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

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
@dataclass
class     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     42




class     lowerCAmelCase_     (       __lowercase,  __lowercase ):







							@register_to_config
							def __init__(   self      :     int ,			_A      :     int = 32 ,			_A      :     int = 64 ,			_A      :     int = 20 ,			_A      :     int = 768 ,			_A      :     Union[str, Any]=77 ,			_A      :     Optional[Any]=4 ,			_A      :     float = 0.0 ,			_A      :     str = "silu" ,			_A      :     Optional[str] = None ,			_A      :     Optional[str] = None ,			_A      :     Optional[str] = "linear" ,			_A      :     Optional[str] = "prd" ,			_A      :     Optional[int] = None ,			_A      :     Optional[int] = None ,			_A      :     Optional[int] = None ,			):
												super().__init__()
												_UpperCamelCase			     =     num_attention_heads
												_UpperCamelCase			     =     attention_head_dim
												_UpperCamelCase			     =     num_attention_heads * attention_head_dim
												_UpperCamelCase			     =     additional_embeddings

												_UpperCamelCase			     =     time_embed_dim or inner_dim
												_UpperCamelCase			     =     embedding_proj_dim or embedding_dim
												_UpperCamelCase			     =     clip_embed_dim or embedding_dim

												_UpperCamelCase			     =     Timesteps(_A ,			_A ,			0		)
												_UpperCamelCase			     =     TimestepEmbedding(_A ,			_A ,			out_dim=_A ,			act_fn=_A		)

												_UpperCamelCase			     =     nn.Linear(_A ,			_A		)

												if embedding_proj_norm_type is None:
																	_UpperCamelCase			     =     None
												elif embedding_proj_norm_type == "layer":
																	_UpperCamelCase			     =     nn.LayerNorm(_A		)
												else:
																	raise ValueError(F"""unsupported embedding_proj_norm_type: {embedding_proj_norm_type}"""		)

												_UpperCamelCase			     =     nn.Linear(_A ,			_A		)

												if encoder_hid_proj_type is None:
																	_UpperCamelCase			     =     None
												elif encoder_hid_proj_type == "linear":
																	_UpperCamelCase			     =     nn.Linear(_A ,			_A		)
												else:
																	raise ValueError(F"""unsupported encoder_hid_proj_type: {encoder_hid_proj_type}"""		)

												_UpperCamelCase			     =     nn.Parameter(torch.zeros(1 ,			num_embeddings + additional_embeddings ,			_A		)		)

												if added_emb_type == "prd":
																	_UpperCamelCase			     =     nn.Parameter(torch.zeros(1 ,			1 ,			_A		)		)
												elif added_emb_type is None:
																	_UpperCamelCase			     =     None
												else:
																	raise ValueError(
																	    F"""`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`."""		)

												_UpperCamelCase			     =     nn.ModuleList(
												    [
												        BasicTransformerBlock(
												            _A ,			_A ,			_A ,			dropout=_A ,			activation_fn='''gelu''' ,			attention_bias=_A ,			)
												        for d in range(_A		)
												    ]		)

												if norm_in_type == "layer":
																	_UpperCamelCase			     =     nn.LayerNorm(_A		)
												elif norm_in_type is None:
																	_UpperCamelCase			     =     None
												else:
																	raise ValueError(F"""Unsupported norm_in_type: {norm_in_type}."""		)

												_UpperCamelCase			     =     nn.LayerNorm(_A		)

												_UpperCamelCase			     =     nn.Linear(_A ,			_A		)

												_UpperCamelCase			     =     torch.full(
												    [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings] ,			-1_0000.0		)
												causal_attention_mask.triu_(1		)
												_UpperCamelCase			     =     causal_attention_mask[None, ...]
												self.register_buffer('''causal_attention_mask''' ,			_A ,			persistent=_A		)

												_UpperCamelCase			     =     nn.Parameter(torch.zeros(1 ,			_A		)		)
												_UpperCamelCase			     =     nn.Parameter(torch.zeros(1 ,			_A		)		)







							@property
							# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
							def  UpperCamelCase_						(   self      :     Optional[int]		):
												_UpperCamelCase			     =     {}

												def fn_recursive_add_processors(_A      :     str ,			_A      :     torch.nn.Module ,			_A      :     Dict[str, AttentionProcessor]		):
																	if hasattr(_A ,			'''set_processor'''		):
																						_UpperCamelCase			     =     module.processor

																	for sub_name, child in module.named_children():
																						fn_recursive_add_processors(F"""{name}.{sub_name}""" ,			_A ,			_A		)

																	return processors

												for name, module in self.named_children():
																	fn_recursive_add_processors(_A ,			_A ,			_A		)

												return processors







							def  UpperCamelCase_						(   self      :     Optional[Any] ,			_A      :     Union[AttentionProcessor, Dict[str, AttentionProcessor]]		):
												_UpperCamelCase			     =     len(self.attn_processors.keys()		)

												if isinstance(_A ,			_A		) and len(_A		) != count:
																	raise ValueError(
																	    F"""A dict of processors was passed, but the number of processors {len(_A		)} does not match the"""
																	    F""" number of attention layers: {count}. Please make sure to pass {count} processor classes."""		)

												def fn_recursive_attn_processor(_A      :     str ,			_A      :     torch.nn.Module ,			_A      :     List[str]		):
																	if hasattr(_A ,			'''set_processor'''		):
																						if not isinstance(_A ,			_A		):
																											module.set_processor(_A		)
																						else:
																											module.set_processor(processor.pop(F"""{name}.processor"""		)		)

																	for sub_name, child in module.named_children():
																						fn_recursive_attn_processor(F"""{name}.{sub_name}""" ,			_A ,			_A		)

												for name, module in self.named_children():
																	fn_recursive_attn_processor(_A ,			_A ,			_A		)







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												self.set_attn_processor(AttnProcessor()		)







							def  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     Optional[Any] ,			_A      :     Union[torch.Tensor, float, int] ,			_A      :     torch.FloatTensor ,			_A      :     Optional[torch.FloatTensor] = None ,			_A      :     Optional[torch.BoolTensor] = None ,			_A      :     bool = True ,			):
												_UpperCamelCase			     =     hidden_states.shape[0]

												_UpperCamelCase			     =     timestep
												if not torch.is_tensor(_A		):
																	_UpperCamelCase			     =     torch.tensor([timesteps] ,			dtype=torch.long ,			device=hidden_states.device		)
												elif torch.is_tensor(_A		) and len(timesteps.shape		) == 0:
																	_UpperCamelCase			     =     timesteps[None].to(hidden_states.device		)

												# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
												_UpperCamelCase			     =     timesteps * torch.ones(_A ,			dtype=timesteps.dtype ,			device=timesteps.device		)

												_UpperCamelCase			     =     self.time_proj(_A		)

												# timesteps does not contain any weights and will always return f32 tensors
												# but time_embedding might be fp16, so we need to cast here.
												_UpperCamelCase			     =     timesteps_projected.to(dtype=self.dtype		)
												_UpperCamelCase			     =     self.time_embedding(_A		)

												if self.embedding_proj_norm is not None:
																	_UpperCamelCase			     =     self.embedding_proj_norm(_A		)

												_UpperCamelCase			     =     self.embedding_proj(_A		)
												if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
																	_UpperCamelCase			     =     self.encoder_hidden_states_proj(_A		)
												elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
																	raise ValueError('''`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set'''		)

												_UpperCamelCase			     =     self.proj_in(_A		)

												_UpperCamelCase			     =     self.positional_embedding.to(hidden_states.dtype		)

												_UpperCamelCase			     =     []
												_UpperCamelCase			     =     0

												if encoder_hidden_states is not None:
																	additional_embeds.append(_A		)
																	additional_embeddings_len += encoder_hidden_states.shape[1]

												if len(proj_embeddings.shape		) == 2:
																	_UpperCamelCase			     =     proj_embeddings[:, None, :]

												if len(hidden_states.shape		) == 2:
																	_UpperCamelCase			     =     hidden_states[:, None, :]

												_UpperCamelCase			     =     additional_embeds + [
												    proj_embeddings,
												    time_embeddings[:, None, :],
												    hidden_states,
												]

												if self.prd_embedding is not None:
																	_UpperCamelCase			     =     self.prd_embedding.to(hidden_states.dtype		).expand(_A ,			-1 ,			-1		)
																	additional_embeds.append(_A		)

												_UpperCamelCase			     =     torch.cat(
												    _A ,			dim=1 ,			)

												# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
												_UpperCamelCase			     =     additional_embeddings_len + proj_embeddings.shape[1] + 1
												if positional_embeddings.shape[1] < hidden_states.shape[1]:
																	_UpperCamelCase			     =     F.pad(
																	    _A ,			(
																	        0,
																	        0,
																	        additional_embeddings_len,
																	        self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
																	    ) ,			value=0.0 ,			)

												_UpperCamelCase			     =     hidden_states + positional_embeddings

												if attention_mask is not None:
																	_UpperCamelCase			     =     (1 - attention_mask.to(hidden_states.dtype		)) * -1_0000.0
																	_UpperCamelCase			     =     F.pad(_A ,			(0, self.additional_embeddings) ,			value=0.0		)
																	_UpperCamelCase			     =     (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype		)
																	_UpperCamelCase			     =     attention_mask.repeat_interleave(self.config.num_attention_heads ,			dim=0		)

												if self.norm_in is not None:
																	_UpperCamelCase			     =     self.norm_in(_A		)

												for block in self.transformer_blocks:
																	_UpperCamelCase			     =     block(_A ,			attention_mask=_A		)

												_UpperCamelCase			     =     self.norm_out(_A		)

												if self.prd_embedding is not None:
																	_UpperCamelCase			     =     hidden_states[:, -1]
												else:
																	_UpperCamelCase			     =     hidden_states[:, additional_embeddings_len:]

												_UpperCamelCase			     =     self.proj_to_clip_embeddings(_A		)

												if not return_dict:
																	return (predicted_image_embedding,)

												return PriorTransformerOutput(predicted_image_embedding=_A		)







							def  UpperCamelCase_						(   self      :     List[Any] ,			_A      :     Dict		):
												_UpperCamelCase			     =     (prior_latents * self.clip_std) + self.clip_mean
												return prior_latents



import os
import tempfile
import unittest

from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, 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 (
			    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
			    DistilBertForMaskedLM,
			    DistilBertForMultipleChoice,
			    DistilBertForQuestionAnswering,
			    DistilBertForSequenceClassification,
			    DistilBertForTokenClassification,
			    DistilBertModel,
			)






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			def __init__(      self				:			Dict   ,   a				:			Tuple   ,   a				:			Any=13   ,   a				:			Any=7   ,   a				:			Union[str, Any]=True   ,   a				:			List[Any]=True   ,   a				:			List[str]=False   ,   a				:			List[str]=True   ,   a				:			Any=99   ,   a				:			str=32   ,   a				:			Any=5   ,   a				:			Optional[int]=4   ,   a				:			Union[str, Any]=37   ,   a				:			Dict="gelu"   ,   a				:			List[Any]=0.1   ,   a				:			Optional[Any]=0.1   ,   a				:			List[str]=512   ,   a				:			Union[str, Any]=16   ,   a				:			str=2   ,   a				:			Dict=0.02   ,   a				:			Optional[int]=3   ,   a				:			Union[str, Any]=4   ,   a				:			int=None   ,   )				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							parent
				SCREAMING_SNAKE_CASE :   Any					  =							batch_size
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							seq_length
				SCREAMING_SNAKE_CASE :   List[Any]					  =							is_training
				SCREAMING_SNAKE_CASE :   int					  =							use_input_mask
				SCREAMING_SNAKE_CASE :   Tuple					  =							use_token_type_ids
				SCREAMING_SNAKE_CASE :   str					  =							use_labels
				SCREAMING_SNAKE_CASE :   Any					  =							vocab_size
				SCREAMING_SNAKE_CASE :   List[Any]					  =							hidden_size
				SCREAMING_SNAKE_CASE :   str					  =							num_hidden_layers
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							num_attention_heads
				SCREAMING_SNAKE_CASE :   Tuple					  =							intermediate_size
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							hidden_act
				SCREAMING_SNAKE_CASE :   Dict					  =							hidden_dropout_prob
				SCREAMING_SNAKE_CASE :   str					  =							attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE :   Tuple					  =							max_position_embeddings
				SCREAMING_SNAKE_CASE :   List[str]					  =							type_vocab_size
				SCREAMING_SNAKE_CASE :   List[str]					  =							type_sequence_label_size
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							initializer_range
				SCREAMING_SNAKE_CASE :   Tuple					  =							num_labels
				SCREAMING_SNAKE_CASE :   Tuple					  =							num_choices
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							scope
			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							ids_tensor([self.batch_size, self.seq_length]   ,   self.vocab_size						)

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

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

				SCREAMING_SNAKE_CASE :   int					  =							self.get_config()

				return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
			def 				__UpperCamelCase			(      self				:			Dict						)				->							str:

				"""simple docstring"""

				return DistilBertConfig(
				    vocab_size=self.vocab_size   ,   dim=self.hidden_size   ,   n_layers=self.num_hidden_layers   ,   n_heads=self.num_attention_heads   ,   hidden_dim=self.intermediate_size   ,   hidden_act=self.hidden_act   ,   dropout=self.hidden_dropout_prob   ,   attention_dropout=self.attention_probs_dropout_prob   ,   max_position_embeddings=self.max_position_embeddings   ,   initializer_range=self.initializer_range   ,   )
			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			int   ,   a				:			Optional[int]   ,   a				:			Optional[int]   ,   a				:			Dict   ,   a				:			str   ,   a				:			str						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							DistilBertModel(config=a						)
				model.to(a						)
				model.eval()
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							model(a   ,   a						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							model(a						)
				self.parent.assertEqual(result.last_hidden_state.shape   ,   (self.batch_size, self.seq_length, self.hidden_size)						)
			def 				__UpperCamelCase			(      self				:			Tuple   ,   a				:			Optional[int]   ,   a				:			Dict   ,   a				:			Tuple   ,   a				:			int   ,   a				:			int   ,   a				:			Any						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							DistilBertForMaskedLM(config=a						)
				model.to(a						)
				model.eval()
				SCREAMING_SNAKE_CASE :   str					  =							model(a   ,   attention_mask=a   ,   labels=a						)
				self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.seq_length, self.vocab_size)						)
			def 				__UpperCamelCase			(      self				:			List[Any]   ,   a				:			int   ,   a				:			Optional[Any]   ,   a				:			Optional[Any]   ,   a				:			str   ,   a				:			str   ,   a				:			Tuple						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							DistilBertForQuestionAnswering(config=a						)
				model.to(a						)
				model.eval()
				SCREAMING_SNAKE_CASE :   List[str]					  =							model(
				    a   ,   attention_mask=a   ,   start_positions=a   ,   end_positions=a						)
				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				:			Optional[int]   ,   a				:			str   ,   a				:			Any   ,   a				:			int   ,   a				:			Optional[Any]   ,   a				:			int   ,   a				:			str						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							self.num_labels
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							DistilBertForSequenceClassification(a						)
				model.to(a						)
				model.eval()
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							model(a   ,   attention_mask=a   ,   labels=a						)
				self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.num_labels)						)
			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			List[Any]   ,   a				:			Optional[int]   ,   a				:			Union[str, Any]   ,   a				:			Dict   ,   a				:			Any   ,   a				:			Optional[Any]						)				->							Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							self.num_labels
				SCREAMING_SNAKE_CASE :   List[str]					  =							DistilBertForTokenClassification(config=a						)
				model.to(a						)
				model.eval()

				SCREAMING_SNAKE_CASE :   List[str]					  =							model(a   ,   attention_mask=a   ,   labels=a						)
				self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.seq_length, self.num_labels)						)
			def 				__UpperCamelCase			(      self				:			int   ,   a				:			Any   ,   a				:			Optional[int]   ,   a				:			Union[str, Any]   ,   a				:			Tuple   ,   a				:			Optional[int]   ,   a				:			Tuple						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							self.num_choices
				SCREAMING_SNAKE_CASE :   Any					  =							DistilBertForMultipleChoice(config=a						)
				model.to(a						)
				model.eval()
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							input_ids.unsqueeze(1						).expand(-1   ,   self.num_choices   ,   -1						).contiguous()
				SCREAMING_SNAKE_CASE :   Dict					  =							input_mask.unsqueeze(1						).expand(-1   ,   self.num_choices   ,   -1						).contiguous()
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							model(
				    a   ,   attention_mask=a   ,   labels=a   ,   )
				self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.num_choices)						)






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

				"""simple docstring"""

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






@require_torch
class _UpperCamelCase				(				__A				,						__A				,						unittest.TestCase ):
			'''simple docstring'''







			lowerCamelCase__							     =(
			    (
			        DistilBertModel,
			        DistilBertForMaskedLM,
			        DistilBertForMultipleChoice,
			        DistilBertForQuestionAnswering,
			        DistilBertForSequenceClassification,
			        DistilBertForTokenClassification,
			    )
			    if is_torch_available()
			    else None
			)
			lowerCamelCase__							     =(
			    {
			        'feature-extraction': DistilBertModel,
			        'fill-mask': DistilBertForMaskedLM,
			        'question-answering': DistilBertForQuestionAnswering,
			        'text-classification': DistilBertForSequenceClassification,
			        'token-classification': DistilBertForTokenClassification,
			        'zero-shot': DistilBertForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			lowerCamelCase__							     =True
			lowerCamelCase__							     =True
			lowerCamelCase__							     =True
			lowerCamelCase__							     =True
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							DistilBertModelTester(self						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							ConfigTester(self   ,   config_class=a   ,   dim=37						)
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Tuple:

				"""simple docstring"""

				self.config_tester.run_common_tests()
			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_distilbert_model(*a						)
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_distilbert_for_masked_lm(*a						)
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_distilbert_for_question_answering(*a						)
			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_distilbert_for_sequence_classification(*a						)
			def 				__UpperCamelCase			(      self				:			str						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_distilbert_for_token_classification(*a						)
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_distilbert_for_multiple_choice(*a						)
			@slow
			def 				__UpperCamelCase			(      self				:			int						)				->							Any:

				"""simple docstring"""

				for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							DistilBertModel.from_pretrained(a						)
					self.assertIsNotNone(a						)






			@slow
			@require_torch_gpu
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Dict					  =							self.model_tester.prepare_config_and_inputs_for_common()
				for model_class in self.all_model_classes:
					# BertForMultipleChoice behaves incorrectly in JIT environments.
					if model_class == DistilBertForMultipleChoice:
						return

					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							True
					SCREAMING_SNAKE_CASE :   Any					  =							model_class(config=a						)

					SCREAMING_SNAKE_CASE :   List[Any]					  =							self._prepare_for_class(a   ,   a						)
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							torch.jit.trace(
					    a   ,   (inputs_dict["input_ids"].to("cpu"						), inputs_dict["attention_mask"].to("cpu"						))						)

					with tempfile.TemporaryDirectory() as tmp:
						torch.jit.save(a   ,   os.path.join(a   ,   "traced_model.pt"						)						)
						SCREAMING_SNAKE_CASE :   Tuple					  =							torch.jit.load(os.path.join(a   ,   "traced_model.pt"						)   ,   map_location=a						)
						loaded(inputs_dict["input_ids"].to(a						)   ,   inputs_dict["attention_mask"].to(a						)						)






@require_torch
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			@slow
			def 				__UpperCamelCase			(      self				:			int						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							DistilBertModel.from_pretrained("distilbert-base-uncased"						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
				with torch.no_grad():
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							model(a   ,   attention_mask=a						)[0]
				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.Size((1, 11, 768)						)
				self.assertEqual(output.shape   ,   a						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							torch.tensor(
				    [[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]]						)

				self.assertTrue(torch.allclose(output[:, 1:4, 1:4]   ,   a   ,   atol=1e-4						)						)