


'''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__					(	UpperCAmelCase_    :				List[str]      ,    UpperCAmelCase_    :				Tuple      ,    UpperCAmelCase_    :				Optional[Any]      ,    UpperCAmelCase_    :				Optional[int]=None      ,    UpperCAmelCase_    :				Tuple=None      ,    UpperCAmelCase_    :				Dict=None      ,    UpperCAmelCase_    :				Optional[int]=None      ,    UpperCAmelCase_    :				Union[str, Any]=None      ,    )     ->		List[str]:
     if attention_mask is None:
          __lowerCamelCase   :	Optional[int]								=		input_ids.ne(config.pad_token_id							)
     if decoder_attention_mask is None:
          __lowerCamelCase   :	str								=		decoder_input_ids.ne(config.pad_token_id							)
     if head_mask is None:
          __lowerCamelCase   :	Dict								=		torch.ones(config.encoder_layers      ,    config.encoder_attention_heads      ,    device=UpperCAmelCase_							)
     if decoder_head_mask is None:
          __lowerCamelCase   :	Optional[int]								=		torch.ones(config.decoder_layers      ,    config.decoder_attention_heads      ,    device=UpperCAmelCase_							)
     if cross_attn_head_mask is None:
          __lowerCamelCase   :	int								=		torch.ones(config.decoder_layers      ,    config.decoder_attention_heads      ,    device=UpperCAmelCase_							)
     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			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=13			,     SCREAMING_SNAKE_CASE_=7			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_=False			,     SCREAMING_SNAKE_CASE_=99			,     SCREAMING_SNAKE_CASE_=16			,     SCREAMING_SNAKE_CASE_=2			,     SCREAMING_SNAKE_CASE_=4			,     SCREAMING_SNAKE_CASE_=4			,     SCREAMING_SNAKE_CASE_="relu"			,     SCREAMING_SNAKE_CASE_=0.1			,     SCREAMING_SNAKE_CASE_=0.1			,     SCREAMING_SNAKE_CASE_=0.0			,     SCREAMING_SNAKE_CASE_=0.0			,     SCREAMING_SNAKE_CASE_=20			,     SCREAMING_SNAKE_CASE_=2			,     SCREAMING_SNAKE_CASE_=1			,     SCREAMING_SNAKE_CASE_=0			,     )				->     Dict:
         __lowerCamelCase   :	Union[str, Any]								=		parent
         __lowerCamelCase   :	Optional[int]								=		batch_size
         __lowerCamelCase   :	Dict								=		seq_length
         __lowerCamelCase   :	Optional[Any]								=		is_training
         __lowerCamelCase   :	Optional[int]								=		use_labels
         __lowerCamelCase   :	Tuple								=		vocab_size
         __lowerCamelCase   :	Optional[int]								=		hidden_size
         __lowerCamelCase   :	Any								=		num_hidden_layers
         __lowerCamelCase   :	Optional[Any]								=		num_attention_heads
         __lowerCamelCase   :	List[Any]								=		intermediate_size
         __lowerCamelCase   :	str								=		hidden_act
         __lowerCamelCase   :	Tuple								=		hidden_dropout_prob
         __lowerCamelCase   :	str								=		attention_probs_dropout_prob
         __lowerCamelCase   :	List[Any]								=		encoder_layerdrop
         __lowerCamelCase   :	List[str]								=		decoder_layerdrop
         __lowerCamelCase   :	List[str]								=		max_position_embeddings
         __lowerCamelCase   :	Any								=		eos_token_id
         __lowerCamelCase   :	Union[str, Any]								=		pad_token_id
         __lowerCamelCase   :	List[Any]								=		bos_token_id







    def       lowercase_				(							self	)				->     Any:
         __lowerCamelCase   :	Optional[Any]								=		ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size	)
         __lowerCamelCase   :	Dict								=		self.eos_token_id  # Eos Token
         __lowerCamelCase   :	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
         __lowerCamelCase   :	List[Any]								=		input_ids.clamp(self.pad_token_id + 1	)
         __lowerCamelCase   :	Union[str, Any]								=		decoder_input_ids.clamp(self.pad_token_id + 1	)

         __lowerCamelCase   :	Optional[Any]								=		self.get_config()
         __lowerCamelCase   :	List[str]								=		prepare_mam_aaa_inputs_dict(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)
         return config, inputs_dict







    def       lowercase_				(							self	)				->     str:
         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	)				->     Tuple:
         __lowerCamelCase				,       __lowerCamelCase   :	int								=		self.prepare_config_and_inputs()
         return config, inputs_dict







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     int:
         __lowerCamelCase   :	Tuple								=		MaMaaaModel(config=SCREAMING_SNAKE_CASE_	).get_decoder().to(SCREAMING_SNAKE_CASE_	).eval()
         __lowerCamelCase   :	List[str]								=		inputs_dict['input_ids']
         __lowerCamelCase   :	Union[str, Any]								=		inputs_dict['attention_mask']
         __lowerCamelCase   :	Union[str, Any]								=		inputs_dict['head_mask']

         # first forward pass
         __lowerCamelCase   :	List[str]								=		model(SCREAMING_SNAKE_CASE_			,     attention_mask=SCREAMING_SNAKE_CASE_			,     head_mask=SCREAMING_SNAKE_CASE_			,     use_cache=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase				,       __lowerCamelCase   :	Optional[int]								=		outputs.to_tuple()

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

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

         __lowerCamelCase   :	Optional[int]								=		model(SCREAMING_SNAKE_CASE_			,     attention_mask=SCREAMING_SNAKE_CASE_	)['last_hidden_state']
         __lowerCamelCase   :	Union[str, Any]								=		model(SCREAMING_SNAKE_CASE_			,     attention_mask=SCREAMING_SNAKE_CASE_			,     past_key_values=SCREAMING_SNAKE_CASE_	)[
             'last_hidden_state'
         ]

         # select random slice
         __lowerCamelCase   :	List[Any]								=		ids_tensor((1,)			,     output_from_past.shape[-1]	).item()
         __lowerCamelCase   :	int								=		output_from_no_past[:, -3:, random_slice_idx].detach()
         __lowerCamelCase   :	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(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     atol=1E-2	)	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     str:
         __lowerCamelCase   :	List[Any]								=		MaMaaaModel(config=SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	).eval()
         __lowerCamelCase   :	List[Any]								=		model(**SCREAMING_SNAKE_CASE_	)

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

         with tempfile.TemporaryDirectory() as tmpdirname:
              __lowerCamelCase   :	List[str]								=		model.get_encoder()
              encoder.save_pretrained(SCREAMING_SNAKE_CASE_	)
              __lowerCamelCase   :	int								=		MaMaaaEncoder.from_pretrained(SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	List[Any]								=		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:
              __lowerCamelCase   :	Tuple								=		model.get_decoder()
              decoder.save_pretrained(SCREAMING_SNAKE_CASE_	)
              __lowerCamelCase   :	Any								=		MaMaaaDecoder.from_pretrained(SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	str								=		decoder(
             input_ids=inputs_dict['decoder_input_ids']			,     attention_mask=inputs_dict['decoder_attention_mask']			,     encoder_hidden_states=SCREAMING_SNAKE_CASE_			,     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"""






    lowerCamelCase  :					str							     =			(
        (
            MaMaaaModel,
            MaMaaaForConditionalGeneration,
        )
        if is_torch_available()
        else ()
    )
    lowerCamelCase  :					Dict							     =			(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
    lowerCamelCase  :					Dict							     =			(
        {
            'conversational': MaMaaaForConditionalGeneration,
            'feature-extraction': MaMaaaModel,
            'summarization': MaMaaaForConditionalGeneration,
            'text2text-generation': MaMaaaForConditionalGeneration,
            'translation': MaMaaaForConditionalGeneration,
        }
        if is_torch_available()
        else {}
    )
    lowerCamelCase  :					List[str]							     =			True
    lowerCamelCase  :					int							     =			True
    lowerCamelCase  :					str							     =			False
    lowerCamelCase  :					Optional[Any]							     =			False







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     str:
         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	)				->     Dict:
         __lowerCamelCase   :	Tuple								=		MaMaaaModelTester(self	)
         __lowerCamelCase   :	List[Any]								=		ConfigTester(self			,     config_class=SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self	)				->     Tuple:
         self.config_tester.run_common_tests()







    def       lowercase_				(							self	)				->     Optional[Any]:
         __lowerCamelCase				,       __lowerCamelCase   :	Tuple								=		self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
              __lowerCamelCase   :	List[Any]								=		model_class(SCREAMING_SNAKE_CASE_	)

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







    def       lowercase_				(							self	)				->     int:
         __lowerCamelCase   :	Dict								=		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self	)				->     List[Any]:
         __lowerCamelCase   :	Optional[int]								=		self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_encoder_decoder_model_standalone(*SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self	)				->     Optional[Any]:
         __lowerCamelCase				,       __lowerCamelCase   :	int								=		self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
              __lowerCamelCase   :	Optional[Any]								=		model_class(SCREAMING_SNAKE_CASE_	)
              model.to(SCREAMING_SNAKE_CASE_	)
              model.eval()

              __lowerCamelCase   :	Union[str, Any]								=		copy.deepcopy(self._prepare_for_class(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)	)

              if not self.is_encoder_decoder:
                   __lowerCamelCase   :	Any								=		inputs['input_ids']
                   del inputs["input_ids"]
              else:
                   __lowerCamelCase   :	Dict								=		inputs['input_ids']
                   __lowerCamelCase   :	Union[str, Any]								=		inputs.get('decoder_input_ids'			,     SCREAMING_SNAKE_CASE_	)
                   del inputs["input_ids"]
                   inputs.pop('decoder_input_ids'			,     SCREAMING_SNAKE_CASE_	)

              __lowerCamelCase   :	Any								=		model.get_input_embeddings()
              if not self.is_encoder_decoder:
                   __lowerCamelCase   :	Union[str, Any]								=		wte(SCREAMING_SNAKE_CASE_	)
              else:
                   __lowerCamelCase   :	Optional[Any]								=		wte(SCREAMING_SNAKE_CASE_	)
                   __lowerCamelCase   :	Optional[Any]								=		wte(SCREAMING_SNAKE_CASE_	)

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







    def       lowercase_				(							self	)				->     str:
         __lowerCamelCase				,       __lowerCamelCase   :	List[str]								=		self.model_tester.prepare_config_and_inputs()
         __lowerCamelCase   :	Any								=		input_dict['input_ids']
         __lowerCamelCase   :	Optional[Any]								=		input_ids.ne(1	).to(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	int								=		MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE_	).eval().to(SCREAMING_SNAKE_CASE_	)
         if torch_device == "cuda":
              model.half()
         model.generate(SCREAMING_SNAKE_CASE_			,     attention_mask=SCREAMING_SNAKE_CASE_	)
         model.generate(num_beams=4			,     do_sample=SCREAMING_SNAKE_CASE_			,     early_stopping=SCREAMING_SNAKE_CASE_			,     num_return_sequences=3	)







def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Dict							)     ->		Tuple:
     return torch.tensor(UpperCAmelCase_      ,    dtype=torch.long      ,    device=UpperCAmelCase_							)


A__		:					Tuple												=  1e-4





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





    """simple docstring"""






    @cached_property
    def       lowercase_				(							self	)				->     List[Any]:
         return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'	)







    def       lowercase_				(							self	)				->     Union[str, Any]:
         __lowerCamelCase   :	Optional[int]								=		MaMaaaModel.from_pretrained('facebook/m2m100_418M'	).to(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	List[Any]								=		_long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]	)
         __lowerCamelCase   :	Optional[int]								=		_long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]	)
         __lowerCamelCase   :	Optional[Any]								=		prepare_mam_aaa_inputs_dict(model.config			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)
         with torch.no_grad():
              __lowerCamelCase   :	Any								=		model(**SCREAMING_SNAKE_CASE_	)[0]
         __lowerCamelCase   :	Union[str, Any]								=		torch.Size((1, 11, 10_24)	)
         self.assertEqual(output.shape			,     SCREAMING_SNAKE_CASE_	)
         # change to expected output here
         __lowerCamelCase   :	int								=		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=SCREAMING_SNAKE_CASE_	)
         self.assertTrue(torch.allclose(output[:, :3, :3]			,     SCREAMING_SNAKE_CASE_			,     atol=SCREAMING_SNAKE_CASE_	)	)







    def       lowercase_				(							self	)				->     Union[str, Any]:
         __lowerCamelCase   :	Any								=		MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'	).to(SCREAMING_SNAKE_CASE_	)

         # change to intended input
         __lowerCamelCase   :	Tuple								=		_long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]	)
         __lowerCamelCase   :	Tuple								=		_long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]	)
         __lowerCamelCase   :	Optional[int]								=		prepare_mam_aaa_inputs_dict(model.config			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)
         with torch.no_grad():
              __lowerCamelCase   :	List[str]								=		model(**SCREAMING_SNAKE_CASE_	)[0]
         __lowerCamelCase   :	Optional[int]								=		torch.Size((1, 11, model.config.vocab_size)	)
         self.assertEqual(output.shape			,     SCREAMING_SNAKE_CASE_	)
         # change to expected output here
         __lowerCamelCase   :	Dict								=		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=SCREAMING_SNAKE_CASE_	)
         self.assertTrue(torch.allclose(output[:, :3, :3]			,     SCREAMING_SNAKE_CASE_			,     atol=SCREAMING_SNAKE_CASE_	)	)







    def       lowercase_				(							self	)				->     int:
         __lowerCamelCase   :	Any								=		MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'	).to(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	str								=		MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'			,     src_lang='fr'			,     tgt_lang='en'	)

         __lowerCamelCase   :	str								=		[
             '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
         __lowerCamelCase   :	List[Any]								=		tokenizer(SCREAMING_SNAKE_CASE_			,     padding=SCREAMING_SNAKE_CASE_			,     return_tensors='pt'	)

         __lowerCamelCase   :	List[str]								=		model.generate(
             input_ids=dct['input_ids'].to(SCREAMING_SNAKE_CASE_	)			,     attention_mask=dct['attention_mask'].to(SCREAMING_SNAKE_CASE_	)			,     num_beams=5			,     forced_bos_token_id=tokenizer.get_lang_id('en'	)			,     )

         __lowerCamelCase   :	Dict								=		[
             '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.',
         ]

         __lowerCamelCase   :	str								=		tokenizer.batch_decode(
             hypotheses_batch.tolist()			,     clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_			,     skip_special_tokens=SCREAMING_SNAKE_CASE_	)
         assert generated == expected_en










'''simple docstring'''



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A__		:					Tuple												=  logging.get_logger(__name__)

A__		:					List[str]												=  {"""vocab_file""": """spm_char.model"""}

A__		:					str												=  {
    """vocab_file""": {
        """microsoft/speecht5_asr""": """https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model""",
        """microsoft/speecht5_tts""": """https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model""",
        """microsoft/speecht5_vc""": """https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model""",
    }
}

A__		:					List[Any]												=  {
    """microsoft/speecht5_asr""": 1024,
    """microsoft/speecht5_tts""": 1024,
    """microsoft/speecht5_vc""": 1024,
}





class    UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  :					Optional[Any]							     =			VOCAB_FILES_NAMES
    lowerCamelCase  :					Any							     =			PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase  :					Union[str, Any]							     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase  :					int							     =			['input_ids', 'attention_mask']







    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_="<s>"			,     SCREAMING_SNAKE_CASE_="</s>"			,     SCREAMING_SNAKE_CASE_="<unk>"			,     SCREAMING_SNAKE_CASE_="<pad>"			,     SCREAMING_SNAKE_CASE_ = None			,     **SCREAMING_SNAKE_CASE_			,     )				->     None:
         __lowerCamelCase   :	Any								=		{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=SCREAMING_SNAKE_CASE_			,     eos_token=SCREAMING_SNAKE_CASE_			,     unk_token=SCREAMING_SNAKE_CASE_			,     pad_token=SCREAMING_SNAKE_CASE_			,     sp_model_kwargs=self.sp_model_kwargs			,     **SCREAMING_SNAKE_CASE_			,     )

         __lowerCamelCase   :	List[Any]								=		vocab_file

         __lowerCamelCase   :	str								=		spm.SentencePieceProcessor(**self.sp_model_kwargs	)
         self.sp_model.Load(SCREAMING_SNAKE_CASE_	)







    @property
    def       lowercase_				(							self	)				->     List[str]:
         return self.sp_model.get_piece_size()







    def       lowercase_				(							self	)				->     Tuple:
         __lowerCamelCase   :	str								=		{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_	): i for i in range(self.vocab_size	)}
         vocab.update(self.added_tokens_encoder	)
         return vocab







    def __getstate__(							self	)				->     Optional[Any]:
         __lowerCamelCase   :	Dict								=		self.__dict__.copy()
         __lowerCamelCase   :	int								=		None
         return state







    def __setstate__(							self			,     SCREAMING_SNAKE_CASE_	)				->     List[str]:
         __lowerCamelCase   :	Union[str, Any]								=		d

         # for backward compatibility
         if not hasattr(self			,     'sp_model_kwargs'	):
              __lowerCamelCase   :	List[str]								=		{}

         __lowerCamelCase   :	Dict								=		spm.SentencePieceProcessor(**self.sp_model_kwargs	)
         self.sp_model.Load(self.vocab_file	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     List[str]:
         return self.sp_model.encode(SCREAMING_SNAKE_CASE_			,     out_type=SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     str:
         return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     Dict:
         __lowerCamelCase   :	Any								=		self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE_	)
         return token







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     List[Any]:
         __lowerCamelCase   :	Union[str, Any]								=		[]
         __lowerCamelCase   :	str								=		''
         for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                   out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_	) + token
                   __lowerCamelCase   :	int								=		[]
              else:
                   current_sub_tokens.append(SCREAMING_SNAKE_CASE_	)
         out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_	)
         return out_string.strip()







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=None	)				->     List[int]:
         if token_ids_a is None:
              return token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_a + token_ids_a + [self.eos_token_id]







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = None			,     SCREAMING_SNAKE_CASE_ = False	)				->     List[int]:
         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=SCREAMING_SNAKE_CASE_			,     token_ids_a=SCREAMING_SNAKE_CASE_			,     already_has_special_tokens=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	str								=		[1]
         if token_ids_a is None:
              return ([0] * len(SCREAMING_SNAKE_CASE_	)) + suffix_ones
         return ([0] * len(SCREAMING_SNAKE_CASE_	)) + ([0] * len(SCREAMING_SNAKE_CASE_	)) + suffix_ones







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = None	)				->     Tuple[str]:
         if not os.path.isdir(SCREAMING_SNAKE_CASE_	):
              logger.error(f'Vocabulary path ({save_directory}) should be a directory'	)
              return
         __lowerCamelCase   :	Tuple								=		os.path.join(
             SCREAMING_SNAKE_CASE_			,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

         if os.path.abspath(self.vocab_file	) != os.path.abspath(SCREAMING_SNAKE_CASE_	) and os.path.isfile(self.vocab_file	):
              copyfile(self.vocab_file			,     SCREAMING_SNAKE_CASE_	)
         elif not os.path.isfile(self.vocab_file	):
              with open(SCREAMING_SNAKE_CASE_			,     'wb'	) as fi:
                   __lowerCamelCase   :	str								=		self.sp_model.serialized_model_proto()
                   fi.write(SCREAMING_SNAKE_CASE_	)

         return (out_vocab_file,)






