


"""simple docstring"""







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import MobileNetVaImageProcessor




class 				UpperCamelCase_			(				unittest.TestCase):

	"""simple docstring"""





	def __init__(				self    :       Any    ,    UpperCAmelCase__    :       Any    ,    UpperCAmelCase__    :       Optional[int]=7    ,    UpperCAmelCase__    :       Tuple=3    ,    UpperCAmelCase__    :       Dict=1_8    ,    UpperCAmelCase__    :       List[Any]=3_0    ,    UpperCAmelCase__    :       List[str]=4_0_0    ,    UpperCAmelCase__    :       Union[str, Any]=True    ,    UpperCAmelCase__    :       Dict=None    ,    UpperCAmelCase__    :       Tuple=True    ,    UpperCAmelCase__    :       Tuple=None    ,    ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       size if size is not None else {"shortest_edge": 2_0}
						__SCREAMING_SNAKE_CASE		     =       crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
						__SCREAMING_SNAKE_CASE		     =       parent
						__SCREAMING_SNAKE_CASE		     =       batch_size
						__SCREAMING_SNAKE_CASE		     =       num_channels
						__SCREAMING_SNAKE_CASE		     =       image_size
						__SCREAMING_SNAKE_CASE		     =       min_resolution
						__SCREAMING_SNAKE_CASE		     =       max_resolution
						__SCREAMING_SNAKE_CASE		     =       do_resize
						__SCREAMING_SNAKE_CASE		     =       size
						__SCREAMING_SNAKE_CASE		     =       do_center_crop
						__SCREAMING_SNAKE_CASE		     =       crop_size




	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			Tuple:
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						}




@require_torch
@require_vision
class 				UpperCamelCase_			(				UpperCamelCase    ,						unittest.TestCase):

	"""simple docstring"""





	snake_case__       :						List[Any]					      =    MobileNetVaImageProcessor if is_vision_available() else None




	def 						UpperCAmelCase_   (				self    :       str     ) ->			Tuple:
						__SCREAMING_SNAKE_CASE		     =       MobileNetVaImageProcessingTester(self     )




	@property
	def 						UpperCAmelCase_   (				self    :       Any     ) ->			List[Any]:
						return self.image_processor_tester.prepare_image_processor_dict()




	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       self.image_processing_class(**self.image_processor_dict     )
						self.assertTrue(hasattr(UpperCAmelCase__    ,    "do_resize"     )     )
						self.assertTrue(hasattr(UpperCAmelCase__    ,    "size"     )     )
						self.assertTrue(hasattr(UpperCAmelCase__    ,    "do_center_crop"     )     )
						self.assertTrue(hasattr(UpperCAmelCase__    ,    "crop_size"     )     )




	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       self.image_processing_class.from_dict(self.image_processor_dict     )
						self.assertEqual(image_processor.size    ,    {"shortest_edge": 2_0}     )
						self.assertEqual(image_processor.crop_size    ,    {"height": 1_8, "width": 1_8}     )

						__SCREAMING_SNAKE_CASE		     =       self.image_processing_class.from_dict(self.image_processor_dict    ,    size=4_2    ,    crop_size=8_4     )
						self.assertEqual(image_processor.size    ,    {"shortest_edge": 4_2}     )
						self.assertEqual(image_processor.crop_size    ,    {"height": 8_4, "width": 8_4}     )




	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			Tuple:
						pass




	def 						UpperCAmelCase_   (				self    :       Tuple     ) ->			int:
						# Initialize image_processing
						__SCREAMING_SNAKE_CASE		     =       self.image_processing_class(**self.image_processor_dict     )
						# create random PIL images
						__SCREAMING_SNAKE_CASE		     =       prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=UpperCAmelCase__     )
						for image in image_inputs:
											self.assertIsInstance(UpperCAmelCase__    ,    Image.Image     )

						# Test not batched input
						__SCREAMING_SNAKE_CASE		     =       image_processing(image_inputs[0]    ,    return_tensors="pt"     ).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,    (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )    ,    )

						# Test batched
						__SCREAMING_SNAKE_CASE		     =       image_processing(UpperCAmelCase__    ,    return_tensors="pt"     ).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )    ,    )




	def 						UpperCAmelCase_   (				self    :       int     ) ->			Optional[Any]:
						# Initialize image_processing
						__SCREAMING_SNAKE_CASE		     =       self.image_processing_class(**self.image_processor_dict     )
						# create random numpy tensors
						__SCREAMING_SNAKE_CASE		     =       prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=UpperCAmelCase__    ,    numpify=UpperCAmelCase__     )
						for image in image_inputs:
											self.assertIsInstance(UpperCAmelCase__    ,    np.ndarray     )

						# Test not batched input
						__SCREAMING_SNAKE_CASE		     =       image_processing(image_inputs[0]    ,    return_tensors="pt"     ).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,    (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )    ,    )

						# Test batched
						__SCREAMING_SNAKE_CASE		     =       image_processing(UpperCAmelCase__    ,    return_tensors="pt"     ).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )    ,    )




	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			List[str]:
						# Initialize image_processing
						__SCREAMING_SNAKE_CASE		     =       self.image_processing_class(**self.image_processor_dict     )
						# create random PyTorch tensors
						__SCREAMING_SNAKE_CASE		     =       prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=UpperCAmelCase__    ,    torchify=UpperCAmelCase__     )
						for image in image_inputs:
											self.assertIsInstance(UpperCAmelCase__    ,    torch.Tensor     )

						# Test not batched input
						__SCREAMING_SNAKE_CASE		     =       image_processing(image_inputs[0]    ,    return_tensors="pt"     ).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,    (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )    ,    )

						# Test batched
						__SCREAMING_SNAKE_CASE		     =       image_processing(UpperCAmelCase__    ,    return_tensors="pt"     ).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )    ,    )




"""simple docstring"""







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      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_=None						,		lowerCAmelCase_=None						,		lowerCAmelCase_=None						,		lowerCAmelCase_=None						,		lowerCAmelCase_=None						,		):




					'''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=lowerCAmelCase_     )
					if decoder_head_mask is None:
										__SCREAMING_SNAKE_CASE		     =       torch.ones(config.decoder_layers						,		config.decoder_attention_heads						,		device=lowerCAmelCase_     )
					if cross_attn_head_mask is None:
										__SCREAMING_SNAKE_CASE		     =       torch.ones(config.decoder_layers						,		config.decoder_attention_heads						,		device=lowerCAmelCase_     )
					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_			:

	"""simple docstring"""





	def __init__(				self    :       Dict    ,    UpperCAmelCase__    :       Any    ,    UpperCAmelCase__    :       Dict=1_3    ,    UpperCAmelCase__    :       List[str]=7    ,    UpperCAmelCase__    :       Any=True    ,    UpperCAmelCase__    :       Any=False    ,    UpperCAmelCase__    :       Optional[int]=9_9    ,    UpperCAmelCase__    :       Dict=1_6    ,    UpperCAmelCase__    :       List[str]=2    ,    UpperCAmelCase__    :       str=4    ,    UpperCAmelCase__    :       str=4    ,    UpperCAmelCase__    :       Optional[int]="relu"    ,    UpperCAmelCase__    :       int=0.1    ,    UpperCAmelCase__    :       List[str]=0.1    ,    UpperCAmelCase__    :       List[Any]=0.0    ,    UpperCAmelCase__    :       Union[str, Any]=0.0    ,    UpperCAmelCase__    :       List[str]=2_0    ,    UpperCAmelCase__    :       List[str]=2    ,    UpperCAmelCase__    :       Any=1    ,    UpperCAmelCase__    :       Union[str, Any]=0    ,    ) ->			Any:
						__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[Any]     ) ->			Optional[int]:
						__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(UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
						return config, inputs_dict




	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			Any:
						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    :       Union[str, Any]     ) ->			int:
						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       self.prepare_config_and_inputs()
						return config, inputs_dict




	def 						UpperCAmelCase_   (				self    :       str    ,    UpperCAmelCase__    :       List[str]    ,    UpperCAmelCase__    :       Optional[Any]     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       MaMaaaModel(config=UpperCAmelCase__     ).get_decoder().to(UpperCAmelCase__     ).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(UpperCAmelCase__    ,    attention_mask=UpperCAmelCase__    ,    head_mask=UpperCAmelCase__    ,    use_cache=UpperCAmelCase__     )

						__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(UpperCAmelCase__    ,    attention_mask=UpperCAmelCase__     )["last_hidden_state"]
						__SCREAMING_SNAKE_CASE		     =       model(UpperCAmelCase__    ,    attention_mask=UpperCAmelCase__    ,    past_key_values=UpperCAmelCase__     )[
						    "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(UpperCAmelCase__    ,    UpperCAmelCase__    ,    atol=1E-2     )     )




	def 						UpperCAmelCase_   (				self    :       Tuple    ,    UpperCAmelCase__    :       Optional[int]    ,    UpperCAmelCase__    :       Union[str, Any]     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       MaMaaaModel(config=UpperCAmelCase__     ).to(UpperCAmelCase__     ).eval()
						__SCREAMING_SNAKE_CASE		     =       model(**UpperCAmelCase__     )

						__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(UpperCAmelCase__     )
											__SCREAMING_SNAKE_CASE		     =       MaMaaaEncoder.from_pretrained(UpperCAmelCase__     ).to(UpperCAmelCase__     )

						__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(UpperCAmelCase__     )
											__SCREAMING_SNAKE_CASE		     =       MaMaaaDecoder.from_pretrained(UpperCAmelCase__     ).to(UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       decoder(
						    input_ids=inputs_dict["decoder_input_ids"]    ,    attention_mask=inputs_dict["decoder_attention_mask"]    ,    encoder_hidden_states=UpperCAmelCase__    ,    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_			(				UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						unittest.TestCase):

	"""simple docstring"""





	snake_case__       :						List[str]					      =    (
	    (
	        MaMaaaModel,
	        MaMaaaForConditionalGeneration,
	    )
	    if is_torch_available()
	    else ()
	)
	snake_case__       :						Tuple					      =    (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
	snake_case__       :						Dict					      =    (
	    {
	        "conversational": MaMaaaForConditionalGeneration,
	        "feature-extraction": MaMaaaModel,
	        "summarization": MaMaaaForConditionalGeneration,
	        "text2text-generation": MaMaaaForConditionalGeneration,
	        "translation": MaMaaaForConditionalGeneration,
	    }
	    if is_torch_available()
	    else {}
	)
	snake_case__       :						Optional[int]					      =    True
	snake_case__       :						Optional[Any]					      =    True
	snake_case__       :						Union[str, Any]					      =    False
	snake_case__       :						Tuple					      =    False




	def 						UpperCAmelCase_   (				self    :       Dict    ,    UpperCAmelCase__    :       Any    ,    UpperCAmelCase__    :       Union[str, Any]    ,    UpperCAmelCase__    :       Dict    ,    UpperCAmelCase__    :       Dict    ,    UpperCAmelCase__    :       Dict     ) ->			Optional[Any]:
						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    :       Dict     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       MaMaaaModelTester(self     )
						__SCREAMING_SNAKE_CASE		     =       ConfigTester(self    ,    config_class=UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			Tuple:
						self.config_tester.run_common_tests()




	def 						UpperCAmelCase_   (				self    :       Dict     ) ->			Tuple:
						__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(UpperCAmelCase__     )

											with tempfile.TemporaryDirectory() as tmpdirname:
																model.save_pretrained(UpperCAmelCase__     )
																__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       model_class.from_pretrained(UpperCAmelCase__    ,    output_loading_info=UpperCAmelCase__     )
											self.assertEqual(info["missing_keys"]    ,    []     )




	def 						UpperCAmelCase_   (				self    :       Tuple     ) ->			Tuple:
						__SCREAMING_SNAKE_CASE		     =       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_decoder_model_past_large_inputs(*UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			Optional[Any]:
						__SCREAMING_SNAKE_CASE		     =       self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_encoder_decoder_model_standalone(*UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       str     ) ->			Optional[int]:
						__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(UpperCAmelCase__     )
											model.to(UpperCAmelCase__     )
											model.eval()

											__SCREAMING_SNAKE_CASE		     =       copy.deepcopy(self._prepare_for_class(UpperCAmelCase__    ,    UpperCAmelCase__     )     )

											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"    ,    UpperCAmelCase__     )
																del inputs["input_ids"]
																inputs.pop("decoder_input_ids"    ,    UpperCAmelCase__     )

											__SCREAMING_SNAKE_CASE		     =       model.get_input_embeddings()
											if not self.is_encoder_decoder:
																__SCREAMING_SNAKE_CASE		     =       wte(UpperCAmelCase__     )
											else:
																__SCREAMING_SNAKE_CASE		     =       wte(UpperCAmelCase__     )
																__SCREAMING_SNAKE_CASE		     =       wte(UpperCAmelCase__     )

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




	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			Union[str, Any]:
						__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(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       MaMaaaForConditionalGeneration(UpperCAmelCase__     ).eval().to(UpperCAmelCase__     )
						if torch_device == "cuda":
											model.half()
						model.generate(UpperCAmelCase__    ,    attention_mask=UpperCAmelCase__     )
						model.generate(num_beams=4    ,    do_sample=UpperCAmelCase__    ,    early_stopping=UpperCAmelCase__    ,    num_return_sequences=3     )




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


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


a__       :							Union[str, Any]        =  1E-4




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

	"""simple docstring"""





	@cached_property
	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			List[str]:
						return MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"     )




	def 						UpperCAmelCase_   (				self    :       Dict     ) ->			List[Any]:
						__SCREAMING_SNAKE_CASE		     =       MaMaaaModel.from_pretrained("facebook/m2m100_418M"     ).to(UpperCAmelCase__     )
						__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    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
						with torch.no_grad():
											__SCREAMING_SNAKE_CASE		     =       model(**UpperCAmelCase__     )[0]
						__SCREAMING_SNAKE_CASE		     =       torch.Size((1, 1_1, 1_0_2_4)     )
						self.assertEqual(output.shape    ,    UpperCAmelCase__     )
						# 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=UpperCAmelCase__     )
						self.assertTrue(torch.allclose(output[:, :3, :3]    ,    UpperCAmelCase__    ,    atol=UpperCAmelCase__     )     )




	def 						UpperCAmelCase_   (				self    :       Tuple     ) ->			str:
						__SCREAMING_SNAKE_CASE		     =       MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"     ).to(UpperCAmelCase__     )

						# 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    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
						with torch.no_grad():
											__SCREAMING_SNAKE_CASE		     =       model(**UpperCAmelCase__     )[0]
						__SCREAMING_SNAKE_CASE		     =       torch.Size((1, 1_1, model.config.vocab_size)     )
						self.assertEqual(output.shape    ,    UpperCAmelCase__     )
						# 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=UpperCAmelCase__     )
						self.assertTrue(torch.allclose(output[:, :3, :3]    ,    UpperCAmelCase__    ,    atol=UpperCAmelCase__     )     )




	def 						UpperCAmelCase_   (				self    :       Tuple     ) ->			Any:
						__SCREAMING_SNAKE_CASE		     =       MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"     ).to(UpperCAmelCase__     )
						__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(UpperCAmelCase__    ,    padding=UpperCAmelCase__    ,    return_tensors="pt"     )

						__SCREAMING_SNAKE_CASE		     =       model.generate(
						    input_ids=dct["input_ids"].to(UpperCAmelCase__     )    ,    attention_mask=dct["attention_mask"].to(UpperCAmelCase__     )    ,    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=UpperCAmelCase__    ,    skip_special_tokens=UpperCAmelCase__     )
						assert generated == expected_en
