import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
    from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


__lowercase							 =					logging.get_logger(__name__)  # pylint: disable=invalid-name

__lowercase							 =					256


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Tuple						=      ["""melgan"""]







    def __init__(	self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , )       ->	None:
        super().__init__()

        # From MELGAN
        __UpperCamelCase     :int			  =		math.log(1E-5)  # Matches MelGAN training.
        __UpperCamelCase     :int			  =		4.0  # Largest value for most examples
        __UpperCamelCase     :str			  =		128

        self.register_modules(
            notes_encoder=__lowercase , continuous_encoder=__lowercase , decoder=__lowercase , scheduler=__lowercase , melgan=__lowercase , )







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase=(-1.0, 1.0) , __lowercase=False)       ->	Dict:
        __UpperCamelCase  ,       __UpperCamelCase     :str			  =		output_range
        if clip:
            __UpperCamelCase     :Union[str, Any]			  =		torch.clip(__lowercase , self.min_value , self.max_value)
        # Scale to [0, 1].
        __UpperCamelCase     :Union[str, Any]			  =		(features - self.min_value) / (self.max_value - self.min_value)
        # Scale to [min_out, max_out].
        return zero_one * (max_out - min_out) + min_out







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase=(-1.0, 1.0) , __lowercase=False)       ->	Optional[int]:
        __UpperCamelCase  ,       __UpperCamelCase     :int			  =		input_range
        __UpperCamelCase     :Optional[int]			  =		torch.clip(__lowercase , __lowercase , __lowercase) if clip else outputs
        # Scale to [0, 1].
        __UpperCamelCase     :List[str]			  =		(outputs - min_out) / (max_out - min_out)
        # Scale to [self.min_value, self.max_value].
        return zero_one * (self.max_value - self.min_value) + self.min_value







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase)       ->	List[Any]:
        __UpperCamelCase     :List[str]			  =		input_tokens > 0
        __UpperCamelCase  ,       __UpperCamelCase     :Union[str, Any]			  =		self.notes_encoder(
            encoder_input_tokens=__lowercase , encoder_inputs_mask=__lowercase)

        __UpperCamelCase  ,       __UpperCamelCase     :Union[str, Any]			  =		self.continuous_encoder(
            encoder_inputs=__lowercase , encoder_inputs_mask=__lowercase)

        return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase)       ->	str:
        __UpperCamelCase     :Optional[int]			  =		noise_time
        if not torch.is_tensor(__lowercase):
            __UpperCamelCase     :str			  =		torch.tensor([timesteps] , dtype=torch.long , device=input_tokens.device)
        elif torch.is_tensor(__lowercase) and len(timesteps.shape) == 0:
            __UpperCamelCase     :Dict			  =		timesteps[None].to(input_tokens.device)

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        __UpperCamelCase     :List[str]			  =		timesteps * torch.ones(input_tokens.shape[0] , dtype=timesteps.dtype , device=timesteps.device)

        __UpperCamelCase     :Tuple			  =		self.decoder(
            encodings_and_masks=__lowercase , decoder_input_tokens=__lowercase , decoder_noise_time=__lowercase)
        return logits







    @torch.no_grad()
    def __call__(	self , __lowercase , __lowercase = None , __lowercase = 100 , __lowercase = True , __lowercase = "numpy" , __lowercase = None , __lowercase = 1 , )       ->	Union[AudioPipelineOutput, Tuple]:
        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(__lowercase , __lowercase) or callback_steps <= 0)
        ):
            raise ValueError(
                f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
                f""" {type(__lowercase)}.""")

        __UpperCamelCase     :Union[str, Any]			  =		np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims] , dtype=np.floataa)
        __UpperCamelCase     :Union[str, Any]			  =		np.zeros([1, 0, self.n_dims] , np.floataa)
        __UpperCamelCase     :Union[str, Any]			  =		torch.ones((1, TARGET_FEATURE_LENGTH) , dtype=__lowercase , device=self.device)

        for i, encoder_input_tokens in enumerate(__lowercase):
            if i == 0:
                __UpperCamelCase     :int			  =		torch.from_numpy(pred_mel[:1].copy()).to(
                    device=self.device , dtype=self.decoder.dtype)
                # The first chunk has no previous context.
                __UpperCamelCase     :int			  =		torch.zeros((1, TARGET_FEATURE_LENGTH) , dtype=__lowercase , device=self.device)
            else:
                # The full song pipeline does not feed in a context feature, so the mask
                # will be all 0s after the feature converter. Because we know we're
                # feeding in a full context chunk from the previous prediction, set it
                # to all 1s.
                __UpperCamelCase     :Tuple			  =		ones

            __UpperCamelCase     :Optional[Any]			  =		self.scale_features(
                __lowercase , output_range=[-1.0, 1.0] , clip=__lowercase)

            __UpperCamelCase     :int			  =		self.encode(
                input_tokens=torch.IntTensor([encoder_input_tokens]).to(device=self.device) , continuous_inputs=__lowercase , continuous_mask=__lowercase , )

            # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
            __UpperCamelCase     :int			  =		randn_tensor(
                shape=encoder_continuous_inputs.shape , generator=__lowercase , device=self.device , dtype=self.decoder.dtype , )

            # set step values
            self.scheduler.set_timesteps(__lowercase)

            # Denoising diffusion loop
            for j, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
                __UpperCamelCase     :Optional[int]			  =		self.decode(
                    encodings_and_masks=__lowercase , input_tokens=__lowercase , noise_time=t / self.scheduler.config.num_train_timesteps , )

                # Compute previous output: x_t -> x_t-1
                __UpperCamelCase     :int			  =		self.scheduler.step(__lowercase , __lowercase , __lowercase , generator=__lowercase).prev_sample

            __UpperCamelCase     :Tuple			  =		self.scale_to_features(__lowercase , input_range=[-1.0, 1.0])
            __UpperCamelCase     :List[Any]			  =		mel[:1]
            __UpperCamelCase     :Optional[Any]			  =		mel.cpu().float().numpy()

            __UpperCamelCase     :Any			  =		np.concatenate([full_pred_mel, pred_mel[:1]] , axis=1)

            # call the callback, if provided
            if callback is not None and i % callback_steps == 0:
                callback(__lowercase , __lowercase)

            logger.info('''Generated segment''' , __lowercase)

        if output_type == "numpy" and not is_onnx_available():
            raise ValueError(
                '''Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.''')
        elif output_type == "numpy" and self.melgan is None:
            raise ValueError(
                '''Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.''')

        if output_type == "numpy":
            __UpperCamelCase     :Optional[Any]			  =		self.melgan(input_features=full_pred_mel.astype(np.floataa))
        else:
            __UpperCamelCase     :List[str]			  =		full_pred_mel

        if not return_dict:
            return (output,)

        return AudioPipelineOutput(audios=__lowercase)










"""simple docstring"""






import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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


if is_torch_available():
	import torch

	from transformers import (
	    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
	    SqueezeBertForMaskedLM,
	    SqueezeBertForMultipleChoice,
	    SqueezeBertForQuestionAnswering,
	    SqueezeBertForSequenceClassification,
	    SqueezeBertForTokenClassification,
	    SqueezeBertModel,
	)



class 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	def __init__(     self:		Tuple       ,			_SCREAMING_SNAKE_CASE:		Dict       ,			_SCREAMING_SNAKE_CASE:		List[str]=13       ,			_SCREAMING_SNAKE_CASE:		Tuple=7       ,			_SCREAMING_SNAKE_CASE:		int=True       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=True       ,			_SCREAMING_SNAKE_CASE:		str=False       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=True       ,			_SCREAMING_SNAKE_CASE:		int=99       ,			_SCREAMING_SNAKE_CASE:		int=32       ,			_SCREAMING_SNAKE_CASE:		List[str]=5       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=4       ,			_SCREAMING_SNAKE_CASE:		int=64       ,			_SCREAMING_SNAKE_CASE:		List[str]="gelu"       ,			_SCREAMING_SNAKE_CASE:		str=0.1       ,			_SCREAMING_SNAKE_CASE:		Any=0.1       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=512       ,			_SCREAMING_SNAKE_CASE:		Tuple=16       ,			_SCREAMING_SNAKE_CASE:		Any=2       ,			_SCREAMING_SNAKE_CASE:		List[str]=0.02       ,			_SCREAMING_SNAKE_CASE:		Tuple=3       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=4       ,			_SCREAMING_SNAKE_CASE:		int=None       ,			_SCREAMING_SNAKE_CASE:		int=2       ,			_SCREAMING_SNAKE_CASE:		str=2       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=2       ,			_SCREAMING_SNAKE_CASE:		List[Any]=2       ,			_SCREAMING_SNAKE_CASE:		int=4       ,			_SCREAMING_SNAKE_CASE:		List[str]=1       ,			)				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       parent
				__lowerCAmelCase      :  Optional[Any]												=       batch_size
				__lowerCAmelCase      :  Union[str, Any]												=       seq_length
				__lowerCAmelCase      :  Optional[Any]												=       is_training
				__lowerCAmelCase      :  Optional[int]												=       use_input_mask
				__lowerCAmelCase      :  Dict												=       use_token_type_ids
				__lowerCAmelCase      :  Dict												=       use_labels
				__lowerCAmelCase      :  Dict												=       vocab_size
				__lowerCAmelCase      :  Tuple												=       hidden_size
				__lowerCAmelCase      :  List[Any]												=       num_hidden_layers
				__lowerCAmelCase      :  Union[str, Any]												=       num_attention_heads
				__lowerCAmelCase      :  Tuple												=       intermediate_size
				__lowerCAmelCase      :  List[Any]												=       hidden_act
				__lowerCAmelCase      :  Optional[Any]												=       hidden_dropout_prob
				__lowerCAmelCase      :  Optional[Any]												=       attention_probs_dropout_prob
				__lowerCAmelCase      :  Optional[int]												=       max_position_embeddings
				__lowerCAmelCase      :  Union[str, Any]												=       type_vocab_size
				__lowerCAmelCase      :  Optional[int]												=       type_sequence_label_size
				__lowerCAmelCase      :  Dict												=       initializer_range
				__lowerCAmelCase      :  Tuple												=       num_labels
				__lowerCAmelCase      :  Optional[Any]												=       num_choices
				__lowerCAmelCase      :  Union[str, Any]												=       scope
				__lowerCAmelCase      :  Optional[Any]												=       q_groups
				__lowerCAmelCase      :  Optional[int]												=       k_groups
				__lowerCAmelCase      :  Any												=       v_groups
				__lowerCAmelCase      :  int												=       post_attention_groups
				__lowerCAmelCase      :  List[str]												=       intermediate_groups
				__lowerCAmelCase      :  Optional[Any]												=       output_groups

	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       List[str]:

				"""simple docstring"""

				__lowerCAmelCase      :  str												=       ids_tensor([self.batch_size, self.seq_length]       ,			self.vocab_size)

				__lowerCAmelCase      :  Union[str, Any]												=       None
				if self.use_input_mask:
							__lowerCAmelCase      :  Tuple												=       random_attention_mask([self.batch_size, self.seq_length])

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

				__lowerCAmelCase      :  Any												=       self.get_config()

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

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       int:

				"""simple docstring"""

				return SqueezeBertConfig(
				    embedding_size=self.hidden_size       ,			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       ,			attention_probs_dropout_prob=self.hidden_dropout_prob       ,			attention_dropout=self.attention_probs_dropout_prob       ,			max_position_embeddings=self.max_position_embeddings       ,			initializer_range=self.initializer_range       ,			q_groups=self.q_groups       ,			k_groups=self.k_groups       ,			v_groups=self.v_groups       ,			post_attention_groups=self.post_attention_groups       ,			intermediate_groups=self.intermediate_groups       ,			output_groups=self.output_groups       ,			)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple       ,			_SCREAMING_SNAKE_CASE:		List[str]       ,			_SCREAMING_SNAKE_CASE:		List[Any]       ,			_SCREAMING_SNAKE_CASE:		Tuple       ,			_SCREAMING_SNAKE_CASE:		Dict       ,			_SCREAMING_SNAKE_CASE:		Dict       ,			_SCREAMING_SNAKE_CASE:		Tuple)				->       Tuple:

				"""simple docstring"""

				__lowerCAmelCase      :  List[Any]												=       SqueezeBertModel(config=_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()
				__lowerCAmelCase      :  List[Any]												=       model(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Dict												=       model(_SCREAMING_SNAKE_CASE)
				self.parent.assertEqual(result.last_hidden_state.shape       ,			(self.batch_size, self.seq_length, self.hidden_size))

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

				"""simple docstring"""

				__lowerCAmelCase      :  int												=       SqueezeBertForMaskedLM(config=_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()
				__lowerCAmelCase      :  Dict												=       model(_SCREAMING_SNAKE_CASE       ,			attention_mask=_SCREAMING_SNAKE_CASE       ,			labels=_SCREAMING_SNAKE_CASE)
				self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.seq_length, self.vocab_size))

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]       ,			_SCREAMING_SNAKE_CASE:		Dict       ,			_SCREAMING_SNAKE_CASE:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any])				->       int:

				"""simple docstring"""

				__lowerCAmelCase      :  str												=       SqueezeBertForQuestionAnswering(config=_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()
				__lowerCAmelCase      :  Union[str, Any]												=       model(
				    _SCREAMING_SNAKE_CASE       ,			attention_mask=_SCREAMING_SNAKE_CASE       ,			start_positions=_SCREAMING_SNAKE_CASE       ,			end_positions=_SCREAMING_SNAKE_CASE)
				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[Any]       ,			_SCREAMING_SNAKE_CASE:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]       ,			_SCREAMING_SNAKE_CASE:		Tuple       ,			_SCREAMING_SNAKE_CASE:		Any       ,			_SCREAMING_SNAKE_CASE:		Tuple)				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  List[Any]												=       self.num_labels
				__lowerCAmelCase      :  Union[str, Any]												=       SqueezeBertForSequenceClassification(_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()
				__lowerCAmelCase      :  int												=       model(_SCREAMING_SNAKE_CASE       ,			attention_mask=_SCREAMING_SNAKE_CASE       ,			labels=_SCREAMING_SNAKE_CASE)
				self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.num_labels))

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple       ,			_SCREAMING_SNAKE_CASE:		List[Any]       ,			_SCREAMING_SNAKE_CASE:		Any       ,			_SCREAMING_SNAKE_CASE:		List[Any]       ,			_SCREAMING_SNAKE_CASE:		List[Any]       ,			_SCREAMING_SNAKE_CASE:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		Optional[int])				->       Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Dict												=       self.num_labels
				__lowerCAmelCase      :  Optional[int]												=       SqueezeBertForTokenClassification(config=_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()

				__lowerCAmelCase      :  List[str]												=       model(_SCREAMING_SNAKE_CASE       ,			attention_mask=_SCREAMING_SNAKE_CASE       ,			labels=_SCREAMING_SNAKE_CASE)
				self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.seq_length, self.num_labels))

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any]       ,			_SCREAMING_SNAKE_CASE:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]       ,			_SCREAMING_SNAKE_CASE:		Any       ,			_SCREAMING_SNAKE_CASE:		List[Any]       ,			_SCREAMING_SNAKE_CASE:		int)				->       Tuple:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       self.num_choices
				__lowerCAmelCase      :  str												=       SqueezeBertForMultipleChoice(config=_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()
				__lowerCAmelCase      :  int												=       input_ids.unsqueeze(1).expand(-1       ,			self.num_choices       ,			-1).contiguous()
				__lowerCAmelCase      :  Union[str, Any]												=       input_mask.unsqueeze(1).expand(-1       ,			self.num_choices       ,			-1).contiguous()
				__lowerCAmelCase      :  str												=       model(
				    _SCREAMING_SNAKE_CASE       ,			attention_mask=_SCREAMING_SNAKE_CASE       ,			labels=_SCREAMING_SNAKE_CASE       ,			)
				self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.num_choices))







	def 							_SCREAMING_SNAKE_CASE   (     self:		str)				->       List[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       self.prepare_config_and_inputs()
				((__lowerCAmelCase)						,	(__lowerCAmelCase)						,	(__lowerCAmelCase)						,	(__lowerCAmelCase)						,	(__lowerCAmelCase)						,	(__lowerCAmelCase))      :  Union[str, Any]												=       config_and_inputs
				__lowerCAmelCase      :  int												=       {"input_ids": input_ids, "attention_mask": input_mask}
				return config, inputs_dict






@require_torch
class 			A__   (  __SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE  ,		unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	(
	    (
	        SqueezeBertModel,
	        SqueezeBertForMaskedLM,
	        SqueezeBertForMultipleChoice,
	        SqueezeBertForQuestionAnswering,
	        SqueezeBertForSequenceClassification,
	        SqueezeBertForTokenClassification,
	    )
	    if is_torch_available()
	    else None
	)
	SCREAMING_SNAKE_CASE          =	(
	    {
	        'feature-extraction': SqueezeBertModel,
	        'fill-mask': SqueezeBertForMaskedLM,
	        'question-answering': SqueezeBertForQuestionAnswering,
	        'text-classification': SqueezeBertForSequenceClassification,
	        'token-classification': SqueezeBertForTokenClassification,
	        'zero-shot': SqueezeBertForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	SCREAMING_SNAKE_CASE          =	False
	SCREAMING_SNAKE_CASE          =	True
	SCREAMING_SNAKE_CASE          =	False

	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Any												=       SqueezeBertModelTester(self)
				__lowerCAmelCase      :  Optional[int]												=       ConfigTester(self       ,			config_class=_SCREAMING_SNAKE_CASE       ,			dim=37)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Any)				->       Tuple:

				"""simple docstring"""

				self.config_tester.run_common_tests()

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       Any:

				"""simple docstring"""

				__lowerCAmelCase      :  Any												=       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_squeezebert_model(*_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any])				->       List[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  int												=       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_squeezebert_for_masked_lm(*_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  List[Any]												=       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_squeezebert_for_question_answering(*_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       int:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_squeezebert_for_sequence_classification(*_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Any)				->       int:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_squeezebert_for_token_classification(*_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Any												=       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_squeezebert_for_multiple_choice(*_SCREAMING_SNAKE_CASE)







	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		Any)				->       Dict:

				"""simple docstring"""

				for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							__lowerCAmelCase      :  Optional[Any]												=       SqueezeBertModel.from_pretrained(_SCREAMING_SNAKE_CASE)
							self.assertIsNotNone(_SCREAMING_SNAKE_CASE)






@require_sentencepiece
@require_tokenizers
@require_torch
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       List[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       SqueezeBertForSequenceClassification.from_pretrained("squeezebert/squeezebert-mnli")

				__lowerCAmelCase      :  List[Any]												=       torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]])
				__lowerCAmelCase      :  List[Any]												=       model(_SCREAMING_SNAKE_CASE)[0]
				__lowerCAmelCase      :  Any												=       torch.Size((1, 3))
				self.assertEqual(output.shape       ,			_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Union[str, Any]												=       torch.tensor([[0.6401, -0.0349, -0.6041]])
				self.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE       ,			atol=1e-4))