


"""simple docstring"""







import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					return 1.0 / (1.0 + np.exp(-_outputs     ))




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       np.max(_outputs						,		axis=-1						,		keepdims=lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       np.exp(_outputs - maxes     )
					return shifted_exp / shifted_exp.sum(axis=-1						,		keepdims=lowerCAmelCase_     )




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	snake_case__       :						str					      =    "sigmoid"
	snake_case__       :						List[str]					      =    "softmax"
	snake_case__       :						str					      =    "none"




@add_end_docstrings(
    UpperCamelCase    ,						R"\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `\"default\"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `\"sigmoid\"`: Applies the sigmoid function on the output.\n            - `\"softmax\"`: Applies the softmax function on the output.\n            - `\"none\"`: Does not apply any function on the output.\n    "    ,						)
class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	snake_case__       :						Dict					      =    False
	snake_case__       :						Optional[Any]					      =    ClassificationFunction.NONE




	def __init__(				self    :       Tuple    ,    **UpperCAmelCase__    :       Optional[Any]     ) ->			List[Any]:
						super().__init__(**UpperCAmelCase__     )

						self.check_model_type(
						    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
						    if self.framework == "tf"
						    else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING     )




	def 						UpperCAmelCase_   (				self    :       Optional[Any]    ,    UpperCAmelCase__    :       Dict=None    ,    UpperCAmelCase__    :       Optional[Any]=None    ,    UpperCAmelCase__    :       str=""    ,    **UpperCAmelCase__    :       int     ) ->			Union[str, Any]:
						# Using "" as default argument because we're going to use `top_k=None` in user code to declare
						# "No top_k"
						__SCREAMING_SNAKE_CASE		     =       tokenizer_kwargs

						__SCREAMING_SNAKE_CASE		     =       {}
						if hasattr(self.model.config    ,    "return_all_scores"     ) and return_all_scores is None:
											__SCREAMING_SNAKE_CASE		     =       self.model.config.return_all_scores

						if isinstance(UpperCAmelCase__    ,    UpperCAmelCase__     ) or top_k is None:
											__SCREAMING_SNAKE_CASE		     =       top_k
											__SCREAMING_SNAKE_CASE		     =       False
						elif return_all_scores is not None:
											warnings.warn(
											    "`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of"
											    " `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`."    ,    UpperCAmelCase__    ,    )
											if return_all_scores:
																__SCREAMING_SNAKE_CASE		     =       None
											else:
																__SCREAMING_SNAKE_CASE		     =       1

						if isinstance(UpperCAmelCase__    ,    UpperCAmelCase__     ):
											__SCREAMING_SNAKE_CASE		     =       ClassificationFunction[function_to_apply.upper()]

						if function_to_apply is not None:
											__SCREAMING_SNAKE_CASE		     =       function_to_apply
						return preprocess_params, {}, postprocess_params




	def __call__(				self    :       List[str]    ,    *UpperCAmelCase__    :       Any    ,    **UpperCAmelCase__    :       List[Any]     ) ->			str:
						__SCREAMING_SNAKE_CASE		     =       super().__call__(*UpperCAmelCase__    ,    **UpperCAmelCase__     )
						# TODO try and retrieve it in a nicer way from _sanitize_parameters.
						__SCREAMING_SNAKE_CASE		     =       "top_k" not in kwargs
						if isinstance(args[0]    ,    UpperCAmelCase__     ) and _legacy:
											# This pipeline is odd, and return a list when single item is run
											return [result]
						else:
											return result




	def 						UpperCAmelCase_   (				self    :       Tuple    ,    UpperCAmelCase__    :       str    ,    **UpperCAmelCase__    :       Any     ) ->			Dict[str, GenericTensor]:
						__SCREAMING_SNAKE_CASE		     =       self.framework
						if isinstance(UpperCAmelCase__    ,    UpperCAmelCase__     ):
											return self.tokenizer(**UpperCAmelCase__    ,    return_tensors=UpperCAmelCase__    ,    **UpperCAmelCase__     )
						elif isinstance(UpperCAmelCase__    ,    UpperCAmelCase__     ) and len(UpperCAmelCase__     ) == 1 and isinstance(inputs[0]    ,    UpperCAmelCase__     ) and len(inputs[0]     ) == 2:
											# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
											return self.tokenizer(
											    text=inputs[0][0]    ,    text_pair=inputs[0][1]    ,    return_tensors=UpperCAmelCase__    ,    **UpperCAmelCase__     )
						elif isinstance(UpperCAmelCase__    ,    UpperCAmelCase__     ):
											# This is likely an invalid usage of the pipeline attempting to pass text pairs.
											raise ValueError(
											    "The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"
											    " dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair."     )
						return self.tokenizer(UpperCAmelCase__    ,    return_tensors=UpperCAmelCase__    ,    **UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       List[str]    ,    UpperCAmelCase__    :       int     ) ->			int:
						return self.model(**UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Optional[Any]    ,    UpperCAmelCase__    :       str    ,    UpperCAmelCase__    :       Optional[int]=None    ,    UpperCAmelCase__    :       Union[str, Any]=1    ,    UpperCAmelCase__    :       Any=True     ) ->			Dict:
						# `_legacy` is used to determine if we're running the naked pipeline and in backward
						# compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
						# the more natural result containing the list.
						# Default value before `set_parameters`
						if function_to_apply is None:
											if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
																__SCREAMING_SNAKE_CASE		     =       ClassificationFunction.SIGMOID
											elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
																__SCREAMING_SNAKE_CASE		     =       ClassificationFunction.SOFTMAX
											elif hasattr(self.model.config    ,    "function_to_apply"     ) and function_to_apply is None:
																__SCREAMING_SNAKE_CASE		     =       self.model.config.function_to_apply
											else:
																__SCREAMING_SNAKE_CASE		     =       ClassificationFunction.NONE

						__SCREAMING_SNAKE_CASE		     =       model_outputs["logits"][0]
						__SCREAMING_SNAKE_CASE		     =       outputs.numpy()

						if function_to_apply == ClassificationFunction.SIGMOID:
											__SCREAMING_SNAKE_CASE		     =       sigmoid(UpperCAmelCase__     )
						elif function_to_apply == ClassificationFunction.SOFTMAX:
											__SCREAMING_SNAKE_CASE		     =       softmax(UpperCAmelCase__     )
						elif function_to_apply == ClassificationFunction.NONE:
											__SCREAMING_SNAKE_CASE		     =       outputs
						else:
											raise ValueError(F"""Unrecognized `function_to_apply` argument: {function_to_apply}"""     )

						if top_k == 1 and _legacy:
											return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

						__SCREAMING_SNAKE_CASE		     =       [
						    {"label": self.model.config.idalabel[i], "score": score.item()} for i, score in enumerate(UpperCAmelCase__     )
						]
						if not _legacy:
											dict_scores.sort(key=lambda UpperCAmelCase__     : x["score"]    ,    reverse=UpperCAmelCase__     )
											if top_k is not None:
																__SCREAMING_SNAKE_CASE		     =       dict_scores[:top_k]
						return dict_scores








"""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
