





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 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase=None							,   _lowerCAmelCase=None							,   _lowerCAmelCase=None							,   _lowerCAmelCase=None							,   _lowerCAmelCase=None							,   )						-> Dict:
 if attention_mask is None:
  UpperCamelCase	:					int       	=   input_ids.ne(config.pad_token_id     )
 if decoder_attention_mask is None:
  UpperCamelCase	:					Dict       	=   decoder_input_ids.ne(config.pad_token_id     )
 if head_mask is None:
  UpperCamelCase	:					List[Any]       	=   torch.ones(config.encoder_layers							,   config.encoder_attention_heads							,   device=_lowerCAmelCase     )
 if decoder_head_mask is None:
  UpperCamelCase	:					Tuple       	=   torch.ones(config.decoder_layers							,   config.decoder_attention_heads							,   device=_lowerCAmelCase     )
 if cross_attn_head_mask is None:
  UpperCamelCase	:					int       	=   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 					A__   :





      def __init__( self       ,							A_       ,							A_=13       ,							A_=7       ,							A_=True       ,							A_=False       ,							A_=99       ,							A_=16       ,							A_=2       ,							A_=4       ,							A_=4       ,							A_="relu"       ,							A_=0.1       ,							A_=0.1       ,							A_=0.0       ,							A_=0.0       ,							A_=20       ,							A_=2       ,							A_=1       ,							A_=0       ,							):
       '''simple docstring'''




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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




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

       UpperCamelCase	:					int       	=   self.get_config()
       UpperCamelCase	:					Dict       	=   prepare_mam_aaa_inputs_dict(A_       ,							A_       ,							A_   )
       return config, inputs_dict





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return MaMaaaConfig(
           vocab_size=self.vocab_size       ,							d_model=self.hidden_size       ,							encoder_layers=self.num_hidden_layers       ,							decoder_layers=self.num_hidden_layers       ,							encoder_attention_heads=self.num_attention_heads       ,							decoder_attention_heads=self.num_attention_heads       ,							encoder_ffn_dim=self.intermediate_size       ,							decoder_ffn_dim=self.intermediate_size       ,							dropout=self.hidden_dropout_prob       ,							attention_dropout=self.attention_probs_dropout_prob       ,							encoder_layerdrop=self.encoder_layerdrop       ,							decoder_layerdrop=self.decoder_layerdrop       ,							max_position_embeddings=self.max_position_embeddings       ,							eos_token_id=self.eos_token_id       ,							bos_token_id=self.bos_token_id       ,							pad_token_id=self.pad_token_id       ,							)





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	,    UpperCamelCase	:					str       	=   self.prepare_config_and_inputs()
       return config, inputs_dict





      def        __UpperCamelCase( self       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					str       	=   MaMaaaModel(config=A_   ).get_decoder().to(A_   ).eval()
       UpperCamelCase	:					List[str]       	=   inputs_dict["input_ids"]
       UpperCamelCase	:					Dict       	=   inputs_dict["attention_mask"]
       UpperCamelCase	:					Dict       	=   inputs_dict["head_mask"]

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

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

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

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

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

       # select random slice
       UpperCamelCase	:					Optional[Any]       	=   ids_tensor((1,)       ,							output_from_past.shape[-1]   ).item()
       UpperCamelCase	:					Optional[Any]       	=   output_from_no_past[:, -3:, random_slice_idx].detach()
       UpperCamelCase	:					Tuple       	=   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        __UpperCamelCase( self       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   MaMaaaModel(config=A_   ).to(A_   ).eval()
       UpperCamelCase	:					str       	=   model(**A_   )

       UpperCamelCase	:					List[Any]       	=   outputs.encoder_last_hidden_state
       UpperCamelCase	:					Tuple       	=   outputs.last_hidden_state

       with tempfile.TemporaryDirectory() as tmpdirname:
        UpperCamelCase	:					Union[str, Any]       	=   model.get_encoder()
        encoder.save_pretrained(A_   )
        UpperCamelCase	:					List[Any]       	=   MaMaaaEncoder.from_pretrained(A_   ).to(A_   )

       UpperCamelCase	:					Optional[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:
        UpperCamelCase	:					Dict       	=   model.get_decoder()
        decoder.save_pretrained(A_   )
        UpperCamelCase	:					Any       	=   MaMaaaDecoder.from_pretrained(A_   ).to(A_   )

       UpperCamelCase	:					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 					A__   (       __snake_case       ,   __snake_case       ,   __snake_case       ,   unittest.TestCase			):
      _UpperCAmelCase	:List[Any]						    =   (
          (
              MaMaaaModel,
              MaMaaaForConditionalGeneration,
          )
          if is_torch_available()
          else ()
      )
      _UpperCAmelCase	:Any						    =   (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
      _UpperCAmelCase	:Dict						    =   (
          {
              'conversational': MaMaaaForConditionalGeneration,
              'feature-extraction': MaMaaaModel,
              'summarization': MaMaaaForConditionalGeneration,
              'text2text-generation': MaMaaaForConditionalGeneration,
              'translation': MaMaaaForConditionalGeneration,
          }
          if is_torch_available()
          else {}
      )
      _UpperCAmelCase	:List[Any]						    =   True
      _UpperCAmelCase	:Optional[int]						    =   True
      _UpperCAmelCase	:List[Any]						    =   False
      _UpperCAmelCase	:Any						    =   False





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       if pipeline_test_casse_name == "TranslationPipelineTests":
        # Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
        # `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
        return True

       return False





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[int]       	=   MaMaaaModelTester(self   )
       UpperCamelCase	:					Any       	=   ConfigTester(self       ,							config_class=A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       self.config_tester.run_common_tests()





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	,    UpperCamelCase	:					List[Any]       	=   self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
        UpperCamelCase	:					Optional[Any]       	=   model_class(A_   )

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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					str       	=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past_large_inputs(*A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_encoder_decoder_model_standalone(*A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	,    UpperCamelCase	:					Dict       	=   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
        UpperCamelCase	:					List[Any]       	=   model_class(A_   )
        model.to(A_   )
        model.eval()

        UpperCamelCase	:					Optional[Any]       	=   copy.deepcopy(self._prepare_for_class(A_       ,							A_   )   )

        if not self.is_encoder_decoder:
         UpperCamelCase	:					Tuple       	=   inputs["input_ids"]
         del inputs["input_ids"]
        else:
         UpperCamelCase	:					Tuple       	=   inputs["input_ids"]
         UpperCamelCase	:					List[Any]       	=   inputs.get("decoder_input_ids"       ,							A_   )
         del inputs["input_ids"]
         inputs.pop("decoder_input_ids"       ,							A_   )

        UpperCamelCase	:					Optional[Any]       	=   model.get_input_embeddings()
        if not self.is_encoder_decoder:
         UpperCamelCase	:					str       	=   wte(A_   )
        else:
         UpperCamelCase	:					Optional[Any]       	=   wte(A_   )
         UpperCamelCase	:					Optional[Any]       	=   wte(A_   )

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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	,    UpperCamelCase	:					str       	=   self.model_tester.prepare_config_and_inputs()
       UpperCamelCase	:					Optional[int]       	=   input_dict["input_ids"]
       UpperCamelCase	:					Optional[Any]       	=   input_ids.ne(1   ).to(A_   )
       UpperCamelCase	:					Optional[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 		A_				(     _lowerCAmelCase     )						-> Optional[Any]:
 return torch.tensor(_lowerCAmelCase							,   dtype=torch.long							,   device=_lowerCAmelCase     )


__lowerCamelCase       :     str   =     1E-4



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





      @cached_property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					str       	=   MaMaaaModel.from_pretrained("facebook/m2m100_418M"   ).to(A_   )
       UpperCamelCase	:					Any       	=   _long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]]   )
       UpperCamelCase	:					Optional[int]       	=   _long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]]   )
       UpperCamelCase	:					Any       	=   prepare_mam_aaa_inputs_dict(model.config       ,							A_       ,							A_   )
       with torch.no_grad():
        UpperCamelCase	:					Union[str, Any]       	=   model(**A_   )[0]
       UpperCamelCase	:					int       	=   torch.Size((1, 11, 1024)   )
       self.assertEqual(output.shape       ,							A_   )
       # change to expected output here
       UpperCamelCase	:					List[str]       	=   torch.tensor(
           [[-0.77_80, -0.16_76, 0.10_38], [-6.75_56, -1.39_92, 0.05_67], [-7.53_83, -0.59_20, -0.27_79]]       ,							device=A_   )
       self.assertTrue(torch.allclose(output[:, :3, :3]       ,							A_       ,							atol=A_   )   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"   ).to(A_   )

       # change to intended input
       UpperCamelCase	:					Dict       	=   _long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]]   )
       UpperCamelCase	:					Tuple       	=   _long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]]   )
       UpperCamelCase	:					Any       	=   prepare_mam_aaa_inputs_dict(model.config       ,							A_       ,							A_   )
       with torch.no_grad():
        UpperCamelCase	:					Dict       	=   model(**A_   )[0]
       UpperCamelCase	:					Any       	=   torch.Size((1, 11, model.config.vocab_size)   )
       self.assertEqual(output.shape       ,							A_   )
       # change to expected output here
       UpperCamelCase	:					Union[str, Any]       	=   torch.tensor(
           [[-1.04_48, -1.04_11, 3.79_92], [-3.21_91, -3.23_86, -1.34_51], [-3.62_10, -3.59_93, 0.49_25]]       ,							device=A_   )
       self.assertTrue(torch.allclose(output[:, :3, :3]       ,							A_       ,							atol=A_   )   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"   ).to(A_   )
       UpperCamelCase	:					List[str]       	=   MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"       ,							src_lang="fr"       ,							tgt_lang="en"   )

       UpperCamelCase	:					Dict       	=   [
           "L'affaire NSA souligne l'absence totale de débat sur le renseignement",
           "Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
           "Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"
           " Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"
           " l'ampleur de la surveillance américaine sur l'ensemble des communications en France.",
       ]

       # The below article tests that we don't add any hypotheses outside of the top n_beams
       UpperCamelCase	:					Optional[int]       	=   tokenizer(A_       ,							padding=A_       ,							return_tensors="pt"   )

       UpperCamelCase	:					Union[str, Any]       	=   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"   )       ,							)

       UpperCamelCase	:					int       	=   [
           "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.",
       ]

       UpperCamelCase	:					Optional[int]       	=   tokenizer.batch_decode(
           hypotheses_batch.tolist()       ,							clean_up_tokenization_spaces=A_       ,							skip_special_tokens=A_   )
       assert generated == expected_en







from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


__lowerCamelCase       :     str   =     logging.get_logger(__name__)  # pylint: disable=invalid-name



class 					A__   (       __snake_case			):





      def __init__( self       ,							A_       ,							A_   ):
       '''simple docstring'''




       super().__init__()
       self.register_modules(unet=A_       ,							scheduler=A_   )





      @torch.no_grad()
      def __call__( self       ,							A_ = 1       ,							A_ = 100       ,							A_ = None       ,							A_ = None       ,							A_ = True       ,							):
       '''simple docstring'''




       if audio_length_in_s is None:
        UpperCamelCase	:					str       	=   self.unet.config.sample_size / self.unet.config.sample_rate

       UpperCamelCase	:					Optional[Any]       	=   audio_length_in_s * self.unet.config.sample_rate

       UpperCamelCase	:					Any       	=   2 ** len(self.unet.up_blocks   )
       if sample_size < 3 * down_scale_factor:
        raise ValueError(
            F"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
            F""" {3 * down_scale_factor / self.unet.config.sample_rate}."""   )

       UpperCamelCase	:					Union[str, Any]       	=   int(A_   )
       if sample_size % down_scale_factor != 0:
        UpperCamelCase	:					List[str]       	=   (
            (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
        ) * down_scale_factor
        logger.info(
            F"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
            F""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
            " process."   )
       UpperCamelCase	:					Any       	=   int(A_   )

       UpperCamelCase	:					Union[str, Any]       	=   next(iter(self.unet.parameters()   )   ).dtype
       UpperCamelCase	:					Optional[int]       	=   (batch_size, self.unet.config.in_channels, sample_size)
       if isinstance(A_       ,							A_   ) and len(A_   ) != batch_size:
        raise ValueError(
            F"""You have passed a list of generators of length {len(A_   )}, but requested an effective batch"""
            F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""   )

       UpperCamelCase	:					Optional[Any]       	=   randn_tensor(A_       ,							generator=A_       ,							device=self.device       ,							dtype=A_   )

       # set step values
       self.scheduler.set_timesteps(A_       ,							device=audio.device   )
       UpperCamelCase	:					Optional[int]       	=   self.scheduler.timesteps.to(A_   )

       for t in self.progress_bar(self.scheduler.timesteps   ):
        # 1. predict noise model_output
        UpperCamelCase	:					Dict       	=   self.unet(A_       ,							A_   ).sample

        # 2. compute previous image: x_t -> t_t-1
        UpperCamelCase	:					int       	=   self.scheduler.step(A_       ,							A_       ,							A_   ).prev_sample

       UpperCamelCase	:					Optional[Any]       	=   audio.clamp(-1       ,							1   ).float().cpu().numpy()

       UpperCamelCase	:					Dict       	=   audio[:, :, :original_sample_size]

       if not return_dict:
        return (audio,)

       return AudioPipelineOutput(audios=A_   )
