



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






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






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

									a_     :      Optional[int] 						=     self.get_config()
									a_     :      List[Any] 						=     prepare_mam_aaa_inputs_dict(a_			,     a_			,     a_      )
									return config, inputs_dict






							def snake_case_  (   self      ):
									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 snake_case_  (   self      ):
									a_						,				a_     :      Any 						=     self.prepare_config_and_inputs()
									return config, inputs_dict






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

									# first forward pass
									a_     :      Optional[int] 						=     model(a_			,     attention_mask=a_			,     head_mask=a_			,     use_cache=a_      )

									a_						,				a_     :      List[str] 						=     outputs.to_tuple()

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

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

									a_     :      List[str] 						=     model(a_			,     attention_mask=a_      )["last_hidden_state"]
									a_     :      str 						=     model(a_			,     attention_mask=a_			,     past_key_values=a_      )[
									    "last_hidden_state"
									]

									# select random slice
									a_     :      int 						=     ids_tensor((1,)			,     output_from_past.shape[-1]      ).item()
									a_     :      int 						=     output_from_no_past[:, -3:, random_slice_idx].detach()
									a_     :      str 						=     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 snake_case_  (   self			,     a_			,     a_      ):
									a_     :      int 						=     MaMaaaModel(config=a_      ).to(a_      ).eval()
									a_     :      Optional[int] 						=     model(**a_      )

									a_     :      int 						=     outputs.encoder_last_hidden_state
									a_     :      str 						=     outputs.last_hidden_state

									with tempfile.TemporaryDirectory() as tmpdirname:
											a_     :      Optional[int] 						=     model.get_encoder()
											encoder.save_pretrained(a_      )
											a_     :      Union[str, Any] 						=     MaMaaaEncoder.from_pretrained(a_      ).to(a_      )

									a_     :      int 						=     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:
											a_     :      List[str] 						=     model.get_decoder()
											decoder.save_pretrained(a_      )
											a_     :      List[str] 						=     MaMaaaDecoder.from_pretrained(a_      ).to(a_      )

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






							def snake_case_  (   self			,     a_			,     a_			,     a_			,     a_			,     a_      ):
									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 snake_case_  (   self      ):
									a_     :      List[str] 						=     MaMaaaModelTester(self      )
									a_     :      Dict 						=     ConfigTester(self			,     config_class=a_      )






							def snake_case_  (   self      ):
									self.config_tester.run_common_tests()






							def snake_case_  (   self      ):
									a_						,				a_     :      Optional[int] 						=     self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
											a_     :      int 						=     model_class(a_      )

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






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_decoder_model_past_large_inputs(*a_      )






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_encoder_decoder_model_standalone(*a_      )






							def snake_case_  (   self      ):
									a_						,				a_     :      str 						=     self.model_tester.prepare_config_and_inputs_for_common()

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

											a_     :      Tuple 						=     copy.deepcopy(self._prepare_for_class(a_			,     a_      )      )

											if not self.is_encoder_decoder:
													a_     :      Dict 						=     inputs["input_ids"]
													del inputs["input_ids"]
											else:
													a_     :      List[str] 						=     inputs["input_ids"]
													a_     :      Tuple 						=     inputs.get("decoder_input_ids"			,     a_      )
													del inputs["input_ids"]
													inputs.pop("decoder_input_ids"			,     a_      )

											a_     :      int 						=     model.get_input_embeddings()
											if not self.is_encoder_decoder:
													a_     :      List[Any] 						=     wte(a_      )
											else:
													a_     :      Dict 						=     wte(a_      )
													a_     :      str 						=     wte(a_      )

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






							def snake_case_  (   self      ):
									a_						,				a_     :      str 						=     self.model_tester.prepare_config_and_inputs()
									a_     :      Dict 						=     input_dict["input_ids"]
									a_     :      List[Any] 						=     input_ids.ne(1      ).to(a_      )
									a_     :      int 						=     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        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Dict:
		return torch.tensor(SCREAMING_SNAKE_CASE__, dtype=torch.long, device=SCREAMING_SNAKE_CASE__	)


SCREAMING_SNAKE_CASE_				= 1E-4




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






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






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     MaMaaaModel.from_pretrained("facebook/m2m100_418M"      ).to(a_      )
									a_     :      List[str] 						=     _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]]      )
									a_     :      Tuple 						=     _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]]      )
									a_     :      Tuple 						=     prepare_mam_aaa_inputs_dict(model.config			,     a_			,     a_      )
									with torch.no_grad():
											a_     :      Optional[Any] 						=     model(**a_      )[0]
									a_     :      Any 						=     torch.Size((1, 1_1, 1_0_2_4)      )
									self.assertEqual(output.shape			,     a_      )
									# change to expected output here
									a_     :      Any 						=     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=a_      )
									self.assertTrue(torch.allclose(output[:, :3, :3]			,     a_			,     atol=a_      )      )






							def snake_case_  (   self      ):
									a_     :      Dict 						=     MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"      ).to(a_      )

									# change to intended input
									a_     :      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]]      )
									a_     :      Union[str, Any] 						=     _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]]      )
									a_     :      str 						=     prepare_mam_aaa_inputs_dict(model.config			,     a_			,     a_      )
									with torch.no_grad():
											a_     :      Union[str, Any] 						=     model(**a_      )[0]
									a_     :      Any 						=     torch.Size((1, 1_1, model.config.vocab_size)      )
									self.assertEqual(output.shape			,     a_      )
									# change to expected output here
									a_     :      Dict 						=     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=a_      )
									self.assertTrue(torch.allclose(output[:, :3, :3]			,     a_			,     atol=a_      )      )






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"      ).to(a_      )
									a_     :      int 						=     MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"			,     src_lang="fr"			,     tgt_lang="en"      )

									a_     :      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
									a_     :      Tuple 						=     tokenizer(a_			,     padding=a_			,     return_tensors="pt"      )

									a_     :      str 						=     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"      )			,     )

									a_     :      List[Any] 						=     [
									    "The NSA case highlights the total absence of intelligence debate",
									    "I think there are two levels of response from the French government.",
									    "When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."
									    " Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"
									    " communications in France.",
									]

									a_     :      List[str] 						=     tokenizer.batch_decode(
									    hypotheses_batch.tolist()			,     clean_up_tokenization_spaces=a_			,     skip_special_tokens=a_      )
									assert generated == expected_en




"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_				= {
    """configuration_bigbird_pegasus""": [
        """BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BigBirdPegasusConfig""",
        """BigBirdPegasusOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= [
					    """BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """BigBirdPegasusForCausalLM""",
					    """BigBirdPegasusForConditionalGeneration""",
					    """BigBirdPegasusForQuestionAnswering""",
					    """BigBirdPegasusForSequenceClassification""",
					    """BigBirdPegasusModel""",
					    """BigBirdPegasusPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_bigbird_pegasus import (
					    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BigBirdPegasusConfig,
					    BigBirdPegasusOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bigbird_pegasus import (
										    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BigBirdPegasusForCausalLM,
										    BigBirdPegasusForConditionalGeneration,
										    BigBirdPegasusForQuestionAnswering,
										    BigBirdPegasusForSequenceClassification,
										    BigBirdPegasusModel,
										    BigBirdPegasusPreTrainedModel,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE_				= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)