

import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation

a_      =     logging.get_logger(__name__)


a_      =     {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

a_      =     {
    'vocab_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'},
    'merges_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'},
    'tokenizer_config_file': {
        'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'
    },
}

a_      =     {'facebook/blenderbot-3B': 128}

@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 			lowerCamelCase__			(       ):
	SCREAMING_SNAKE_CASE :   Dict					  =							(
	    list(range(ord("!")				,      ord("~") + 1)) + list(range(ord("¡")				,      ord("¬") + 1)) + list(range(ord("®")				,      ord("ÿ") + 1))
	)
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							bs[:]
	SCREAMING_SNAKE_CASE :   Dict					  =							0
	for b in range(2**8):
		if b not in bs:
			bs.append(_a)
			cs.append(2**8 + n)
			n += 1
	SCREAMING_SNAKE_CASE :   int					  =							[chr(_a) for n in cs]
	return dict(zip(_a				,      _a))

def 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							set()
	SCREAMING_SNAKE_CASE :   List[Any]					  =							word[0]
	for char in word[1:]:
		pairs.add((prev_char, char))
		SCREAMING_SNAKE_CASE :   Optional[int]					  =							char
	return pairs






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







			lowerCamelCase__							     =VOCAB_FILES_NAMES
			lowerCamelCase__							     =PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__							     =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__							     =['input_ids', 'attention_mask']
			def __init__(      self				:			Optional[Any]   ,   a				:			Any   ,   a				:			List[str]   ,   a				:			Union[str, Any]="replace"   ,   a				:			Optional[Any]="<s>"   ,   a				:			Tuple="</s>"   ,   a				:			str="</s>"   ,   a				:			Optional[int]="<s>"   ,   a				:			Dict="<unk>"   ,   a				:			Dict="<pad>"   ,   a				:			List[Any]="<mask>"   ,   a				:			int=False   ,   **a				:			int   ,   )				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							AddedToken(a   ,   lstrip=a   ,   rstrip=a						) if isinstance(a   ,   a						) else bos_token
				SCREAMING_SNAKE_CASE :   str					  =							AddedToken(a   ,   lstrip=a   ,   rstrip=a						) if isinstance(a   ,   a						) else eos_token
				SCREAMING_SNAKE_CASE :   Any					  =							AddedToken(a   ,   lstrip=a   ,   rstrip=a						) if isinstance(a   ,   a						) else sep_token
				SCREAMING_SNAKE_CASE :   List[str]					  =							AddedToken(a   ,   lstrip=a   ,   rstrip=a						) if isinstance(a   ,   a						) else cls_token
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							AddedToken(a   ,   lstrip=a   ,   rstrip=a						) if isinstance(a   ,   a						) else unk_token
				SCREAMING_SNAKE_CASE :   str					  =							AddedToken(a   ,   lstrip=a   ,   rstrip=a						) if isinstance(a   ,   a						) else pad_token

				# Mask token behave like a normal word, i.e. include the space before it
				SCREAMING_SNAKE_CASE :   Dict					  =							AddedToken(a   ,   lstrip=a   ,   rstrip=a						) if isinstance(a   ,   a						) else mask_token

				super().__init__(
				    errors=a   ,   bos_token=a   ,   eos_token=a   ,   unk_token=a   ,   sep_token=a   ,   cls_token=a   ,   pad_token=a   ,   mask_token=a   ,   add_prefix_space=a   ,   **a   ,   )

				with open(a   ,   encoding="utf-8"						) as vocab_handle:
					SCREAMING_SNAKE_CASE :   Any					  =							json.load(a						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							{v: k for k, v in self.encoder.items()}
				SCREAMING_SNAKE_CASE :   List[str]					  =							errors  # how to handle errors in decoding
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							bytes_to_unicode()
				SCREAMING_SNAKE_CASE :   Dict					  =							{v: k for k, v in self.byte_encoder.items()}
				with open(a   ,   encoding="utf-8"						) as merges_handle:
					SCREAMING_SNAKE_CASE :   Dict					  =							merges_handle.read().split("\n"						)[1:-1]
				SCREAMING_SNAKE_CASE :   Dict					  =							[tuple(merge.split()						) for merge in bpe_merges]
				SCREAMING_SNAKE_CASE :   int					  =							dict(zip(a   ,   range(len(a						)						)						)						)
				SCREAMING_SNAKE_CASE :   Any					  =							{}
				SCREAMING_SNAKE_CASE :   Dict					  =							add_prefix_space

				# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
				SCREAMING_SNAKE_CASE :   List[str]					  =							re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"						)
			@property
			# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
			def 				__UpperCamelCase			(      self				:			int						)				->							Any:

				"""simple docstring"""

				return len(self.encoder						)
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							Optional[int]:

				"""simple docstring"""

				return dict(self.encoder   ,   **self.added_tokens_encoder						)
			def 				__UpperCamelCase			(      self				:			int   ,   a				:			str						)				->							Tuple:

				"""simple docstring"""

				if token in self.cache:
					return self.cache[token]
				SCREAMING_SNAKE_CASE :   int					  =							tuple(a						)
				SCREAMING_SNAKE_CASE :   Dict					  =							get_pairs(a						)

				if not pairs:
					return token

				while True:
					SCREAMING_SNAKE_CASE :   Tuple					  =							min(a   ,   key=lambda a						: self.bpe_ranks.get(a   ,   float("inf"						)						)						)
					if bigram not in self.bpe_ranks:
						break
					SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[str]					  =							bigram
					SCREAMING_SNAKE_CASE :   Tuple					  =							[]
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							0
					while i < len(a						):
						try:
							SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							word.index(a   ,   a						)
						except ValueError:
							new_word.extend(word[i:]						)
							break
						else:
							new_word.extend(word[i:j]						)
							SCREAMING_SNAKE_CASE :   Optional[int]					  =							j

						if word[i] == first and i < len(a						) - 1 and word[i + 1] == second:
							new_word.append(first + second						)
							i += 2
						else:
							new_word.append(word[i]						)
							i += 1
					SCREAMING_SNAKE_CASE :   Tuple					  =							tuple(a						)
					SCREAMING_SNAKE_CASE :   str					  =							new_word
					if len(a						) == 1:
						break
					else:
						SCREAMING_SNAKE_CASE :   Any					  =							get_pairs(a						)
				SCREAMING_SNAKE_CASE :   Dict					  =							" ".join(a						)
				SCREAMING_SNAKE_CASE :   str					  =							word
				return word
			def 				__UpperCamelCase			(      self				:			Optional[int]   ,   a				:			Tuple						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							[]
				for token in re.findall(self.pat   ,   a						):
					SCREAMING_SNAKE_CASE :   Dict					  =							"".join(
					    self.byte_encoder[b] for b in token.encode("utf-8"						)						)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
					bpe_tokens.extend(bpe_token for bpe_token in self.bpe(a						).split(" "						)						)
				return bpe_tokens
			def 				__UpperCamelCase			(      self				:			Any   ,   a				:			str						)				->							Dict:

				"""simple docstring"""

				return self.encoder.get(a   ,   self.encoder.get(self.unk_token						)						)
			def 				__UpperCamelCase			(      self				:			Any   ,   a				:			str						)				->							Optional[int]:

				"""simple docstring"""

				return self.decoder.get(a						)
			def 				__UpperCamelCase			(      self				:			Any   ,   a				:			Optional[int]						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							"".join(a						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							bytearray([self.byte_decoder[c] for c in text]						).decode("utf-8"   ,   errors=self.errors						)
				return text
			def 				__UpperCamelCase			(      self				:			Tuple   ,   a				:			str   ,   a				:			Optional[str] = None						)				->							Tuple[str]:

				"""simple docstring"""

				if not os.path.isdir(a						):
					logger.error(F"Vocabulary path ({save_directory}) should be a directory"						)
					return
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							os.path.join(
				    a   ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							os.path.join(
				    a   ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]						)

				with open(a   ,   "w"   ,   encoding="utf-8"						) as f:
					f.write(json.dumps(self.encoder   ,   indent=2   ,   sort_keys=a   ,   ensure_ascii=a						) + "\n"						)

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							0
				with open(a   ,   "w"   ,   encoding="utf-8"						) as writer:
					writer.write("#version: 0.2\n"						)
					for bpe_tokens, token_index in sorted(self.bpe_ranks.items()   ,   key=lambda a						: kv[1]						):
						if index != token_index:
							logger.warning(
							    F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
							    " Please check that the tokenizer is not corrupted!"						)
							SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							token_index
						writer.write(" ".join(a						) + "\n"						)
						index += 1

				return vocab_file, merge_file
			def 				__UpperCamelCase			(      self				:			str   ,   a				:			List[int]   ,   a				:			Optional[List[int]] = None   ,   a				:			bool = False						)				->							List[int]:

				"""simple docstring"""

				if already_has_special_tokens:
					return super().get_special_tokens_mask(
					    token_ids_a=a   ,   token_ids_a=a   ,   already_has_special_tokens=a						)

				if token_ids_a is None:
					return [1] + ([0] * len(a						)) + [1]
				return [1] + ([0] * len(a						)) + [1, 1] + ([0] * len(a						)) + [1]
			def 				__UpperCamelCase			(      self				:			Dict   ,   a				:			List[int]   ,   a				:			Optional[List[int]] = None						)				->							List[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							[self.sep_token_id]
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							[self.cls_token_id]

				if token_ids_a is None:
					return len(cls + token_ids_a + sep						) * [0]
				return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]
			def 				__UpperCamelCase			(      self				:			Union[str, Any]   ,   a				:			List[Any]   ,   a				:			List[str]=False   ,   **a				:			List[str]						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							kwargs.pop("add_prefix_space"   ,   self.add_prefix_space						)
				if (is_split_into_words or add_prefix_space) and (len(a						) > 0 and not text[0].isspace()):
					SCREAMING_SNAKE_CASE :   List[str]					  =							" " + text
				return (text, kwargs)
			def 				__UpperCamelCase			(      self				:			Dict   ,   a				:			List[int]   ,   a				:			Optional[List[int]] = None						)				->							Dict:

				"""simple docstring"""

				return token_ids_a + [self.eos_token_id]






			def 				__UpperCamelCase			(      self				:			Union[str, Any]   ,   a				:			"Conversation"						)				->							List[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							[]
				for is_user, text in conversation.iter_texts():
					if is_user:
						# We need to space prefix as it's being done within blenderbot
						inputs.append(" " + text						)
					else:
						# Generated responses should contain them already.
						inputs.append(a						)

				SCREAMING_SNAKE_CASE :   Dict					  =							"  ".join(a						)
				SCREAMING_SNAKE_CASE :   Dict					  =							self.encode(a						)
				if len(a						) > self.model_max_length:
					SCREAMING_SNAKE_CASE :   int					  =							input_ids[-self.model_max_length :]
					logger.warning(F"Trimmed input from conversation as it was longer than {self.model_max_length} tokens."						)
				return input_ids


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						)						)