




import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
 from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

 from .test_feature_extraction_speechta import floats_list


_lowerCamelCase			 =						get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')



@require_torch
class 			a     (   unittest.TestCase			):

     '''simple docstring'''





     def   lowerCamelCase_   (	self   :			str				):
         UpperCAmelCase_							 =     tempfile.mkdtemp()

         UpperCAmelCase_							 =     SpeechTaTokenizer(__snake_case				)
         tokenizer.save_pretrained(self.tmpdirname				)

         UpperCAmelCase_							 =     {
             '''feature_size''': 1,
             '''padding_value''': 0.0,
             '''sampling_rate''': 1_60_00,
             '''do_normalize''': False,
             '''num_mel_bins''': 80,
             '''hop_length''': 16,
             '''win_length''': 64,
             '''win_function''': '''hann_window''',
             '''fmin''': 80,
             '''fmax''': 76_00,
             '''mel_floor''': 1E-10,
             '''reduction_factor''': 2,
             '''return_attention_mask''': True,
         }

         UpperCAmelCase_							 =     os.path.join(self.tmpdirname					,    __snake_case				)
         with open(self.feature_extraction_file					,    '''w'''					,    encoding='''utf-8'''				) as fp:
             fp.write(json.dumps(__snake_case				) + '''\n'''				)
     def   lowerCamelCase_   (	self   :			int					,    **__snake_case   :			Any				):
         return SpeechTaTokenizer.from_pretrained(self.tmpdirname					,    **__snake_case				)
     def   lowerCamelCase_   (	self   :			Tuple					,    **__snake_case   :			str				):
         return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname					,    **__snake_case				)
     def   lowerCamelCase_   (	self   :			List[str]				):
         shutil.rmtree(self.tmpdirname				)
     def   lowerCamelCase_   (	self   :			Any				):
         UpperCAmelCase_							 =     self.get_tokenizer()
         UpperCAmelCase_							 =     self.get_feature_extractor()

         UpperCAmelCase_							 =     SpeechTaProcessor(tokenizer=__snake_case					,    feature_extractor=__snake_case				)

         processor.save_pretrained(self.tmpdirname				)
         UpperCAmelCase_							 =     SpeechTaProcessor.from_pretrained(self.tmpdirname				)

         self.assertEqual(processor.tokenizer.get_vocab()					,    tokenizer.get_vocab()				)
         self.assertIsInstance(processor.tokenizer					,    __snake_case				)

         self.assertEqual(processor.feature_extractor.to_json_string()					,    feature_extractor.to_json_string()				)
         self.assertIsInstance(processor.feature_extractor					,    __snake_case				)
     def   lowerCamelCase_   (	self   :			List[Any]				):
         UpperCAmelCase_							 =     SpeechTaProcessor(tokenizer=self.get_tokenizer()					,    feature_extractor=self.get_feature_extractor()				)
         processor.save_pretrained(self.tmpdirname				)

         UpperCAmelCase_							 =     self.get_tokenizer(bos_token='''(BOS)'''					,    eos_token='''(EOS)'''				)
         UpperCAmelCase_							 =     self.get_feature_extractor(do_normalize=__snake_case					,    padding_value=1.0				)

         UpperCAmelCase_							 =     SpeechTaProcessor.from_pretrained(
             self.tmpdirname					,    bos_token='''(BOS)'''					,    eos_token='''(EOS)'''					,    do_normalize=__snake_case					,    padding_value=1.0				)

         self.assertEqual(processor.tokenizer.get_vocab()					,    tokenizer_add_kwargs.get_vocab()				)
         self.assertIsInstance(processor.tokenizer					,    __snake_case				)

         self.assertEqual(processor.feature_extractor.to_json_string()					,    feature_extractor_add_kwargs.to_json_string()				)
         self.assertIsInstance(processor.feature_extractor					,    __snake_case				)
     def   lowerCamelCase_   (	self   :			Union[str, Any]				):
         UpperCAmelCase_							 =     self.get_feature_extractor()
         UpperCAmelCase_							 =     self.get_tokenizer()

         UpperCAmelCase_							 =     SpeechTaProcessor(tokenizer=__snake_case					,    feature_extractor=__snake_case				)

         UpperCAmelCase_							 =     floats_list((3, 10_00)				)

         UpperCAmelCase_							 =     feature_extractor(audio=__snake_case					,    return_tensors='''np'''				)
         UpperCAmelCase_							 =     processor(audio=__snake_case					,    return_tensors='''np'''				)

         for key in input_feat_extract.keys():
             self.assertAlmostEqual(input_feat_extract[key].sum()					,    input_processor[key].sum()					,    delta=1E-2				)
     def   lowerCamelCase_   (	self   :			int				):
         UpperCAmelCase_							 =     self.get_feature_extractor()
         UpperCAmelCase_							 =     self.get_tokenizer()

         UpperCAmelCase_							 =     SpeechTaProcessor(tokenizer=__snake_case					,    feature_extractor=__snake_case				)

         UpperCAmelCase_							 =     floats_list((3, 10_00)				)

         UpperCAmelCase_							 =     feature_extractor(audio_target=__snake_case					,    return_tensors='''np'''				)
         UpperCAmelCase_							 =     processor(audio_target=__snake_case					,    return_tensors='''np'''				)

         for key in input_feat_extract.keys():
             self.assertAlmostEqual(input_feat_extract[key].sum()					,    input_processor[key].sum()					,    delta=1E-2				)
     def   lowerCamelCase_   (	self   :			Dict				):
         UpperCAmelCase_							 =     self.get_feature_extractor()
         UpperCAmelCase_							 =     self.get_tokenizer()

         UpperCAmelCase_							 =     SpeechTaProcessor(tokenizer=__snake_case					,    feature_extractor=__snake_case				)

         UpperCAmelCase_							 =     '''This is a test string'''

         UpperCAmelCase_							 =     processor(text=__snake_case				)
         UpperCAmelCase_							 =     tokenizer(__snake_case				)

         for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]					,    encoded_processor[key]				)
     def   lowerCamelCase_   (	self   :			str				):
         UpperCAmelCase_							 =     self.get_feature_extractor()
         UpperCAmelCase_							 =     self.get_tokenizer()

         UpperCAmelCase_							 =     SpeechTaProcessor(tokenizer=__snake_case					,    feature_extractor=__snake_case				)

         UpperCAmelCase_							 =     '''This is a test string'''

         UpperCAmelCase_							 =     processor(text_target=__snake_case				)
         UpperCAmelCase_							 =     tokenizer(__snake_case				)

         for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]					,    encoded_processor[key]				)
     def   lowerCamelCase_   (	self   :			Dict				):
         UpperCAmelCase_							 =     self.get_feature_extractor()
         UpperCAmelCase_							 =     self.get_tokenizer()

         UpperCAmelCase_							 =     SpeechTaProcessor(tokenizer=__snake_case					,    feature_extractor=__snake_case				)

         UpperCAmelCase_							 =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         UpperCAmelCase_							 =     processor.batch_decode(__snake_case				)
         UpperCAmelCase_							 =     tokenizer.batch_decode(__snake_case				)

         self.assertListEqual(__snake_case					,    __snake_case				)




     def   lowerCamelCase_   (	self   :			Dict				):
         UpperCAmelCase_							 =     self.get_feature_extractor()
         UpperCAmelCase_							 =     self.get_tokenizer()

         UpperCAmelCase_							 =     SpeechTaProcessor(tokenizer=__snake_case					,    feature_extractor=__snake_case				)

         self.assertListEqual(
             processor.model_input_names					,    feature_extractor.model_input_names					,    msg='''`processor` and `feature_extractor` model input names do not match'''					,    )











from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase					:     Optional[Any]					  =  logging.get_logger(__name__)

_lowerCamelCase					:     str					  =  {
    "google/reformer-crime-and-punishment": (
        "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json"
    ),
    "google/reformer-enwik8": "https://huggingface.co/google/reformer-enwik8/resolve/main/config.json",
}
class 				__snake_case     (_a						):
				lowerCAmelCase__			=   "reformer"
				lowerCAmelCase__			=   ["past_buckets_states"]
				lowerCAmelCase__			=   {}



				def __init__(					self					:						Optional[int]						,		_UpperCAmelCase					:						str=64						,		_UpperCAmelCase					:						Tuple=["local", "lsh", "local", "lsh", "local", "lsh"]						,		_UpperCAmelCase					:						Optional[int]=1.0						,		_UpperCAmelCase					:						str=True						,		_UpperCAmelCase					:						List[Any]=[64, 64]						,		_UpperCAmelCase					:						str=[64, 192]						,		_UpperCAmelCase					:						List[Any]=0						,		_UpperCAmelCase					:						Optional[Any]=2						,		_UpperCAmelCase					:						Dict=512						,		_UpperCAmelCase					:						Optional[int]=None						,		_UpperCAmelCase					:						Tuple="relu"						,		_UpperCAmelCase					:						Tuple=0.05						,		_UpperCAmelCase					:						int=256						,		_UpperCAmelCase					:						List[str]=0.02						,		_UpperCAmelCase					:						Any=False						,		_UpperCAmelCase					:						Any=1E-12						,		_UpperCAmelCase					:						List[Any]=1						,		_UpperCAmelCase					:						Optional[Any]=0						,		_UpperCAmelCase					:						Union[str, Any]=0.05						,		_UpperCAmelCase					:						int=64						,		_UpperCAmelCase					:						List[str]=64						,		_UpperCAmelCase					:						Any=0.0						,		_UpperCAmelCase					:						Any=1						,		_UpperCAmelCase					:						Optional[Any]=0						,		_UpperCAmelCase					:						Union[str, Any]=4096						,		_UpperCAmelCase					:						Dict=12						,		_UpperCAmelCase					:						int=None						,		_UpperCAmelCase					:						List[Any]=1						,		_UpperCAmelCase					:						Union[str, Any]=0						,		_UpperCAmelCase					:						Any=320						,		_UpperCAmelCase					:						Any=False						,		_UpperCAmelCase					:						List[str]=True						,		_UpperCAmelCase					:						Optional[int]=None						,		**_UpperCAmelCase					:						Tuple						,		)     ->			List[str]:





								'''simple docstring'''




								_lowerCAmelCase			:			Union[str, Any]        =					hash_seed
								_lowerCAmelCase			:			int        =					vocab_size
								_lowerCAmelCase			:			Optional[Any]        =					attention_head_size
								_lowerCAmelCase			:			Dict        =					hidden_size
								_lowerCAmelCase			:			Union[str, Any]        =					num_attention_heads
								_lowerCAmelCase			:			int        =					num_hashes
								_lowerCAmelCase			:			Union[str, Any]        =					len(_UpperCAmelCase					)
								_lowerCAmelCase			:			int        =					tuple(_UpperCAmelCase					) if isinstance(_UpperCAmelCase						,		_UpperCAmelCase					) else num_buckets
								_lowerCAmelCase			:			Union[str, Any]        =					lsh_attn_chunk_length
								_lowerCAmelCase			:			List[str]        =					local_attn_chunk_length
								_lowerCAmelCase			:			int        =					lsh_num_chunks_after
								_lowerCAmelCase			:			Union[str, Any]        =					lsh_num_chunks_before
								_lowerCAmelCase			:			str        =					local_num_chunks_after
								_lowerCAmelCase			:			Dict        =					local_num_chunks_before
								_lowerCAmelCase			:			Any        =					hidden_act
								_lowerCAmelCase			:			List[str]        =					feed_forward_size
								_lowerCAmelCase			:			str        =					hidden_dropout_prob
								_lowerCAmelCase			:			Any        =					lsh_attention_probs_dropout_prob
								_lowerCAmelCase			:			Dict        =					local_attention_probs_dropout_prob
								_lowerCAmelCase			:			Tuple        =					max_position_embeddings
								_lowerCAmelCase			:			Any        =					initializer_range
								_lowerCAmelCase			:			List[str]        =					layer_norm_eps
								_lowerCAmelCase			:			Dict        =					axial_pos_embds
								_lowerCAmelCase			:			Union[str, Any]        =					tuple(_UpperCAmelCase					)
								_lowerCAmelCase			:			int        =					tuple(_UpperCAmelCase					)
								_lowerCAmelCase			:			List[str]        =					axial_norm_std
								_lowerCAmelCase			:			List[str]        =					chunk_size_lm_head
								_lowerCAmelCase			:			int        =					attn_layers
								_lowerCAmelCase			:			List[Any]        =					use_cache
								_lowerCAmelCase			:			Optional[int]        =					classifier_dropout
								super().__init__(
								    pad_token_id=_UpperCAmelCase						,		eos_token_id=_UpperCAmelCase						,		is_decoder=_UpperCAmelCase						,		tie_word_embeddings=_UpperCAmelCase						,		**_UpperCAmelCase						,		)

