





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    lowerCAmelCase__				:






						"""simple docstring"""




						def __init__(   self				,							a_				,							a_=13				,							a_=7				,							a_=True				,							a_=True				,							a_=True				,							a_=True				,							a_=99				,							a_=16				,							a_=32				,							a_=2				,							a_=4				,							a_=37				,							a_="gelu"				,							a_=0.1				,							a_=0.1				,							a_=512				,							a_=16				,							a_=2				,							a_=0.02				,							a_=3				,							a_=4				,							a_=None				,							):
							lowerCamelCase_			:							Dict      =       parent
							lowerCamelCase_			:							Tuple      =       13
							lowerCamelCase_			:							str      =       7
							lowerCamelCase_			:							List[str]      =       True
							lowerCamelCase_			:							Any      =       True
							lowerCamelCase_			:							List[str]      =       True
							lowerCamelCase_			:							int      =       True
							lowerCamelCase_			:							List[Any]      =       99
							lowerCamelCase_			:							Optional[int]      =       16
							lowerCamelCase_			:							List[str]      =       32
							lowerCamelCase_			:							Any      =       2
							lowerCamelCase_			:							int      =       4
							lowerCamelCase_			:							Union[str, Any]      =       37
							lowerCamelCase_			:							Union[str, Any]      =       "gelu"
							lowerCamelCase_			:							Union[str, Any]      =       0.1
							lowerCamelCase_			:							List[str]      =       0.1
							lowerCamelCase_			:							Optional[Any]      =       512
							lowerCamelCase_			:							Dict      =       16
							lowerCamelCase_			:							Tuple      =       2
							lowerCamelCase_			:							Union[str, Any]      =       0.02
							lowerCamelCase_			:							Any      =       3
							lowerCamelCase_			:							Tuple      =       4
							lowerCamelCase_			:							Optional[Any]      =       None




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

							lowerCamelCase_			:							str      =       None
							if self.use_input_mask:
								lowerCamelCase_			:							int      =       random_attention_mask([self.batch_size, self.seq_length]     )

							lowerCamelCase_			:							int      =       None
							if self.use_token_type_ids:
								lowerCamelCase_			:							Optional[Any]      =       ids_tensor([self.batch_size, self.seq_length]				,							self.type_vocab_size     )

							lowerCamelCase_			:							Dict      =       None
							lowerCamelCase_			:							Any      =       None
							lowerCamelCase_			:							str      =       None
							if self.use_labels:
								lowerCamelCase_			:							str      =       ids_tensor([self.batch_size]				,							self.type_sequence_label_size     )
								lowerCamelCase_			:							Optional[int]      =       ids_tensor([self.batch_size, self.seq_length]				,							self.num_labels     )
								lowerCamelCase_			:							List[Any]      =       ids_tensor([self.batch_size]				,							self.num_choices     )

							lowerCamelCase_			:							Optional[int]      =       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 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_				,							a_				,							a_				,							a_				,							a_     ):
							lowerCamelCase_			:							Optional[int]      =       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_			:							List[Any]      =       {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
							lowerCamelCase_			:							Dict      =       model(lowerCAmelCase__     )

							lowerCamelCase_			:							Optional[int]      =       [input_ids, input_mask]
							lowerCamelCase_			:							Any      =       model(lowerCAmelCase__     )

							lowerCamelCase_			:							Optional[Any]      =       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 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_				,							a_				,							a_				,							a_				,							a_     ):
							lowerCamelCase_			:							Optional[int]      =       self.num_labels
							lowerCamelCase_			:							List[str]      =       TFAlbertForPreTraining(config=lowerCAmelCase__     )
							lowerCamelCase_			:							Any      =       {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
							lowerCamelCase_			:							Optional[int]      =       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 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_				,							a_				,							a_				,							a_				,							a_     ):
							lowerCamelCase_			:							Optional[int]      =       TFAlbertForMaskedLM(config=lowerCAmelCase__     )
							lowerCamelCase_			:							Optional[int]      =       {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
							lowerCamelCase_			:							Union[str, Any]      =       model(lowerCAmelCase__     )
							self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.seq_length, self.vocab_size)     )




						def 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_				,							a_				,							a_				,							a_				,							a_     ):
							lowerCamelCase_			:							Optional[int]      =       self.num_labels
							lowerCamelCase_			:							int      =       TFAlbertForSequenceClassification(config=lowerCAmelCase__     )
							lowerCamelCase_			:							Any      =       {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
							lowerCamelCase_			:							Optional[int]      =       model(lowerCAmelCase__     )
							self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.num_labels)     )




						def 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_				,							a_				,							a_				,							a_				,							a_     ):
							lowerCamelCase_			:							Union[str, Any]      =       TFAlbertForQuestionAnswering(config=lowerCAmelCase__     )
							lowerCamelCase_			:							Optional[int]      =       {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
							lowerCamelCase_			:							str      =       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 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_				,							a_				,							a_				,							a_				,							a_     ):
							lowerCamelCase_			:							List[str]      =       self.num_choices
							lowerCamelCase_			:							str      =       TFAlbertForMultipleChoice(config=lowerCAmelCase__     )
							lowerCamelCase_			:							Any      =       tf.tile(tf.expand_dims(lowerCAmelCase__				,							1     )				,							(1, self.num_choices, 1)     )
							lowerCamelCase_			:							List[Any]      =       tf.tile(tf.expand_dims(lowerCAmelCase__				,							1     )				,							(1, self.num_choices, 1)     )
							lowerCamelCase_			:							int      =       tf.tile(tf.expand_dims(lowerCAmelCase__				,							1     )				,							(1, self.num_choices, 1)     )
							lowerCamelCase_			:							List[Any]      =       {
							    "input_ids": multiple_choice_inputs_ids,
							    "attention_mask": multiple_choice_input_mask,
							    "token_type_ids": multiple_choice_token_type_ids,
							}
							lowerCamelCase_			:							List[Any]      =       model(lowerCAmelCase__     )
							self.parent.assertListEqual(list(result["logits"].shape     )				,							[self.batch_size, self.num_choices]     )




						def 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_				,							a_				,							a_				,							a_				,							a_     ):
							lowerCamelCase_			:							List[str]      =       self.num_labels
							lowerCamelCase_			:							Optional[Any]      =       TFAlbertForTokenClassification(config=lowerCAmelCase__     )
							lowerCamelCase_			:							Union[str, Any]      =       {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}
							lowerCamelCase_			:							str      =       model(lowerCAmelCase__     )
							self.parent.assertListEqual(list(result["logits"].shape     )				,							[self.batch_size, self.seq_length, self.num_labels]     )




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Dict      =       self.prepare_config_and_inputs()
							(
							    (
							    lowerCamelCase_ 
							)   ,(
							    lowerCamelCase_ 
							)   ,(
							    lowerCamelCase_ 
							)   ,(
							    lowerCamelCase_ 
							)   ,(
							    lowerCamelCase_ 
							)   ,(
							    lowerCamelCase_ 
							)   ,(
							    lowerCamelCase_ 
							)   , 
							)			:							int      =       config_and_inputs
							lowerCamelCase_			:							Optional[Any]      =       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
							return config, inputs_dict





@require_tf
class    lowerCAmelCase__				(			lowercase__,    lowercase__,    unittest.TestCase       ):






						"""simple docstring"""
						__UpperCAmelCase   :		Dict  					=	(
						    (
						        TFAlbertModel,
						        TFAlbertForPreTraining,
						        TFAlbertForMaskedLM,
						        TFAlbertForSequenceClassification,
						        TFAlbertForQuestionAnswering,
						        TFAlbertForTokenClassification,
						        TFAlbertForMultipleChoice,
						    )
						    if is_tf_available()
						    else ()
						)
						__UpperCAmelCase   :		Optional[int]  					=	(
						    {
						        '''feature-extraction''': TFAlbertModel,
						        '''fill-mask''': TFAlbertForMaskedLM,
						        '''question-answering''': TFAlbertForQuestionAnswering,
						        '''text-classification''': TFAlbertForSequenceClassification,
						        '''token-classification''': TFAlbertForTokenClassification,
						        '''zero-shot''': TFAlbertForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						__UpperCAmelCase   :		List[Any]  					=	False
						__UpperCAmelCase   :		List[Any]  					=	False




						def 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_=False     ):
							lowerCamelCase_			:							Union[str, Any]      =       super()._prepare_for_class(lowerCAmelCase__				,							lowerCAmelCase__				,							return_labels=lowerCAmelCase__     )

							if return_labels:
								if model_class in get_values(lowerCAmelCase__     ):
									lowerCamelCase_			:							int      =       tf.zeros(self.model_tester.batch_size				,							dtype=tf.intaa     )

							return inputs_dict




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Dict      =       TFAlbertModelTester(self     )
							lowerCamelCase_			:							List[str]      =       ConfigTester(self				,							config_class=lowerCAmelCase__				,							hidden_size=37     )




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




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Union[str, Any]      =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_albert_model(*lowerCAmelCase__     )




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Dict      =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_albert_for_pretraining(*lowerCAmelCase__     )




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Any      =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_albert_for_masked_lm(*lowerCAmelCase__     )




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							str      =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_albert_for_multiple_choice(*lowerCAmelCase__     )




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							List[str]      =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_albert_for_sequence_classification(*lowerCAmelCase__     )




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							str      =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_albert_for_question_answering(*lowerCAmelCase__     )




						@slow
						def 					_UpperCamelCase			(   self     ):
							for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								lowerCamelCase_			:							Dict      =       TFAlbertModel.from_pretrained(lowerCAmelCase__     )
								self.assertIsNotNone(lowerCAmelCase__     )





@require_tf
class    lowerCAmelCase__				(			unittest.TestCase       ):






						"""simple docstring"""




						@slow
						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Optional[int]      =       TFAlbertForPreTraining.from_pretrained("albert-base-v2"     )
							lowerCamelCase_			:							Optional[int]      =       tf.constant([[0, 1, 2, 3, 4, 5]]     )
							lowerCamelCase_			:							int      =       model(lowerCAmelCase__     )[0]

							lowerCamelCase_			:							Any      =       [1, 6, 3_0000]
							self.assertEqual(output.shape				,							lowerCAmelCase__     )

							lowerCamelCase_			:							Optional[Any]      =       tf.constant(
							    [
							        [
							            [4.59_56_68, 0.74_46_27_54, -1.81_81_47],
							            [4.5_95_43_47, 0.7_45_41_84, -1.8_18_82_58],
							            [4.5_95_49_05, 0.7_44_82_35, -1.8_18_23_16],
							        ]
							    ]     )
							tf.debugging.assert_near(output[:, :3, :3]				,							lowerCAmelCase__				,							atol=1E-4     )





import math
import os

import cva
import numpy as np

__magic_name__       					=      2_55.0







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							List[Any]      =       np.mean((original - contrast) ** 2)
	if mse == 0:
		return 100

	return 20 * math.logaa(PIXEL_MAX / math.sqrt(lowerCAmelCase_))







def     __magic_name__  (      ):
	'''simple docstring'''


	lowerCamelCase_			:							List[str]      =       os.path.dirname(os.path.realpath(lowerCAmelCase_))
	# Loading images (original image and compressed image)
	lowerCamelCase_			:							Optional[int]      =       cva.imread(os.path.join(lowerCAmelCase_      ,					"image_data/original_image.png"))
	lowerCamelCase_			:							List[str]      =       cva.imread(os.path.join(lowerCAmelCase_      ,					"image_data/compressed_image.png")      ,					1)

	lowerCamelCase_			:							str      =       cva.imread(os.path.join(lowerCAmelCase_      ,					"image_data/PSNR-example-base.png"))
	lowerCamelCase_			:							int      =       cva.imread(
	    os.path.join(lowerCAmelCase_      ,					"image_data/PSNR-example-comp-10.jpg")      ,					1)

	# Value expected: 29.73dB
	print("-- First Test --")
	print(F"""PSNR value is {peak_signal_to_noise_ratio(lowerCAmelCase_      ,					lowerCAmelCase_)} dB""")

	# # Value expected: 31.53dB (Wikipedia Example)
	print("\n-- Second Test --")
	print(F"""PSNR value is {peak_signal_to_noise_ratio(lowerCAmelCase_      ,					lowerCAmelCase_)} dB""")


if __name__ == "__main__":
							main()

