



import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
       import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin



@require_torch
class       UpperCAmelCase__(							unittest.TestCase     ,							lowerCamelCase	):

   '''simple docstring'''




   def 			UpperCAmelCase		(						self  :			List[Any])	->				List[Any]:




         """simple docstring"""

         lowercase__     =  load_tool('text-to-speech')
         self.tool.setup()


   def 			UpperCAmelCase		(						self  :			Any)	->				Dict:




         """simple docstring"""

         torch.manual_seed(0)
         lowercase__     =  self.tool('hey')
         lowercase__     =  result.to_raw()
         self.assertTrue(
             torch.allclose(
                 resulting_tensor[:3]			,				torch.tensor([-0.0_00_59_66_66_88_32_11_58_29, -0.0_00_36_57_64_01_90_79_50_64, -0.00_01_34_39_50_27_99_88_34_85])			,				))



   def 			UpperCAmelCase		(						self  :			Dict)	->				Any:




         """simple docstring"""

         torch.manual_seed(0)
         lowercase__     =  self.tool('hey')
         lowercase__     =  result.to_raw()
         self.assertTrue(
             torch.allclose(
                 resulting_tensor[:3]			,				torch.tensor([-0.0_00_59_66_66_88_32_11_58_29, -0.0_00_36_57_64_01_90_79_50_64, -0.00_01_34_39_50_27_99_88_34_85])			,				))





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a__      :				Any   	=			{"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Optional[int]   	=			["ReformerTokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Union[str, Any]   	=			["ReformerTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Any   	=			[
           "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "ReformerAttention",
           "ReformerForMaskedLM",
           "ReformerForQuestionAnswering",
           "ReformerForSequenceClassification",
           "ReformerLayer",
           "ReformerModel",
           "ReformerModelWithLMHead",
           "ReformerPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_reformer import ReformerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_reformer_fast import ReformerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_reformer import (
                  REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ReformerAttention,
                  ReformerForMaskedLM,
                  ReformerForQuestionAnswering,
                  ReformerForSequenceClassification,
                  ReformerLayer,
                  ReformerModel,
                  ReformerModelWithLMHead,
                  ReformerPreTrainedModel,
              )

else:
       import sys

       a__      :				Tuple   	=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
