


from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor




class       lowercase_					:





				def __init__(				self       ,  lowercase_       ,  lowercase_=13       ,  lowercase_=30       ,  lowercase_=2       ,  lowercase_=3       ,  lowercase_=True       ,  lowercase_=True       ,  lowercase_=32       ,  lowercase_=2       ,  lowercase_=4       ,  lowercase_=37       ,  lowercase_="gelu"       ,  lowercase_=0.1       ,  lowercase_=0.1       ,  lowercase_=10       ,  lowercase_=0.02       ,  lowercase_=3       ,  lowercase_=0.6       ,  lowercase_=None       ,  )  -> Dict:
											a__      =parent
											a__      =batch_size
											a__      =image_size
											a__      =patch_size
											a__      =num_channels
											a__      =is_training
											a__      =use_labels
											a__      =hidden_size
											a__      =num_hidden_layers
											a__      =num_attention_heads
											a__      =intermediate_size
											a__      =hidden_act
											a__      =hidden_dropout_prob
											a__      =attention_probs_dropout_prob
											a__      =type_sequence_label_size
											a__      =initializer_range
											a__      =mask_ratio
											a__      =scope

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





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

											a__      =self.get_config()

											return config, pixel_values, labels





				def 						__UpperCamelCase    (				self)  -> Dict:
											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       ,  decoder_hidden_size=self.hidden_size       ,  decoder_num_hidden_layers=self.num_hidden_layers       ,  decoder_num_attention_heads=self.num_attention_heads       ,  decoder_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=lowercase_       ,  initializer_range=self.initializer_range       ,  mask_ratio=self.mask_ratio       ,  )





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_)  -> Any:
											a__      =TFViTMAEModel(config=lowercase_)
											a__      =model(lowercase_       ,  training=lowercase_)
											self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.seq_length, self.hidden_size))





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_)  -> Optional[int]:
											a__      =TFViTMAEForPreTraining(lowercase_)
											a__      =model(lowercase_       ,  training=lowercase_)
											# expected sequence length = num_patches
											a__      =(self.image_size // self.patch_size) ** 2
											a__      =self.patch_size**2 * self.num_channels
											self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, num_patches, expected_num_channels))

											# test greyscale images
											a__      =1
											a__      =TFViTMAEForPreTraining(lowercase_)

											a__      =floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
											a__      =model(lowercase_       ,  training=lowercase_)
											a__      =self.patch_size**2
											self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, num_patches, expected_num_channels))





				def 						__UpperCamelCase    (				self)  -> List[str]:
											a__      =self.prepare_config_and_inputs()
											((a__)    ,     (a__)    ,     (a__))      =config_and_inputs
											a__      ={'pixel_values': pixel_values}
											return config, inputs_dict







@require_tf
class       lowercase_					(lowercase__      ,					lowercase__      ,					unittest.TestCase   ):
				snake_case	    =(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
				snake_case	    ={'feature-extraction': TFViTMAEModel} if is_tf_available() else {}

				snake_case	    =False
				snake_case	    =False
				snake_case	    =False
				snake_case	    =False





				def 						__UpperCamelCase    (				self)  -> Dict:
											a__      =TFViTMAEModelTester(self)
											a__      =ConfigTester(self       ,  config_class=lowercase_       ,  has_text_modality=lowercase_       ,  hidden_size=37)





				def 						__UpperCamelCase    (				self)  -> Tuple:
											self.config_tester.run_common_tests()





				@unittest.skip(reason='ViTMAE does not use inputs_embeds')
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											pass





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__    ,     a__      =self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		a__      =model_class(lowercase_)
																		self.assertIsInstance(model.get_input_embeddings()       ,  (tf.keras.layers.Layer))
																		a__      =model.get_output_embeddings()
																		self.assertTrue(x is None or isinstance(lowercase_       ,  tf.keras.layers.Layer))





				def 						__UpperCamelCase    (				self)  -> Dict:
											a__    ,     a__      =self.model_tester.prepare_config_and_inputs_for_common()

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

																		a__      =['pixel_values']
																		self.assertListEqual(arg_names[:1]       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*lowercase_)





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											a__      =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_pretraining(*lowercase_)





				def 						__UpperCamelCase    (				self)  -> Any:
											# make the mask reproducible
											np.random.seed(2)

											a__    ,     a__      =self.model_tester.prepare_config_and_inputs_for_common()

											a__      =int((config.image_size // config.patch_size) ** 2)
											a__      =np.random.uniform(size=(self.model_tester.batch_size, num_patches))

											for model_class in self.all_model_classes:
																		a__      =model_class(lowercase_)
																		a__      =self._prepare_for_class(lowercase_       ,  lowercase_)

																		a__      =model(lowercase_       ,  noise=lowercase_)

																		a__      =copy.deepcopy(self._prepare_for_class(lowercase_       ,  lowercase_))
																		a__      =model(**lowercase_       ,  noise=lowercase_)
																		a__      =outputs_dict[0].numpy()
																		a__      =outputs_keywords[0].numpy()

																		self.assertLess(np.sum(np.abs(output_dict - output_keywords))       ,  1e-6)





				def 						__UpperCamelCase    (				self)  -> Any:
											# make the mask reproducible
											np.random.seed(2)

											a__    ,     a__      =self.model_tester.prepare_config_and_inputs_for_common()

											a__      =int((config.image_size // config.patch_size) ** 2)
											a__      =np.random.uniform(size=(self.model_tester.batch_size, num_patches))

											def prepare_numpy_arrays(lowercase_):
																		a__      ={}
																		for k, v in inputs_dict.items():
																									if tf.is_tensor(lowercase_):
																																a__      =v.numpy()
																									else:
																																a__      =np.array(lowercase_)

																		return inputs_np_dict

											for model_class in self.all_model_classes:
																		a__      =model_class(lowercase_)

																		a__      =self._prepare_for_class(lowercase_       ,  lowercase_)
																		a__      =prepare_numpy_arrays(lowercase_)

																		a__      =model(lowercase_       ,  noise=lowercase_)
																		a__      =model(**lowercase_       ,  noise=lowercase_)
																		self.assert_outputs_same(lowercase_       ,  lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_)  -> Union[str, Any]:
											# make masks reproducible
											np.random.seed(2)

											a__      =int((tf_model.config.image_size // tf_model.config.patch_size) ** 2)
											a__      =np.random.uniform(size=(self.model_tester.batch_size, num_patches))
											a__      =tf.constant(lowercase_)

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

											super().check_pt_tf_models(lowercase_       ,  lowercase_       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											# make mask reproducible
											np.random.seed(2)

											a__    ,     a__      =self.model_tester.prepare_config_and_inputs_for_common()

											a__      ={
											    module_member
											    for model_class in self.all_model_classes
											    for module in (import_module(model_class.__module__),)
											    for module_member_name in dir(lowercase_)
											    if module_member_name.endswith('MainLayer')
											    # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
											    and module_member_name[: -len('MainLayer')] == model_class.__name__[: -len('Model')]
											    for module_member in (getattr(lowercase_       ,  lowercase_),)
											    if isinstance(lowercase_       ,  lowercase_)
											    and tf.keras.layers.Layer in module_member.__bases__
											    and getattr(lowercase_       ,  '_keras_serializable'       ,  lowercase_)
											}

											a__      =int((config.image_size // config.patch_size) ** 2)
											a__      =np.random.uniform(size=(self.model_tester.batch_size, num_patches))
											a__      =tf.convert_to_tensor(lowercase_)
											inputs_dict.update({'noise': noise})

											for main_layer_class in tf_main_layer_classes:
																		a__      =main_layer_class(lowercase_)

																		a__      ={
																		    name: tf.keras.Input(tensor.shape[1:]       ,  dtype=tensor.dtype) for name, tensor in inputs_dict.items()
																		}

																		a__      =tf.keras.Model(lowercase_       ,  outputs=main_layer(lowercase_))
																		a__      =model(lowercase_)

																		with tempfile.TemporaryDirectory() as tmpdirname:
																									a__      =os.path.join(lowercase_       ,  'keras_model.h5')
																									model.save(lowercase_)
																									a__      =tf.keras.models.load_model(
																									    lowercase_       ,  custom_objects={main_layer_class.__name__: main_layer_class})
																									assert isinstance(lowercase_       ,  tf.keras.Model)
																									a__      =model(lowercase_)
																									self.assert_outputs_same(lowercase_       ,  lowercase_)





				@slow
				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											# make mask reproducible
											np.random.seed(2)

											a__    ,     a__      =self.model_tester.prepare_config_and_inputs_for_common()

											a__      =int((config.image_size // config.patch_size) ** 2)
											a__      =np.random.uniform(size=(self.model_tester.batch_size, num_patches))

											for model_class in self.all_model_classes:
																		a__      =model_class(lowercase_)
																		a__      =self._prepare_for_class(lowercase_       ,  lowercase_)
																		a__      =model(lowercase_       ,  noise=lowercase_)

																		if model_class.__name__ == "TFViTMAEModel":
																									a__      =outputs.last_hidden_state.numpy()
																									a__      =0
																		else:
																									a__      =outputs.logits.numpy()
																									a__      =0

																		with tempfile.TemporaryDirectory() as tmpdirname:
																									model.save_pretrained(lowercase_       ,  saved_model=lowercase_)
																									a__      =model_class.from_pretrained(lowercase_)
																									a__      =model(lowercase_       ,  noise=lowercase_)

																									if model_class.__name__ == "TFViTMAEModel":
																																a__      =after_outputs['last_hidden_state'].numpy()
																																a__      =0
																									else:
																																a__      =after_outputs['logits'].numpy()
																																a__      =0

																									a__      =np.amax(np.abs(out_a - out_a))
																									self.assertLessEqual(lowercase_       ,  1e-5)





				def 						__UpperCamelCase    (				self)  -> Any:
											# make mask reproducible
											np.random.seed(2)

											a__    ,     a__      =self.model_tester.prepare_config_and_inputs_for_common()

											a__      =int((config.image_size // config.patch_size) ** 2)
											a__      =np.random.uniform(size=(self.model_tester.batch_size, num_patches))

											for model_class in self.all_model_classes:
																		a__      =model_class(lowercase_)
																		a__      =self._prepare_for_class(lowercase_       ,  lowercase_)

																		a__      =model(lowercase_       ,  noise=lowercase_)
																		a__      =model.get_config()
																		# make sure that returned config is jsonifiable, which is required by keras
																		json.dumps(lowercase_)
																		a__      =model_class.from_config(model.get_config())
																		# make sure it also accepts a normal config
																		a__      =model_class.from_config(model.config)
																		a__      =new_model(lowercase_)  # Build model
																		new_model.set_weights(model.get_weights())
																		a__      =new_model(lowercase_       ,  noise=lowercase_)

																		self.assert_outputs_same(lowercase_       ,  lowercase_)





				@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:
											pass





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





				@slow
				def 						__UpperCamelCase    (				self)  -> int:
											a__      =TFViTMAEModel.from_pretrained('google/vit-base-patch16-224')
											self.assertIsNotNone(lowercase_)







def   _lowercase(					):
							a__      =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
							return image







@require_tf
@require_vision
class       lowercase_					(unittest.TestCase   ):





				@cached_property
				def 						__UpperCamelCase    (				self)  -> Dict:
											return ViTImageProcessor.from_pretrained('facebook/vit-mae-base') if is_vision_available() else None





				@slow
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											# make random mask reproducible across the PT and TF model
											np.random.seed(2)

											a__      =TFViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base')

											a__      =self.default_image_processor
											a__      =prepare_img()
											a__      =image_processor(images=lowercase_       ,  return_tensors='tf')

											# 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)
											a__      =ViTMAEConfig()
											a__      =int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2)
											a__      =np.random.uniform(size=(1, num_patches))

											# forward pass
											a__      =model(**lowercase_       ,  noise=lowercase_)

											# verify the logits
											a__      =tf.convert_to_tensor([1, 196, 768])
											self.assertEqual(outputs.logits.shape       ,  lowercase_)

											a__      =tf.convert_to_tensor(
											    [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]])

											tf.debugging.assert_near(outputs.logits[0, :3, :3]       ,  lowercase_       ,  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

