

'''simple docstring'''





import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
				import torch
				from torch import nn

				from transformers import ViTMAEForPreTraining, ViTMAEModel
				from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor


class  UpperCAmelCase :

		'''simple docstring'''






		def __init__(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_=13   ,			SCREAMING_SNAKE_CASE_=30   ,			SCREAMING_SNAKE_CASE_=2   ,			SCREAMING_SNAKE_CASE_=3   ,			SCREAMING_SNAKE_CASE_=True   ,			SCREAMING_SNAKE_CASE_=True   ,			SCREAMING_SNAKE_CASE_=32   ,			SCREAMING_SNAKE_CASE_=5   ,			SCREAMING_SNAKE_CASE_=4   ,			SCREAMING_SNAKE_CASE_=37   ,			SCREAMING_SNAKE_CASE_="gelu"   ,			SCREAMING_SNAKE_CASE_=0.1   ,			SCREAMING_SNAKE_CASE_=0.1   ,			SCREAMING_SNAKE_CASE_=10   ,			SCREAMING_SNAKE_CASE_=0.02   ,			SCREAMING_SNAKE_CASE_=3   ,			SCREAMING_SNAKE_CASE_=0.6   ,			SCREAMING_SNAKE_CASE_=None   ,			)  ->     Any:






							'''simple docstring'''



							lowerCamelCase_  				=      parent
							lowerCamelCase_  				=      batch_size
							lowerCamelCase_  				=      image_size
							lowerCamelCase_  				=      patch_size
							lowerCamelCase_  				=      num_channels
							lowerCamelCase_  				=      is_training
							lowerCamelCase_  				=      use_labels
							lowerCamelCase_  				=      hidden_size
							lowerCamelCase_  				=      num_hidden_layers
							lowerCamelCase_  				=      num_attention_heads
							lowerCamelCase_  				=      intermediate_size
							lowerCamelCase_  				=      hidden_act
							lowerCamelCase_  				=      hidden_dropout_prob
							lowerCamelCase_  				=      attention_probs_dropout_prob
							lowerCamelCase_  				=      type_sequence_label_size
							lowerCamelCase_  				=      initializer_range
							lowerCamelCase_  				=      mask_ratio
							lowerCamelCase_  				=      scope

							# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
							# (we add 1 for the [CLS] token)
							lowerCamelCase_  				=      (image_size // patch_size) ** 2
							lowerCamelCase_  				=      int(math.ceil((1 - mask_ratio) * (num_patches + 1)		)		)
		def 			UpperCamelCase(					self		)  ->     Optional[int]:






							'''simple docstring'''



							lowerCamelCase_  				=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

							lowerCamelCase_  				=      None
							if self.use_labels:
												lowerCamelCase_  				=      ids_tensor([self.batch_size]   ,			self.type_sequence_label_size		)

							lowerCamelCase_  				=      self.get_config()

							return config, pixel_values, labels
		def 			UpperCamelCase(					self		)  ->     Any:






							'''simple docstring'''



							return ViTMAEConfig(
							    image_size=self.image_size   ,			patch_size=self.patch_size   ,			num_channels=self.num_channels   ,			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   ,			is_decoder=SCREAMING_SNAKE_CASE_   ,			initializer_range=self.initializer_range   ,			mask_ratio=self.mask_ratio   ,			)
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     List[str]:






							'''simple docstring'''



							lowerCamelCase_  				=      ViTMAEModel(config=SCREAMING_SNAKE_CASE_		)
							model.to(SCREAMING_SNAKE_CASE_		)
							model.eval()
							lowerCamelCase_  				=      model(SCREAMING_SNAKE_CASE_		)
							self.parent.assertEqual(result.last_hidden_state.shape   ,			(self.batch_size, self.seq_length, self.hidden_size)		)
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     Optional[Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      ViTMAEForPreTraining(SCREAMING_SNAKE_CASE_		)
							model.to(SCREAMING_SNAKE_CASE_		)
							model.eval()
							lowerCamelCase_  				=      model(SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      (self.image_size // self.patch_size) ** 2
							lowerCamelCase_  				=      self.patch_size**2 * self.num_channels
							self.parent.assertEqual(result.logits.shape   ,			(self.batch_size, num_patches, expected_num_channels)		)

							# test greyscale images
							lowerCamelCase_  				=      1
							lowerCamelCase_  				=      ViTMAEForPreTraining(SCREAMING_SNAKE_CASE_		)
							model.to(SCREAMING_SNAKE_CASE_		)
							model.eval()
							lowerCamelCase_  				=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
							lowerCamelCase_  				=      model(SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      self.patch_size**2
							self.parent.assertEqual(result.logits.shape   ,			(self.batch_size, num_patches, expected_num_channels)		)






		def 			UpperCamelCase(					self		)  ->     List[str]:






							'''simple docstring'''



							lowerCamelCase_  				=      self.prepare_config_and_inputs()
							lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_  				=      config_and_inputs
							lowerCamelCase_  				=      {'pixel_values': pixel_values}
							return config, inputs_dict


@require_torch
class  UpperCAmelCase (							UpperCAmelCase__  , UpperCAmelCase__  , unittest.TestCase    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
		SCREAMING_SNAKE_CASE_           =					{'feature-extraction': ViTMAEModel} if is_torch_available() else {}

		SCREAMING_SNAKE_CASE_           =					False
		SCREAMING_SNAKE_CASE_           =					False
		SCREAMING_SNAKE_CASE_           =					False
		SCREAMING_SNAKE_CASE_           =					False
		def 			UpperCamelCase(					self		)  ->     int:






							'''simple docstring'''



							lowerCamelCase_  				=      ViTMAEModelTester(self		)
							lowerCamelCase_  				=      ConfigTester(self   ,			config_class=SCREAMING_SNAKE_CASE_   ,			has_text_modality=SCREAMING_SNAKE_CASE_   ,			hidden_size=37		)
		def 			UpperCamelCase(					self		)  ->     Dict:






							'''simple docstring'''



							self.config_tester.run_common_tests()
		@unittest.skip(reason='ViTMAE does not use inputs_embeds'		)
		def 			UpperCamelCase(					self		)  ->     Any:






							'''simple docstring'''



							pass
		def 			UpperCamelCase(					self		)  ->     Dict:






							'''simple docstring'''



							lowerCamelCase_				,lowerCamelCase_  				=      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												lowerCamelCase_  				=      model_class(SCREAMING_SNAKE_CASE_		)
												self.assertIsInstance(model.get_input_embeddings()   ,			(nn.Module)		)
												lowerCamelCase_  				=      model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_   ,			nn.Linear		)		)
		def 			UpperCamelCase(					self		)  ->     Union[str, Any]:






							'''simple docstring'''



							lowerCamelCase_				,lowerCamelCase_  				=      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												lowerCamelCase_  				=      model_class(SCREAMING_SNAKE_CASE_		)
												lowerCamelCase_  				=      inspect.signature(model.forward		)
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												lowerCamelCase_  				=      [*signature.parameters.keys()]

												lowerCamelCase_  				=      ['pixel_values']
												self.assertListEqual(arg_names[:1]   ,			SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self		)  ->     Tuple:






							'''simple docstring'''



							lowerCamelCase_  				=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self		)  ->     Optional[Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     Dict:






							'''simple docstring'''



							np.random.seed(2		)

							lowerCamelCase_  				=      int((pt_model.config.image_size // pt_model.config.patch_size) ** 2		)
							lowerCamelCase_  				=      np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)
							lowerCamelCase_  				=      torch.from_numpy(SCREAMING_SNAKE_CASE_		)

							# Add `noise` argument.
							# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
							lowerCamelCase_  				=      pt_noise

							super().check_pt_tf_models(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self		)  ->     Union[str, Any]:






							'''simple docstring'''



							lowerCamelCase_				,lowerCamelCase_  				=      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												lowerCamelCase_  				=      model_class(SCREAMING_SNAKE_CASE_		)
												model.to(SCREAMING_SNAKE_CASE_		)
												model.eval()
												# make random mask reproducible
												torch.manual_seed(2		)
												with torch.no_grad():
																	lowerCamelCase_  				=      model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)		)

												lowerCamelCase_  				=      outputs[0].cpu().numpy()
												lowerCamelCase_  				=      0

												with tempfile.TemporaryDirectory() as tmpdirname:
																	model.save_pretrained(SCREAMING_SNAKE_CASE_		)
																	lowerCamelCase_  				=      model_class.from_pretrained(SCREAMING_SNAKE_CASE_		)
																	model.to(SCREAMING_SNAKE_CASE_		)
																	# make random mask reproducible
																	torch.manual_seed(2		)
																	with torch.no_grad():
																						lowerCamelCase_  				=      model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)		)

																	# Make sure we don't have nans
																	lowerCamelCase_  				=      after_outputs[0].cpu().numpy()
																	lowerCamelCase_  				=      0
																	lowerCamelCase_  				=      np.amax(np.abs(out_a - out_a		)		)
																	self.assertLessEqual(SCREAMING_SNAKE_CASE_   ,			1E-5		)
		@unittest.skip(
		    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'		)
		def 			UpperCamelCase(					self		)  ->     Any:






							'''simple docstring'''



							pass
		@unittest.skip(
		    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'		)
		def 			UpperCamelCase(					self		)  ->     int:






							'''simple docstring'''



							pass
		@unittest.skip(
		    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'		)
		def 			UpperCamelCase(					self		)  ->     Tuple:






							'''simple docstring'''



							pass
		@unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'		)
		def 			UpperCamelCase(					self		)  ->     Union[str, Any]:






							'''simple docstring'''



							pass
		@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'		)
		def 			UpperCamelCase(					self		)  ->     str:






							'''simple docstring'''



							pass






		@slow
		def 			UpperCamelCase(					self		)  ->     Optional[int]:






							'''simple docstring'''



							for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowerCamelCase_  				=      ViTMAEModel.from_pretrained(SCREAMING_SNAKE_CASE_		)
												self.assertIsNotNone(SCREAMING_SNAKE_CASE_		)







def   _UpperCamelCase	(		)       ->			Optional[int]:
					lowerCamelCase_  				=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
					return image







@require_torch
@require_vision
class  UpperCAmelCase (							unittest.TestCase    ):

		'''simple docstring'''






		@cached_property
		def 			UpperCamelCase(					self		)  ->     Optional[Any]:






							'''simple docstring'''



							return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'		) if is_vision_available() else None






		@slow
		def 			UpperCamelCase(					self		)  ->     Optional[int]:






							'''simple docstring'''



							np.random.seed(2		)

							lowerCamelCase_  				=      ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'		).to(SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      self.default_image_processor
							lowerCamelCase_  				=      prepare_img()
							lowerCamelCase_  				=      image_processor(images=SCREAMING_SNAKE_CASE_   ,			return_tensors='pt'		).to(SCREAMING_SNAKE_CASE_		)

							# prepare a noise vector that will be also used for testing the TF model
							# (this way we can ensure that the PT and TF models operate on the same inputs)
							lowerCamelCase_  				=      ViTMAEConfig()
							lowerCamelCase_  				=      int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2		)
							lowerCamelCase_  				=      np.random.uniform(size=(1, num_patches)		)

							# forward pass
							with torch.no_grad():
												lowerCamelCase_  				=      model(**SCREAMING_SNAKE_CASE_   ,			noise=torch.from_numpy(SCREAMING_SNAKE_CASE_		).to(device=SCREAMING_SNAKE_CASE_		)		)

							# verify the logits
							lowerCamelCase_  				=      torch.Size((1, 196, 768)		)
							self.assertEqual(outputs.logits.shape   ,			SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      torch.tensor(
							    [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]		)

							self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]   ,			expected_slice.to(SCREAMING_SNAKE_CASE_		)   ,			atol=1E-4		)		)






import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
      from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder


def 							__lowerCAmelCase     (							_UpperCamelCase		:	int		,				_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	Dict=None		,				_UpperCamelCase		:	Dict=None		,				_UpperCamelCase		:	int=None		,				_UpperCamelCase		:	Optional[Any]=None		,				_UpperCamelCase		:	List[Any]=None		,				)		->				Tuple:
 '''simple docstring'''


 if attention_mask is None:
  SCREAMING_SNAKE_CASE		    =      input_ids.ne(config.pad_token_id						)
 if decoder_attention_mask is None:
  SCREAMING_SNAKE_CASE		    =      decoder_input_ids.ne(config.pad_token_id						)
 if head_mask is None:
  SCREAMING_SNAKE_CASE		    =      torch.ones(config.encoder_layers		,				config.encoder_attention_heads		,				device=_UpperCamelCase						)
 if decoder_head_mask is None:
  SCREAMING_SNAKE_CASE		    =      torch.ones(config.decoder_layers		,				config.decoder_attention_heads		,				device=_UpperCamelCase						)
 if cross_attn_head_mask is None:
  SCREAMING_SNAKE_CASE		    =      torch.ones(config.decoder_layers		,				config.decoder_attention_heads		,				device=_UpperCamelCase						)
 return {
     "input_ids": input_ids,
     "decoder_input_ids": decoder_input_ids,
     "attention_mask": attention_mask,
     "decoder_attention_mask": attention_mask,
     "head_mask": head_mask,
     "decoder_head_mask": decoder_head_mask,
     "cross_attn_head_mask": cross_attn_head_mask,
 }




class   UpperCamelCase			:

    def __init__(      self			:   Optional[Any]				,     snake_case__			:   List[str]				,     snake_case__			:   Any=1_3				,     snake_case__			:   List[str]=7				,     snake_case__			:   Optional[int]=True				,     snake_case__			:   Tuple=False				,     snake_case__			:   Optional[int]=9_9				,     snake_case__			:   List[str]=1_6				,     snake_case__			:   int=2				,     snake_case__			:   Optional[int]=4				,     snake_case__			:   str=4				,     snake_case__			:   Dict="relu"				,     snake_case__			:   Tuple=0.1				,     snake_case__			:   Union[str, Any]=0.1				,     snake_case__			:   Any=0.0				,     snake_case__			:   Dict=0.0				,     snake_case__			:   Optional[Any]=2_0				,     snake_case__			:   int=2				,     snake_case__			:   Optional[Any]=1				,     snake_case__			:   Any=0				,     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      parent
     SCREAMING_SNAKE_CASE		    =      batch_size
     SCREAMING_SNAKE_CASE		    =      seq_length
     SCREAMING_SNAKE_CASE		    =      is_training
     SCREAMING_SNAKE_CASE		    =      use_labels
     SCREAMING_SNAKE_CASE		    =      vocab_size
     SCREAMING_SNAKE_CASE		    =      hidden_size
     SCREAMING_SNAKE_CASE		    =      num_hidden_layers
     SCREAMING_SNAKE_CASE		    =      num_attention_heads
     SCREAMING_SNAKE_CASE		    =      intermediate_size
     SCREAMING_SNAKE_CASE		    =      hidden_act
     SCREAMING_SNAKE_CASE		    =      hidden_dropout_prob
     SCREAMING_SNAKE_CASE		    =      attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE		    =      encoder_layerdrop
     SCREAMING_SNAKE_CASE		    =      decoder_layerdrop
     SCREAMING_SNAKE_CASE		    =      max_position_embeddings
     SCREAMING_SNAKE_CASE		    =      eos_token_id
     SCREAMING_SNAKE_CASE		    =      pad_token_id
     SCREAMING_SNAKE_CASE		    =      bos_token_id

    def UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size     )
     SCREAMING_SNAKE_CASE		    =      self.eos_token_id  # Eos Token
     SCREAMING_SNAKE_CASE		    =      ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size     )

     # we need to clamp the input ids here to avoid having pad token in between
     # this is because for M2M100 the position_ids are prepared such that
     # all pad tokens have pos id = 2 and rest are between 2..seq_length
     # and the seq_length here is seq_length - num_pad_tokens
     # but when using past, there is no way of knowing if the past input ids had
     # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
     # position_ids being off by num_pad_tokens in past input
     SCREAMING_SNAKE_CASE		    =      input_ids.clamp(self.pad_token_id + 1     )
     SCREAMING_SNAKE_CASE		    =      decoder_input_ids.clamp(self.pad_token_id + 1     )

     SCREAMING_SNAKE_CASE		    =      self.get_config()
     SCREAMING_SNAKE_CASE		    =      prepare_mam_aaa_inputs_dict(snake_case__				,     snake_case__				,     snake_case__     )
     return config, inputs_dict

    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     return MaMaaaConfig(
         vocab_size=self.vocab_size				,     d_model=self.hidden_size				,     encoder_layers=self.num_hidden_layers				,     decoder_layers=self.num_hidden_layers				,     encoder_attention_heads=self.num_attention_heads				,     decoder_attention_heads=self.num_attention_heads				,     encoder_ffn_dim=self.intermediate_size				,     decoder_ffn_dim=self.intermediate_size				,     dropout=self.hidden_dropout_prob				,     attention_dropout=self.attention_probs_dropout_prob				,     encoder_layerdrop=self.encoder_layerdrop				,     decoder_layerdrop=self.decoder_layerdrop				,     max_position_embeddings=self.max_position_embeddings				,     eos_token_id=self.eos_token_id				,     bos_token_id=self.bos_token_id				,     pad_token_id=self.pad_token_id				,     )

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      self.prepare_config_and_inputs()
     return config, inputs_dict

    def UpperCamelCase      (      self			:   Tuple				,     snake_case__			:   List[str]				,     snake_case__			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      MaMaaaModel(config=snake_case__     ).get_decoder().to(snake_case__     ).eval()
     SCREAMING_SNAKE_CASE		    =      inputs_dict['input_ids']
     SCREAMING_SNAKE_CASE		    =      inputs_dict['attention_mask']
     SCREAMING_SNAKE_CASE		    =      inputs_dict['head_mask']

     # first forward pass
     SCREAMING_SNAKE_CASE		    =      model(snake_case__				,     attention_mask=snake_case__				,     head_mask=snake_case__				,     use_cache=snake_case__     )

     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      outputs.to_tuple()

     # create hypothetical multiple next token and extent to next_input_ids
     SCREAMING_SNAKE_CASE		    =      ids_tensor((self.batch_size, 3)				,     config.vocab_size     )
     SCREAMING_SNAKE_CASE		    =      ids_tensor((self.batch_size, 3)				,     2     )

     # append to next input_ids and
     SCREAMING_SNAKE_CASE		    =      torch.cat([input_ids, next_tokens]				,     dim=-1     )
     SCREAMING_SNAKE_CASE		    =      torch.cat([attention_mask, next_attn_mask]				,     dim=-1     )

     SCREAMING_SNAKE_CASE		    =      model(snake_case__				,     attention_mask=snake_case__     )['last_hidden_state']
     SCREAMING_SNAKE_CASE		    =      model(snake_case__				,     attention_mask=snake_case__				,     past_key_values=snake_case__     )[
         'last_hidden_state'
     ]

     # select random slice
     SCREAMING_SNAKE_CASE		    =      ids_tensor((1,)				,     output_from_past.shape[-1]     ).item()
     SCREAMING_SNAKE_CASE		    =      output_from_no_past[:, -3:, random_slice_idx].detach()
     SCREAMING_SNAKE_CASE		    =      output_from_past[:, :, random_slice_idx].detach()

     self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]     )

     # test that outputs are equal for slice
     self.parent.assertTrue(torch.allclose(snake_case__				,     snake_case__				,     atol=1E-2     )     )




    def UpperCamelCase      (      self			:   List[str]				,     snake_case__			:   Any				,     snake_case__			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      MaMaaaModel(config=snake_case__     ).to(snake_case__     ).eval()
     SCREAMING_SNAKE_CASE		    =      model(**snake_case__     )

     SCREAMING_SNAKE_CASE		    =      outputs.encoder_last_hidden_state
     SCREAMING_SNAKE_CASE		    =      outputs.last_hidden_state

     with tempfile.TemporaryDirectory() as tmpdirname:
      SCREAMING_SNAKE_CASE		    =      model.get_encoder()
      encoder.save_pretrained(snake_case__     )
      SCREAMING_SNAKE_CASE		    =      MaMaaaEncoder.from_pretrained(snake_case__     ).to(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      encoder(inputs_dict['input_ids']				,     attention_mask=inputs_dict['attention_mask']     )[
         0
     ]

     self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3     )

     with tempfile.TemporaryDirectory() as tmpdirname:
      SCREAMING_SNAKE_CASE		    =      model.get_decoder()
      decoder.save_pretrained(snake_case__     )
      SCREAMING_SNAKE_CASE		    =      MaMaaaDecoder.from_pretrained(snake_case__     ).to(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      decoder(
         input_ids=inputs_dict['decoder_input_ids']				,     attention_mask=inputs_dict['decoder_attention_mask']				,     encoder_hidden_states=snake_case__				,     encoder_attention_mask=inputs_dict['attention_mask']				,     )[0]

     self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3     )




@require_torch
class   UpperCamelCase			(  SCREAMING_SNAKE_CASE						,      SCREAMING_SNAKE_CASE						,      SCREAMING_SNAKE_CASE						,      unittest.TestCase ):
    __UpperCamelCase    =(
        (
            MaMaaaModel,
            MaMaaaForConditionalGeneration,
        )
        if is_torch_available()
        else ()
    )
    __UpperCamelCase    =(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
    __UpperCamelCase    =(
        {
            "conversational": MaMaaaForConditionalGeneration,
            "feature-extraction": MaMaaaModel,
            "summarization": MaMaaaForConditionalGeneration,
            "text2text-generation": MaMaaaForConditionalGeneration,
            "translation": MaMaaaForConditionalGeneration,
        }
        if is_torch_available()
        else {}
    )
    __UpperCamelCase    =True
    __UpperCamelCase    =True
    __UpperCamelCase    =False
    __UpperCamelCase    =False

    def UpperCamelCase      (      self			:   Optional[Any]				,     snake_case__			:   Any				,     snake_case__			:   Tuple				,     snake_case__			:   List[str]				,     snake_case__			:   Optional[int]				,     snake_case__			:   Optional[Any]     ):

     """simple docstring"""






     if pipeline_test_casse_name == "TranslationPipelineTests":
      # Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
      # `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
      return True

     return False

    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      MaMaaaModelTester(self     )
     SCREAMING_SNAKE_CASE		    =      ConfigTester(self				,     config_class=snake_case__     )

    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






     self.config_tester.run_common_tests()

    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
      SCREAMING_SNAKE_CASE		    =      model_class(snake_case__     )

      with tempfile.TemporaryDirectory() as tmpdirname:
       model.save_pretrained(snake_case__     )
       SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      model_class.from_pretrained(snake_case__				,     output_loading_info=snake_case__     )
      self.assertEqual(info['missing_keys']				,     []     )

    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_decoder_model_past_large_inputs(*snake_case__     )

    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_encoder_decoder_model_standalone(*snake_case__     )

    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
      SCREAMING_SNAKE_CASE		    =      model_class(snake_case__     )
      model.to(snake_case__     )
      model.eval()

      SCREAMING_SNAKE_CASE		    =      copy.deepcopy(self._prepare_for_class(snake_case__				,     snake_case__     )     )

      if not self.is_encoder_decoder:
       SCREAMING_SNAKE_CASE		    =      inputs['input_ids']
       del inputs["input_ids"]
      else:
       SCREAMING_SNAKE_CASE		    =      inputs['input_ids']
       SCREAMING_SNAKE_CASE		    =      inputs.get('decoder_input_ids'				,     snake_case__     )
       del inputs["input_ids"]
       inputs.pop('decoder_input_ids'				,     snake_case__     )

      SCREAMING_SNAKE_CASE		    =      model.get_input_embeddings()
      if not self.is_encoder_decoder:
       SCREAMING_SNAKE_CASE		    =      wte(snake_case__     )
      else:
       SCREAMING_SNAKE_CASE		    =      wte(snake_case__     )
       SCREAMING_SNAKE_CASE		    =      wte(snake_case__     )

      with torch.no_grad():
       model(**snake_case__     )[0]




    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE		    =      input_dict['input_ids']
     SCREAMING_SNAKE_CASE		    =      input_ids.ne(1     ).to(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      MaMaaaForConditionalGeneration(snake_case__     ).eval().to(snake_case__     )
     if torch_device == "cuda":
      model.half()
     model.generate(snake_case__				,     attention_mask=snake_case__     )
     model.generate(num_beams=4				,     do_sample=snake_case__				,     early_stopping=snake_case__				,     num_return_sequences=3     )


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Tuple						)		->				Dict:
 '''simple docstring'''


 return torch.tensor(_UpperCamelCase		,				dtype=torch.long		,				device=_UpperCamelCase						)


a_     :    Optional[int]     		=   1e-4




@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class   UpperCamelCase			(  unittest.TestCase ):

    @cached_property
    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'     )

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      MaMaaaModel.from_pretrained('facebook/m2m100_418M'     ).to(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      _long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]]     )
     SCREAMING_SNAKE_CASE		    =      _long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]]     )
     SCREAMING_SNAKE_CASE		    =      prepare_mam_aaa_inputs_dict(model.config				,     snake_case__				,     snake_case__     )
     with torch.no_grad():
      SCREAMING_SNAKE_CASE		    =      model(**snake_case__     )[0]
     SCREAMING_SNAKE_CASE		    =      torch.Size((1, 1_1, 1_0_2_4)     )
     self.assertEqual(output.shape				,     snake_case__     )
     # change to expected output here
     SCREAMING_SNAKE_CASE		    =      torch.tensor(
         [[-0.7_780, -0.1_676, 0.1_038], [-6.7_556, -1.3_992, 0.0_567], [-7.5_383, -0.5_920, -0.2_779]]				,     device=snake_case__     )
     self.assertTrue(torch.allclose(output[:, :3, :3]				,     snake_case__				,     atol=snake_case__     )     )

    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'     ).to(snake_case__     )

     # change to intended input
     SCREAMING_SNAKE_CASE		    =      _long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]]     )
     SCREAMING_SNAKE_CASE		    =      _long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]]     )
     SCREAMING_SNAKE_CASE		    =      prepare_mam_aaa_inputs_dict(model.config				,     snake_case__				,     snake_case__     )
     with torch.no_grad():
      SCREAMING_SNAKE_CASE		    =      model(**snake_case__     )[0]
     SCREAMING_SNAKE_CASE		    =      torch.Size((1, 1_1, model.config.vocab_size)     )
     self.assertEqual(output.shape				,     snake_case__     )
     # change to expected output here
     SCREAMING_SNAKE_CASE		    =      torch.tensor(
         [[-1.0_448, -1.0_411, 3.7_992], [-3.2_191, -3.2_386, -1.3_451], [-3.6_210, -3.5_993, 0.4_925]]				,     device=snake_case__     )
     self.assertTrue(torch.allclose(output[:, :3, :3]				,     snake_case__				,     atol=snake_case__     )     )




    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'     ).to(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'				,     src_lang='fr'				,     tgt_lang='en'     )

     SCREAMING_SNAKE_CASE		    =      [
         'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
         'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
         'Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'
         ' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'
         ' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.',
     ]

     # The below article tests that we don't add any hypotheses outside of the top n_beams
     SCREAMING_SNAKE_CASE		    =      tokenizer(snake_case__				,     padding=snake_case__				,     return_tensors='pt'     )

     SCREAMING_SNAKE_CASE		    =      model.generate(
         input_ids=dct['input_ids'].to(snake_case__     )				,     attention_mask=dct['attention_mask'].to(snake_case__     )				,     num_beams=5				,     forced_bos_token_id=tokenizer.get_lang_id('en'     )				,     )

     SCREAMING_SNAKE_CASE		    =      [
         'The NSA case highlights the total absence of intelligence debate',
         'I think there are two levels of response from the French government.',
         'When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'
         ' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'
         ' communications in France.',
     ]

     SCREAMING_SNAKE_CASE		    =      tokenizer.batch_decode(
         hypotheses_batch.tolist()				,     clean_up_tokenization_spaces=snake_case__				,     skip_special_tokens=snake_case__     )
     assert generated == expected_en

