



"""simple docstring"""
import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
  import torch

if is_soundfile_availble():
  import soundfile as sf

if is_vision_available():
  from PIL import Image





def  lowerCamelCase_						(UpperCamelCase__		:		Dict=""							):
    _UpperCAmelCase							:  Optional[Any]											=    tempfile.mkdtemp()
    return os.path.join(UpperCamelCase__     ,       str(uuid.uuida()							) + suffix							)
@require_soundfile
@require_torch
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     def  __lowerCAmelCase	(					self		)							->					Optional[int]:
         _UpperCAmelCase							:  Tuple											=    torch.rand(1_2       ,	dtype=torch.floataa		) - 0.5
         _UpperCAmelCase							:  int											=    AgentAudio(A		)
         _UpperCAmelCase							:  List[str]											=    str(agent_type.to_string()		)

         # Ensure that the tensor and the agent_type's tensor are the same
         self.assertTrue(torch.allclose(A       ,	agent_type.to_raw()       ,	atol=1E-4		)		)

         del agent_type

         # Ensure the path remains even after the object deletion
         self.assertTrue(os.path.exists(A		)		)

         # Ensure that the file contains the same value as the original tensor
         _UpperCAmelCase							,				_UpperCAmelCase							:  List[str]											=    sf.read(A		)
         self.assertTrue(torch.allclose(A       ,	torch.tensor(A		)       ,	atol=1E-4		)		)





     def  __lowerCAmelCase	(					self		)							->					Optional[Any]:
         _UpperCAmelCase							:  Dict											=    torch.rand(1_2       ,	dtype=torch.floataa		) - 0.5
         _UpperCAmelCase							:  Optional[int]											=    get_new_path(suffix='''.wav'''		)
         sf.write(A       ,	A       ,	1_6_0_0_0		)

         _UpperCAmelCase							:  List[Any]											=    AgentAudio(A		)

         self.assertTrue(torch.allclose(A       ,	agent_type.to_raw()       ,	atol=1E-4		)		)
         self.assertEqual(agent_type.to_string()       ,	A		)



@require_vision
@require_torch
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  List[str]											=    torch.randint(0       ,	2_5_6       ,	(6_4, 6_4, 3)		)
         _UpperCAmelCase							:  str											=    AgentImage(A		)
         _UpperCAmelCase							:  Union[str, Any]											=    str(agent_type.to_string()		)

         # Ensure that the tensor and the agent_type's tensor are the same
         self.assertTrue(torch.allclose(A       ,	agent_type._tensor       ,	atol=1E-4		)		)

         self.assertIsInstance(agent_type.to_raw()       ,	Image.Image		)

         # Ensure the path remains even after the object deletion
         del agent_type
         self.assertTrue(os.path.exists(A		)		)





     def  __lowerCAmelCase	(					self		)							->					int:
         _UpperCAmelCase							:  Union[str, Any]											=    Path(get_tests_dir('''fixtures/tests_samples/COCO'''		)		) / '''000000039769.png'''
         _UpperCAmelCase							:  Dict											=    Image.open(A		)
         _UpperCAmelCase							:  Optional[int]											=    AgentImage(A		)

         self.assertTrue(path.samefile(agent_type.to_string()		)		)
         self.assertTrue(image == agent_type.to_raw()		)

         # Ensure the path remains even after the object deletion
         del agent_type
         self.assertTrue(os.path.exists(A		)		)





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         _UpperCAmelCase							:  Any											=    Path(get_tests_dir('''fixtures/tests_samples/COCO'''		)		) / '''000000039769.png'''
         _UpperCAmelCase							:  List[str]											=    Image.open(A		)
         _UpperCAmelCase							:  Union[str, Any]											=    AgentImage(A		)

         self.assertFalse(path.samefile(agent_type.to_string()		)		)
         self.assertTrue(image == agent_type.to_raw()		)

         # Ensure the path remains even after the object deletion
         del agent_type
         self.assertTrue(os.path.exists(A		)		)



class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     def  __lowerCAmelCase	(					self		)							->					Tuple:
         _UpperCAmelCase							:  List[Any]											=    '''Hey!'''
         _UpperCAmelCase							:  Any											=    AgentText(A		)

         self.assertEqual(A       ,	agent_type.to_string()		)
         self.assertEqual(A       ,	agent_type.to_raw()		)
         self.assertEqual(A       ,	A		)







"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCAmelCase    :str     =					{
    'configuration_squeezebert': [
        'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SqueezeBertConfig',
        'SqueezeBertOnnxConfig',
    ],
    'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Optional[int]     =					['SqueezeBertTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :str     =					[
      'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'SqueezeBertForMaskedLM',
      'SqueezeBertForMultipleChoice',
      'SqueezeBertForQuestionAnswering',
      'SqueezeBertForSequenceClassification',
      'SqueezeBertForTokenClassification',
      'SqueezeBertModel',
      'SqueezeBertModule',
      'SqueezeBertPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_squeezebert import (
      SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      SqueezeBertConfig,
      SqueezeBertOnnxConfig,
  )
  from .tokenization_squeezebert import SqueezeBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_squeezebert import (
        SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        SqueezeBertForMaskedLM,
        SqueezeBertForMultipleChoice,
        SqueezeBertForQuestionAnswering,
        SqueezeBertForSequenceClassification,
        SqueezeBertForTokenClassification,
        SqueezeBertModel,
        SqueezeBertModule,
        SqueezeBertPreTrainedModel,
    )

else:
  import sys

  _lowerCAmelCase    :Optional[Any]     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


