
import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
		    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
		    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
		    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
		    LayoutLMvaConfig,
		    LayoutLMvaForQuestionAnswering,
		    LayoutLMvaForSequenceClassification,
		    LayoutLMvaForTokenClassification,
		    LayoutLMvaModel,
		)
		from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
		from PIL import Image

		from transformers import LayoutLMvaImageProcessor




class  lowerCamelCase__							:




	'''simple docstring'''




	def __init__(					self		:Any     ,   a		:Any     ,   a		:List[str]=2     ,   a		:Tuple=3     ,   a		:Optional[Any]=4     ,   a		:Tuple=2     ,   a		:Dict=7     ,   a		:Union[str, Any]=True     ,   a		:Dict=True     ,   a		:List[Any]=True     ,   a		:Optional[int]=True     ,   a		:List[Any]=9_9     ,   a		:Dict=3_6     ,   a		:Dict=3     ,   a		:Optional[Any]=4     ,   a		:Union[str, Any]=3_7     ,   a		:List[str]="gelu"     ,   a		:List[Any]=0.1     ,   a		:List[Any]=0.1     ,   a		:Optional[Any]=5_1_2     ,   a		:List[str]=1_6     ,   a		:Union[str, Any]=2     ,   a		:str=0.02     ,   a		:Dict=6     ,   a		:Optional[Any]=6     ,   a		:Optional[Any]=3     ,   a		:Optional[Any]=4     ,   a		:Any=None     ,   a		:Tuple=1_0_0_0     ,   )		-> Optional[int]:
								__UpperCamelCase	:   Any			   =					parent
								__UpperCamelCase	:   int			   =					batch_size
								__UpperCamelCase	:   Any			   =					num_channels
								__UpperCamelCase	:   Any			   =					image_size
								__UpperCamelCase	:   Dict			   =					patch_size
								__UpperCamelCase	:   int			   =					text_seq_length
								__UpperCamelCase	:   Optional[Any]			   =					is_training
								__UpperCamelCase	:   Optional[int]			   =					use_input_mask
								__UpperCamelCase	:   List[str]			   =					use_token_type_ids
								__UpperCamelCase	:   Optional[int]			   =					use_labels
								__UpperCamelCase	:   str			   =					vocab_size
								__UpperCamelCase	:   str			   =					hidden_size
								__UpperCamelCase	:   Tuple			   =					num_hidden_layers
								__UpperCamelCase	:   Dict			   =					num_attention_heads
								__UpperCamelCase	:   Tuple			   =					intermediate_size
								__UpperCamelCase	:   List[Any]			   =					hidden_act
								__UpperCamelCase	:   str			   =					hidden_dropout_prob
								__UpperCamelCase	:   Any			   =					attention_probs_dropout_prob
								__UpperCamelCase	:   Any			   =					max_position_embeddings
								__UpperCamelCase	:   Optional[Any]			   =					type_vocab_size
								__UpperCamelCase	:   Optional[int]			   =					type_sequence_label_size
								__UpperCamelCase	:   Any			   =					initializer_range
								__UpperCamelCase	:   int			   =					coordinate_size
								__UpperCamelCase	:   List[str]			   =					shape_size
								__UpperCamelCase	:   Optional[Any]			   =					num_labels
								__UpperCamelCase	:   Optional[Any]			   =					num_choices
								__UpperCamelCase	:   Optional[int]			   =					scope
								__UpperCamelCase	:   Any			   =					range_bbox

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







	def    _lowerCamelCase				(					self		:int						)		-> Union[str, Any]:
								__UpperCamelCase	:   Any			   =					ids_tensor([self.batch_size, self.text_seq_length]     ,   self.vocab_size						)

								__UpperCamelCase	:   Union[str, Any]			   =					ids_tensor([self.batch_size, self.text_seq_length, 4]     ,   self.range_bbox						)
								# 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[str]			   =					bbox[i, j, 3]
																													__UpperCamelCase	:   Tuple			   =					bbox[i, j, 1]
																													__UpperCamelCase	:   Dict			   =					t
																						if bbox[i, j, 2] < bbox[i, j, 0]:
																													__UpperCamelCase	:   str			   =					bbox[i, j, 2]
																													__UpperCamelCase	:   List[str]			   =					bbox[i, j, 0]
																													__UpperCamelCase	:   Optional[int]			   =					t

								__UpperCamelCase	:   List[str]			   =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

								__UpperCamelCase	:   Any			   =					None
								if self.use_input_mask:
															__UpperCamelCase	:   int			   =					random_attention_mask([self.batch_size, self.text_seq_length]						)

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

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

								__UpperCamelCase	:   int			   =					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    _lowerCamelCase				(					self		:List[Any]     ,   a		:Optional[Any]     ,   a		:Union[str, Any]     ,   a		:Dict     ,   a		:List[Any]     ,   a		:Optional[Any]     ,   a		:Optional[int]     ,   a		:int     ,   a		:Dict						)		-> Optional[int]:
								__UpperCamelCase	:   Dict			   =					LayoutLMvaModel(config=UpperCamelCase__						)
								model.to(UpperCamelCase__						)
								model.eval()

								# text + image
								__UpperCamelCase	:   Tuple			   =					model(UpperCamelCase__     ,   pixel_values=UpperCamelCase__						)
								__UpperCamelCase	:   Union[str, Any]			   =					model(
								    UpperCamelCase__     ,   bbox=UpperCamelCase__     ,   pixel_values=UpperCamelCase__     ,   attention_mask=UpperCamelCase__     ,   token_type_ids=UpperCamelCase__						)
								__UpperCamelCase	:   str			   =					model(UpperCamelCase__     ,   bbox=UpperCamelCase__     ,   pixel_values=UpperCamelCase__     ,   token_type_ids=UpperCamelCase__						)
								__UpperCamelCase	:   Optional[Any]			   =					model(UpperCamelCase__     ,   bbox=UpperCamelCase__     ,   pixel_values=UpperCamelCase__						)

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

								# text only
								__UpperCamelCase	:   Any			   =					model(UpperCamelCase__						)
								self.parent.assertEqual(
								    result.last_hidden_state.shape     ,   (self.batch_size, self.text_seq_length, self.hidden_size)						)

								# image only
								__UpperCamelCase	:   List[Any]			   =					model(pixel_values=UpperCamelCase__						)
								self.parent.assertEqual(
								    result.last_hidden_state.shape     ,   (self.batch_size, self.image_seq_length, self.hidden_size)						)







	def    _lowerCamelCase				(					self		:Union[str, Any]     ,   a		:Dict     ,   a		:Optional[Any]     ,   a		:Optional[int]     ,   a		:str     ,   a		:Any     ,   a		:Tuple     ,   a		:List[str]     ,   a		:Dict						)		-> Tuple:
								__UpperCamelCase	:   Optional[int]			   =					self.num_labels
								__UpperCamelCase	:   Any			   =					LayoutLMvaForSequenceClassification(UpperCamelCase__						)
								model.to(UpperCamelCase__						)
								model.eval()
								__UpperCamelCase	:   Optional[int]			   =					model(
								    UpperCamelCase__     ,   bbox=UpperCamelCase__     ,   pixel_values=UpperCamelCase__     ,   attention_mask=UpperCamelCase__     ,   token_type_ids=UpperCamelCase__     ,   labels=UpperCamelCase__     ,   )
								self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.num_labels)						)







	def    _lowerCamelCase				(					self		:List[str]     ,   a		:Union[str, Any]     ,   a		:Any     ,   a		:Optional[int]     ,   a		:List[Any]     ,   a		:List[str]     ,   a		:str     ,   a		:List[str]     ,   a		:Tuple						)		-> List[Any]:
								__UpperCamelCase	:   Any			   =					self.num_labels
								__UpperCamelCase	:   Union[str, Any]			   =					LayoutLMvaForTokenClassification(config=UpperCamelCase__						)
								model.to(UpperCamelCase__						)
								model.eval()
								__UpperCamelCase	:   Any			   =					model(
								    UpperCamelCase__     ,   bbox=UpperCamelCase__     ,   pixel_values=UpperCamelCase__     ,   attention_mask=UpperCamelCase__     ,   token_type_ids=UpperCamelCase__     ,   labels=UpperCamelCase__     ,   )
								self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.text_seq_length, self.num_labels)						)







	def    _lowerCamelCase				(					self		:Any     ,   a		:Union[str, Any]     ,   a		:int     ,   a		:List[Any]     ,   a		:int     ,   a		:Tuple     ,   a		:int     ,   a		:str     ,   a		:Optional[int]						)		-> List[Any]:
								__UpperCamelCase	:   List[str]			   =					LayoutLMvaForQuestionAnswering(config=UpperCamelCase__						)
								model.to(UpperCamelCase__						)
								model.eval()
								__UpperCamelCase	:   int			   =					model(
								    UpperCamelCase__     ,   bbox=UpperCamelCase__     ,   pixel_values=UpperCamelCase__     ,   attention_mask=UpperCamelCase__     ,   token_type_ids=UpperCamelCase__     ,   start_positions=UpperCamelCase__     ,   end_positions=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    _lowerCamelCase				(					self		:int						)		-> List[str]:
								__UpperCamelCase	:   List[Any]			   =					self.prepare_config_and_inputs()
								(
								    (
								    __UpperCamelCase    
								)   ,						(
								    __UpperCamelCase    
								)   ,						(
								    __UpperCamelCase    
								)   ,						(
								    __UpperCamelCase    
								)   ,						(
								    __UpperCamelCase    
								)   ,						(
								    __UpperCamelCase    
								)   ,						(
								    __UpperCamelCase    
								)   ,						(
								    __UpperCamelCase    
								)   ,						    
								)	:   Optional[int]			   =					config_and_inputs
								__UpperCamelCase	:   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_torch
class  lowerCamelCase__							(				_A     ,						_A     ,						unittest.TestCase):




	'''simple docstring'''




	_A								=     False
	_A								=     False
	_A								=     False

	_A								=     (
	    (
	        LayoutLMvaModel,
	        LayoutLMvaForSequenceClassification,
	        LayoutLMvaForTokenClassification,
	        LayoutLMvaForQuestionAnswering,
	    )
	    if is_torch_available()
	    else ()
	)
	_A								=     (
	    {'document-question-answering': LayoutLMvaForQuestionAnswering, 'feature-extraction': LayoutLMvaModel}
	    if is_torch_available()
	    else {}
	)







	def    _lowerCamelCase				(					self		:Any     ,   a		:List[str]     ,   a		:List[Any]     ,   a		:Any     ,   a		:List[Any]     ,   a		:Optional[int]						)		-> List[str]:
								return True







	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> Optional[Any]:
								__UpperCamelCase	:   List[str]			   =					LayoutLMvaModelTester(self						)
								__UpperCamelCase	:   Optional[Any]			   =					ConfigTester(self     ,   config_class=UpperCamelCase__     ,   hidden_size=3_7						)







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:List[str]     ,   a		:Any     ,   a		:Union[str, Any]=False						)		-> Optional[Any]:
								__UpperCamelCase	:   str			   =					copy.deepcopy(UpperCamelCase__						)
								if model_class in get_values(UpperCamelCase__						):
															__UpperCamelCase	:   List[str]			   =					{
															    k: v.unsqueeze(1						).expand(-1     ,   self.model_tester.num_choices     ,   -1						).contiguous()
															    if isinstance(UpperCamelCase__     ,   torch.Tensor						) and v.ndim > 1
															    else v
															    for k, v in inputs_dict.items()
															}
								if return_labels:
															if model_class in get_values(UpperCamelCase__						):
																						__UpperCamelCase	:   Optional[Any]			   =					torch.ones(self.model_tester.batch_size     ,   dtype=torch.long     ,   device=UpperCamelCase__						)
															elif model_class in get_values(UpperCamelCase__						):
																						__UpperCamelCase	:   Any			   =					torch.zeros(
																						    self.model_tester.batch_size     ,   dtype=torch.long     ,   device=UpperCamelCase__						)
																						__UpperCamelCase	:   Any			   =					torch.zeros(
																						    self.model_tester.batch_size     ,   dtype=torch.long     ,   device=UpperCamelCase__						)
															elif model_class in [
															    *get_values(UpperCamelCase__						),
															]:
																						__UpperCamelCase	:   List[str]			   =					torch.zeros(
																						    self.model_tester.batch_size     ,   dtype=torch.long     ,   device=UpperCamelCase__						)
															elif model_class in [
															    *get_values(UpperCamelCase__						),
															]:
																						__UpperCamelCase	:   Optional[int]			   =					torch.zeros(
																						    (self.model_tester.batch_size, self.model_tester.text_seq_length)     ,   dtype=torch.long     ,   device=UpperCamelCase__     ,   )

								return inputs_dict







	def    _lowerCamelCase				(					self		:Dict						)		-> Union[str, Any]:
								self.config_tester.run_common_tests()







	def    _lowerCamelCase				(					self		:Any						)		-> Optional[int]:
								__UpperCamelCase	:   Any			   =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCamelCase__						)







	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> List[str]:
								__UpperCamelCase	:   Tuple			   =					self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
															__UpperCamelCase	:   Dict			   =					type
															self.model_tester.create_and_check_model(*UpperCamelCase__						)







	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> Optional[Any]:
								__UpperCamelCase	:   Optional[Any]			   =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__						)







	def    _lowerCamelCase				(					self		:str						)		-> Tuple:
								__UpperCamelCase	:   List[str]			   =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__						)







	def    _lowerCamelCase				(					self		:Tuple						)		-> Any:
								__UpperCamelCase	:   List[str]			   =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__						)







	@slow
	def    _lowerCamelCase				(					self		:int						)		-> Optional[Any]:






								for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															__UpperCamelCase	:   Union[str, Any]			   =					LayoutLMvaModel.from_pretrained(UpperCamelCase__						)
															self.assertIsNotNone(UpperCamelCase__						)




def 	_SCREAMING_SNAKE_CASE					(		)					->    Any:






							'''simple docstring'''





							__UpperCamelCase	:   str			   =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
							return image




@require_torch
class  lowerCamelCase__							(				unittest.TestCase):




	'''simple docstring'''




	@cached_property
	def    _lowerCamelCase				(					self		:Dict						)		-> int:
								return LayoutLMvaImageProcessor(apply_ocr=UpperCamelCase__						) if is_vision_available() else None







	@slow
	def    _lowerCamelCase				(					self		:str						)		-> Optional[Any]:
								__UpperCamelCase	:   Optional[Any]			   =					LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"						).to(UpperCamelCase__						)

								__UpperCamelCase	:   Any			   =					self.default_image_processor
								__UpperCamelCase	:   List[str]			   =					prepare_img()
								__UpperCamelCase	:   Optional[int]			   =					image_processor(images=UpperCamelCase__     ,   return_tensors="pt"						).pixel_values.to(UpperCamelCase__						)

								__UpperCamelCase	:   Dict			   =					torch.tensor([[1, 2]]						)
								__UpperCamelCase	:   Tuple			   =					torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]						).unsqueeze(0						)

								# forward pass
								__UpperCamelCase	:   List[Any]			   =					model(
								    input_ids=input_ids.to(UpperCamelCase__						)     ,   bbox=bbox.to(UpperCamelCase__						)     ,   pixel_values=pixel_values.to(UpperCamelCase__						)     ,   )

								# verify the logits
								__UpperCamelCase	:   Optional[Any]			   =					torch.Size((1, 1_9_9, 7_6_8)						)
								self.assertEqual(outputs.last_hidden_state.shape     ,   UpperCamelCase__						)

								__UpperCamelCase	:   Dict			   =					torch.tensor(
								    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]						).to(UpperCamelCase__						)

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

import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
__lowerCAmelCase			:     Optional[int]           =   {
    'E': 12.70,
    'T': 9.06,
    'A': 8.17,
    'O': 7.51,
    'I': 6.97,
    'N': 6.75,
    'S': 6.33,
    'H': 6.09,
    'R': 5.99,
    'D': 4.25,
    'L': 4.03,
    'C': 2.78,
    'U': 2.76,
    'M': 2.41,
    'W': 2.36,
    'F': 2.23,
    'G': 2.02,
    'Y': 1.97,
    'P': 1.93,
    'B': 1.29,
    'V': 0.98,
    'K': 0.77,
    'J': 0.15,
    'X': 0.15,
    'Q': 0.10,
    'Z': 0.07,
}
__lowerCAmelCase			:     Optional[Any]           =   'ETAOINSHRDLCUMWFGYPBVKJXQZ'
__lowerCAmelCase			:     Optional[Any]           =   'ABCDEFGHIJKLMNOPQRSTUVWXYZ'

def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       {letter: 0 for letter in string.ascii_uppercase}
  for letter in message.upper():
    if letter in LETTERS:
      letter_count[letter] += 1

  return letter_count

def a__    (       A_					):






  '''simple docstring'''





  return x[0]

def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       get_letter_count(A_					)
  __magic_name__				     =       {
      freq: [] for letter, freq in letter_to_freq.items()
  }
  for letter in LETTERS:
    freq_to_letter[letter_to_freq[letter]].append(A_					)

  __magic_name__				     =       {}

  for freq in freq_to_letter:
    freq_to_letter[freq].sort(key=ETAOIN.find,     reverse=A_					)
    __magic_name__				     =       """""".join(freq_to_letter[freq]					)

  __magic_name__				     =       list(freq_to_letter_str.items()					)
  freq_pairs.sort(key=A_,     reverse=A_					)

  __magic_name__				     =       [freq_pair[1] for freq_pair in freq_pairs]

  return "".join(A_					)

def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       get_frequency_order(A_					)
  __magic_name__				     =       0
  for common_letter in ETAOIN[:6]:
    if common_letter in freq_order[:6]:
      match_score += 1

  for uncommon_letter in ETAOIN[-6:]:
    if uncommon_letter in freq_order[-6:]:
      match_score += 1

  return match_score


if __name__ == "__main__":
    import doctest

    doctest.testmod()

