



"""simple docstring"""






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

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block



@dataclass
class 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	42






class 			A__   (  __SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	@register_to_config
	def __init__(     self:		Dict       ,			_SCREAMING_SNAKE_CASE:		int = 6_5536       ,			_SCREAMING_SNAKE_CASE:		Optional[int] = None       ,			_SCREAMING_SNAKE_CASE:		int = 2       ,			_SCREAMING_SNAKE_CASE:		int = 2       ,			_SCREAMING_SNAKE_CASE:		int = 0       ,			_SCREAMING_SNAKE_CASE:		str = "fourier"       ,			_SCREAMING_SNAKE_CASE:		bool = True       ,			_SCREAMING_SNAKE_CASE:		bool = False       ,			_SCREAMING_SNAKE_CASE:		float = 0.0       ,			_SCREAMING_SNAKE_CASE:		Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")       ,			_SCREAMING_SNAKE_CASE:		Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")       ,			_SCREAMING_SNAKE_CASE:		Tuple[str] = "UNetMidBlock1D"       ,			_SCREAMING_SNAKE_CASE:		str = None       ,			_SCREAMING_SNAKE_CASE:		Tuple[int] = (32, 32, 64)       ,			_SCREAMING_SNAKE_CASE:		str = None       ,			_SCREAMING_SNAKE_CASE:		int = 8       ,			_SCREAMING_SNAKE_CASE:		int = 1       ,			_SCREAMING_SNAKE_CASE:		bool = False       ,			)				->       int:

				"""simple docstring"""

				super().__init__()
				__lowerCAmelCase      :  Any												=       sample_size

				# time
				if time_embedding_type == "fourier":
							__lowerCAmelCase      :  Union[str, Any]												=       GaussianFourierProjection(
							    embedding_size=8       ,			set_W_to_weight=_SCREAMING_SNAKE_CASE       ,			log=_SCREAMING_SNAKE_CASE       ,			flip_sin_to_cos=_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase      :  Optional[int]												=       2 * block_out_channels[0]
				elif time_embedding_type == "positional":
							__lowerCAmelCase      :  List[Any]												=       Timesteps(
							    block_out_channels[0]       ,			flip_sin_to_cos=_SCREAMING_SNAKE_CASE       ,			downscale_freq_shift=_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase      :  Optional[Any]												=       block_out_channels[0]

				if use_timestep_embedding:
							__lowerCAmelCase      :  str												=       block_out_channels[0] * 4
							__lowerCAmelCase      :  Union[str, Any]												=       TimestepEmbedding(
							    in_channels=_SCREAMING_SNAKE_CASE       ,			time_embed_dim=_SCREAMING_SNAKE_CASE       ,			act_fn=_SCREAMING_SNAKE_CASE       ,			out_dim=block_out_channels[0]       ,			)

				__lowerCAmelCase      :  int												=       nn.ModuleList([])
				__lowerCAmelCase      :  Any												=       None
				__lowerCAmelCase      :  Any												=       nn.ModuleList([])
				__lowerCAmelCase      :  Tuple												=       None

				# down
				__lowerCAmelCase      :  List[str]												=       in_channels
				for i, down_block_type in enumerate(_SCREAMING_SNAKE_CASE):
							__lowerCAmelCase      :  Optional[Any]												=       output_channel
							__lowerCAmelCase      :  List[str]												=       block_out_channels[i]

							if i == 0:
										input_channel += extra_in_channels

							__lowerCAmelCase      :  Optional[Any]												=       i == len(_SCREAMING_SNAKE_CASE) - 1

							__lowerCAmelCase      :  Any												=       get_down_block(
							    _SCREAMING_SNAKE_CASE       ,			num_layers=_SCREAMING_SNAKE_CASE       ,			in_channels=_SCREAMING_SNAKE_CASE       ,			out_channels=_SCREAMING_SNAKE_CASE       ,			temb_channels=block_out_channels[0]       ,			add_downsample=not is_final_block or downsample_each_block       ,			)
							self.down_blocks.append(_SCREAMING_SNAKE_CASE)

				# mid
				__lowerCAmelCase      :  Optional[int]												=       get_mid_block(
				    _SCREAMING_SNAKE_CASE       ,			in_channels=block_out_channels[-1]       ,			mid_channels=block_out_channels[-1]       ,			out_channels=block_out_channels[-1]       ,			embed_dim=block_out_channels[0]       ,			num_layers=_SCREAMING_SNAKE_CASE       ,			add_downsample=_SCREAMING_SNAKE_CASE       ,			)

				# up
				__lowerCAmelCase      :  Optional[int]												=       list(reversed(_SCREAMING_SNAKE_CASE))
				__lowerCAmelCase      :  str												=       reversed_block_out_channels[0]
				if out_block_type is None:
							__lowerCAmelCase      :  List[Any]												=       out_channels
				else:
							__lowerCAmelCase      :  Dict												=       block_out_channels[0]

				for i, up_block_type in enumerate(_SCREAMING_SNAKE_CASE):
							__lowerCAmelCase      :  Tuple												=       output_channel
							__lowerCAmelCase      :  str												=       (
							    reversed_block_out_channels[i + 1] if i < len(_SCREAMING_SNAKE_CASE) - 1 else final_upsample_channels
							)

							__lowerCAmelCase      :  str												=       i == len(_SCREAMING_SNAKE_CASE) - 1

							__lowerCAmelCase      :  List[Any]												=       get_up_block(
							    _SCREAMING_SNAKE_CASE       ,			num_layers=_SCREAMING_SNAKE_CASE       ,			in_channels=_SCREAMING_SNAKE_CASE       ,			out_channels=_SCREAMING_SNAKE_CASE       ,			temb_channels=block_out_channels[0]       ,			add_upsample=not is_final_block       ,			)
							self.up_blocks.append(_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase      :  Optional[Any]												=       output_channel

				# out
				__lowerCAmelCase      :  Any												=       norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4       ,			32)
				__lowerCAmelCase      :  List[str]												=       get_out_block(
				    out_block_type=_SCREAMING_SNAKE_CASE       ,			num_groups_out=_SCREAMING_SNAKE_CASE       ,			embed_dim=block_out_channels[0]       ,			out_channels=_SCREAMING_SNAKE_CASE       ,			act_fn=_SCREAMING_SNAKE_CASE       ,			fc_dim=block_out_channels[-1] // 4       ,			)







	def 							_SCREAMING_SNAKE_CASE   (     self:		Any       ,			_SCREAMING_SNAKE_CASE:		torch.FloatTensor       ,			_SCREAMING_SNAKE_CASE:		Union[torch.Tensor, float, int]       ,			_SCREAMING_SNAKE_CASE:		bool = True       ,			)				->       Union[UNetaDOutput, Tuple]:

				"""simple docstring"""

				__lowerCAmelCase      :  Any												=       timestep
				if not torch.is_tensor(_SCREAMING_SNAKE_CASE):
							__lowerCAmelCase      :  Dict												=       torch.tensor([timesteps]       ,			dtype=torch.long       ,			device=sample.device)
				elif torch.is_tensor(_SCREAMING_SNAKE_CASE) and len(timesteps.shape) == 0:
							__lowerCAmelCase      :  Any												=       timesteps[None].to(sample.device)

				__lowerCAmelCase      :  int												=       self.time_proj(_SCREAMING_SNAKE_CASE)
				if self.config.use_timestep_embedding:
							__lowerCAmelCase      :  Optional[Any]												=       self.time_mlp(_SCREAMING_SNAKE_CASE)
				else:
							__lowerCAmelCase      :  int												=       timestep_embed[..., None]
							__lowerCAmelCase      :  Dict												=       timestep_embed.repeat([1, 1, sample.shape[2]]).to(sample.dtype)
							__lowerCAmelCase      :  Dict												=       timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]))

				# 2. down
				__lowerCAmelCase      :  Tuple												=       ()
				for downsample_block in self.down_blocks:
							__lowerCAmelCase						,	__lowerCAmelCase      :  str												=       downsample_block(hidden_states=_SCREAMING_SNAKE_CASE       ,			temb=_SCREAMING_SNAKE_CASE)
							down_block_res_samples += res_samples

				# 3. mid
				if self.mid_block:
							__lowerCAmelCase      :  Dict												=       self.mid_block(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE)

				# 4. up
				for i, upsample_block in enumerate(self.up_blocks):
							__lowerCAmelCase      :  Optional[int]												=       down_block_res_samples[-1:]
							__lowerCAmelCase      :  Tuple												=       down_block_res_samples[:-1]
							__lowerCAmelCase      :  str												=       upsample_block(_SCREAMING_SNAKE_CASE       ,			res_hidden_states_tuple=_SCREAMING_SNAKE_CASE       ,			temb=_SCREAMING_SNAKE_CASE)

				# 5. post-process
				if self.out_block:
							__lowerCAmelCase      :  Optional[int]												=       self.out_block(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE)

				if not return_dict:
							return (sample,)

				return UNetaDOutput(sample=_SCREAMING_SNAKE_CASE)




"""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))