
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:

					def __init__(						self       :  Union[str, Any] ,       __UpperCamelCase       :  Union[str, Any] ,       __UpperCamelCase       :  Optional[Any]=2 ,       __UpperCamelCase       :  List[str]=3 ,       __UpperCamelCase       :  List[Any]=4 ,       __UpperCamelCase       :  Tuple=2 ,       __UpperCamelCase       :  List[Any]=7 ,       __UpperCamelCase       :  Dict=True ,       __UpperCamelCase       :  List[str]=True ,       __UpperCamelCase       :  str=True ,       __UpperCamelCase       :  Tuple=True ,       __UpperCamelCase       :  int=9_9 ,       __UpperCamelCase       :  str=3_6 ,       __UpperCamelCase       :  Dict=2 ,       __UpperCamelCase       :  str=4 ,       __UpperCamelCase       :  Tuple=3_7 ,       __UpperCamelCase       :  List[str]="gelu" ,       __UpperCamelCase       :  int=0.1 ,       __UpperCamelCase       :  str=0.1 ,       __UpperCamelCase       :  Tuple=5_1_2 ,       __UpperCamelCase       :  Tuple=1_6 ,       __UpperCamelCase       :  str=2 ,       __UpperCamelCase       :  Optional[int]=0.02 ,       __UpperCamelCase       :  Tuple=6 ,       __UpperCamelCase       :  Dict=6 ,       __UpperCamelCase       :  Any=3 ,       __UpperCamelCase       :  str=4 ,       __UpperCamelCase       :  Union[str, Any]=None ,       __UpperCamelCase       :  List[Any]=1_0_0_0 ,       ):
												lowerCamelCase_					=       parent
												lowerCamelCase_					=       batch_size
												lowerCamelCase_					=       num_channels
												lowerCamelCase_					=       image_size
												lowerCamelCase_					=       patch_size
												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_					=       coordinate_size
												lowerCamelCase_					=       shape_size
												lowerCamelCase_					=       num_labels
												lowerCamelCase_					=       num_choices
												lowerCamelCase_					=       scope
												lowerCamelCase_					=       range_bbox

												# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
												lowerCamelCase_					=       text_seq_length
												lowerCamelCase_					=       (image_size // patch_size) ** 2 + 1
												lowerCamelCase_					=       self.text_seq_length + self.image_seq_length

					def  lowercase__   (						self       :  Optional[Any] ):
												lowerCamelCase_					=       ids_tensor([self.batch_size, self.text_seq_length] ,       self.vocab_size )

												lowerCamelCase_					=       ids_tensor([self.batch_size, self.text_seq_length, 4] ,       self.range_bbox )
												lowerCamelCase_					=       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_					=       tmp_coordinate
																										if bbox[i, j, 2] < bbox[i, j, 0]:
																																	lowerCamelCase_					=       bbox[i, j, 2]
																																	lowerCamelCase_					=       bbox[i, j, 0]
																																	lowerCamelCase_					=       tmp_coordinate
												lowerCamelCase_					=       tf.constant(__UpperCamelCase )

												lowerCamelCase_					=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

												lowerCamelCase_					=       None
												if self.use_input_mask:
																			lowerCamelCase_					=       random_attention_mask([self.batch_size, self.text_seq_length] )

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

												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.text_seq_length] ,       self.num_labels )

												lowerCamelCase_					=       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  lowercase__   (						self       :  List[str] ,       __UpperCamelCase       :  Tuple ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  Union[str, Any] ,       __UpperCamelCase       :  List[Any] ,       __UpperCamelCase       :  Optional[int] ,       __UpperCamelCase       :  List[str] ):
												lowerCamelCase_					=       TFLayoutLMvaModel(config=__UpperCamelCase )

												# text + image
												lowerCamelCase_					=       model(__UpperCamelCase ,       pixel_values=__UpperCamelCase ,       training=__UpperCamelCase )
												lowerCamelCase_					=       model(
												    __UpperCamelCase ,       bbox=__UpperCamelCase ,       pixel_values=__UpperCamelCase ,       attention_mask=__UpperCamelCase ,       token_type_ids=__UpperCamelCase ,       training=__UpperCamelCase ,       )
												lowerCamelCase_					=       model(__UpperCamelCase ,       bbox=__UpperCamelCase ,       pixel_values=__UpperCamelCase ,       training=__UpperCamelCase )

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

												# text only
												lowerCamelCase_					=       model(__UpperCamelCase ,       training=__UpperCamelCase )
												self.parent.assertEqual(
												    result.last_hidden_state.shape ,       (self.batch_size, self.text_seq_length, self.hidden_size) )

												# image only
												lowerCamelCase_					=       model({"""pixel_values""": pixel_values} ,       training=__UpperCamelCase )
												self.parent.assertEqual(
												    result.last_hidden_state.shape ,       (self.batch_size, self.image_seq_length, self.hidden_size) )

					def  lowercase__   (						self       :  Dict ,       __UpperCamelCase       :  List[str] ,       __UpperCamelCase       :  Dict ,       __UpperCamelCase       :  Any ,       __UpperCamelCase       :  List[str] ,       __UpperCamelCase       :  Optional[Any] ,       __UpperCamelCase       :  int ,       __UpperCamelCase       :  List[str] ):
												lowerCamelCase_					=       self.num_labels
												lowerCamelCase_					=       TFLayoutLMvaForSequenceClassification(config=__UpperCamelCase )
												lowerCamelCase_					=       model(
												    __UpperCamelCase ,       bbox=__UpperCamelCase ,       pixel_values=__UpperCamelCase ,       attention_mask=__UpperCamelCase ,       token_type_ids=__UpperCamelCase ,       labels=__UpperCamelCase ,       training=__UpperCamelCase ,       )
												self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.num_labels) )

					def  lowercase__   (						self       :  Optional[Any] ,       __UpperCamelCase       :  Tuple ,       __UpperCamelCase       :  Union[str, Any] ,       __UpperCamelCase       :  Optional[Any] ,       __UpperCamelCase       :  List[Any] ,       __UpperCamelCase       :  Dict ,       __UpperCamelCase       :  int ,       __UpperCamelCase       :  Tuple ):
												lowerCamelCase_					=       self.num_labels
												lowerCamelCase_					=       TFLayoutLMvaForTokenClassification(config=__UpperCamelCase )
												lowerCamelCase_					=       model(
												    __UpperCamelCase ,       bbox=__UpperCamelCase ,       pixel_values=__UpperCamelCase ,       attention_mask=__UpperCamelCase ,       token_type_ids=__UpperCamelCase ,       labels=__UpperCamelCase ,       training=__UpperCamelCase ,       )
												self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.text_seq_length, self.num_labels) )

					def  lowercase__   (						self       :  Tuple ,       __UpperCamelCase       :  Union[str, Any] ,       __UpperCamelCase       :  Dict ,       __UpperCamelCase       :  Any ,       __UpperCamelCase       :  Optional[Any] ,       __UpperCamelCase       :  Any ,       __UpperCamelCase       :  Union[str, Any] ,       __UpperCamelCase       :  Dict ):
												lowerCamelCase_					=       2
												lowerCamelCase_					=       TFLayoutLMvaForQuestionAnswering(config=__UpperCamelCase )
												lowerCamelCase_					=       model(
												    __UpperCamelCase ,       bbox=__UpperCamelCase ,       pixel_values=__UpperCamelCase ,       attention_mask=__UpperCamelCase ,       token_type_ids=__UpperCamelCase ,       start_positions=__UpperCamelCase ,       end_positions=__UpperCamelCase ,       training=__UpperCamelCase ,       )
												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       :  Any ):
												lowerCamelCase_					=       self.prepare_config_and_inputs()
												((lowerCamelCase_)	, (lowerCamelCase_)	, (lowerCamelCase_)	, (lowerCamelCase_)	, (lowerCamelCase_)	, (lowerCamelCase_)	, (lowerCamelCase_)	, (lowerCamelCase_))					=       config_and_inputs
												lowerCamelCase_					=       {
												    """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(					UpperCAmelCase  ,		UpperCAmelCase  ,		unittest.TestCase							):
					SCREAMING_SNAKE_CASE           =     (
					    (
					        TFLayoutLMvaModel,
					        TFLayoutLMvaForQuestionAnswering,
					        TFLayoutLMvaForSequenceClassification,
					        TFLayoutLMvaForTokenClassification,
					    )
					    if is_tf_available()
					    else ()
					)
					SCREAMING_SNAKE_CASE           =     (
					    {'''document-question-answering''': TFLayoutLMvaForQuestionAnswering, '''feature-extraction''': TFLayoutLMvaModel}
					    if is_tf_available()
					    else {}
					)

					SCREAMING_SNAKE_CASE           =     False
					SCREAMING_SNAKE_CASE           =     False
					SCREAMING_SNAKE_CASE           =     False

					def  lowercase__   (						self       :  str ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  Optional[Any] ,       __UpperCamelCase       :  List[str] ,       __UpperCamelCase       :  Union[str, Any] ,       __UpperCamelCase       :  Union[str, Any] ):
												return True

					def  lowercase__   (						self       :  Union[str, Any] ,       __UpperCamelCase       :  int ,       __UpperCamelCase       :  List[Any] ,       __UpperCamelCase       :  List[Any]=False ):
												lowerCamelCase_					=       copy.deepcopy(__UpperCamelCase )

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

												if return_labels:
																			if model_class in get_values(__UpperCamelCase ):
																										lowerCamelCase_					=       tf.ones(self.model_tester.batch_size ,       dtype=tf.intaa )
																			elif model_class in get_values(__UpperCamelCase ):
																										lowerCamelCase_					=       tf.zeros(self.model_tester.batch_size ,       dtype=tf.intaa )
																										lowerCamelCase_					=       tf.zeros(self.model_tester.batch_size ,       dtype=tf.intaa )
																			elif model_class in get_values(__UpperCamelCase ):
																										lowerCamelCase_					=       tf.zeros(self.model_tester.batch_size ,       dtype=tf.intaa )
																			elif model_class in get_values(__UpperCamelCase ):
																										lowerCamelCase_					=       tf.zeros(
																										    (self.model_tester.batch_size, self.model_tester.text_seq_length) ,       dtype=tf.intaa )

												return inputs_dict

					def  lowercase__   (						self       :  Union[str, Any] ):
												lowerCamelCase_					=       TFLayoutLMvaModelTester(self )
												lowerCamelCase_					=       ConfigTester(self ,       config_class=__UpperCamelCase ,       hidden_size=3_7 )

					def  lowercase__   (						self       :  Dict ):
												self.config_tester.run_common_tests()

					def  lowercase__   (						self       :  Dict ):
												lowerCamelCase_	, lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs_for_common()
												for model_class in self.all_model_classes:
																			lowerCamelCase_					=       model_class(__UpperCamelCase )
																			if getattr(__UpperCamelCase ,       """hf_compute_loss""" ,       __UpperCamelCase ):
																										# The number of elements in the loss should be the same as the number of elements in the label
																										lowerCamelCase_					=       self._prepare_for_class(inputs_dict.copy() ,       __UpperCamelCase ,       return_labels=__UpperCamelCase )
																										lowerCamelCase_					=       prepared_for_class[
																										    sorted(prepared_for_class.keys() - inputs_dict.keys() ,       reverse=__UpperCamelCase )[0]
																										]
																										lowerCamelCase_					=       added_label.shape.as_list()[:1]

																										# Test that model correctly compute the loss with kwargs
																										lowerCamelCase_					=       self._prepare_for_class(inputs_dict.copy() ,       __UpperCamelCase ,       return_labels=__UpperCamelCase )
																										lowerCamelCase_					=       prepared_for_class.pop("""input_ids""" )

																										lowerCamelCase_					=       model(__UpperCamelCase ,       **__UpperCamelCase )[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
																										lowerCamelCase_					=       self._prepare_for_class(inputs_dict.copy() ,       __UpperCamelCase ,       return_labels=__UpperCamelCase )
																										lowerCamelCase_					=       prepared_for_class.pop("""input_ids""" )
																										if "labels" in prepared_for_class:
																																	lowerCamelCase_					=       prepared_for_class["""labels"""].numpy()
																																	if len(labels.shape ) > 1 and labels.shape[1] != 1:
																																								lowerCamelCase_					=       -1_0_0
																																								lowerCamelCase_					=       tf.convert_to_tensor(__UpperCamelCase )
																																								lowerCamelCase_					=       model(__UpperCamelCase ,       **__UpperCamelCase )[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
																										lowerCamelCase_					=       self._prepare_for_class(inputs_dict.copy() ,       __UpperCamelCase ,       return_labels=__UpperCamelCase )
																										lowerCamelCase_					=       model(__UpperCamelCase )[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
																										lowerCamelCase_					=       self._prepare_for_class(inputs_dict.copy() ,       __UpperCamelCase ,       return_labels=__UpperCamelCase )

																										# Get keys that were added with the _prepare_for_class function
																										lowerCamelCase_					=       prepared_for_class.keys() - inputs_dict.keys()
																										lowerCamelCase_					=       inspect.signature(model.call ).parameters
																										lowerCamelCase_					=       list(signature.keys() )

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

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

																										for index, value in sorted_tuple_index_mapping:
																																	lowerCamelCase_					=       prepared_for_class[value]

																										lowerCamelCase_					=       tuple(__UpperCamelCase )

																										# Send to model
																										lowerCamelCase_					=       model(tuple_input[:-1] )[0]

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

					def  lowercase__   (						self       :  List[Any] ):
												(
												    (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, 
												)					=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )

					def  lowercase__   (						self       :  str ):
												(
												    (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, 
												)					=       self.model_tester.prepare_config_and_inputs()
												for type in ["absolute", "relative_key", "relative_key_query"]:
																			lowerCamelCase_					=       type
																			self.model_tester.create_and_check_model(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )

					def  lowercase__   (						self       :  Union[str, Any] ):
												(
												    (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, 
												)					=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(
												    __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )

					def  lowercase__   (						self       :  List[str] ):
												(
												    (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, 
												)					=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(
												    __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )

					def  lowercase__   (						self       :  Union[str, Any] ):
												(
												    (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, 
												)					=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(
												    __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )



					@slow
					def  lowercase__   (						self       :  int ):
												for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			lowerCamelCase_					=       TFLayoutLMvaModel.from_pretrained(__UpperCamelCase )
																			self.assertIsNotNone(__UpperCamelCase )



def   __lowerCAmelCase    (				)	->     str:
							lowerCamelCase_					=       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
							return image





@require_tf
class    __A(					unittest.TestCase							):

					@cached_property
					def  lowercase__   (						self       :  int ):
												return LayoutLMvaImageProcessor(apply_ocr=__UpperCamelCase ) if is_vision_available() else None



					@slow
					def  lowercase__   (						self       :  Any ):
												lowerCamelCase_					=       TFLayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base""" )

												lowerCamelCase_					=       self.default_image_processor
												lowerCamelCase_					=       prepare_img()
												lowerCamelCase_					=       image_processor(images=__UpperCamelCase ,       return_tensors="""tf""" ).pixel_values

												lowerCamelCase_					=       tf.constant([[1, 2]] )
												lowerCamelCase_					=       tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]] ) ,       axis=0 )

												# forward pass
												lowerCamelCase_					=       model(input_ids=__UpperCamelCase ,       bbox=__UpperCamelCase ,       pixel_values=__UpperCamelCase ,       training=__UpperCamelCase )

												# verify the logits
												lowerCamelCase_					=       (1, 1_9_9, 7_6_8)
												self.assertEqual(outputs.last_hidden_state.shape ,       __UpperCamelCase )

												lowerCamelCase_					=       tf.constant(
												    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]] )

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







import os
from itertools import chain
from random import randrange, shuffle

import pytest

from .sola import PokerHand

lowercase   =	(
    '''4S 3H 2C 7S 5H''',
    '''9D 8H 2C 6S 7H''',
    '''2D 6D 9D TH 7D''',
    '''TC 8C 2S JH 6C''',
    '''JH 8S TH AH QH''',
    '''TS KS 5S 9S AC''',
    '''KD 6S 9D TH AD''',
    '''KS 8D 4D 9S 4S''',  # pair
    '''8C 4S KH JS 4D''',  # pair
    '''QH 8H KD JH 8S''',  # pair
    '''KC 4H KS 2H 8D''',  # pair
    '''KD 4S KC 3H 8S''',  # pair
    '''AH 8S AS KC JH''',  # pair
    '''3H 4C 4H 3S 2H''',  # 2 pairs
    '''5S 5D 2C KH KH''',  # 2 pairs
    '''3C KH 5D 5S KH''',  # 2 pairs
    '''AS 3C KH AD KH''',  # 2 pairs
    '''7C 7S 3S 7H 5S''',  # 3 of a kind
    '''7C 7S KH 2H 7H''',  # 3 of a kind
    '''AC KH QH AH AS''',  # 3 of a kind
    '''2H 4D 3C AS 5S''',  # straight (low ace)
    '''3C 5C 4C 2C 6H''',  # straight
    '''6S 8S 7S 5H 9H''',  # straight
    '''JS QS 9H TS KH''',  # straight
    '''QC KH TS JS AH''',  # straight (high ace)
    '''8C 9C 5C 3C TC''',  # flush
    '''3S 8S 9S 5S KS''',  # flush
    '''4C 5C 9C 8C KC''',  # flush
    '''JH 8H AH KH QH''',  # flush
    '''3D 2H 3H 2C 2D''',  # full house
    '''2H 2C 3S 3H 3D''',  # full house
    '''KH KC 3S 3H 3D''',  # full house
    '''JC 6H JS JD JH''',  # 4 of a kind
    '''JC 7H JS JD JH''',  # 4 of a kind
    '''JC KH JS JD JH''',  # 4 of a kind
    '''2S AS 4S 5S 3S''',  # straight flush (low ace)
    '''2D 6D 3D 4D 5D''',  # straight flush
    '''5C 6C 3C 7C 4C''',  # straight flush
    '''JH 9H TH KH QH''',  # straight flush
    '''JH AH TH KH QH''',  # royal flush (high ace straight flush)
)

lowercase   =	(
    ('''2H 3H 4H 5H 6H''', '''KS AS TS QS JS''', '''Loss'''),
    ('''2H 3H 4H 5H 6H''', '''AS AD AC AH JD''', '''Win'''),
    ('''AS AH 2H AD AC''', '''JS JD JC JH 3D''', '''Win'''),
    ('''2S AH 2H AS AC''', '''JS JD JC JH AD''', '''Loss'''),
    ('''2S AH 2H AS AC''', '''2H 3H 5H 6H 7H''', '''Win'''),
    ('''AS 3S 4S 8S 2S''', '''2H 3H 5H 6H 7H''', '''Win'''),
    ('''2H 3H 5H 6H 7H''', '''2S 3H 4H 5S 6C''', '''Win'''),
    ('''2S 3H 4H 5S 6C''', '''3D 4C 5H 6H 2S''', '''Tie'''),
    ('''2S 3H 4H 5S 6C''', '''AH AC 5H 6H AS''', '''Win'''),
    ('''2S 2H 4H 5S 4C''', '''AH AC 5H 6H AS''', '''Loss'''),
    ('''2S 2H 4H 5S 4C''', '''AH AC 5H 6H 7S''', '''Win'''),
    ('''6S AD 7H 4S AS''', '''AH AC 5H 6H 7S''', '''Loss'''),
    ('''2S AH 4H 5S KC''', '''AH AC 5H 6H 7S''', '''Loss'''),
    ('''2S 3H 6H 7S 9C''', '''7H 3C TH 6H 9S''', '''Loss'''),
    ('''4S 5H 6H TS AC''', '''3S 5H 6H TS AC''', '''Win'''),
    ('''2S AH 4H 5S 6C''', '''AD 4C 5H 6H 2C''', '''Tie'''),
    ('''AS AH 3H AD AC''', '''AS AH 2H AD AC''', '''Win'''),
    ('''AH AC 5H 5C QS''', '''AH AC 5H 5C KS''', '''Loss'''),
    ('''AH AC 5H 5C QS''', '''KH KC 5H 5C QS''', '''Win'''),
    ('''7C 7S KH 2H 7H''', '''3C 3S AH 2H 3H''', '''Win'''),
    ('''3C 3S AH 2H 3H''', '''7C 7S KH 2H 7H''', '''Loss'''),
    ('''6H 5H 4H 3H 2H''', '''5H 4H 3H 2H AH''', '''Win'''),
    ('''5H 4H 3H 2H AH''', '''5H 4H 3H 2H AH''', '''Tie'''),
    ('''5H 4H 3H 2H AH''', '''6H 5H 4H 3H 2H''', '''Loss'''),
    ('''AH AD KS KC AC''', '''AH KD KH AC KC''', '''Win'''),
    ('''2H 4D 3C AS 5S''', '''2H 4D 3C 6S 5S''', '''Loss'''),
    ('''2H 3S 3C 3H 2S''', '''3S 3C 2S 2H 2D''', '''Win'''),
    ('''4D 6D 5D 2D JH''', '''3S 8S 3H TC KH''', '''Loss'''),
    ('''4S 6C 8S 3S 7S''', '''AD KS 2D 7D 7C''', '''Loss'''),
    ('''6S 4C 7H 8C 3H''', '''5H JC AH 9D 9C''', '''Loss'''),
    ('''9D 9H JH TC QH''', '''3C 2S JS 5C 7H''', '''Win'''),
    ('''2H TC 8S AD 9S''', '''4H TS 7H 2C 5C''', '''Win'''),
    ('''9D 3S 2C 7S 7C''', '''JC TD 3C TC 9H''', '''Loss'''),
)

lowercase   =	(
    ('''2H 3H 4H 5H 6H''', True),
    ('''AS AH 2H AD AC''', False),
    ('''2H 3H 5H 6H 7H''', True),
    ('''KS AS TS QS JS''', True),
    ('''8H 9H QS JS TH''', False),
    ('''AS 3S 4S 8S 2S''', True),
)

lowercase   =	(
    ('''2H 3H 4H 5H 6H''', True),
    ('''AS AH 2H AD AC''', False),
    ('''2H 3H 5H 6H 7H''', False),
    ('''KS AS TS QS JS''', True),
    ('''8H 9H QS JS TH''', True),
)

lowercase   =	(
    ('''2H 4D 3C AS 5S''', True, [5, 4, 3, 2, 1_4]),
    ('''2H 5D 3C AS 5S''', False, [1_4, 5, 5, 3, 2]),
    ('''JH QD KC AS TS''', False, [1_4, 1_3, 1_2, 1_1, 1_0]),
    ('''9D 3S 2C 7S 7C''', False, [9, 7, 7, 3, 2]),
)

lowercase   =	(
    ('''JH AH TH KH QH''', 0),
    ('''JH 9H TH KH QH''', 0),
    ('''JC KH JS JD JH''', 7),
    ('''KH KC 3S 3H 3D''', 6),
    ('''8C 9C 5C 3C TC''', 0),
    ('''JS QS 9H TS KH''', 0),
    ('''7C 7S KH 2H 7H''', 3),
    ('''3C KH 5D 5S KH''', 2),
    ('''QH 8H KD JH 8S''', 1),
    ('''2D 6D 9D TH 7D''', 0),
)

lowercase   =	(
    ('''JH AH TH KH QH''', 2_3),
    ('''JH 9H TH KH QH''', 2_2),
    ('''JC KH JS JD JH''', 2_1),
    ('''KH KC 3S 3H 3D''', 2_0),
    ('''8C 9C 5C 3C TC''', 1_9),
    ('''JS QS 9H TS KH''', 1_8),
    ('''7C 7S KH 2H 7H''', 1_7),
    ('''3C KH 5D 5S KH''', 1_6),
    ('''QH 8H KD JH 8S''', 1_5),
    ('''2D 6D 9D TH 7D''', 1_4),
)



def   __lowerCAmelCase    (				)	->     List[str]:
							lowerCamelCase_	, lowerCamelCase_					=       randrange(len(UpperCAmelCase__							)							), randrange(len(UpperCAmelCase__							)							)
							lowerCamelCase_					=       ["""Loss""", """Tie""", """Win"""][(play >= oppo) + (play > oppo)]
							lowerCamelCase_	, lowerCamelCase_					=       SORTED_HANDS[play], SORTED_HANDS[oppo]
							return hand, other, expected



def   __lowerCAmelCase    (				UpperCAmelCase__					:   int = 1_0_0							)	->     Optional[int]:
							return (generate_random_hand() for _ in range(UpperCAmelCase__							))



@pytest.mark.parametrize("""hand, expected"""      ,					UpperCAmelCase__							)
def   __lowerCAmelCase    (				UpperCAmelCase__					:   Union[str, Any]      ,					UpperCAmelCase__					:   Union[str, Any]							)	->     List[Any]:
							assert PokerHand(UpperCAmelCase__							)._is_flush() == expected



@pytest.mark.parametrize("""hand, expected"""      ,					UpperCAmelCase__							)
def   __lowerCAmelCase    (				UpperCAmelCase__					:   Tuple      ,					UpperCAmelCase__					:   List[Any]							)	->     int:
							assert PokerHand(UpperCAmelCase__							)._is_straight() == expected



@pytest.mark.parametrize("""hand, expected, card_values"""      ,					UpperCAmelCase__							)
def   __lowerCAmelCase    (				UpperCAmelCase__					:   List[str]      ,					UpperCAmelCase__					:   List[Any]      ,					UpperCAmelCase__					:   Union[str, Any]							)	->     Union[str, Any]:
							lowerCamelCase_					=       PokerHand(UpperCAmelCase__							)
							assert player._is_five_high_straight() == expected
							assert player._card_values == card_values



@pytest.mark.parametrize("""hand, expected"""      ,					UpperCAmelCase__							)
def   __lowerCAmelCase    (				UpperCAmelCase__					:   List[Any]      ,					UpperCAmelCase__					:   Dict							)	->     str:
							assert PokerHand(UpperCAmelCase__							)._is_same_kind() == expected



@pytest.mark.parametrize("""hand, expected"""      ,					UpperCAmelCase__							)
def   __lowerCAmelCase    (				UpperCAmelCase__					:   Any      ,					UpperCAmelCase__					:   Dict							)	->     Tuple:
							assert PokerHand(UpperCAmelCase__							)._hand_type == expected



@pytest.mark.parametrize("""hand, other, expected"""      ,					UpperCAmelCase__							)
def   __lowerCAmelCase    (				UpperCAmelCase__					:   int      ,					UpperCAmelCase__					:   Tuple      ,					UpperCAmelCase__					:   Union[str, Any]							)	->     Any:
							assert PokerHand(UpperCAmelCase__							).compare_with(PokerHand(UpperCAmelCase__							)							) == expected



@pytest.mark.parametrize("""hand, other, expected"""      ,					generate_random_hands()							)
def   __lowerCAmelCase    (				UpperCAmelCase__					:   Any      ,					UpperCAmelCase__					:   Tuple      ,					UpperCAmelCase__					:   Dict							)	->     List[str]:
							assert PokerHand(UpperCAmelCase__							).compare_with(PokerHand(UpperCAmelCase__							)							) == expected



def   __lowerCAmelCase    (				)	->     Tuple:
							lowerCamelCase_					=       [PokerHand(UpperCAmelCase__							) for hand in SORTED_HANDS]
							lowerCamelCase_					=       poker_hands.copy()
							shuffle(UpperCAmelCase__							)
							lowerCamelCase_					=       chain(sorted(UpperCAmelCase__							)							)
							for index, hand in enumerate(UpperCAmelCase__							):
														assert hand == poker_hands[index]



def   __lowerCAmelCase    (				)	->     List[Any]:
							# Test that five high straights are compared correctly.
							lowerCamelCase_					=       [PokerHand("""2D AC 3H 4H 5S"""							), PokerHand("""2S 3H 4H 5S 6C"""							)]
							pokerhands.sort(reverse=UpperCAmelCase__							)
							assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"



def   __lowerCAmelCase    (				)	->     List[str]:
							# Multiple calls to five_high_straight function should still return True
							# and shouldn't mutate the list in every call other than the first.
							lowerCamelCase_					=       PokerHand("""2C 4S AS 3D 5C"""							)
							lowerCamelCase_					=       True
							lowerCamelCase_					=       [5, 4, 3, 2, 1_4]
							for _ in range(1_0							):
														assert pokerhand._is_five_high_straight() == expected
														assert pokerhand._card_values == expected_card_values



def   __lowerCAmelCase    (				)	->     List[Any]:
							# Problem number 54 from Project Euler
							# Testing from poker_hands.txt file
							lowerCamelCase_					=       0
							lowerCamelCase_					=       os.path.abspath(os.path.dirname(UpperCAmelCase__							)							)
							lowerCamelCase_					=       os.path.join(UpperCAmelCase__      ,					"""poker_hands.txt"""							)
							with open(UpperCAmelCase__							) as file_hand:
														for line in file_hand:
																					lowerCamelCase_					=       line[:1_4].strip()
																					lowerCamelCase_					=       line[1_5:].strip()
																					lowerCamelCase_	, lowerCamelCase_					=       PokerHand(UpperCAmelCase__							), PokerHand(UpperCAmelCase__							)
																					lowerCamelCase_					=       player.compare_with(UpperCAmelCase__							)
																					if output == "Win":
																												answer += 1
							assert answer == 3_7_6





