
"""simple docstring"""


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 					lowerCAmelCase_					:



						'''simple docstring'''


						def __init__(			self  :   List[str]	,A_  :   Union[str, Any]	,A_  :   str=2	,A_  :   Union[str, Any]=3	,A_  :   Dict=4	,A_  :   Optional[Any]=2	,A_  :   Any=7	,A_  :   Any=True	,A_  :   List[str]=True	,A_  :   Tuple=True	,A_  :   Optional[int]=True	,A_  :   Dict=99	,A_  :   int=36	,A_  :   Dict=2	,A_  :   Tuple=4	,A_  :   List[Any]=37	,A_  :   int="gelu"	,A_  :   Tuple=0.1	,A_  :   Union[str, Any]=0.1	,A_  :   Optional[int]=512	,A_  :   Dict=16	,A_  :   Dict=2	,A_  :   int=0.02	,A_  :   Dict=6	,A_  :   Dict=6	,A_  :   int=3	,A_  :   str=4	,A_  :   List[Any]=None	,A_  :   str=1000	,)  -> Optional[Any]:
							A     = parent
							A     = batch_size
							A     = num_channels
							A     = image_size
							A     = patch_size
							A     = is_training
							A     = use_input_mask
							A     = use_token_type_ids
							A     = use_labels
							A     = vocab_size
							A     = hidden_size
							A     = num_hidden_layers
							A     = num_attention_heads
							A     = intermediate_size
							A     = hidden_act
							A     = hidden_dropout_prob
							A     = attention_probs_dropout_prob
							A     = max_position_embeddings
							A     = type_vocab_size
							A     = type_sequence_label_size
							A     = initializer_range
							A     = coordinate_size
							A     = shape_size
							A     = num_labels
							A     = num_choices
							A     = scope
							A     = range_bbox

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> Any:
							A     = ids_tensor([self.batch_size, self.text_seq_length]	,self.vocab_size   )

							A     = ids_tensor([self.batch_size, self.text_seq_length, 4]	,self.range_bbox   )
							A     = 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]:
										A     = bbox[i, j, 3]
										A     = bbox[i, j, 1]
										A     = tmp_coordinate
									if bbox[i, j, 2] < bbox[i, j, 0]:
										A     = bbox[i, j, 2]
										A     = bbox[i, j, 0]
										A     = tmp_coordinate
							A     = tf.constant(A_   )

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

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

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

							A     = None
							A     = None
							if self.use_labels:
								A     = ids_tensor([self.batch_size]	,self.type_sequence_label_size   )
								A     = ids_tensor([self.batch_size, self.text_seq_length]	,self.num_labels   )

							A     = 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     _SCREAMING_SNAKE_CASE   (			self  :   List[str]	,A_  :   Optional[int]	,A_  :   Dict	,A_  :   Optional[Any]	,A_  :   Optional[int]	,A_  :   Optional[int]	,A_  :   Tuple   )  -> Union[str, Any]:
							A     = TFLayoutLMvaModel(config=A_   )

							# text + image
							A     = model(A_	,pixel_values=A_	,training=A_   )
							A     = model(
							    A_	,bbox=A_	,pixel_values=A_	,attention_mask=A_	,token_type_ids=A_	,training=A_	,)
							A     = model(A_	,bbox=A_	,pixel_values=A_	,training=A_   )

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

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

							# image only
							A     = model({'pixel_values': pixel_values}	,training=A_   )
							self.parent.assertEqual(
							    result.last_hidden_state.shape	,(self.batch_size, self.image_seq_length, self.hidden_size)   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   int	,A_  :   List[Any]	,A_  :   Any	,A_  :   str	,A_  :   str	,A_  :   List[str]	,A_  :   Optional[Any]	,A_  :   List[str]   )  -> Union[str, Any]:
							A     = self.num_labels
							A     = TFLayoutLMvaForSequenceClassification(config=A_   )
							A     = model(
							    A_	,bbox=A_	,pixel_values=A_	,attention_mask=A_	,token_type_ids=A_	,labels=A_	,training=A_	,)
							self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.num_labels)   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]	,A_  :   List[str]	,A_  :   Any	,A_  :   List[Any]	,A_  :   List[str]	,A_  :   List[Any]	,A_  :   Union[str, Any]	,A_  :   List[Any]   )  -> Union[str, Any]:
							A     = self.num_labels
							A     = TFLayoutLMvaForTokenClassification(config=A_   )
							A     = model(
							    A_	,bbox=A_	,pixel_values=A_	,attention_mask=A_	,token_type_ids=A_	,labels=A_	,training=A_	,)
							self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.text_seq_length, self.num_labels)   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]	,A_  :   int	,A_  :   List[str]	,A_  :   Tuple	,A_  :   List[str]	,A_  :   Optional[int]	,A_  :   Optional[int]	,A_  :   Optional[Any]   )  -> Optional[Any]:
							A     = 2
							A     = TFLayoutLMvaForQuestionAnswering(config=A_   )
							A     = model(
							    A_	,bbox=A_	,pixel_values=A_	,attention_mask=A_	,token_type_ids=A_	,start_positions=A_	,end_positions=A_	,training=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     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Optional[int]:
							A     = self.prepare_config_and_inputs()
							((A) ,   (A) ,   (A) ,   (A) ,   (A) ,   (A) ,   (A) ,   (A))     = config_and_inputs
							A     = {
							    '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 					lowerCAmelCase_					(   _lowercase		,       _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     str   =     (
						    (
						        TFLayoutLMvaModel,
						        TFLayoutLMvaForQuestionAnswering,
						        TFLayoutLMvaForSequenceClassification,
						        TFLayoutLMvaForTokenClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						_lowerCamelCase:     int   =     (
						    {'''document-question-answering''': TFLayoutLMvaForQuestionAnswering, '''feature-extraction''': TFLayoutLMvaModel}
						    if is_tf_available()
						    else {}
						)

						_lowerCamelCase:     int   =     False
						_lowerCamelCase:     Any   =     False
						_lowerCamelCase:     Any   =     False


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any	,A_  :   Tuple	,A_  :   Any	,A_  :   Optional[int]	,A_  :   Dict	,A_  :   Dict   )  -> List[Any]:
							return True


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]	,A_  :   int	,A_  :   Tuple	,A_  :   Optional[int]=False   )  -> dict:
							A     = copy.deepcopy(A_   )

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

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

							return inputs_dict


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> Dict:
							A     = TFLayoutLMvaModelTester(self   )
							A     = ConfigTester(self	,config_class=A_	,hidden_size=37   )


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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> str:
							A ,   A     = self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
								A     = model_class(A_   )
								if getattr(A_	,'hf_compute_loss'	,A_   ):
									# The number of elements in the loss should be the same as the number of elements in the label
									A     = self._prepare_for_class(inputs_dict.copy()	,A_	,return_labels=A_   )
									A     = prepared_for_class[
									    sorted(prepared_for_class.keys() - inputs_dict.keys()	,reverse=A_   )[0]
									]
									A     = added_label.shape.as_list()[:1]

									# Test that model correctly compute the loss with kwargs
									A     = self._prepare_for_class(inputs_dict.copy()	,A_	,return_labels=A_   )
									A     = prepared_for_class.pop('input_ids'   )

									A     = model(A_	,**A_   )[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
									A     = self._prepare_for_class(inputs_dict.copy()	,A_	,return_labels=A_   )
									A     = prepared_for_class.pop('input_ids'   )
									if "labels" in prepared_for_class:
										A     = prepared_for_class['labels'].numpy()
										if len(labels.shape   ) > 1 and labels.shape[1] != 1:
											A     = -100
											A     = tf.convert_to_tensor(A_   )
											A     = model(A_	,**A_   )[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
									A     = self._prepare_for_class(inputs_dict.copy()	,A_	,return_labels=A_   )
									A     = model(A_   )[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
									A     = self._prepare_for_class(inputs_dict.copy()	,A_	,return_labels=A_   )

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

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

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

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

									A     = tuple(A_   )

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

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> List[Any]:
							(
							    (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   
							)     = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(A_	,A_	,A_	,A_	,A_	,A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> List[str]:
							(
							    (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   
							)     = self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
								A     = type
								self.model_tester.create_and_check_model(A_	,A_	,A_	,A_	,A_	,A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple   )  -> Tuple:
							(
							    (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   
							)     = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(
							    A_	,A_	,A_	,A_	,A_	,A_	,A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Any:
							(
							    (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   
							)     = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(
							    A_	,A_	,A_	,A_	,A_	,A_	,A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> Any:
							(
							    (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   
							)     = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(
							    A_	,A_	,A_	,A_	,A_	,A_	,A_   )




						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> List[Any]:
							for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A     = TFLayoutLMvaModel.from_pretrained(A_   )
								self.assertIsNotNone(A_   )






def    _snake_case					(		):
	A     = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
	return image







@require_tf
class 					lowerCAmelCase_					(   unittest.TestCase     ):



						'''simple docstring'''


						@cached_property
						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Any:
							return LayoutLMvaImageProcessor(apply_ocr=A_   ) if is_vision_available() else None




						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple   )  -> Union[str, Any]:
							A     = TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'   )

							A     = self.default_image_processor
							A     = prepare_img()
							A     = image_processor(images=A_	,return_tensors='tf'   ).pixel_values

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

							# forward pass
							A     = model(input_ids=A_	,bbox=A_	,pixel_values=A_	,training=A_   )

							# verify the logits
							A     = (1, 199, 768)
							self.assertEqual(outputs.last_hidden_state.shape	,A_   )

							A     = 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]	,A_	,atol=1e-4   )   )

import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation


def      UpperCamelCase   (       __lowerCamelCase	:					int = 8       ):
       snake_case			:  int     							= ascii_letters + digits + punctuation
       return "".join(secrets.choice(__lowerCamelCase       ) for _ in range(__lowerCamelCase       )       )


def      UpperCamelCase   (       __lowerCamelCase	:					str							,   __lowerCamelCase	:					int       ):
       # Password Generator = full boot with random_number, random_letters, and
       # random_character FUNCTIONS
       # Put your code here...
       i -= len(__lowerCamelCase       )
       snake_case			:  Any     							= i // 3
       snake_case			:  Optional[int]     							= i % 3
       # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
       #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
       snake_case			:  Tuple     							= (
           chars_incl
           + random(__lowerCamelCase							,   quotient + remainder       )
           + random(__lowerCamelCase							,   __lowerCamelCase       )
           + random(__lowerCamelCase							,   __lowerCamelCase       )
       )
       snake_case			:  Optional[Any]     							= list(__lowerCamelCase       )
       shuffle(__lowerCamelCase       )
       return "".join(__lowerCamelCase       )

       # random is a generalised function for letters, characters and numbers


def      UpperCamelCase   (       __lowerCamelCase	:					str							,   __lowerCamelCase	:					int       ):
       return "".join(secrets.choice(__lowerCamelCase       ) for _ in range(__lowerCamelCase       )       )


def      UpperCamelCase   (       __lowerCamelCase	:					List[str]							,   __lowerCamelCase	:					str       ):
       pass  # Put your code here...


def      UpperCamelCase   (       __lowerCamelCase	:					Optional[int]							,   __lowerCamelCase	:					List[str]       ):
       pass  # Put your code here...


def      UpperCamelCase   (       __lowerCamelCase	:					Any							,   __lowerCamelCase	:					List[Any]       ):
       pass  # Put your code here...


def      UpperCamelCase   (       __lowerCamelCase	:					str							,   __lowerCamelCase	:					int = 8       ):

       if len(__lowerCamelCase       ) < min_length:
              # Your Password must be at least 8 characters long
              return False

       snake_case			:  Dict     							= any(char in ascii_uppercase for char in password       )
       snake_case			:  Optional[int]     							= any(char in ascii_lowercase for char in password       )
       snake_case			:  str     							= any(char in digits for char in password       )
       snake_case			:  str     							= any(char in punctuation for char in password       )

       return upper and lower and num and spec_char
       # Passwords should contain UPPERCASE, lowerase
       # numbers, and special characters


def      UpperCamelCase   (       ):
       snake_case			:  int     							= int(input("Please indicate the max length of your password: "       ).strip()       )
       snake_case			:  Union[str, Any]     							= input(
           "Please indicate the characters that must be in your password: "       ).strip()
       print("Password generated:"							,   password_generator(__lowerCamelCase       )       )
       print(
           "Alternative Password generated:"							,   alternative_password_generator(__lowerCamelCase							,   __lowerCamelCase       )							,   )
       print("[If you are thinking of using this passsword, You better save it.]"       )


if __name__ == "__main__":
   main()





