

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     SCREAMING_SNAKE_CASE__     :





       '''simple docstring'''




       def __init__(						self       :						Any    ,		lowerCAmelCase__       :						Optional[int]    ,		lowerCAmelCase__       :						Any=99    ,		lowerCAmelCase__       :						List[str]=13    ,		lowerCAmelCase__       :						Any=16    ,		lowerCAmelCase__       :						Tuple=7    ,		lowerCAmelCase__       :						Dict=True    ,		lowerCAmelCase__       :						Optional[int]=True    ,		lowerCAmelCase__       :						Dict=True    ,		lowerCAmelCase__       :						List[str]=False    ,		lowerCAmelCase__       :						Optional[int]=True    ,		lowerCAmelCase__       :						Dict=2    ,		lowerCAmelCase__       :						Optional[Any]=32    ,		lowerCAmelCase__       :						List[str]=4    ,		lowerCAmelCase__       :						int=4    ,		lowerCAmelCase__       :						str=30    ,		lowerCAmelCase__       :						Any=0    ,		lowerCAmelCase__       :						Tuple=1    ,		lowerCAmelCase__       :						List[Any]=2    ,		lowerCAmelCase__       :						Optional[Any]=None    ,		)			->    Tuple:
             snake_case__					=   parent
             snake_case__					=   batch_size
             snake_case__					=   decoder_seq_length
             # For common tests
             snake_case__					=   self.decoder_seq_length
             snake_case__					=   is_training
             snake_case__					=   use_attention_mask
             snake_case__					=   use_labels

             snake_case__					=   vocab_size
             snake_case__					=   d_model
             snake_case__					=   d_model
             snake_case__					=   decoder_layers
             snake_case__					=   decoder_layers
             snake_case__					=   decoder_ffn_dim
             snake_case__					=   decoder_attention_heads
             snake_case__					=   decoder_attention_heads
             snake_case__					=   eos_token_id
             snake_case__					=   bos_token_id
             snake_case__					=   pad_token_id
             snake_case__					=   decoder_start_token_id
             snake_case__					=   use_cache
             snake_case__					=   max_position_embeddings

             snake_case__					=   None
             snake_case__					=   decoder_seq_length
             snake_case__					=   2
             snake_case__					=   1

       def UpperCAmelCase_						(						self       :						Optional[int]							)			->    int:
             snake_case__					=   ids_tensor([self.batch_size, self.decoder_seq_length]    ,		self.vocab_size							)

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

             snake_case__					=   None
             if self.use_labels:
                   snake_case__					=   ids_tensor([self.batch_size, self.decoder_seq_length]    ,		self.vocab_size							)

             snake_case__					=   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 UpperCAmelCase_						(						self       :						List[str]    ,		lowerCAmelCase__       :						Union[str, Any]    ,		lowerCAmelCase__       :						List[Any]    ,		lowerCAmelCase__       :						str    ,		lowerCAmelCase__       :						Optional[Any]    ,		)			->    str:
             snake_case__					=   True
             snake_case__					=   SpeechaTextaDecoder(config=lowerCAmelCase__							).to(lowerCAmelCase__							).eval()
             snake_case__					=   input_ids[:2]

             input_ids[input_ids == 0] += 1
             # first forward pass
             snake_case__					=   model(lowerCAmelCase__    ,		use_cache=lowerCAmelCase__							)
             snake_case__					=   model(lowerCAmelCase__							)
             snake_case__					=   model(lowerCAmelCase__    ,		use_cache=lowerCAmelCase__							)

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

             snake_case__					=   outputs["""past_key_values"""]

             # create hypothetical next token and extent to next_input_ids
             snake_case__					=   ids_tensor((2, 1)    ,		config.vocab_size - 1							) + 1

             # append to next input_ids and
             snake_case__					=   torch.cat([input_ids, next_tokens]    ,		dim=-1							)

             print(lowerCAmelCase__							)

             snake_case__					=   model(lowerCAmelCase__							)["""last_hidden_state"""]
             snake_case__					=   model(lowerCAmelCase__    ,		past_key_values=lowerCAmelCase__							)["""last_hidden_state"""]

             # select random slice
             snake_case__					=   ids_tensor((1,)    ,		output_from_past.shape[-1]							).item()
             snake_case__					=   output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
             snake_case__					=   output_from_past[:, 0, random_slice_idx].detach()

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

       def UpperCAmelCase_						(						self       :						int							)			->    str:
             snake_case__					=   self.prepare_config_and_inputs()
             (
                 (
                 snake_case__
             )	,			(
                 snake_case__
             )	,			(
                 snake_case__
             )	,			(
                 snake_case__
             )	,			
             )					=   config_and_inputs

             snake_case__					=   {
                 """input_ids""": input_ids,
                 """attention_mask""": attention_mask,
             }
             return config, inputs_dict



@require_torch
class     SCREAMING_SNAKE_CASE__     (						__a ,	__a ,	__a ,	unittest.TestCase		):





       '''simple docstring'''




       UpperCamelCase__ :     Union[str, Any]				    =       (SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
       UpperCamelCase__ :     Optional[Any]				    =       (SpeechaTextaForCausalLM,) if is_torch_available() else ()
       UpperCamelCase__ :     List[Any]				    =       {'''text-generation''': SpeechaTextaForCausalLM} if is_torch_available() else {}
       UpperCamelCase__ :     Optional[int]				    =       True
       UpperCamelCase__ :     int				    =       False

       def UpperCAmelCase_						(						self       :						List[Any]    ,		)			->    List[str]:
             snake_case__					=   SpeechaTextaStandaloneDecoderModelTester(self    ,		is_training=lowerCAmelCase__							)
             snake_case__					=   ConfigTester(self    ,		config_class=lowerCAmelCase__							)

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

       def UpperCAmelCase_						(						self       :						Dict							)			->    Union[str, Any]:
             pass

       def UpperCAmelCase_						(						self       :						Tuple							)			->    List[str]:
             pass

       def UpperCAmelCase_						(						self       :						Optional[int]							)			->    int:
             self.config_tester.run_common_tests()

       def UpperCAmelCase_						(						self       :						List[str]							)			->    Tuple:
             snake_case__					=   self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_decoder_model_past(*lowerCAmelCase__							)

       def UpperCAmelCase_						(						self       :						Dict							)			->    Dict:
             return









import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_reformer import ReformerTokenizer
else:
       lowerCAmelCase						:   Union[str, Any]										=    None


lowerCAmelCase						:   str										=    logging.get_logger(__name__)


lowerCAmelCase						:   Any										=    '''▁'''

lowerCAmelCase						:   Tuple										=    {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase						:   Optional[Any]										=    {
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    },
    '''tokenizer_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'''
        )
    },
}

lowerCAmelCase						:   Union[str, Any]										=    {
    '''google/reformer-crime-and-punishment''': 52_4288,
}
class     SCREAMING_SNAKE_CASE__     (						__a		):





       '''simple docstring'''




       UpperCamelCase__ :     Union[str, Any]				    =       VOCAB_FILES_NAMES
       UpperCamelCase__ :     str				    =       PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase__ :     str				    =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase__ :     str				    =       ['''input_ids''', '''attention_mask''']
       UpperCamelCase__ :     Optional[Any]				    =       ReformerTokenizer

       def __init__(						self       :						List[str]    ,		lowerCAmelCase__       :						Tuple=None    ,		lowerCAmelCase__       :						Optional[Any]=None    ,		lowerCAmelCase__       :						Dict="</s>"    ,		lowerCAmelCase__       :						Tuple="<unk>"    ,		lowerCAmelCase__       :						Dict=[]    ,		**lowerCAmelCase__       :						Optional[Any]    ,		)			->    Dict:
             super().__init__(
                 lowerCAmelCase__    ,		tokenizer_file=lowerCAmelCase__    ,		eos_token=lowerCAmelCase__    ,		unk_token=lowerCAmelCase__    ,		additional_special_tokens=lowerCAmelCase__    ,		**lowerCAmelCase__    ,		)

             snake_case__					=   vocab_file
             snake_case__					=   False if not self.vocab_file else True

       def UpperCAmelCase_						(						self       :						int    ,		lowerCAmelCase__       :						str    ,		lowerCAmelCase__       :						Optional[str] = None							)			->    Tuple[str]:
             if not self.can_save_slow_tokenizer:
                   raise ValueError(
                       """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                       """tokenizer."""							)

             if not os.path.isdir(lowerCAmelCase__							):
                   logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
                   return
             snake_case__					=   os.path.join(
                 lowerCAmelCase__    ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

             if os.path.abspath(self.vocab_file							) != os.path.abspath(lowerCAmelCase__							):
                   copyfile(self.vocab_file    ,		lowerCAmelCase__							)

             return (out_vocab_file,)






