate
      snake_case							:		Optional[Any]        =		tf.constant(SCREAMING_SNAKE_CASE_    )

      snake_case							:		Tuple        =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

      snake_case							:		List[str]        =		None
      if self.use_input_mask:
          snake_case							:		str        =		random_attention_mask([self.batch_size, self.text_seq_length]    )

      snake_case							:		Optional[int]        =		None
      if self.use_token_type_ids:
          snake_case							:		Optional[int]        =		ids_tensor([self.batch_size, self.text_seq_length]   ,self.type_vocab_size    )

      snake_case							:		Tuple        =		None
      snake_case							:		Union[str, Any]        =		None
      if self.use_labels:
          snake_case							:		List[Any]        =		ids_tensor([self.batch_size]   ,self.type_sequence_label_size    )
          snake_case							:		Any        =		ids_tensor([self.batch_size, self.text_seq_length]   ,self.num_labels    )

      snake_case							:		Any        =		LayoutLMvaConfig(
          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   ,coordinate_size=self.coordinate_size   ,shape_size=self.shape_size   ,input_size=self.image_size   ,patch_size=self.patch_size   ,)

      return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		str        =		TFLayoutLMvaModel(config=SCREAMING_SNAKE_CASE_    )

      # text + image
      snake_case							:		Optional[int]        =		model(SCREAMING_SNAKE_CASE_   ,pixel_values=SCREAMING_SNAKE_CASE_   ,training=SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[int]        =		model(
          SCREAMING_SNAKE_CASE_   ,bbox=SCREAMING_SNAKE_CASE_   ,pixel_values=SCREAMING_SNAKE_CASE_   ,attention_mask=SCREAMING_SNAKE_CASE_   ,token_type_ids=SCREAMING_SNAKE_CASE_   ,training=SCREAMING_SNAKE_CASE_   ,)
      snake_case							:		int        =		model(SCREAMING_SNAKE_CASE_   ,bbox=SCREAMING_SNAKE_CASE_   ,pixel_values=SCREAMING_SNAKE_CASE_   ,training=SCREAMING_SNAKE_CASE_    )

      self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size)    )

      # text only
      snake_case							:		str        =		model(SCREAMING_SNAKE_CASE_   ,training=SCREAMING_SNAKE_CASE_    )
      self.parent.assertEqual(
          result.last_hidden_state.shape   ,(self.batch_size, self.text_seq_length, self.hidden_size)    )

      # image only
      snake_case							:		Optional[int]        =		model({"""pixel_values""": pixel_values}   ,training=SCREAMING_SNAKE_CASE_    )
      self.parent.assertEqual(
          result.last_hidden_state.shape   ,(self.batch_size, self.image_seq_length, 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_    ):

      '''simple docstring'''
      snake_case							:		Dict        =		self.num_labels
      snake_case							:		Optional[int]        =		TFLayoutLMvaForSequenceClassification(config=SCREAMING_SNAKE_CASE_    )
      snake_case							:		int        =		model(
          SCREAMING_SNAKE_CASE_   ,bbox=SCREAMING_SNAKE_CASE_   ,pixel_values=SCREAMING_SNAKE_CASE_   ,attention_mask=SCREAMING_SNAKE_CASE_   ,token_type_ids=SCREAMING_SNAKE_CASE_   ,labels=SCREAMING_SNAKE_CASE_   ,training=SCREAMING_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_    ):

      '''simple docstring'''
      snake_case							:		List[str]        =		self.num_labels
      snake_case							:		Optional[int]        =		TFLayoutLMvaForTokenClassification(config=SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[Any]        =		model(
          SCREAMING_SNAKE_CASE_   ,bbox=SCREAMING_SNAKE_CASE_   ,pixel_values=SCREAMING_SNAKE_CASE_   ,attention_mask=SCREAMING_SNAKE_CASE_   ,token_type_ids=SCREAMING_SNAKE_CASE_   ,labels=SCREAMING_SNAKE_CASE_   ,training=SCREAMING_SNAKE_CASE_   ,)
      self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.text_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_    ):

      '''simple docstring'''
      snake_case							:		Optional[int]        =		2
      snake_case							:		Dict        =		TFLayoutLMvaForQuestionAnswering(config=SCREAMING_SNAKE_CASE_    )
      snake_case							:		Tuple        =		model(
          SCREAMING_SNAKE_CASE_   ,bbox=SCREAMING_SNAKE_CASE_   ,pixel_values=SCREAMING_SNAKE_CASE_   ,attention_mask=SCREAMING_SNAKE_CASE_   ,token_type_ids=SCREAMING_SNAKE_CASE_   ,start_positions=SCREAMING_SNAKE_CASE_   ,end_positions=SCREAMING_SNAKE_CASE_   ,training=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    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Optional[Any]        =		self.prepare_config_and_inputs()
      ((snake_case)      ,		(snake_case)      ,		(snake_case)      ,		(snake_case)      ,		(snake_case)      ,		(snake_case)      ,		(snake_case)      ,		(snake_case))							:		Dict        =		config_and_inputs
      snake_case							:		Union[str, Any]        =		{
          """input_ids""": input_ids,
          """bbox""": bbox,
          """pixel_values""": pixel_values,
          """token_type_ids""": token_type_ids,
          """attention_mask""": input_mask,
      }
      return config, inputs_dict







@require_tf
class 						_A     (				snake_case						,    snake_case						,    unittest.TestCase				):





  '''simple docstring'''



  __lowerCamelCase			:      Union[str, Any]					=   (
      (
          TFLayoutLMvaModel,
          TFLayoutLMvaForQuestionAnswering,
          TFLayoutLMvaForSequenceClassification,
          TFLayoutLMvaForTokenClassification,
      )
      if is_tf_available()
      else ()
  )
  __lowerCamelCase			:      List[str]					=   (
      {'''document-question-answering''': TFLayoutLMvaForQuestionAnswering, '''feature-extraction''': TFLayoutLMvaModel}
      if is_tf_available()
      else {}
  )

  __lowerCamelCase			:      Optional[Any]					=   False
  __lowerCamelCase			:      List[Any]					=   False
  __lowerCamelCase			:      Any					=   False





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      return True





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_=False    ):

      '''simple docstring'''
      snake_case							:		str        =		copy.deepcopy(SCREAMING_SNAKE_CASE_    )

      if model_class in get_values(SCREAMING_SNAKE_CASE_    ):
          snake_case							:		Any        =		{
              k: tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_   ,1    )   ,(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)    )
              if isinstance(SCREAMING_SNAKE_CASE_   ,tf.Tensor    ) and v.ndim > 0
              else v
              for k, v in inputs_dict.items()
          }

      if return_labels:
          if model_class in get_values(SCREAMING_SNAKE_CASE_    ):
              snake_case							:		Optional[int]        =		tf.ones(self.model_tester.batch_size   ,dtype=tf.intaa    )
          elif model_class in get_values(SCREAMING_SNAKE_CASE_    ):
              snake_case							:		Dict        =		tf.zeros(self.model_tester.batch_size   ,dtype=tf.intaa    )
              snake_case							:		Tuple        =		tf.zeros(self.model_tester.batch_size   ,dtype=tf.intaa    )
          elif model_class in get_values(SCREAMING_SNAKE_CASE_    ):
              snake_case							:		List[Any]        =		tf.zeros(self.model_tester.batch_size   ,dtype=tf.intaa    )
          elif model_class in get_values(SCREAMING_SNAKE_CASE_    ):
              snake_case							:		Dict        =		tf.zeros(
                  (self.model_tester.batch_size, self.model_tester.text_seq_length)   ,dtype=tf.intaa    )

      return inputs_dict





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		str        =		TFLayoutLMvaModelTester(self    )
      snake_case							:		str        =		ConfigTester(self   ,config_class=SCREAMING_SNAKE_CASE_   ,hidden_size=37    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      self.config_tester.run_common_tests()





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case      ,		snake_case							:		Tuple        =		self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
          snake_case							:		List[str]        =		model_class(SCREAMING_SNAKE_CASE_    )
          if getattr(SCREAMING_SNAKE_CASE_   ,"""hf_compute_loss"""   ,SCREAMING_SNAKE_CASE_    ):
              # The number of elements in the loss should be the same as the number of elements in the label
              snake_case							:		Optional[Any]        =		self._prepare_for_class(inputs_dict.copy()   ,SCREAMING_SNAKE_CASE_   ,return_labels=SCREAMING_SNAKE_CASE_    )
              snake_case							:		List[str]        =		prepared_for_class[
                  sorted(prepared_for_class.keys() - inputs_dict.keys()   ,reverse=SCREAMING_SNAKE_CASE_    )[0]
              ]
              snake_case							:		List[Any]        =		added_label.shape.as_list()[:1]

              # Test that model correctly compute the loss with kwargs
              snake_case							:		Tuple        =		self._prepare_for_class(inputs_dict.copy()   ,SCREAMING_SNAKE_CASE_   ,return_labels=SCREAMING_SNAKE_CASE_    )
              snake_case							:		Optional[int]        =		prepared_for_class.pop("""input_ids"""    )

              snake_case							:		Dict        =		model(SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    )[0]
              self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]    )

              # Test that model correctly compute the loss when we mask some positions
              snake_case							:		Union[str, Any]        =		self._prepare_for_class(inputs_dict.copy()   ,SCREAMING_SNAKE_CASE_   ,return_labels=SCREAMING_SNAKE_CASE_    )
              snake_case							:		Any        =		prepared_for_class.pop("""input_ids"""    )
              if "labels" in prepared_for_class:
                  snake_case							:		Union[str, Any]        =		prepared_for_class["""labels"""].numpy()
                  if len(labels.shape    ) > 1 and labels.shape[1] != 1:
                      snake_case							:		Dict        =		-100
                      snake_case							:		Optional[Any]        =		tf.convert_to_tensor(SCREAMING_SNAKE_CASE_    )
                      snake_case							:		int        =		model(SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    )[0]
                      self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]    )
                      self.assertTrue(not np.any(np.isnan(loss.numpy()    )    )    )

                # Test that model correctly compute the loss with a dict
              snake_case							:		str        =		self._prepare_for_class(inputs_dict.copy()   ,SCREAMING_SNAKE_CASE_   ,return_labels=SCREAMING_SNAKE_CASE_    )
              snake_case							:		Dict        =		model(SCREAMING_SNAKE_CASE_    )[0]
              self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]    )

              # Test that model correctly compute the loss with a tuple
              snake_case							:		Any        =		self._prepare_for_class(inputs_dict.copy()   ,SCREAMING_SNAKE_CASE_   ,return_labels=SCREAMING_SNAKE_CASE_    )

              # Get keys that were added with the _prepare_for_class function
              snake_case							:		Optional[Any]        =		prepared_for_class.keys() - inputs_dict.keys()
              snake_case							:		Optional[int]        =		inspect.signature(model.call    ).parameters
              snake_case							:		Tuple        =		list(signature.keys()    )

              # Create a dictionary holding the location of the tensors in the tuple
              snake_case							:		Optional[Any]        =		{0: """input_ids"""}
              for label_key in label_keys:
                  snake_case							:		Dict        =		signature_names.index(SCREAMING_SNAKE_CASE_    )
                  snake_case							:		Union[str, Any]        =		label_key
              snake_case							:		List[str]        =		sorted(tuple_index_mapping.items()    )
              # Initialize a list with their default values, update the values and convert to a tuple
              snake_case							:		Dict        =		[]

              for name in signature_names:
                  if name != "kwargs":
                      list_input.append(signature[name].default    )

              for index, value in sorted_tuple_index_mapping:
                  snake_case							:		Optional[Any]        =		prepared_for_class[value]

              snake_case							:		Optional[int]        =		tuple(SCREAMING_SNAKE_CASE_    )

              # Send to model
              snake_case							:		Union[str, Any]        =		model(tuple_input[:-1]    )[0]

              self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      (
          (
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		
      )							:		Tuple        =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      (
          (
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		
      )							:		int        =		self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
          snake_case							:		int        =		type
          self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      (
          (
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		
      )							:		Dict        =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(
          SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      (
          (
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		
      )							:		Dict        =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(
          SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      (
          (
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		
      )							:		Optional[int]        =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(
          SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )





  @slow
  def    snake_case_       (  self    ):

      '''simple docstring'''
      for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          snake_case							:		str        =		TFLayoutLMvaModel.from_pretrained(SCREAMING_SNAKE_CASE_    )
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_    )







def       lowercase					(	)				->			Dict:





    '''simple docstring'''




    snake_case							:		List[Any]        =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
    return image



@require_tf
class 						_A     (				unittest.TestCase				):





  '''simple docstring'''



  @cached_property
  def    snake_case_       (  self    ):

      '''simple docstring'''
      return LayoutLMvaImageProcessor(apply_ocr=SCREAMING_SNAKE_CASE_    ) if is_vision_available() else None





  @slow
  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Any        =		TFLayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""    )

      snake_case							:		int        =		self.default_image_processor
      snake_case							:		Optional[Any]        =		prepare_img()
      snake_case							:		str        =		image_processor(images=SCREAMING_SNAKE_CASE_   ,return_tensors="""tf"""    ).pixel_values

      snake_case							:		Optional[int]        =		tf.constant([[1, 2]]    )
      snake_case							:		List[str]        =		tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]    )   ,axis=0    )

      # forward pass
      snake_case							:		List[str]        =		model(input_ids=SCREAMING_SNAKE_CASE_   ,bbox=SCREAMING_SNAKE_CASE_   ,pixel_values=SCREAMING_SNAKE_CASE_   ,training=SCREAMING_SNAKE_CASE_    )

      # verify the logits
      snake_case							:		List[Any]        =		(1, 199, 768)
      self.assertEqual(outputs.last_hidden_state.shape   ,SCREAMING_SNAKE_CASE_    )

      snake_case							:		str        =		tf.constant(
          [[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]]    )

      self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]   ,SCREAMING_SNAKE_CASE_   ,atol=1E-4    )    )








from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands

def 	_lowerCAmelCase (    ):
      lowercase__  =	ArgumentParser('Transformers CLI tool'    ,	usage='transformers-cli <command> [<args>]'						)
      lowercase__  =	parser.add_subparsers(help='transformers-cli command helpers'						)

      # Register commands
      ConvertCommand.register_subcommand(A__						)
      DownloadCommand.register_subcommand(A__						)
      EnvironmentCommand.register_subcommand(A__						)
      RunCommand.register_subcommand(A__						)
      ServeCommand.register_subcommand(A__						)
      UserCommands.register_subcommand(A__						)
      AddNewModelCommand.register_subcommand(A__						)
      AddNewModelLikeCommand.register_subcommand(A__						)
      LfsCommands.register_subcommand(A__						)
      PTtoTFCommand.register_subcommand(A__						)

      # Let's go
      lowercase__  =	parser.parse_args()

      if not hasattr(A__    ,	'func'						):
            parser.print_help()
            exit(1						)

      # Run
      lowercase__  =	args.func(A__						)
      service.run()


if __name__ == "__main__":
       main()
