


"""simple docstring"""







import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
a__       :							Dict        =  {'''LayoutLMv2Config''', '''LayoutLMv3Config'''}




@is_pipeline_test
class 				UpperCamelCase_			(				unittest.TestCase):

	"""simple docstring"""





	snake_case__       :						str					      =    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
	snake_case__       :						Tuple					      =    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

	if model_mapping is not None:
		snake_case__       :						List[Any]					      =    {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
	if tf_model_mapping is not None:
		snake_case__       :						Dict					      =    {
		    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
		}




	@require_torch
	def 						UpperCAmelCase_   (				self    :       str     ) ->			Tuple:
						__SCREAMING_SNAKE_CASE		     =       pipeline(
						    task="text-classification"    ,    model="hf-internal-testing/tiny-random-distilbert"    ,    framework="pt"     )

						__SCREAMING_SNAKE_CASE		     =       text_classifier("This is great !"     )
						self.assertEqual(nested_simplify(UpperCAmelCase__     )    ,    [{"label": "LABEL_0", "score": 0.504}]     )

						__SCREAMING_SNAKE_CASE		     =       text_classifier("This is great !"    ,    top_k=2     )
						self.assertEqual(
						    nested_simplify(UpperCAmelCase__     )    ,    [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]     )

						__SCREAMING_SNAKE_CASE		     =       text_classifier(["This is great !", "This is bad"]    ,    top_k=2     )
						self.assertEqual(
						    nested_simplify(UpperCAmelCase__     )    ,    [
						        [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
						        [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
						    ]    ,    )

						__SCREAMING_SNAKE_CASE		     =       text_classifier("This is great !"    ,    top_k=1     )
						self.assertEqual(nested_simplify(UpperCAmelCase__     )    ,    [{"label": "LABEL_0", "score": 0.504}]     )

						# Legacy behavior
						__SCREAMING_SNAKE_CASE		     =       text_classifier("This is great !"    ,    return_all_scores=UpperCAmelCase__     )
						self.assertEqual(nested_simplify(UpperCAmelCase__     )    ,    [{"label": "LABEL_0", "score": 0.504}]     )

						__SCREAMING_SNAKE_CASE		     =       text_classifier("This is great !"    ,    return_all_scores=UpperCAmelCase__     )
						self.assertEqual(
						    nested_simplify(UpperCAmelCase__     )    ,    [[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]]     )

						__SCREAMING_SNAKE_CASE		     =       text_classifier(["This is great !", "Something else"]    ,    return_all_scores=UpperCAmelCase__     )
						self.assertEqual(
						    nested_simplify(UpperCAmelCase__     )    ,    [
						        [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
						        [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
						    ]    ,    )

						__SCREAMING_SNAKE_CASE		     =       text_classifier(["This is great !", "Something else"]    ,    return_all_scores=UpperCAmelCase__     )
						self.assertEqual(
						    nested_simplify(UpperCAmelCase__     )    ,    [
						        {"label": "LABEL_0", "score": 0.504},
						        {"label": "LABEL_0", "score": 0.504},
						    ]    ,    )




	@require_torch
	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			Optional[int]:
						import torch

						__SCREAMING_SNAKE_CASE		     =       pipeline(
						    task="text-classification"    ,    model="hf-internal-testing/tiny-random-distilbert"    ,    framework="pt"    ,    device=torch.device("cpu"     )    ,    )

						__SCREAMING_SNAKE_CASE		     =       text_classifier("This is great !"     )
						self.assertEqual(nested_simplify(UpperCAmelCase__     )    ,    [{"label": "LABEL_0", "score": 0.504}]     )




	@require_tf
	def 						UpperCAmelCase_   (				self    :       Dict     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       pipeline(
						    task="text-classification"    ,    model="hf-internal-testing/tiny-random-distilbert"    ,    framework="tf"     )

						__SCREAMING_SNAKE_CASE		     =       text_classifier("This is great !"     )
						self.assertEqual(nested_simplify(UpperCAmelCase__     )    ,    [{"label": "LABEL_0", "score": 0.504}]     )




	@slow
	@require_torch
	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			int:
						__SCREAMING_SNAKE_CASE		     =       pipeline("text-classification"     )

						__SCREAMING_SNAKE_CASE		     =       text_classifier("This is great !"     )
						self.assertEqual(nested_simplify(UpperCAmelCase__     )    ,    [{"label": "POSITIVE", "score": 1.0}]     )
						__SCREAMING_SNAKE_CASE		     =       text_classifier("This is bad !"     )
						self.assertEqual(nested_simplify(UpperCAmelCase__     )    ,    [{"label": "NEGATIVE", "score": 1.0}]     )
						__SCREAMING_SNAKE_CASE		     =       text_classifier("Birds are a type of animal"     )
						self.assertEqual(nested_simplify(UpperCAmelCase__     )    ,    [{"label": "POSITIVE", "score": 0.988}]     )




	@slow
	@require_tf
	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       pipeline("text-classification"    ,    framework="tf"     )

						__SCREAMING_SNAKE_CASE		     =       text_classifier("This is great !"     )
						self.assertEqual(nested_simplify(UpperCAmelCase__     )    ,    [{"label": "POSITIVE", "score": 1.0}]     )
						__SCREAMING_SNAKE_CASE		     =       text_classifier("This is bad !"     )
						self.assertEqual(nested_simplify(UpperCAmelCase__     )    ,    [{"label": "NEGATIVE", "score": 1.0}]     )
						__SCREAMING_SNAKE_CASE		     =       text_classifier("Birds are a type of animal"     )
						self.assertEqual(nested_simplify(UpperCAmelCase__     )    ,    [{"label": "POSITIVE", "score": 0.988}]     )




	def 						UpperCAmelCase_   (				self    :       Any    ,    UpperCAmelCase__    :       str    ,    UpperCAmelCase__    :       Any    ,    UpperCAmelCase__    :       Tuple     ) ->			Dict:
						__SCREAMING_SNAKE_CASE		     =       TextClassificationPipeline(model=UpperCAmelCase__    ,    tokenizer=UpperCAmelCase__     )
						return text_classifier, ["HuggingFace is in", "This is another test"]




	def 						UpperCAmelCase_   (				self    :       str    ,    UpperCAmelCase__    :       Optional[int]    ,    UpperCAmelCase__    :       Optional[int]     ) ->			Dict:
						__SCREAMING_SNAKE_CASE		     =       text_classifier.model
						# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
						__SCREAMING_SNAKE_CASE		     =       "HuggingFace is in"
						__SCREAMING_SNAKE_CASE		     =       text_classifier(UpperCAmelCase__     )

						self.assertEqual(nested_simplify(UpperCAmelCase__     )    ,    [{"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}]     )
						self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()     )

						__SCREAMING_SNAKE_CASE		     =       ["HuggingFace is in ", "Paris is in France"]
						__SCREAMING_SNAKE_CASE		     =       text_classifier(UpperCAmelCase__     )
						self.assertEqual(
						    nested_simplify(UpperCAmelCase__     )    ,    [{"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}, {"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}]    ,    )
						self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()     )
						self.assertTrue(outputs[1]["label"] in model.config.idalabel.values()     )

						# Forcing to get all results with `top_k=None`
						# This is NOT the legacy format
						__SCREAMING_SNAKE_CASE		     =       text_classifier(UpperCAmelCase__    ,    top_k=UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       len(model.config.idalabel.values()     )
						self.assertEqual(
						    nested_simplify(UpperCAmelCase__     )    ,    [[{"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}] * N, [{"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}] * N]    ,    )

						__SCREAMING_SNAKE_CASE		     =       {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
						__SCREAMING_SNAKE_CASE		     =       text_classifier(UpperCAmelCase__     )
						self.assertEqual(
						    nested_simplify(UpperCAmelCase__     )    ,    {"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}    ,    )
						self.assertTrue(outputs["label"] in model.config.idalabel.values()     )

						# This might be used a text pair, but tokenizer + pipe interaction
						# makes it hard to understand that it's not using the pair properly
						# https://github.com/huggingface/transformers/issues/17305
						# We disabled this usage instead as it was outputting wrong outputs.
						__SCREAMING_SNAKE_CASE		     =       [["HuggingFace is in ", "Paris is in France"]]
						with self.assertRaises(UpperCAmelCase__     ):
											text_classifier(UpperCAmelCase__     )

						# This used to be valid for doing text pairs
						# We're keeping it working because of backward compatibility
						__SCREAMING_SNAKE_CASE		     =       text_classifier([[["HuggingFace is in ", "Paris is in France"]]]     )
						self.assertEqual(
						    nested_simplify(UpperCAmelCase__     )    ,    [{"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}]    ,    )
						self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()     )




"""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
