






import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import FlavaImageProcessor, FlavaProcessor
					from transformers.models.flava.image_processing_flava import (
					    FLAVA_CODEBOOK_MEAN,
					    FLAVA_CODEBOOK_STD,
					    FLAVA_IMAGE_MEAN,
					    FLAVA_IMAGE_STD,
					)





@require_vision
class        a__      (	unittest.TestCase		):






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				__A    =							tempfile.mkdtemp()

				# fmt: off
				__A    =							["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
				# fmt: on
				__A    =							os.path.join(self.tmpdirname							, VOCAB_FILES_NAMES["vocab_file"]						)

				with open(self.vocab_file							, "w"							, encoding="utf-8"						) as fp:
						fp.write("".join([x + "\n" for x in vocab_tokens]						)						)

				__A    =							{
				    "image_mean": FLAVA_IMAGE_MEAN,
				    "image_std": FLAVA_IMAGE_STD,
				    "do_normalize": True,
				    "do_resize": True,
				    "size": 224,
				    "do_center_crop": True,
				    "crop_size": 224,
				    "input_size_patches": 14,
				    "total_mask_patches": 75,
				    "mask_group_max_patches": None,
				    "mask_group_min_patches": 16,
				    "mask_group_min_aspect_ratio": 0.3,
				    "mask_group_max_aspect_ratio": None,
				    "codebook_do_resize": True,
				    "codebook_size": 112,
				    "codebook_do_center_crop": True,
				    "codebook_crop_size": 112,
				    "codebook_do_map_pixels": True,
				    "codebook_do_normalize": True,
				    "codebook_image_mean": FLAVA_CODEBOOK_MEAN,
				    "codebook_image_std": FLAVA_CODEBOOK_STD,
				}

				__A    =							os.path.join(self.tmpdirname							, _lowerCamelCase						)
				with open(self.image_processor_file							, "w"							, encoding="utf-8"						) as fp:
						json.dump(_lowerCamelCase							, _lowerCamelCase						)






		def 					_lowerCamelCase					(      self							, **lowercase__						)				->				str:
				return BertTokenizer.from_pretrained(self.tmpdirname							, **_lowerCamelCase						)






		def 					_lowerCamelCase					(      self							, **lowercase__						)				->				Optional[int]:
				return BertTokenizerFast.from_pretrained(self.tmpdirname							, **_lowerCamelCase						)






		def 					_lowerCamelCase					(      self							, **lowercase__						)				->				str:
				return FlavaImageProcessor.from_pretrained(self.tmpdirname							, **_lowerCamelCase						)






		def 					_lowerCamelCase					(      self						)				->				List[Any]:
				shutil.rmtree(self.tmpdirname						)






		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				__A    =							[np.random.randint(255							, size=(3, 30, 400)							, dtype=np.uinta						)]

				__A    =							[Image.fromarray(np.moveaxis(_lowerCamelCase							, 0							, -1						)						) for x in image_inputs]

				return image_inputs






		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				__A    =							self.get_tokenizer()
				__A    =							self.get_rust_tokenizer()
				__A    =							self.get_image_processor()

				__A    =							FlavaProcessor(tokenizer=_lowerCamelCase							, image_processor=_lowerCamelCase						)
				processor_slow.save_pretrained(self.tmpdirname						)
				__A    =							FlavaProcessor.from_pretrained(self.tmpdirname							, use_fast=_lowerCamelCase						)

				__A    =							FlavaProcessor(tokenizer=_lowerCamelCase							, image_processor=_lowerCamelCase						)
				processor_fast.save_pretrained(self.tmpdirname						)
				__A    =							FlavaProcessor.from_pretrained(self.tmpdirname						)

				self.assertEqual(processor_slow.tokenizer.get_vocab()							, tokenizer_slow.get_vocab()						)
				self.assertEqual(processor_fast.tokenizer.get_vocab()							, tokenizer_fast.get_vocab()						)
				self.assertEqual(tokenizer_slow.get_vocab()							, tokenizer_fast.get_vocab()						)
				self.assertIsInstance(processor_slow.tokenizer							, _lowerCamelCase						)
				self.assertIsInstance(processor_fast.tokenizer							, _lowerCamelCase						)

				self.assertEqual(processor_slow.image_processor.to_json_string()							, image_processor.to_json_string()						)
				self.assertEqual(processor_fast.image_processor.to_json_string()							, image_processor.to_json_string()						)
				self.assertIsInstance(processor_slow.image_processor							, _lowerCamelCase						)
				self.assertIsInstance(processor_fast.image_processor							, _lowerCamelCase						)






		def 					_lowerCamelCase					(      self						)				->				int:
				__A    =							FlavaProcessor(tokenizer=self.get_tokenizer()							, image_processor=self.get_image_processor()						)
				processor.save_pretrained(self.tmpdirname						)

				__A    =							self.get_tokenizer(bos_token="(BOS)"							, eos_token="(EOS)"						)
				__A    =							self.get_image_processor(do_normalize=_lowerCamelCase							, padding_value=1.0						)

				__A    =							FlavaProcessor.from_pretrained(
				    self.tmpdirname							, bos_token="(BOS)"							, eos_token="(EOS)"							, do_normalize=_lowerCamelCase							, padding_value=1.0						)

				self.assertEqual(processor.tokenizer.get_vocab()							, tokenizer_add_kwargs.get_vocab()						)
				self.assertIsInstance(processor.tokenizer							, _lowerCamelCase						)

				self.assertEqual(processor.image_processor.to_json_string()							, image_processor_add_kwargs.to_json_string()						)
				self.assertIsInstance(processor.image_processor							, _lowerCamelCase						)






		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				__A    =							self.get_image_processor()
				__A    =							self.get_tokenizer()

				__A    =							FlavaProcessor(tokenizer=_lowerCamelCase							, image_processor=_lowerCamelCase						)

				__A    =							self.prepare_image_inputs()

				__A    =							image_processor(_lowerCamelCase							, return_tensors="np"						)
				__A    =							processor(images=_lowerCamelCase							, return_tensors="np"						)

				for key in input_feat_extract.keys():
						self.assertAlmostEqual(input_feat_extract[key].sum()							, input_processor[key].sum()							, delta=1e-2						)

				# With rest of the args
				random.seed(1234						)
				__A    =							image_processor(
				    _lowerCamelCase							, return_image_mask=_lowerCamelCase							, return_codebook_pixels=_lowerCamelCase							, return_tensors="np"						)
				random.seed(1234						)
				__A    =							processor(
				    images=_lowerCamelCase							, return_image_mask=_lowerCamelCase							, return_codebook_pixels=_lowerCamelCase							, return_tensors="np"						)

				for key in input_feat_extract.keys():
						self.assertAlmostEqual(input_feat_extract[key].sum()							, input_processor[key].sum()							, delta=1e-2						)






		def 					_lowerCamelCase					(      self						)				->				Any:
				__A    =							self.get_image_processor()
				__A    =							self.get_tokenizer()

				__A    =							FlavaProcessor(tokenizer=_lowerCamelCase							, image_processor=_lowerCamelCase						)

				__A    =							"lower newer"

				__A    =							processor(text=_lowerCamelCase						)

				__A    =							tokenizer(_lowerCamelCase						)

				for key in encoded_tok.keys():
						self.assertListEqual(encoded_tok[key]							, encoded_processor[key]						)






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				__A    =							self.get_image_processor()
				__A    =							self.get_tokenizer()

				__A    =							FlavaProcessor(tokenizer=_lowerCamelCase							, image_processor=_lowerCamelCase						)

				__A    =							"lower newer"
				__A    =							self.prepare_image_inputs()

				__A    =							processor(text=_lowerCamelCase							, images=_lowerCamelCase						)

				self.assertListEqual(list(inputs.keys()						)							, ["input_ids", "token_type_ids", "attention_mask", "pixel_values"]						)

				# add extra args
				__A    =							processor(text=_lowerCamelCase							, images=_lowerCamelCase							, return_codebook_pixels=_lowerCamelCase							, return_image_mask=_lowerCamelCase						)

				self.assertListEqual(
				    list(inputs.keys()						)							, [
				        "input_ids",
				        "token_type_ids",
				        "attention_mask",
				        "pixel_values",
				        "codebook_pixel_values",
				        "bool_masked_pos",
				    ]							, )

				# test if it raises when no input is passed
				with pytest.raises(_lowerCamelCase						):
						processor()






		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				__A    =							self.get_image_processor()
				__A    =							self.get_tokenizer()

				__A    =							FlavaProcessor(tokenizer=_lowerCamelCase							, image_processor=_lowerCamelCase						)

				__A    =							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				__A    =							processor.batch_decode(_lowerCamelCase						)
				__A    =							tokenizer.batch_decode(_lowerCamelCase						)

				self.assertListEqual(_lowerCamelCase							, _lowerCamelCase						)






		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				__A    =							self.get_image_processor()
				__A    =							self.get_tokenizer()

				__A    =							FlavaProcessor(tokenizer=_lowerCamelCase							, image_processor=_lowerCamelCase						)

				__A    =							"lower newer"
				__A    =							self.prepare_image_inputs()

				__A    =							processor(text=_lowerCamelCase							, images=_lowerCamelCase						)

				self.assertListEqual(list(inputs.keys()						)							, processor.model_input_names						)









"""simple docstring"""



import unittest

from transformers import ElectraConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
    import torch

    from transformers import (
        MODEL_FOR_PRETRAINING_MAPPING,
        ElectraForCausalLM,
        ElectraForMaskedLM,
        ElectraForMultipleChoice,
        ElectraForPreTraining,
        ElectraForQuestionAnswering,
        ElectraForSequenceClassification,
        ElectraForTokenClassification,
        ElectraModel,
    )
    from transformers.models.electra.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST

class 			a	:







    def __init__(     self				,						_lowerCamelCase				,						_lowerCamelCase=1_3				,						_lowerCamelCase=7				,						_lowerCamelCase=True				,						_lowerCamelCase=True				,						_lowerCamelCase=True				,						_lowerCamelCase=True				,						_lowerCamelCase=9_9				,						_lowerCamelCase=3_2				,						_lowerCamelCase=5				,						_lowerCamelCase=4				,						_lowerCamelCase=3_7				,						_lowerCamelCase="gelu"				,						_lowerCamelCase=0.1				,						_lowerCamelCase=0.1				,						_lowerCamelCase=5_1_2				,						_lowerCamelCase=1_6				,						_lowerCamelCase=2				,						_lowerCamelCase=0.0_2				,						_lowerCamelCase=3				,						_lowerCamelCase=4				,						_lowerCamelCase=None				,						):
      lowercase				       =			parent
      lowercase				       =			batch_size
      lowercase				       =			seq_length
      lowercase				       =			is_training
      lowercase				       =			use_input_mask
      lowercase				       =			use_token_type_ids
      lowercase				       =			use_labels
      lowercase				       =			vocab_size
      lowercase				       =			hidden_size
      lowercase				       =			num_hidden_layers
      lowercase				       =			num_attention_heads
      lowercase				       =			intermediate_size
      lowercase				       =			hidden_act
      lowercase				       =			hidden_dropout_prob
      lowercase				       =			attention_probs_dropout_prob
      lowercase				       =			max_position_embeddings
      lowercase				       =			type_vocab_size
      lowercase				       =			type_sequence_label_size
      lowercase				       =			initializer_range
      lowercase				       =			num_labels
      lowercase				       =			num_choices
      lowercase				       =			scope







    def      UpperCamelCase_ (     self			):
      lowercase				       =			ids_tensor([self.batch_size, self.seq_length]				,						self.vocab_size			)

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

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

      lowercase				       =			None
      lowercase				       =			None
      lowercase				       =			None
      if self.use_labels:
        lowercase				       =			ids_tensor([self.batch_size]				,						self.type_sequence_label_size			)
        lowercase				       =			ids_tensor([self.batch_size, self.seq_length]				,						self.num_labels			)
        lowercase				       =			ids_tensor([self.batch_size]				,						self.num_choices			)
        lowercase				       =			ids_tensor([self.batch_size, self.seq_length]				,						1			)

      lowercase				       =			self.get_config()

      return (
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
          fake_token_labels,
      )







    def      UpperCamelCase_ (     self			):
      return ElectraConfig(
          vocab_size=self.vocab_size				,						hidden_size=self.hidden_size				,						num_hidden_layers=self.num_hidden_layers				,						num_attention_heads=self.num_attention_heads				,						intermediate_size=self.intermediate_size				,						hidden_act=self.hidden_act				,						hidden_dropout_prob=self.hidden_dropout_prob				,						attention_probs_dropout_prob=self.attention_probs_dropout_prob				,						max_position_embeddings=self.max_position_embeddings				,						type_vocab_size=self.type_vocab_size				,						is_decoder=_lowerCamelCase				,						initializer_range=self.initializer_range				,						)







    def      UpperCamelCase_ (     self			):
      (
          (
          lowercase
      )       ,	(
          lowercase
      )       ,	(
          lowercase
      )       ,	(
          lowercase
      )       ,	(
          lowercase
      )       ,	(
          lowercase
      )       ,	(
          lowercase
      )       ,	(
          lowercase
      )       ,	
      )				       =			self.prepare_config_and_inputs()

      lowercase				       =			True
      lowercase				       =			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
      lowercase				       =			ids_tensor([self.batch_size, self.seq_length]				,						vocab_size=2			)

      return (
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
          encoder_hidden_states,
          encoder_attention_mask,
      )







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						):
      lowercase				       =			ElectraModel(config=_lowerCamelCase			)
      model.to(_lowerCamelCase			)
      model.eval()
      lowercase				       =			model(_lowerCamelCase				,						attention_mask=_lowerCamelCase				,						token_type_ids=_lowerCamelCase			)
      lowercase				       =			model(_lowerCamelCase				,						token_type_ids=_lowerCamelCase			)
      lowercase				       =			model(_lowerCamelCase			)
      self.parent.assertEqual(result.last_hidden_state.shape				,						(self.batch_size, self.seq_length, self.hidden_size)			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						):
      lowercase				       =			True
      lowercase				       =			ElectraModel(_lowerCamelCase			)
      model.to(_lowerCamelCase			)
      model.eval()
      lowercase				       =			model(
          _lowerCamelCase				,						attention_mask=_lowerCamelCase				,						token_type_ids=_lowerCamelCase				,						encoder_hidden_states=_lowerCamelCase				,						encoder_attention_mask=_lowerCamelCase				,						)
      lowercase				       =			model(
          _lowerCamelCase				,						attention_mask=_lowerCamelCase				,						token_type_ids=_lowerCamelCase				,						encoder_hidden_states=_lowerCamelCase				,						)
      lowercase				       =			model(_lowerCamelCase				,						attention_mask=_lowerCamelCase				,						token_type_ids=_lowerCamelCase			)
      self.parent.assertEqual(result.last_hidden_state.shape				,						(self.batch_size, self.seq_length, self.hidden_size)			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						):
      lowercase				       =			ElectraForMaskedLM(config=_lowerCamelCase			)
      model.to(_lowerCamelCase			)
      model.eval()
      lowercase				       =			model(_lowerCamelCase				,						attention_mask=_lowerCamelCase				,						token_type_ids=_lowerCamelCase				,						labels=_lowerCamelCase			)
      self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.seq_length, self.vocab_size)			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						):
      lowercase				       =			ElectraForCausalLM(config=_lowerCamelCase			)
      model.to(_lowerCamelCase			)
      model.eval()
      lowercase				       =			model(_lowerCamelCase				,						attention_mask=_lowerCamelCase				,						token_type_ids=_lowerCamelCase				,						labels=_lowerCamelCase			)
      self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.seq_length, self.vocab_size)			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						):
      lowercase				       =			self.num_labels
      lowercase				       =			ElectraForTokenClassification(config=_lowerCamelCase			)
      model.to(_lowerCamelCase			)
      model.eval()
      lowercase				       =			model(_lowerCamelCase				,						attention_mask=_lowerCamelCase				,						token_type_ids=_lowerCamelCase				,						labels=_lowerCamelCase			)
      self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.seq_length, self.num_labels)			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						):
      lowercase				       =			self.num_labels
      lowercase				       =			ElectraForPreTraining(config=_lowerCamelCase			)
      model.to(_lowerCamelCase			)
      model.eval()
      lowercase				       =			model(_lowerCamelCase				,						attention_mask=_lowerCamelCase				,						token_type_ids=_lowerCamelCase				,						labels=_lowerCamelCase			)
      self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.seq_length)			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						):
      lowercase				       =			self.num_labels
      lowercase				       =			ElectraForSequenceClassification(_lowerCamelCase			)
      model.to(_lowerCamelCase			)
      model.eval()
      lowercase				       =			model(_lowerCamelCase				,						attention_mask=_lowerCamelCase				,						token_type_ids=_lowerCamelCase				,						labels=_lowerCamelCase			)
      self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.num_labels)			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						):
      lowercase				       =			ElectraForQuestionAnswering(config=_lowerCamelCase			)
      model.to(_lowerCamelCase			)
      model.eval()
      lowercase				       =			model(
          _lowerCamelCase				,						attention_mask=_lowerCamelCase				,						token_type_ids=_lowerCamelCase				,						start_positions=_lowerCamelCase				,						end_positions=_lowerCamelCase				,						)
      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				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						):
      lowercase				       =			self.num_choices
      lowercase				       =			ElectraForMultipleChoice(config=_lowerCamelCase			)
      model.to(_lowerCamelCase			)
      model.eval()
      lowercase				       =			input_ids.unsqueeze(1			).expand(-1				,						self.num_choices				,						-1			).contiguous()
      lowercase				       =			token_type_ids.unsqueeze(1			).expand(-1				,						self.num_choices				,						-1			).contiguous()
      lowercase				       =			input_mask.unsqueeze(1			).expand(-1				,						self.num_choices				,						-1			).contiguous()
      lowercase				       =			model(
          _lowerCamelCase				,						attention_mask=_lowerCamelCase				,						token_type_ids=_lowerCamelCase				,						labels=_lowerCamelCase				,						)
      self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.num_choices)			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.prepare_config_and_inputs()
      (
          (
          lowercase
      )       ,	(
          lowercase
      )       ,	(
          lowercase
      )       ,	(
          lowercase
      )       ,	(
          lowercase
      )       ,	(
          lowercase
      )       ,	(
          lowercase
      )       ,	(
          lowercase
      )       ,	
      )				       =			config_and_inputs
      lowercase				       =			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
      return config, inputs_dict


@require_torch
class 			a	(  a_,							a_,							unittest.TestCase      ):
    UpperCAmelCase_					:	List[str]      	=(
        (
            ElectraModel,
            ElectraForPreTraining,
            ElectraForMaskedLM,
            ElectraForCausalLM,
            ElectraForMultipleChoice,
            ElectraForTokenClassification,
            ElectraForSequenceClassification,
            ElectraForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    UpperCAmelCase_					:	Optional[Any]      	=(
        {
            "feature-extraction": ElectraModel,
            "fill-mask": ElectraForMaskedLM,
            "question-answering": ElectraForQuestionAnswering,
            "text-classification": ElectraForSequenceClassification,
            "text-generation": ElectraForCausalLM,
            "token-classification": ElectraForTokenClassification,
            "zero-shot": ElectraForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    UpperCAmelCase_					:	Tuple      	=True







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase=False			):
      lowercase				       =			super()._prepare_for_class(_lowerCamelCase				,						_lowerCamelCase				,						return_labels=_lowerCamelCase			)

      if return_labels:
        if model_class in get_values(_lowerCamelCase			):
          lowercase				       =			torch.zeros(
              (self.model_tester.batch_size, self.model_tester.seq_length)				,						dtype=torch.long				,						device=_lowerCamelCase			)
      return inputs_dict







    def      UpperCamelCase_ (     self			):
      lowercase				       =			ElectraModelTester(self			)
      lowercase				       =			ConfigTester(self				,						config_class=_lowerCamelCase				,						hidden_size=3_7			)







    def      UpperCamelCase_ (     self			):
      self.config_tester.run_common_tests()







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_electra_model(*_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_electra_model_as_decoder(*_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
        lowercase				       =			type
        self.model_tester.create_and_check_electra_model(*_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_electra_for_masked_lm(*_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_electra_for_token_classification(*_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_electra_for_pretraining(*_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_electra_for_sequence_classification(*_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_electra_for_question_answering(*_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_electra_for_multiple_choice(*_lowerCamelCase			)







    @slow
    def      UpperCamelCase_ (     self			):
      for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowercase				       =			ElectraModel.from_pretrained(_lowerCamelCase			)
        self.assertIsNotNone(_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_electra_for_causal_lm(*_lowerCamelCase			)


@require_torch
class 			a	(  unittest.TestCase      ):







    @slow
    def      UpperCamelCase_ (     self			):
      lowercase				       =			ElectraModel.from_pretrained('google/electra-small-discriminator'			)
      lowercase				       =			torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]			)
      lowercase				       =			torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
      lowercase				       =			model(_lowerCamelCase				,						attention_mask=_lowerCamelCase			)[0]
      lowercase				       =			torch.Size((1, 1_1, 2_5_6)			)
      self.assertEqual(output.shape				,						_lowerCamelCase			)
      lowercase				       =			torch.tensor(
          [[[0.4_4_7_1, 0.6_8_2_1, -0.3_2_6_5], [0.4_6_2_7, 0.5_2_5_5, -0.3_6_6_8], [0.4_5_3_2, 0.3_3_1_3, -0.4_3_4_4]]]			)

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


