from __future__ import annotations

import unittest

from transformers import MobileBertConfig, 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,
	    TFMobileBertForMaskedLM,
	    TFMobileBertForMultipleChoice,
	    TFMobileBertForNextSentencePrediction,
	    TFMobileBertForPreTraining,
	    TFMobileBertForQuestionAnswering,
	    TFMobileBertForSequenceClassification,
	    TFMobileBertForTokenClassification,
	    TFMobileBertModel,
	)


@require_tf
class 						a__							(       __snake_case				,						__snake_case				,						unittest.TestCase		):
			A__    :       Union[str, Any]        =      (
			    (
			        TFMobileBertModel,
			        TFMobileBertForMaskedLM,
			        TFMobileBertForNextSentencePrediction,
			        TFMobileBertForPreTraining,
			        TFMobileBertForQuestionAnswering,
			        TFMobileBertForSequenceClassification,
			        TFMobileBertForTokenClassification,
			        TFMobileBertForMultipleChoice,
			    )
			    if is_tf_available()
			    else ()
			)
			A__    :       int        =      (
			    {
			        'feature-extraction': TFMobileBertModel,
			        'fill-mask': TFMobileBertForMaskedLM,
			        'question-answering': TFMobileBertForQuestionAnswering,
			        'text-classification': TFMobileBertForSequenceClassification,
			        'token-classification': TFMobileBertForTokenClassification,
			        'zero-shot': TFMobileBertForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			A__    :       Optional[Any]        =      False
			A__    :       Union[str, Any]        =      False



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase=False   )			->   Any:
								__a		 =       super()._prepare_for_class(UpperCAmelCase				,						UpperCAmelCase				,						return_labels=UpperCAmelCase   )

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

								return inputs_dict


			class 						a__							(       __snake_case		):



						def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase=1_3				,						UpperCAmelCase=7				,						UpperCAmelCase=True				,						UpperCAmelCase=True				,						UpperCAmelCase=True				,						UpperCAmelCase=True				,						UpperCAmelCase=9_9				,						UpperCAmelCase=3_2				,						UpperCAmelCase=3_2				,						UpperCAmelCase=2				,						UpperCAmelCase=4				,						UpperCAmelCase=3_7				,						UpperCAmelCase="gelu"				,						UpperCAmelCase=0.1				,						UpperCAmelCase=0.1				,						UpperCAmelCase=5_1_2				,						UpperCAmelCase=1_6				,						UpperCAmelCase=2				,						UpperCAmelCase=0.02				,						UpperCAmelCase=3				,						UpperCAmelCase=4				,						UpperCAmelCase=None				,						)			->   List[Any]:
											__a		 =       parent
											__a		 =       batch_size
											__a		 =       seq_length
											__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		 =       num_labels
											__a		 =       num_choices
											__a		 =       scope
											__a		 =       embedding_size



						def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
											__a		 =       ids_tensor([self.batch_size, self.seq_length]				,						self.vocab_size   )

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

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

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

											__a		 =       MobileBertConfig(
											    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				,						embedding_size=self.embedding_size				,						)

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



						def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   int:
											__a		 =       TFMobileBertModel(config=UpperCAmelCase   )
											__a		 =       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
											__a		 =       model(UpperCAmelCase   )

											__a		 =       [input_ids, input_mask]
											__a		 =       model(UpperCAmelCase   )

											__a		 =       model(UpperCAmelCase   )

											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 __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   Any:
											__a		 =       TFMobileBertForMaskedLM(config=UpperCAmelCase   )
											__a		 =       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
											__a		 =       model(UpperCAmelCase   )
											self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.seq_length, self.vocab_size)   )



						def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   List[str]:
											__a		 =       TFMobileBertForNextSentencePrediction(config=UpperCAmelCase   )
											__a		 =       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
											__a		 =       model(UpperCAmelCase   )
											self.parent.assertEqual(result.logits.shape				,						(self.batch_size, 2)   )



						def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   str:
											__a		 =       TFMobileBertForPreTraining(config=UpperCAmelCase   )
											__a		 =       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
											__a		 =       model(UpperCAmelCase   )
											self.parent.assertEqual(
											    result.prediction_logits.shape				,						(self.batch_size, self.seq_length, self.vocab_size)   )
											self.parent.assertEqual(result.seq_relationship_logits.shape				,						(self.batch_size, 2)   )



						def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   Optional[int]:
											__a		 =       self.num_labels
											__a		 =       TFMobileBertForSequenceClassification(config=UpperCAmelCase   )
											__a		 =       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
											__a		 =       model(UpperCAmelCase   )
											self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.num_labels)   )



						def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   List[Any]:
											__a		 =       self.num_choices
											__a		 =       TFMobileBertForMultipleChoice(config=UpperCAmelCase   )
											__a		 =       tf.tile(tf.expand_dims(UpperCAmelCase				,						1   )				,						(1, self.num_choices, 1)   )
											__a		 =       tf.tile(tf.expand_dims(UpperCAmelCase				,						1   )				,						(1, self.num_choices, 1)   )
											__a		 =       tf.tile(tf.expand_dims(UpperCAmelCase				,						1   )				,						(1, self.num_choices, 1)   )
											__a		 =       {
											    'input_ids': multiple_choice_inputs_ids,
											    'attention_mask': multiple_choice_input_mask,
											    'token_type_ids': multiple_choice_token_type_ids,
											}
											__a		 =       model(UpperCAmelCase   )
											self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.num_choices)   )



						def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   List[Any]:
											__a		 =       self.num_labels
											__a		 =       TFMobileBertForTokenClassification(config=UpperCAmelCase   )
											__a		 =       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
											__a		 =       model(UpperCAmelCase   )
											self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.seq_length, self.num_labels)   )



						def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   List[Any]:
											__a		 =       TFMobileBertForQuestionAnswering(config=UpperCAmelCase   )
											__a		 =       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
											__a		 =       model(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 __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
											__a		 =       self.prepare_config_and_inputs()
											(
											    (
											    __a
											) ,				(
											    __a
											) ,				(
											    __a
											) ,				(
											    __a
											) ,				(
											    __a
											) ,				(
											    __a
											) ,				(
											    __a
											) ,				
											)		 =       config_and_inputs
											__a		 =       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
											return config, inputs_dict






			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a		 =       TFMobileBertModelTest.TFMobileBertModelTester(self   )
								__a		 =       ConfigTester(self				,						config_class=UpperCAmelCase				,						hidden_size=3_7   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								self.config_tester.run_common_tests()



			def __SCREAMING_SNAKE_CASE							(						self   )			->   str:
								__a		 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_model(*UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Union[str, Any]:
								__a		 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_masked_lm(*UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a		 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_multiple_choice(*UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a		 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Union[str, Any]:
								__a		 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_pretraining(*UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Union[str, Any]:
								__a		 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_question_answering(*UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								__a		 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_sequence_classification(*UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_token_classification(*UpperCAmelCase   )







			@slow
			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								for model_name in ["google/mobilebert-uncased"]:
													__a		 =       TFMobileBertModel.from_pretrained(UpperCAmelCase   )
													self.assertIsNotNone(UpperCAmelCase   )






@require_tf
class 						a__							(       unittest.TestCase		):



			@slow
			def __SCREAMING_SNAKE_CASE							(						self   )			->   Union[str, Any]:
								__a		 =       TFMobileBertForPreTraining.from_pretrained('google/mobilebert-uncased'   )
								__a		 =       tf.constant([[0, 1, 2, 3, 4, 5]]   )
								__a		 =       model(UpperCAmelCase   )[0]

								__a		 =       [1, 6, 3_0_5_2_2]
								self.assertEqual(output.shape				,						UpperCAmelCase   )

								__a		 =       tf.constant(
								    [
								        [
								            [-4.5_919_547, -9.248_295, -9.645_256],
								            [-6.7_306_175, -6.440_284, -6.6_052_837],
								            [-7.2_743_506, -6.7_847_915, -6.024_673],
								        ]
								    ]   )
								tf.debugging.assert_near(output[:, :3, :3]				,						UpperCAmelCase				,						atol=1e-4   )





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
class 						a__							(       __snake_case		):
			A__    :       torch.FloatTensor
			A__    :       torch.FloatTensor
			A__    :       Optional[torch.FloatTensor]        =      None






class 						a__							(       __snake_case				,						__snake_case		):
			A__    :       Optional[Any]        =      2



			@register_to_config
			def __init__(						self				,						UpperCAmelCase = 0.02				,						UpperCAmelCase = 1_0_0				,						UpperCAmelCase = 1.007				,						UpperCAmelCase = 8_0				,						UpperCAmelCase = 0.05				,						UpperCAmelCase = 5_0				,						)			->   Optional[Any]:
								# standard deviation of the initial noise distribution
								__a		 =       sigma_max

								# setable values
								__a		 =       None
								__a		 =       None
								__a		 =       None  # sigma(t_i)



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   torch.FloatTensor:
								return sample



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   int:
								__a		 =       num_inference_steps
								__a		 =       np.arange(0				,						self.num_inference_steps   )[::-1].copy()
								__a		 =       torch.from_numpy(UpperCAmelCase   ).to(UpperCAmelCase   )
								__a		 =       [
								    (
								        self.config.sigma_max**2
								        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
								    )
								    for i in self.timesteps
								]
								__a		 =       torch.tensor(UpperCAmelCase				,						dtype=torch.floataa				,						device=UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   Tuple[torch.FloatTensor, float]:
								if self.config.s_min <= sigma <= self.config.s_max:
													__a		 =       min(self.config.s_churn / self.num_inference_steps				,						2**0.5 - 1   )
								else:
													__a		 =       0

								# sample eps ~ N(0, S_noise^2 * I)
								__a		 =       self.config.s_noise * randn_tensor(sample.shape				,						generator=UpperCAmelCase   ).to(sample.device   )
								__a		 =       sigma + gamma * sigma
								__a		 =       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

								return sample_hat, sigma_hat



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = True				,						)			->   Union[KarrasVeOutput, Tuple]:
								__a		 =       sample_hat + sigma_hat * model_output
								__a		 =       (sample_hat - pred_original_sample) / sigma_hat
								__a		 =       sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
													return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=UpperCAmelCase				,						derivative=UpperCAmelCase				,						pred_original_sample=UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = True				,						)			->   Union[KarrasVeOutput, Tuple]:
								__a		 =       sample_prev + sigma_prev * model_output
								__a		 =       (sample_prev - pred_original_sample) / sigma_prev
								__a		 =       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

								if not return_dict:
													return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=UpperCAmelCase				,						derivative=UpperCAmelCase				,						pred_original_sample=UpperCAmelCase   )







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   List[Any]:
								raise NotImplementedError()




