import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

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.models.speech_to_text_a.modeling_speech_to_text_a import (
     SpeechaTextaDecoder,
     SpeechaTextaForCausalLM,
 )




@require_torch
class  __lowerCAmelCase	:



 '''simple docstring'''

 def __init__(      self							:		Union[str, Any]							,      lowerCamelCase							:		Optional[Any]							,      lowerCamelCase							:		Optional[Any]=9_9							,      lowerCamelCase							:		str=1_3							,      lowerCamelCase							:		Dict=1_6							,      lowerCamelCase							:		Union[str, Any]=7							,      lowerCamelCase							:		str=True							,      lowerCamelCase							:		str=True							,      lowerCamelCase							:		Any=True							,      lowerCamelCase							:		Dict=False							,      lowerCamelCase							:		List[str]=True							,      lowerCamelCase							:		Dict=2							,      lowerCamelCase							:		int=3_2							,      lowerCamelCase							:		Optional[int]=4							,      lowerCamelCase							:		int=4							,      lowerCamelCase							:		int=3_0							,      lowerCamelCase							:		int=0							,      lowerCamelCase							:		List[str]=1							,      lowerCamelCase							:		Union[str, Any]=2							,      lowerCamelCase							:		Union[str, Any]=None							,      )			->				Optional[int]:
        _UpperCamelCase  :							Optional[Any]	    =	parent
        _UpperCamelCase  :							int	    =	batch_size
        _UpperCamelCase  :							Any	    =	decoder_seq_length
        # For common tests
        _UpperCamelCase  :							Optional[int]	    =	self.decoder_seq_length
        _UpperCamelCase  :							Tuple	    =	is_training
        _UpperCamelCase  :							Any	    =	use_attention_mask
        _UpperCamelCase  :							Optional[Any]	    =	use_labels

        _UpperCamelCase  :							str	    =	vocab_size
        _UpperCamelCase  :							str	    =	d_model
        _UpperCamelCase  :							int	    =	d_model
        _UpperCamelCase  :							int	    =	decoder_layers
        _UpperCamelCase  :							List[Any]	    =	decoder_layers
        _UpperCamelCase  :							List[Any]	    =	decoder_ffn_dim
        _UpperCamelCase  :							Optional[Any]	    =	decoder_attention_heads
        _UpperCamelCase  :							Optional[int]	    =	decoder_attention_heads
        _UpperCamelCase  :							List[str]	    =	eos_token_id
        _UpperCamelCase  :							Tuple	    =	bos_token_id
        _UpperCamelCase  :							List[str]	    =	pad_token_id
        _UpperCamelCase  :							Tuple	    =	decoder_start_token_id
        _UpperCamelCase  :							Optional[int]	    =	use_cache
        _UpperCamelCase  :							List[str]	    =	max_position_embeddings

        _UpperCamelCase  :							Optional[Any]	    =	None
        _UpperCamelCase  :							Union[str, Any]	    =	decoder_seq_length
        _UpperCamelCase  :							Any	    =	2
        _UpperCamelCase  :							Union[str, Any]	    =	1

 def        __snake_case       (      self							:		Optional[int]	)			->				Dict:
        _UpperCamelCase  :							List[Any]	    =	ids_tensor([self.batch_size, self.decoder_seq_length]							,      self.vocab_size	)

        _UpperCamelCase  :							Optional[int]	    =	None
        if self.use_attention_mask:
               _UpperCamelCase  :							int	    =	ids_tensor([self.batch_size, self.decoder_seq_length]							,      vocab_size=2	)

        _UpperCamelCase  :							Optional[Any]	    =	None
        if self.use_labels:
               _UpperCamelCase  :							Optional[int]	    =	ids_tensor([self.batch_size, self.decoder_seq_length]							,      self.vocab_size	)

        _UpperCamelCase  :							Optional[Any]	    =	SpeechaTextaConfig(
            vocab_size=self.vocab_size							,      d_model=self.d_model							,      decoder_layers=self.decoder_layers							,      decoder_ffn_dim=self.decoder_ffn_dim							,      decoder_attention_heads=self.decoder_attention_heads							,      eos_token_id=self.eos_token_id							,      bos_token_id=self.bos_token_id							,      use_cache=self.use_cache							,      pad_token_id=self.pad_token_id							,      decoder_start_token_id=self.decoder_start_token_id							,      max_position_embeddings=self.max_position_embeddings							,      )

        return (
            config,
            input_ids,
            attention_mask,
            lm_labels,
        )

 def        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		Optional[int]							,      lowerCamelCase							:		List[str]							,      lowerCamelCase							:		List[str]							,      lowerCamelCase							:		int							,      )			->				Any:
        _UpperCamelCase  :							Optional[Any]	    =	True
        _UpperCamelCase  :							Optional[int]	    =	SpeechaTextaDecoder(config=lowerCamelCase	).to(lowerCamelCase	).eval()
        _UpperCamelCase  :							List[Any]	    =	input_ids[:2]

        input_ids[input_ids == 0] += 1
        # first forward pass
        _UpperCamelCase  :							int	    =	model(lowerCamelCase							,      use_cache=lowerCamelCase	)
        _UpperCamelCase  :							Optional[Any]	    =	model(lowerCamelCase	)
        _UpperCamelCase  :							Optional[Any]	    =	model(lowerCamelCase							,      use_cache=lowerCamelCase	)

        self.parent.assertTrue(len(lowerCamelCase	) == len(lowerCamelCase	)	)
        self.parent.assertTrue(len(lowerCamelCase	) == len(lowerCamelCase	) + 1	)

        _UpperCamelCase  :							List[Any]	    =	outputs['past_key_values']

        # create hypothetical next token and extent to next_input_ids
        _UpperCamelCase  :							Optional[int]	    =	ids_tensor((2, 1)							,      config.vocab_size - 1	) + 1

        # append to next input_ids and
        _UpperCamelCase  :							List[str]	    =	torch.cat([input_ids, next_tokens]							,      dim=-1	)

        print(lowerCamelCase	)

        _UpperCamelCase  :							Dict	    =	model(lowerCamelCase	)['last_hidden_state']
        _UpperCamelCase  :							str	    =	model(lowerCamelCase							,      past_key_values=lowerCamelCase	)['last_hidden_state']

        # select random slice
        _UpperCamelCase  :							List[str]	    =	ids_tensor((1,)							,      output_from_past.shape[-1]	).item()
        _UpperCamelCase  :							Optional[Any]	    =	output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
        _UpperCamelCase  :							List[Any]	    =	output_from_past[:, 0, random_slice_idx].detach()

        # test that outputs are equal for slice
        assert torch.allclose(lowerCamelCase							,      lowerCamelCase							,      atol=1e-3	)

 def        __snake_case       (      self							:		Any	)			->				str:
        _UpperCamelCase  :							Optional[Any]	    =	self.prepare_config_and_inputs()
        (
            (
            _UpperCamelCase
        )    ,   (
            _UpperCamelCase
        )    ,   (
            _UpperCamelCase
        )    ,   (
            _UpperCamelCase
        )    ,   
        )  :							List[str]	    =	config_and_inputs

        _UpperCamelCase  :							int	    =	{
            'input_ids': input_ids,
            'attention_mask': attention_mask,
        }
        return config, inputs_dict







@require_torch
class  __lowerCAmelCase	(							a_,						a_,						a_,						unittest.TestCase    ):



 '''simple docstring'''
 _lowerCamelCase    :						Any					     =			(SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
 _lowerCamelCase    :						str					     =			(SpeechaTextaForCausalLM,) if is_torch_available() else ()
 _lowerCamelCase    :						Dict					     =			{'text-generation': SpeechaTextaForCausalLM} if is_torch_available() else {}
 _lowerCamelCase    :						Dict					     =			True
 _lowerCamelCase    :						Optional[Any]					     =			False

 def        __snake_case       (      self							:		List[str]							,      )			->				Tuple:
        _UpperCamelCase  :							List[str]	    =	SpeechaTextaStandaloneDecoderModelTester(self							,      is_training=lowerCamelCase	)
        _UpperCamelCase  :							List[Any]	    =	ConfigTester(self							,      config_class=lowerCamelCase	)

 def        __snake_case       (      self							:		Dict	)			->				Dict:
        pass

 def        __snake_case       (      self							:		Optional[int]	)			->				int:
        pass

 def        __snake_case       (      self							:		Union[str, Any]	)			->				Optional[Any]:
        pass

 def        __snake_case       (      self							:		Optional[Any]	)			->				Union[str, Any]:
        self.config_tester.run_common_tests()

 def        __snake_case       (      self							:		Dict	)			->				str:
        _UpperCamelCase  :							List[str]	    =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past(*lowerCamelCase	)

 def        __snake_case       (      self							:		Optional[Any]	)			->				List[str]:
        return











"""simple docstring"""



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


a  :						List[Any]					       =							logging.get_logger(__name__)

a  :						List[Any]					       =							{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

a  :						Any					       =							{
    '''vocab_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt''',
    },
    '''tokenizer_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json''',
    },
}

a  :						str					       =							{
    '''microsoft/mpnet-base''': 512,
}

a  :						Union[str, Any]					       =							{
    '''microsoft/mpnet-base''': {'''do_lower_case''': True},
}


class 	_UpperCamelCase ( __UpperCamelCase							):



    '''simple docstring'''


    __lowercase    :   Optional[Any]											=						VOCAB_FILES_NAMES
    __lowercase    :   Optional[int]											=						PRETRAINED_VOCAB_FILES_MAP
    __lowercase    :   Dict											=						PRETRAINED_INIT_CONFIGURATION
    __lowercase    :   Optional[Any]											=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowercase    :   Dict											=						MPNetTokenizer
    __lowercase    :   Dict											=						['input_ids', 'attention_mask']


    def __init__(      self   ,			__lowercase=None   ,			__lowercase=None   ,			__lowercase=True   ,			__lowercase="<s>"   ,			__lowercase="</s>"   ,			__lowercase="</s>"   ,			__lowercase="<s>"   ,			__lowercase="[UNK]"   ,			__lowercase="<pad>"   ,			__lowercase="<mask>"   ,			__lowercase=True   ,			__lowercase=None   ,			**__lowercase   ,			):
     super().__init__(
         __lowercase   ,			tokenizer_file=__lowercase   ,			do_lower_case=__lowercase   ,			bos_token=__lowercase   ,			eos_token=__lowercase   ,			sep_token=__lowercase   ,			cls_token=__lowercase   ,			unk_token=__lowercase   ,			pad_token=__lowercase   ,			mask_token=__lowercase   ,			tokenize_chinese_chars=__lowercase   ,			strip_accents=__lowercase   ,			**__lowercase   ,			)

     UpperCAmelCase__ 						=				json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
     if (
         pre_tok_state.get("""lowercase"""   ,			__lowercase			) != do_lower_case
         or pre_tok_state.get("""strip_accents"""   ,			__lowercase			) != strip_accents
     ):
      UpperCAmelCase__ 						=				getattr(__lowercase   ,			pre_tok_state.pop("""type"""			)			)
      UpperCAmelCase__ 						=				do_lower_case
      UpperCAmelCase__ 						=				strip_accents
      UpperCAmelCase__ 						=				pre_tok_class(**__lowercase			)

     UpperCAmelCase__ 						=				do_lower_case


    @property
    def 		A__      (      self			):
     if self._mask_token is None:
      if self.verbose:
       logger.error("""Using mask_token, but it is not set yet."""			)
      return None
     return str(self._mask_token			)


    @mask_token.setter
    def 		A__      (      self   ,			__lowercase			):
     UpperCAmelCase__ 						=				AddedToken(__lowercase   ,			lstrip=__lowercase   ,			rstrip=__lowercase			) if isinstance(__lowercase   ,			__lowercase			) else value
     UpperCAmelCase__ 						=				value


    def 		A__      (      self   ,			__lowercase   ,			__lowercase=None			):
     UpperCAmelCase__ 						=				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
      return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


    def 		A__      (      self   ,			__lowercase   ,			__lowercase = None			):
     UpperCAmelCase__ 						=				[self.sep_token_id]
     UpperCAmelCase__ 						=				[self.cls_token_id]

     if token_ids_a is None:
      return len(cls + token_ids_a + sep			) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]






    def 		A__      (      self   ,			__lowercase   ,			__lowercase = None			):
     UpperCAmelCase__ 						=				self._tokenizer.model.save(__lowercase   ,			name=__lowercase			)
     return tuple(__lowercase			)



