





from __future__ import annotations

import unittest

from transformers import AlbertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
						import tensorflow as tf

						from transformers import TF_MODEL_FOR_PRETRAINING_MAPPING
						from transformers.models.albert.modeling_tf_albert import (
						    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFAlbertForMaskedLM,
						    TFAlbertForMultipleChoice,
						    TFAlbertForPreTraining,
						    TFAlbertForQuestionAnswering,
						    TFAlbertForSequenceClassification,
						    TFAlbertForTokenClassification,
						    TFAlbertModel,
						)

class SCREAMING_SNAKE_CASE_			:





					"""simple docstring"""


					def __init__(			self						,	_lowerCAmelCase						,	_lowerCAmelCase=13						,	_lowerCAmelCase=7						,	_lowerCAmelCase=True						,	_lowerCAmelCase=True						,	_lowerCAmelCase=True						,	_lowerCAmelCase=True						,	_lowerCAmelCase=99						,	_lowerCAmelCase=16						,	_lowerCAmelCase=32						,	_lowerCAmelCase=2						,	_lowerCAmelCase=4						,	_lowerCAmelCase=37						,	_lowerCAmelCase="gelu"						,	_lowerCAmelCase=0.1						,	_lowerCAmelCase=0.1						,	_lowerCAmelCase=512						,	_lowerCAmelCase=16						,	_lowerCAmelCase=2						,	_lowerCAmelCase=0.02						,	_lowerCAmelCase=3						,	_lowerCAmelCase=4						,	_lowerCAmelCase=None						,	):
										lowerCamelCase__      =							parent
										lowerCamelCase__      =							13
										lowerCamelCase__      =							7
										lowerCamelCase__      =							True
										lowerCamelCase__      =							True
										lowerCamelCase__      =							True
										lowerCamelCase__      =							True
										lowerCamelCase__      =							99
										lowerCamelCase__      =							16
										lowerCamelCase__      =							32
										lowerCamelCase__      =							2
										lowerCamelCase__      =							4
										lowerCamelCase__      =							37
										lowerCamelCase__      =							"gelu"
										lowerCamelCase__      =							0.1
										lowerCamelCase__      =							0.1
										lowerCamelCase__      =							512
										lowerCamelCase__      =							16
										lowerCamelCase__      =							2
										lowerCamelCase__      =							0.02
										lowerCamelCase__      =							3
										lowerCamelCase__      =							4
										lowerCamelCase__      =							None





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size						)

										lowerCamelCase__      =							None
										if self.use_input_mask:
															lowerCamelCase__      =							random_attention_mask([self.batch_size, self.seq_length]						)

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

										lowerCamelCase__      =							None
										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.seq_length]						,	self.num_labels						)
															lowerCamelCase__      =							ids_tensor([self.batch_size]						,	self.num_choices						)

										lowerCamelCase__      =							AlbertConfig(
										    vocab_size=self.vocab_size						,	hidden_size=self.hidden_size						,	embedding_size=self.embedding_size						,	num_hidden_layers=self.num_hidden_layers						,	num_attention_heads=self.num_attention_heads						,	intermediate_size=self.intermediate_size						,	hidden_act=self.hidden_act						,	hidden_dropout_prob=self.hidden_dropout_prob						,	attention_probs_dropout_prob=self.attention_probs_dropout_prob						,	max_position_embeddings=self.max_position_embeddings						,	type_vocab_size=self.type_vocab_size						,	initializer_range=self.initializer_range						,	)

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							TFAlbertModel(config=_lowerCAmelCase						)
										# inputs = {'input_ids': input_ids,
										#           'attention_mask': input_mask,
										#           'token_type_ids': token_type_ids}
										# sequence_output, pooled_output = model(**inputs)
										lowerCamelCase__      =							{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
										lowerCamelCase__      =							model(_lowerCAmelCase						)

										lowerCamelCase__      =							[input_ids, input_mask]
										lowerCamelCase__      =							model(_lowerCAmelCase						)

										lowerCamelCase__      =							model(_lowerCAmelCase						)

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





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							self.num_labels
										lowerCamelCase__      =							TFAlbertForPreTraining(config=_lowerCAmelCase						)
										lowerCamelCase__      =							{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
										lowerCamelCase__      =							model(_lowerCAmelCase						)
										self.parent.assertEqual(result.prediction_logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size)						)
										self.parent.assertEqual(result.sop_logits.shape						,	(self.batch_size, self.num_labels)						)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							TFAlbertForMaskedLM(config=_lowerCAmelCase						)
										lowerCamelCase__      =							{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
										lowerCamelCase__      =							model(_lowerCAmelCase						)
										self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size)						)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							self.num_labels
										lowerCamelCase__      =							TFAlbertForSequenceClassification(config=_lowerCAmelCase						)
										lowerCamelCase__      =							{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
										lowerCamelCase__      =							model(_lowerCAmelCase						)
										self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels)						)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							TFAlbertForQuestionAnswering(config=_lowerCAmelCase						)
										lowerCamelCase__      =							{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
										lowerCamelCase__      =							model(_lowerCAmelCase						)
										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        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							self.num_choices
										lowerCamelCase__      =							TFAlbertForMultipleChoice(config=_lowerCAmelCase						)
										lowerCamelCase__      =							tf.tile(tf.expand_dims(_lowerCAmelCase						,	1						)						,	(1, self.num_choices, 1)						)
										lowerCamelCase__      =							tf.tile(tf.expand_dims(_lowerCAmelCase						,	1						)						,	(1, self.num_choices, 1)						)
										lowerCamelCase__      =							tf.tile(tf.expand_dims(_lowerCAmelCase						,	1						)						,	(1, self.num_choices, 1)						)
										lowerCamelCase__      =							{
										    "input_ids": multiple_choice_inputs_ids,
										    "attention_mask": multiple_choice_input_mask,
										    "token_type_ids": multiple_choice_token_type_ids,
										}
										lowerCamelCase__      =							model(_lowerCAmelCase						)
										self.parent.assertListEqual(list(result["logits"].shape						)						,	[self.batch_size, self.num_choices]						)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							self.num_labels
										lowerCamelCase__      =							TFAlbertForTokenClassification(config=_lowerCAmelCase						)
										lowerCamelCase__      =							{
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}
										lowerCamelCase__      =							model(_lowerCAmelCase						)
										self.parent.assertListEqual(list(result["logits"].shape						)						,	[self.batch_size, self.seq_length, self.num_labels]						)






					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.prepare_config_and_inputs()
										(
										    (
										    lowerCamelCase__	
										)     ,		(
										    lowerCamelCase__	
										)     ,		(
										    lowerCamelCase__	
										)     ,		(
										    lowerCamelCase__	
										)     ,		(
										    lowerCamelCase__	
										)     ,		(
										    lowerCamelCase__	
										)     ,		(
										    lowerCamelCase__	
										)     ,			
										)      =							config_and_inputs
										lowerCamelCase__      =							{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
										return config, inputs_dict

@require_tf
class SCREAMING_SNAKE_CASE_			(       snake_case_				,							snake_case_				,							unittest.TestCase ):





					"""simple docstring"""


					A__								=     (
					    (
					        TFAlbertModel,
					        TFAlbertForPreTraining,
					        TFAlbertForMaskedLM,
					        TFAlbertForSequenceClassification,
					        TFAlbertForQuestionAnswering,
					        TFAlbertForTokenClassification,
					        TFAlbertForMultipleChoice,
					    )
					    if is_tf_available()
					    else ()
					)
					A__								=     (
					    {
					        "feature-extraction": TFAlbertModel,
					        "fill-mask": TFAlbertForMaskedLM,
					        "question-answering": TFAlbertForQuestionAnswering,
					        "text-classification": TFAlbertForSequenceClassification,
					        "token-classification": TFAlbertForTokenClassification,
					        "zero-shot": TFAlbertForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					A__								=     False
					A__								=     False





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase=False						):
										lowerCamelCase__      =							super()._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						,	return_labels=_lowerCAmelCase						)

										if return_labels:
															if model_class in get_values(_lowerCAmelCase						):
																				lowerCamelCase__      =							tf.zeros(self.model_tester.batch_size						,	dtype=tf.intaa						)

										return inputs_dict





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							TFAlbertModelTester(self						)
										lowerCamelCase__      =							ConfigTester(self						,	config_class=_lowerCAmelCase						,	hidden_size=37						)





					def        __magic_name__			(			self						):
										self.config_tester.run_common_tests()





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_albert_model(*_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_albert_for_pretraining(*_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_albert_for_masked_lm(*_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_albert_for_multiple_choice(*_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_albert_for_sequence_classification(*_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_albert_for_question_answering(*_lowerCAmelCase						)






					@slow
					def        __magic_name__			(			self						):






										for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															lowerCamelCase__      =							TFAlbertModel.from_pretrained(_lowerCAmelCase						)
															self.assertIsNotNone(_lowerCAmelCase						)

@require_tf
class SCREAMING_SNAKE_CASE_			(       unittest.TestCase ):





					"""simple docstring"""


					@slow
					def        __magic_name__			(			self						):
										lowerCamelCase__      =							TFAlbertForPreTraining.from_pretrained("albert-base-v2"						)
										lowerCamelCase__      =							tf.constant([[0, 1, 2, 3, 4, 5]]						)
										lowerCamelCase__      =							model(_lowerCAmelCase						)[0]

										lowerCamelCase__      =							[1, 6, 3_0000]
										self.assertEqual(output.shape						,	_lowerCAmelCase						)

										lowerCamelCase__      =							tf.constant(
										    [
										        [
										            [4.595_668, 0.74_462_754, -1.818_147],
										            [4.5_954_347, 0.7_454_184, -1.8_188_258],
										            [4.5_954_905, 0.7_448_235, -1.8_182_316],
										        ]
										    ]						)
										tf.debugging.assert_near(output[:, :3, :3]						,	_lowerCAmelCase						,	atol=1E-4						)





import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey







class     __lowerCamelCase     :


							"""simple docstring"""

							def __init__(  self						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       Tuple:






								'''simple docstring'''






								lowercase_			 = 0
								# max greyscale value for #FFFFFF
								lowercase_			 = int(self.get_greyscale(255						,       255						,       255  )  )

								if not self.min_threshold < threshold < self.max_threshold:
									lowercase_			 = F'Factor value should be from 0 to {self.max_threshold}'
									raise ValueError(UpperCAmelCase  )

								lowercase_			 = input_img
								lowercase_			 = threshold
								lowercase_ ,	lowercase_			 = self.input_img.shape[1], self.input_img.shape[0]

								# error table size (+4 columns and +1 row) greater than input image because of
								# lack of if statements
								lowercase_			 = [
								    [0 for _ in range(self.height + 4  )] for __ in range(self.width + 1  )
								]
								lowercase_			 = np.ones((self.width, self.height, 3)						,       np.uinta  ) * 255




							@classmethod
							def 				A__      (  cls						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       float:






								'''simple docstring'''






								return 0.114 * blue + 0.587 * green + 0.2126 * red




							def 				A__      (  self  )     ->       None:






								'''simple docstring'''






								for y in range(self.height  ):
									for x in range(self.width  ):
										lowercase_			 = int(self.get_greyscale(*self.input_img[y][x]  )  )
										if self.threshold > greyscale + self.error_table[y][x]:
											lowercase_			 = (0, 0, 0)
											lowercase_			 = greyscale + self.error_table[x][y]
										else:
											lowercase_			 = (255, 255, 255)
											lowercase_			 = greyscale + self.error_table[x][y] - 255
										self.error_table[y][x + 1] += int(8 / 32 * current_error  )
										self.error_table[y][x + 2] += int(4 / 32 * current_error  )
										self.error_table[y + 1][x] += int(8 / 32 * current_error  )
										self.error_table[y + 1][x + 1] += int(4 / 32 * current_error  )
										self.error_table[y + 1][x + 2] += int(2 / 32 * current_error  )
										self.error_table[y + 1][x - 1] += int(4 / 32 * current_error  )
										self.error_table[y + 1][x - 2] += int(2 / 32 * current_error  )


if __name__ == "__main__":
							# create Burke's instances with original images in greyscale
							SCREAMING_SNAKE_CASE__       			=				[
							    Burkes(imread("""image_data/lena.jpg""", 1), threshold)
							    for threshold in (1, 1_2_6, 1_3_0, 1_4_0)
							]

							for burkes in burkes_instances:
														burkes.process()

							for burkes in burkes_instances:
														imshow(
														    f"""Original image with dithering threshold: {burkes.threshold}""",
														    burkes.output_img,
														)

							waitKey(0)
							destroyAllWindows()

