import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast






@require_vision
class 		a__						(					unittest.TestCase					):

					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    tempfile.mkdtemp()

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    BlipImageProcessor()
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel"    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    BlipProcessor(_A,_A    )

							processor.save_pretrained(self.tmpdirname    )

					def 				__UpperCamelCase   (    self      :       Dict,**_A      :       str    ):







							"""simple docstring"""






							return AutoProcessor.from_pretrained(self.tmpdirname,**_A    ).tokenizer

					def 				__UpperCamelCase   (    self      :       Any,**_A      :       int    ):







							"""simple docstring"""






							return AutoProcessor.from_pretrained(self.tmpdirname,**_A    ).image_processor

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							shutil.rmtree(self.tmpdirname    )

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    [np.random.randint(255,size=(3, 30, 400),dtype=np.uinta    )]

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    [Image.fromarray(np.moveaxis(_A,0,-1    )    ) for x in image_inputs]

							return image_inputs

					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    BlipProcessor(tokenizer=self.get_tokenizer(),image_processor=self.get_image_processor()    )
							processor.save_pretrained(self.tmpdirname    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.get_tokenizer(bos_token="(BOS)",eos_token="(EOS)"    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.get_image_processor(do_normalize=_A,padding_value=1.0    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    BlipProcessor.from_pretrained(
							    self.tmpdirname,bos_token="(BOS)",eos_token="(EOS)",do_normalize=_A,padding_value=1.0    )

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

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

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.get_image_processor()
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.get_tokenizer()

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    BlipProcessor(tokenizer=_A,image_processor=_A    )

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    image_processor(_A,return_tensors="np"    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    processor(images=_A,return_tensors="np"    )

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

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.get_image_processor()
							SCREAMING_SNAKE_CASE_  :		str				      =    self.get_tokenizer()

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    BlipProcessor(tokenizer=_A,image_processor=_A    )

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    "lower newer"

							SCREAMING_SNAKE_CASE_  :		int				      =    processor(text=_A    )

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    tokenizer(_A,return_token_type_ids=_A    )

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

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.get_image_processor()
							SCREAMING_SNAKE_CASE_  :		str				      =    self.get_tokenizer()

							SCREAMING_SNAKE_CASE_  :		str				      =    BlipProcessor(tokenizer=_A,image_processor=_A    )

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    "lower newer"
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE_  :		Any				      =    processor(text=_A,images=_A    )

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

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

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.get_image_processor()
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.get_tokenizer()

							SCREAMING_SNAKE_CASE_  :		str				      =    BlipProcessor(tokenizer=_A,image_processor=_A    )

							SCREAMING_SNAKE_CASE_  :		str				      =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    processor.batch_decode(_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    tokenizer.batch_decode(_A    )

							self.assertListEqual(_A,_A    )






					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    self.get_image_processor()
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.get_tokenizer()

							SCREAMING_SNAKE_CASE_  :		int				      =    BlipProcessor(tokenizer=_A,image_processor=_A    )

							SCREAMING_SNAKE_CASE_  :		str				      =    "lower newer"
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    processor(text=_A,images=_A    )

							# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
							self.assertListEqual(list(inputs.keys()    ),["pixel_values", "input_ids", "attention_mask"]    )








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 					snake_case_						(   lowerCAmelCase_				:  Dict				,				lowerCAmelCase_				:  Dict				,				lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  List[Any]=None				,				lowerCAmelCase_				:  Any=None				,				lowerCAmelCase_				:  List[Any]=None				,				lowerCAmelCase_				:  Optional[Any]=None				,				lowerCAmelCase_				:  Tuple=None				,				):
							if attention_mask is None:
														__lowercase					:							Tuple			=   input_ids.ne(config.pad_token_id					)
							if decoder_attention_mask is None:
														__lowercase					:							Any			=   decoder_input_ids.ne(config.pad_token_id					)
							if head_mask is None:
														__lowercase					:							Any			=   torch.ones(config.encoder_layers				,				config.encoder_attention_heads				,				device=lowerCAmelCase_					)
							if decoder_head_mask is None:
														__lowercase					:							List[Any]			=   torch.ones(config.decoder_layers				,				config.decoder_attention_heads				,				device=lowerCAmelCase_					)
							if cross_attn_head_mask is None:
														__lowercase					:							List[Any]			=   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 						lowerCAmelCase   :

					'''simple docstring'''

					def __init__(     self :		Union[str, Any]						,   __a :		str						,   __a :		Tuple=13						,   __a :		List[Any]=7						,   __a :		Any=True						,   __a :		List[str]=False						,   __a :		Optional[Any]=99						,   __a :		Tuple=16						,   __a :		int=2						,   __a :		Optional[Any]=4						,   __a :		int=4						,   __a :		Any="relu"						,   __a :		Optional[int]=0.1						,   __a :		List[str]=0.1						,   __a :		Dict=0.0						,   __a :		List[str]=0.0						,   __a :		Union[str, Any]=20						,   __a :		str=2						,   __a :		str=1						,   __a :		Optional[int]=0						,   )	->     Optional[int]:


												"""simple docstring"""

												__lowercase					:							Any			=   parent
												__lowercase					:							Tuple			=   batch_size
												__lowercase					:							Any			=   seq_length
												__lowercase					:							Tuple			=   is_training
												__lowercase					:							Optional[Any]			=   use_labels
												__lowercase					:							Dict			=   vocab_size
												__lowercase					:							Optional[Any]			=   hidden_size
												__lowercase					:							Dict			=   num_hidden_layers
												__lowercase					:							Dict			=   num_attention_heads
												__lowercase					:							Dict			=   intermediate_size
												__lowercase					:							Union[str, Any]			=   hidden_act
												__lowercase					:							Union[str, Any]			=   hidden_dropout_prob
												__lowercase					:							Tuple			=   attention_probs_dropout_prob
												__lowercase					:							Tuple			=   encoder_layerdrop
												__lowercase					:							List[str]			=   decoder_layerdrop
												__lowercase					:							Any			=   max_position_embeddings
												__lowercase					:							Any			=   eos_token_id
												__lowercase					:							Dict			=   pad_token_id
												__lowercase					:							List[str]			=   bos_token_id



					def 					lowerCAmelCase (     self :		Union[str, Any]							)	->     Optional[int]:


												"""simple docstring"""

												__lowercase					:							Tuple			=   ids_tensor([self.batch_size, self.seq_length]						,   self.vocab_size							)
												__lowercase					:							str			=   self.eos_token_id  # Eos Token
												__lowercase					:							str			=   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
												__lowercase					:							Tuple			=   input_ids.clamp(self.pad_token_id + 1							)
												__lowercase					:							Optional[int]			=   decoder_input_ids.clamp(self.pad_token_id + 1							)

												__lowercase					:							List[str]			=   self.get_config()
												__lowercase					:							str			=   prepare_mam_aaa_inputs_dict(__a						,   __a						,   __a							)
												return config, inputs_dict



					def 					lowerCAmelCase (     self :		str							)	->     Dict:


												"""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 					lowerCAmelCase (     self :		Dict							)	->     str:


												"""simple docstring"""

												__lowercase	,	__lowercase					:							int			=   self.prepare_config_and_inputs()
												return config, inputs_dict



					def 					lowerCAmelCase (     self :		int						,   __a :		str						,   __a :		str							)	->     int:


												"""simple docstring"""

												__lowercase					:							Optional[Any]			=   MaMaaaModel(config=__a							).get_decoder().to(__a							).eval()
												__lowercase					:							List[str]			=   inputs_dict["""input_ids"""]
												__lowercase					:							Dict			=   inputs_dict["""attention_mask"""]
												__lowercase					:							List[Any]			=   inputs_dict["""head_mask"""]

												# first forward pass
												__lowercase					:							List[str]			=   model(__a						,   attention_mask=__a						,   head_mask=__a						,   use_cache=__a							)

												__lowercase	,	__lowercase					:							str			=   outputs.to_tuple()

												# create hypothetical multiple next token and extent to next_input_ids
												__lowercase					:							Union[str, Any]			=   ids_tensor((self.batch_size, 3)						,   config.vocab_size							)
												__lowercase					:							str			=   ids_tensor((self.batch_size, 3)						,   2							)

												# append to next input_ids and
												__lowercase					:							List[Any]			=   torch.cat([input_ids, next_tokens]						,   dim=-1							)
												__lowercase					:							Optional[Any]			=   torch.cat([attention_mask, next_attn_mask]						,   dim=-1							)

												__lowercase					:							Optional[int]			=   model(__a						,   attention_mask=__a							)["""last_hidden_state"""]
												__lowercase					:							Union[str, Any]			=   model(__a						,   attention_mask=__a						,   past_key_values=__a							)[
												    """last_hidden_state"""
												]

												# select random slice
												__lowercase					:							Optional[int]			=   ids_tensor((1,)						,   output_from_past.shape[-1]							).item()
												__lowercase					:							int			=   output_from_no_past[:, -3:, random_slice_idx].detach()
												__lowercase					:							Optional[Any]			=   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(__a						,   __a						,   atol=1E-2							)							)



					def 					lowerCAmelCase (     self :		List[str]						,   __a :		Tuple						,   __a :		List[str]							)	->     str:


												"""simple docstring"""

												__lowercase					:							Dict			=   MaMaaaModel(config=__a							).to(__a							).eval()
												__lowercase					:							Any			=   model(**__a							)

												__lowercase					:							str			=   outputs.encoder_last_hidden_state
												__lowercase					:							Optional[Any]			=   outputs.last_hidden_state

												with tempfile.TemporaryDirectory() as tmpdirname:
																			__lowercase					:							str			=   model.get_encoder()
																			encoder.save_pretrained(__a							)
																			__lowercase					:							List[Any]			=   MaMaaaEncoder.from_pretrained(__a							).to(__a							)

												__lowercase					:							Tuple			=   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:
																			__lowercase					:							Tuple			=   model.get_decoder()
																			decoder.save_pretrained(__a							)
																			__lowercase					:							Tuple			=   MaMaaaDecoder.from_pretrained(__a							).to(__a							)

												__lowercase					:							Tuple			=   decoder(
												    input_ids=inputs_dict["""decoder_input_ids"""]						,   attention_mask=inputs_dict["""decoder_attention_mask"""]						,   encoder_hidden_states=__a						,   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 						lowerCAmelCase   (       __a					,				__a					,				__a					,				unittest.TestCase	):

					'''simple docstring'''

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



					def 					lowerCAmelCase (     self :		str						,   __a :		Union[str, Any]						,   __a :		Optional[int]						,   __a :		List[Any]						,   __a :		Tuple						,   __a :		Optional[int]							)	->     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 					lowerCAmelCase (     self :		Union[str, Any]							)	->     List[Any]:


												"""simple docstring"""

												__lowercase					:							int			=   MaMaaaModelTester(self							)
												__lowercase					:							Union[str, Any]			=   ConfigTester(self						,   config_class=__a							)



					def 					lowerCAmelCase (     self :		Any							)	->     str:


												"""simple docstring"""

												self.config_tester.run_common_tests()



					def 					lowerCAmelCase (     self :		List[Any]							)	->     Any:


												"""simple docstring"""

												__lowercase	,	__lowercase					:							Any			=   self.model_tester.prepare_config_and_inputs()
												for model_class in self.all_model_classes:
																			__lowercase					:							List[str]			=   model_class(__a							)

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



					def 					lowerCAmelCase (     self :		int							)	->     List[Any]:


												"""simple docstring"""

												__lowercase					:							List[Any]			=   self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_decoder_model_past_large_inputs(*__a							)



					def 					lowerCAmelCase (     self :		Union[str, Any]							)	->     Optional[Any]:


												"""simple docstring"""

												__lowercase					:							Optional[int]			=   self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.check_encoder_decoder_model_standalone(*__a							)



					def 					lowerCAmelCase (     self :		int							)	->     Any:


												"""simple docstring"""

												__lowercase	,	__lowercase					:							Optional[Any]			=   self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
																			__lowercase					:							Union[str, Any]			=   model_class(__a							)
																			model.to(__a							)
																			model.eval()

																			__lowercase					:							Any			=   copy.deepcopy(self._prepare_for_class(__a						,   __a							)							)

																			if not self.is_encoder_decoder:
																										__lowercase					:							int			=   inputs["""input_ids"""]
																										del inputs["input_ids"]
																			else:
																										__lowercase					:							Optional[int]			=   inputs["""input_ids"""]
																										__lowercase					:							Optional[Any]			=   inputs.get("""decoder_input_ids"""						,   __a							)
																										del inputs["input_ids"]
																										inputs.pop("""decoder_input_ids"""						,   __a							)

																			__lowercase					:							Union[str, Any]			=   model.get_input_embeddings()
																			if not self.is_encoder_decoder:
																										__lowercase					:							Dict			=   wte(__a							)
																			else:
																										__lowercase					:							str			=   wte(__a							)
																										__lowercase					:							Union[str, Any]			=   wte(__a							)

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



					def 					lowerCAmelCase (     self :		Optional[int]							)	->     Optional[int]:


												"""simple docstring"""

												__lowercase	,	__lowercase					:							str			=   self.model_tester.prepare_config_and_inputs()
												__lowercase					:							List[str]			=   input_dict["""input_ids"""]
												__lowercase					:							Optional[int]			=   input_ids.ne(1							).to(__a							)
												__lowercase					:							List[Any]			=   MaMaaaForConditionalGeneration(__a							).eval().to(__a							)
												if torch_device == "cuda":
																			model.half()
												model.generate(__a						,   attention_mask=__a							)
												model.generate(num_beams=4						,   do_sample=__a						,   early_stopping=__a						,   num_return_sequences=3							)







def 					snake_case_						(   lowerCAmelCase_				:  Optional[Any]					):
							return torch.tensor(lowerCAmelCase_				,				dtype=torch.long				,				device=lowerCAmelCase_					)


lowerCamelCase       :       Dict        =				1E-4



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

					'''simple docstring'''

					@cached_property
					def 					lowerCAmelCase (     self :		List[str]							)	->     Tuple:


												"""simple docstring"""

												return MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""							)



					def 					lowerCAmelCase (     self :		Any							)	->     Tuple:


												"""simple docstring"""

												__lowercase					:							List[str]			=   MaMaaaModel.from_pretrained("""facebook/m2m100_418M"""							).to(__a							)
												__lowercase					:							Union[str, Any]			=   _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]							)
												__lowercase					:							int			=   _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]							)
												__lowercase					:							int			=   prepare_mam_aaa_inputs_dict(model.config						,   __a						,   __a							)
												with torch.no_grad():
																			__lowercase					:							int			=   model(**__a							)[0]
												__lowercase					:							int			=   torch.Size((1, 11, 1024)							)
												self.assertEqual(output.shape						,   __a							)
												# change to expected output here
												__lowercase					:							Dict			=   torch.tensor(
												    [[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]]						,   device=__a							)
												self.assertTrue(torch.allclose(output[:, :3, :3]						,   __a						,   atol=__a							)							)



					def 					lowerCAmelCase (     self :		Union[str, Any]							)	->     Optional[Any]:


												"""simple docstring"""

												__lowercase					:							Tuple			=   MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""							).to(__a							)

												# change to intended input
												__lowercase					:							Any			=   _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]							)
												__lowercase					:							Union[str, Any]			=   _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]							)
												__lowercase					:							Tuple			=   prepare_mam_aaa_inputs_dict(model.config						,   __a						,   __a							)
												with torch.no_grad():
																			__lowercase					:							Optional[Any]			=   model(**__a							)[0]
												__lowercase					:							Tuple			=   torch.Size((1, 11, model.config.vocab_size)							)
												self.assertEqual(output.shape						,   __a							)
												# change to expected output here
												__lowercase					:							Union[str, Any]			=   torch.tensor(
												    [[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]]						,   device=__a							)
												self.assertTrue(torch.allclose(output[:, :3, :3]						,   __a						,   atol=__a							)							)



					def 					lowerCAmelCase (     self :		Optional[int]							)	->     int:


												"""simple docstring"""

												__lowercase					:							List[str]			=   MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""							).to(__a							)
												__lowercase					:							Tuple			=   MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""						,   src_lang="""fr"""						,   tgt_lang="""en"""							)

												__lowercase					:							Dict			=   [
												    """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
												__lowercase					:							Union[str, Any]			=   tokenizer(__a						,   padding=__a						,   return_tensors="""pt"""							)

												__lowercase					:							Dict			=   model.generate(
												    input_ids=dct["""input_ids"""].to(__a							)						,   attention_mask=dct["""attention_mask"""].to(__a							)						,   num_beams=5						,   forced_bos_token_id=tokenizer.get_lang_id("""en"""							)						,   )

												__lowercase					:							Any			=   [
												    """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.""",
												]

												__lowercase					:							Dict			=   tokenizer.batch_decode(
												    hypotheses_batch.tolist()						,   clean_up_tokenization_spaces=__a						,   skip_special_tokens=__a							)
												assert generated == expected_en