


import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
   from .testing_utils import (
       floats_tensor,
       load_hf_numpy,
       load_image,
       load_numpy,
       load_pt,
       nightly,
       parse_flag_from_env,
       print_tensor_test,
       require_torch_a,
       require_torch_gpu,
       skip_mps,
       slow,
       torch_all_close,
       torch_device,
   )
   from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


SCREAMING_SNAKE_CASE				:						List[Any]			   = get_logger(__name__)




def 			UpperCamelCase			(					_a						)    ->							Tuple:
     '''simple docstring'''





     if version.parse(_a						) < version.parse(_a						):
          if "dev" in min_version:
               lowercase_				:str       			=				(
                   '''This example requires a source install from HuggingFace diffusers (see '''
                   '''`https://huggingface.co/docs/diffusers/installation#install-from-source`),'''
               )
          else:
               lowercase_				:Tuple       			=				f"This example requires a minimum version of {min_version},"
          error_message += f" but the version found is {__version__}.\n"
          raise ImportError(_a						)



"""simple docstring"""

import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list
class    _UpperCAmelCase							(						unittest.TestCase							):




      def 			a_					(							self							)  ->    Any:
          UpperCAmelCase  	=    '<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'.split(' '							)
          UpperCAmelCase  	=    dict(zip(lowercase_      ,				range(len(lowercase_							)							)							)							)

          UpperCAmelCase  	=    {
              'pad_token': '<pad>',
              'unk_token': '<unk>',
              'bos_token': '<s>',
              'eos_token': '</s>',
          }
          UpperCAmelCase  	=    {
              'feature_size': 1,
              'padding_value': 0.0,
              'sampling_rate': 1_6_0_0_0,
              'return_attention_mask': False,
              'do_normalize': True,
          }

          UpperCAmelCase  	=    tempfile.mkdtemp()
          UpperCAmelCase  	=    os.path.join(self.tmpdirname      ,				VOCAB_FILES_NAMES['vocab_file']							)
          UpperCAmelCase  	=    os.path.join(self.tmpdirname      ,				lowercase_							)
          with open(self.vocab_file      ,				'w'      ,				encoding='utf-8'							) as fp:
              fp.write(json.dumps(lowercase_							) + '\n'							)

          with open(self.feature_extraction_file      ,				'w'      ,				encoding='utf-8'							) as fp:
              fp.write(json.dumps(lowercase_							) + '\n'							)




      def 			a_					(							self      ,				**lowercase_							)  ->    Any:
          UpperCAmelCase  	=    self.add_kwargs_tokens_map.copy()
          kwargs.update(lowercase_							)
          return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname      ,				**lowercase_							)




      def 			a_					(							self      ,				**lowercase_							)  ->    Dict:
          return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname      ,				**lowercase_							)




      def 			a_					(							self							)  ->    Any:
          shutil.rmtree(self.tmpdirname							)




      def 			a_					(							self							)  ->    Tuple:
          UpperCAmelCase  	=    self.get_tokenizer()
          UpperCAmelCase  	=    self.get_feature_extractor()

          UpperCAmelCase  	=    WavaVecaProcessor(tokenizer=lowercase_      ,				feature_extractor=lowercase_							)

          processor.save_pretrained(self.tmpdirname							)
          UpperCAmelCase  	=    WavaVecaProcessor.from_pretrained(self.tmpdirname							)

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

          self.assertEqual(processor.feature_extractor.to_json_string()      ,				feature_extractor.to_json_string()							)
          self.assertIsInstance(processor.feature_extractor      ,				lowercase_							)




      def 			a_					(							self							)  ->    Dict:
          UpperCAmelCase  	=    WavaVecaProcessor(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=lowercase_      ,				padding_value=1.0							)

          UpperCAmelCase  	=    WavaVecaProcessor.from_pretrained(
              self.tmpdirname      ,				bos_token='(BOS)'      ,				eos_token='(EOS)'      ,				do_normalize=lowercase_      ,				padding_value=1.0							)

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

          self.assertEqual(processor.feature_extractor.to_json_string()      ,				feature_extractor_add_kwargs.to_json_string()							)
          self.assertIsInstance(processor.feature_extractor      ,				lowercase_							)




      def 			a_					(							self							)  ->    Tuple:
          UpperCAmelCase  	=    self.get_feature_extractor()
          UpperCAmelCase  	=    self.get_tokenizer()

          UpperCAmelCase  	=    WavaVecaProcessor(tokenizer=lowercase_      ,				feature_extractor=lowercase_							)

          UpperCAmelCase  	=    floats_list((3, 1_0_0_0)							)

          UpperCAmelCase  	=    feature_extractor(lowercase_      ,				return_tensors='np'							)
          UpperCAmelCase  	=    processor(lowercase_      ,				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 			a_					(							self							)  ->    List[str]:
          UpperCAmelCase  	=    self.get_feature_extractor()
          UpperCAmelCase  	=    self.get_tokenizer()

          UpperCAmelCase  	=    WavaVecaProcessor(tokenizer=lowercase_      ,				feature_extractor=lowercase_							)

          UpperCAmelCase  	=    'This is a test string'

          UpperCAmelCase  	=    processor(text=lowercase_							)

          UpperCAmelCase  	=    tokenizer(lowercase_							)

          for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]      ,				encoded_processor[key]							)




      def 			a_					(							self							)  ->    List[str]:
          UpperCAmelCase  	=    self.get_feature_extractor()
          UpperCAmelCase  	=    self.get_tokenizer()

          UpperCAmelCase  	=    WavaVecaProcessor(tokenizer=lowercase_      ,				feature_extractor=lowercase_							)

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

          UpperCAmelCase  	=    processor.batch_decode(lowercase_							)
          UpperCAmelCase  	=    tokenizer.batch_decode(lowercase_							)

          self.assertListEqual(lowercase_      ,				lowercase_							)




      def 			a_					(							self							)  ->    Union[str, Any]:
          UpperCAmelCase  	=    self.get_feature_extractor()
          UpperCAmelCase  	=    self.get_tokenizer()

          UpperCAmelCase  	=    WavaVecaProcessor(tokenizer=lowercase_      ,				feature_extractor=lowercase_							)

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



