






import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
	import transformers







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int     ):
	random.seed(snake_case_     )
	np.random.seed(snake_case_     )
	torch.manual_seed(snake_case_     )
	torch.cuda.manual_seed_all(snake_case_     )
	# ^^ safe to call this function even if cuda is not available





class 							SCREAMING_SNAKE_CASE__		:





					"""simple docstring"""






					def __init__(      self  :						Dict							,	__A  :						Iterable[torch.nn.Parameter]							,	__A  :						float = 0.9_9_9_9							,	__A  :						float = 0.0							,	__A  :						int = 0							,	__A  :						bool = False							,	__A  :						Union[float, int] = 1.0							,	__A  :						Union[float, int] = 2 / 3							,	__A  :						Optional[Any] = None							,	__A  :						Dict[str, Any] = None							,	**__A  :						List[str]							,	):

						if isinstance(__A							,	torch.nn.Module ):
							snake_case__				:   Tuple     =						(
							    "Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. "
							    "Please pass the parameters of the module instead."
							)
							deprecate(
							    "passing a `torch.nn.Module` to `ExponentialMovingAverage`"							,	"1.0.0"							,	__A							,	standard_warn=__A							,	)
							snake_case__				:   Union[str, Any]     =						parameters.parameters()

							# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
							snake_case__				:   List[str]     =						True

						if kwargs.get("max_value"							,	__A ) is not None:
							snake_case__				:   Any     =						"The `max_value` argument is deprecated. Please use `decay` instead."
							deprecate("max_value"							,	"1.0.0"							,	__A							,	standard_warn=__A )
							snake_case__				:   str     =						kwargs["max_value"]

						if kwargs.get("min_value"							,	__A ) is not None:
							snake_case__				:   Dict     =						"The `min_value` argument is deprecated. Please use `min_decay` instead."
							deprecate("min_value"							,	"1.0.0"							,	__A							,	standard_warn=__A )
							snake_case__				:   Any     =						kwargs["min_value"]

						snake_case__				:   Tuple     =						list(__A )
						snake_case__				:   str     =						[p.clone().detach() for p in parameters]

						if kwargs.get("device"							,	__A ) is not None:
							snake_case__				:   Dict     =						"The `device` argument is deprecated. Please use `to` instead."
							deprecate("device"							,	"1.0.0"							,	__A							,	standard_warn=__A )
							self.to(device=kwargs["device"] )

						snake_case__				:   List[str]     =						None

						snake_case__				:   Tuple     =						decay
						snake_case__				:   Dict     =						min_decay
						snake_case__				:   int     =						update_after_step
						snake_case__				:   Any     =						use_ema_warmup
						snake_case__				:   Union[str, Any]     =						inv_gamma
						snake_case__				:   Optional[int]     =						power
						snake_case__				:   List[Any]     =						0
						snake_case__				:   Optional[Any]     =						None  # set in `step()`

						snake_case__				:   List[Any]     =						model_cls
						snake_case__				:   int     =						model_config



					@classmethod
					def  _lowercase  (      cls  :						Dict							,	__A  :						Optional[int]							,	__A  :						int ):
						snake_case__,		snake_case__				:   Any     =						model_cls.load_config(__A							,	return_unused_kwargs=__A )
						snake_case__				:   Tuple     =						model_cls.from_pretrained(__A )

						snake_case__				:   List[Any]     =						cls(model.parameters()							,	model_cls=__A							,	model_config=model.config )

						ema_model.load_state_dict(__A )
						return ema_model



					def  _lowercase  (      self  :						int							,	__A  :						int ):
						if self.model_cls is None:
							raise ValueError("`save_pretrained` can only be used if `model_cls` was defined at __init__." )

						if self.model_config is None:
							raise ValueError("`save_pretrained` can only be used if `model_config` was defined at __init__." )

						snake_case__				:   Any     =						self.model_cls.from_config(self.model_config )
						snake_case__				:   Union[str, Any]     =						self.state_dict()
						state_dict.pop("shadow_params"							,	__A )

						model.register_to_config(**__A )
						self.copy_to(model.parameters() )
						model.save_pretrained(__A )



					def  _lowercase  (      self  :						Tuple							,	__A  :						int ):
						snake_case__				:   str     =						max(0							,	optimization_step - self.update_after_step - 1 )

						if step <= 0:
							return 0.0

						if self.use_ema_warmup:
							snake_case__				:   int     =						1 - (1 + step / self.inv_gamma) ** -self.power
						else:
							snake_case__				:   Union[str, Any]     =						(1 + step) / (1_0 + step)

						snake_case__				:   List[Any]     =						min(__A							,	self.decay )
						# make sure decay is not smaller than min_decay
						snake_case__				:   Union[str, Any]     =						max(__A							,	self.min_decay )
						return cur_decay_value



					@torch.no_grad()
					def  _lowercase  (      self  :						Any							,	__A  :						Iterable[torch.nn.Parameter] ):
						if isinstance(__A							,	torch.nn.Module ):
							snake_case__				:   int     =						(
							    "Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. "
							    "Please pass the parameters of the module instead."
							)
							deprecate(
							    "passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"							,	"1.0.0"							,	__A							,	standard_warn=__A							,	)
							snake_case__				:   str     =						parameters.parameters()

						snake_case__				:   List[Any]     =						list(__A )

						self.optimization_step += 1

						# Compute the decay factor for the exponential moving average.
						snake_case__				:   str     =						self.get_decay(self.optimization_step )
						snake_case__				:   Optional[Any]     =						decay
						snake_case__				:   str     =						1 - decay

						snake_case__				:   Tuple     =						contextlib.nullcontext
						if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
							import deepspeed

						for s_param, param in zip(self.shadow_params							,	__A ):
							if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
								snake_case__				:   Any     =						deepspeed.zero.GatheredParameters(__A							,	modifier_rank=__A )

							with context_manager():
								if param.requires_grad:
									s_param.sub_(one_minus_decay * (s_param - param) )
								else:
									s_param.copy_(__A )



					def  _lowercase  (      self  :						str							,	__A  :						Iterable[torch.nn.Parameter] ):
						snake_case__				:   Union[str, Any]     =						list(__A )
						for s_param, param in zip(self.shadow_params							,	__A ):
							param.data.copy_(s_param.to(param.device ).data )



					def  _lowercase  (      self  :						Optional[int]							,	__A  :						Tuple=None							,	__A  :						Tuple=None ):
						snake_case__				:   Union[str, Any]     =						[
						    p.to(device=__A							,	dtype=__A ) if p.is_floating_point() else p.to(device=__A )
						    for p in self.shadow_params
						]



					def  _lowercase  (      self  :						Dict ):
						return {
						    "decay": self.decay,
						    "min_decay": self.min_decay,
						    "optimization_step": self.optimization_step,
						    "update_after_step": self.update_after_step,
						    "use_ema_warmup": self.use_ema_warmup,
						    "inv_gamma": self.inv_gamma,
						    "power": self.power,
						    "shadow_params": self.shadow_params,
						}



					def  _lowercase  (      self  :						Tuple							,	__A  :						Iterable[torch.nn.Parameter] ):
						snake_case__				:   Any     =						[param.detach().cpu().clone() for param in parameters]



					def  _lowercase  (      self  :						str							,	__A  :						Iterable[torch.nn.Parameter] ):
						if self.temp_stored_params is None:
							raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`" )
						for c_param, param in zip(self.temp_stored_params							,	__A ):
							param.data.copy_(c_param.data )

						# Better memory-wise.
						snake_case__				:   Dict     =						None






					def  _lowercase  (      self  :						List[str]							,	__A  :						dict ):
						snake_case__				:   Tuple     =						copy.deepcopy(__A )

						snake_case__				:   List[Any]     =						state_dict.get("decay"							,	self.decay )
						if self.decay < 0.0 or self.decay > 1.0:
							raise ValueError("Decay must be between 0 and 1" )

						snake_case__				:   Optional[Any]     =						state_dict.get("min_decay"							,	self.min_decay )
						if not isinstance(self.min_decay							,	__A ):
							raise ValueError("Invalid min_decay" )

						snake_case__				:   int     =						state_dict.get("optimization_step"							,	self.optimization_step )
						if not isinstance(self.optimization_step							,	__A ):
							raise ValueError("Invalid optimization_step" )

						snake_case__				:   List[str]     =						state_dict.get("update_after_step"							,	self.update_after_step )
						if not isinstance(self.update_after_step							,	__A ):
							raise ValueError("Invalid update_after_step" )

						snake_case__				:   Dict     =						state_dict.get("use_ema_warmup"							,	self.use_ema_warmup )
						if not isinstance(self.use_ema_warmup							,	__A ):
							raise ValueError("Invalid use_ema_warmup" )

						snake_case__				:   Any     =						state_dict.get("inv_gamma"							,	self.inv_gamma )
						if not isinstance(self.inv_gamma							,	(float, int) ):
							raise ValueError("Invalid inv_gamma" )

						snake_case__				:   Union[str, Any]     =						state_dict.get("power"							,	self.power )
						if not isinstance(self.power							,	(float, int) ):
							raise ValueError("Invalid power" )

						snake_case__				:   List[str]     =						state_dict.get("shadow_params"							,	__A )
						if shadow_params is not None:
							snake_case__				:   Dict     =						shadow_params
							if not isinstance(self.shadow_params							,	__A ):
								raise ValueError("shadow_params must be a list" )
							if not all(isinstance(__A							,	torch.Tensor ) for p in self.shadow_params ):
								raise ValueError("shadow_params must all be Tensors" )











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





					"""simple docstring"""






					def __init__(      self  :						List[str]							,	__A  :						Any							,	__A  :						List[str]=1_3							,	__A  :						List[Any]=7							,	__A  :						Union[str, Any]=True							,	__A  :						Union[str, Any]=False							,	__A  :						str=9_9							,	__A  :						Optional[Any]=1_6							,	__A  :						Optional[Any]=2							,	__A  :						Any=4							,	__A  :						List[Any]=4							,	__A  :						int="relu"							,	__A  :						Optional[int]=0.1							,	__A  :						Tuple=0.1							,	__A  :						Optional[int]=0.0							,	__A  :						Optional[Any]=0.0							,	__A  :						List[Any]=2_0							,	__A  :						Optional[Any]=2							,	__A  :						int=1							,	__A  :						Union[str, Any]=0							,	):
						snake_case__				:   Optional[Any]     =						parent
						snake_case__				:   List[str]     =						batch_size
						snake_case__				:   Union[str, Any]     =						seq_length
						snake_case__				:   Optional[Any]     =						is_training
						snake_case__				:   List[str]     =						use_labels
						snake_case__				:   Tuple     =						vocab_size
						snake_case__				:   Optional[Any]     =						hidden_size
						snake_case__				:   Union[str, Any]     =						num_hidden_layers
						snake_case__				:   List[Any]     =						num_attention_heads
						snake_case__				:   Tuple     =						intermediate_size
						snake_case__				:   str     =						hidden_act
						snake_case__				:   Optional[Any]     =						hidden_dropout_prob
						snake_case__				:   int     =						attention_probs_dropout_prob
						snake_case__				:   int     =						encoder_layerdrop
						snake_case__				:   Tuple     =						decoder_layerdrop
						snake_case__				:   List[str]     =						max_position_embeddings
						snake_case__				:   Tuple     =						eos_token_id
						snake_case__				:   Dict     =						pad_token_id
						snake_case__				:   str     =						bos_token_id



					def  _lowercase  (      self  :						Tuple ):
						snake_case__				:   List[Any]     =						ids_tensor([self.batch_size, self.seq_length]							,	self.vocab_size )
						snake_case__				:   Union[str, Any]     =						self.eos_token_id  # Eos Token
						snake_case__				:   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
						snake_case__				:   int     =						input_ids.clamp(self.pad_token_id + 1 )
						snake_case__				:   Optional[Any]     =						decoder_input_ids.clamp(self.pad_token_id + 1 )

						snake_case__				:   Union[str, Any]     =						self.get_config()
						snake_case__				:   Union[str, Any]     =						prepare_mam_aaa_inputs_dict(__A							,	__A							,	__A )
						return config, inputs_dict



					def  _lowercase  (      self  :						Dict ):
						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  _lowercase  (      self  :						List[str] ):
						snake_case__,		snake_case__				:   Any     =						self.prepare_config_and_inputs()
						return config, inputs_dict



					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						int							,	__A  :						Dict ):
						snake_case__				:   Union[str, Any]     =						MaMaaaModel(config=__A ).get_decoder().to(__A ).eval()
						snake_case__				:   List[Any]     =						inputs_dict["input_ids"]
						snake_case__				:   Optional[Any]     =						inputs_dict["attention_mask"]
						snake_case__				:   Union[str, Any]     =						inputs_dict["head_mask"]

						# first forward pass
						snake_case__				:   Dict     =						model(__A							,	attention_mask=__A							,	head_mask=__A							,	use_cache=__A )

						snake_case__,		snake_case__				:   Dict     =						outputs.to_tuple()

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

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

						snake_case__				:   Tuple     =						model(__A							,	attention_mask=__A )["last_hidden_state"]
						snake_case__				:   Tuple     =						model(__A							,	attention_mask=__A							,	past_key_values=__A )[
						    "last_hidden_state"
						]

						# select random slice
						snake_case__				:   Optional[Any]     =						ids_tensor((1,)							,	output_from_past.shape[-1] ).item()
						snake_case__				:   Optional[Any]     =						output_from_no_past[:, -3:, random_slice_idx].detach()
						snake_case__				:   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  _lowercase  (      self  :						str							,	__A  :						Dict							,	__A  :						Optional[Any] ):
						snake_case__				:   Union[str, Any]     =						MaMaaaModel(config=__A ).to(__A ).eval()
						snake_case__				:   Union[str, Any]     =						model(**__A )

						snake_case__				:   Tuple     =						outputs.encoder_last_hidden_state
						snake_case__				:   Union[str, Any]     =						outputs.last_hidden_state

						with tempfile.TemporaryDirectory() as tmpdirname:
							snake_case__				:   Dict     =						model.get_encoder()
							encoder.save_pretrained(__A )
							snake_case__				:   Any     =						MaMaaaEncoder.from_pretrained(__A ).to(__A )

						snake_case__				:   List[str]     =						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:
							snake_case__				:   Dict     =						model.get_decoder()
							decoder.save_pretrained(__A )
							snake_case__				:   Optional[Any]     =						MaMaaaDecoder.from_pretrained(__A ).to(__A )

						snake_case__				:   List[str]     =						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 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     unittest.TestCase		):





					"""simple docstring"""






					a_			=	(
					    (
					        MaMaaaModel,
					        MaMaaaForConditionalGeneration,
					    )
					    if is_torch_available()
					    else ()
					)
					a_			=	(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
					a_			=	(
					    {
					        "conversational": MaMaaaForConditionalGeneration,
					        "feature-extraction": MaMaaaModel,
					        "summarization": MaMaaaForConditionalGeneration,
					        "text2text-generation": MaMaaaForConditionalGeneration,
					        "translation": MaMaaaForConditionalGeneration,
					    }
					    if is_torch_available()
					    else {}
					)
					a_			=	True
					a_			=	True
					a_			=	False
					a_			=	False



					def  _lowercase  (      self  :						int							,	__A  :						Tuple							,	__A  :						Any							,	__A  :						Optional[Any]							,	__A  :						Optional[Any]							,	__A  :						Union[str, 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  _lowercase  (      self  :						Tuple ):
						snake_case__				:   Any     =						MaMaaaModelTester(self )
						snake_case__				:   Dict     =						ConfigTester(self							,	config_class=__A )



					def  _lowercase  (      self  :						Optional[Any] ):
						self.config_tester.run_common_tests()



					def  _lowercase  (      self  :						Union[str, Any] ):
						snake_case__,		snake_case__				:   int     =						self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
							snake_case__				:   int     =						model_class(__A )

							with tempfile.TemporaryDirectory() as tmpdirname:
								model.save_pretrained(__A )
								snake_case__,		snake_case__				:   Optional[int]     =						model_class.from_pretrained(__A							,	output_loading_info=__A )
							self.assertEqual(info["missing_keys"]							,	[] )



					def  _lowercase  (      self  :						Dict ):
						snake_case__				:   Dict     =						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_decoder_model_past_large_inputs(*__A )



					def  _lowercase  (      self  :						Any ):
						snake_case__				:   Tuple     =						self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_encoder_decoder_model_standalone(*__A )



					def  _lowercase  (      self  :						Union[str, Any] ):
						snake_case__,		snake_case__				:   Dict     =						self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
							snake_case__				:   str     =						model_class(__A )
							model.to(__A )
							model.eval()

							snake_case__				:   str     =						copy.deepcopy(self._prepare_for_class(__A							,	__A ) )

							if not self.is_encoder_decoder:
								snake_case__				:   Optional[Any]     =						inputs["input_ids"]
								del inputs["input_ids"]
							else:
								snake_case__				:   Union[str, Any]     =						inputs["input_ids"]
								snake_case__				:   List[str]     =						inputs.get("decoder_input_ids"							,	__A )
								del inputs["input_ids"]
								inputs.pop("decoder_input_ids"							,	__A )

							snake_case__				:   Tuple     =						model.get_input_embeddings()
							if not self.is_encoder_decoder:
								snake_case__				:   List[Any]     =						wte(__A )
							else:
								snake_case__				:   Any     =						wte(__A )
								snake_case__				:   Optional[int]     =						wte(__A )

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






					def  _lowercase  (      self  :						Optional[Any] ):
						snake_case__,		snake_case__				:   Union[str, Any]     =						self.model_tester.prepare_config_and_inputs()
						snake_case__				:   Any     =						input_dict["input_ids"]
						snake_case__				:   int     =						input_ids.ne(1 ).to(__A )
						snake_case__				:   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   SCREAMING_SNAKE_CASE       (				snake_case_      :							int     ):
	return torch.tensor(snake_case_  ,						dtype=torch.long  ,						device=snake_case_     )


__lowerCamelCase      :    Optional[Any]   		=   1e-4





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





					"""simple docstring"""






					@cached_property
					def  _lowercase  (      self  :						str ):
						return MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M" )



					def  _lowercase  (      self  :						Optional[int] ):
						snake_case__				:   List[str]     =						MaMaaaModel.from_pretrained("facebook/m2m100_418M" ).to(__A )
						snake_case__				:   Optional[Any]     =						_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]] )
						snake_case__				:   str     =						_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]] )
						snake_case__				:   int     =						prepare_mam_aaa_inputs_dict(model.config							,	__A							,	__A )
						with torch.no_grad():
							snake_case__				:   str     =						model(**__A )[0]
						snake_case__				:   Tuple     =						torch.Size((1, 1_1, 1_0_2_4) )
						self.assertEqual(output.shape							,	__A )
						# change to expected output here
						snake_case__				:   Optional[Any]     =						torch.tensor(
						    [[-0.7_7_8_0, -0.1_6_7_6, 0.1_0_3_8], [-6.7_5_5_6, -1.3_9_9_2, 0.0_5_6_7], [-7.5_3_8_3, -0.5_9_2_0, -0.2_7_7_9]]							,	device=__A )
						self.assertTrue(torch.allclose(output[:, :3, :3]							,	__A							,	atol=__A ) )



					def  _lowercase  (      self  :						Union[str, Any] ):
						snake_case__				:   Union[str, Any]     =						MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M" ).to(__A )

						# change to intended input
						snake_case__				:   Union[str, Any]     =						_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]] )
						snake_case__				:   List[str]     =						_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]] )
						snake_case__				:   int     =						prepare_mam_aaa_inputs_dict(model.config							,	__A							,	__A )
						with torch.no_grad():
							snake_case__				:   Union[str, Any]     =						model(**__A )[0]
						snake_case__				:   Tuple     =						torch.Size((1, 1_1, model.config.vocab_size) )
						self.assertEqual(output.shape							,	__A )
						# change to expected output here
						snake_case__				:   List[str]     =						torch.tensor(
						    [[-1.0_4_4_8, -1.0_4_1_1, 3.7_9_9_2], [-3.2_1_9_1, -3.2_3_8_6, -1.3_4_5_1], [-3.6_2_1_0, -3.5_9_9_3, 0.4_9_2_5]]							,	device=__A )
						self.assertTrue(torch.allclose(output[:, :3, :3]							,	__A							,	atol=__A ) )






					def  _lowercase  (      self  :						Optional[Any] ):
						snake_case__				:   List[Any]     =						MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M" ).to(__A )
						snake_case__				:   List[str]     =						MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"							,	src_lang="fr"							,	tgt_lang="en" )

						snake_case__				:   List[Any]     =						[
						    "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
						snake_case__				:   str     =						tokenizer(__A							,	padding=__A							,	return_tensors="pt" )

						snake_case__				:   Tuple     =						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" )							,	)

						snake_case__				:   List[str]     =						[
						    "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.",
						]

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



