



"""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   a__      ( lowerCAmelCase__="" ):
      UpperCAmelCase_     					=				tempfile.mkdtemp()
      return os.path.join(lowerCAmelCase__      ,     str(uuid.uuida() ) + suffix )
@require_soundfile
@require_torch
class  lowercase__  (       unittest.TestCase ):







       '''simple docstring'''


       def 	lowercase__							(				self     :    List[str]       )					->		Any:


             '''simple docstring'''




             UpperCAmelCase_     					=				torch.rand(12						,							dtype=torch.floataa       ) - 0.5
             UpperCAmelCase_     					=				AgentAudio(_UpperCAmelCase       )
             UpperCAmelCase_     					=				str(agent_type.to_string()       )

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

             del agent_type

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

             # Ensure that the file contains the same value as the original tensor
             UpperCAmelCase_					,       UpperCAmelCase_     					=				sf.read(_UpperCAmelCase       )
             self.assertTrue(torch.allclose(_UpperCAmelCase						,							torch.tensor(_UpperCAmelCase       )						,							atol=1e-4       )       )







       def 	lowercase__							(				self     :    Dict       )					->		Any:


             '''simple docstring'''




             UpperCAmelCase_     					=				torch.rand(12						,							dtype=torch.floataa       ) - 0.5
             UpperCAmelCase_     					=				get_new_path(suffix=".wav"       )
             sf.write(_UpperCAmelCase						,							_UpperCAmelCase						,							16000       )

             UpperCAmelCase_     					=				AgentAudio(_UpperCAmelCase       )

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



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







       '''simple docstring'''


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


             '''simple docstring'''




             UpperCAmelCase_     					=				torch.randint(0						,							256						,							(64, 64, 3)       )
             UpperCAmelCase_     					=				AgentImage(_UpperCAmelCase       )
             UpperCAmelCase_     					=				str(agent_type.to_string()       )

             # Ensure that the tensor and the agent_type's tensor are the same
             self.assertTrue(torch.allclose(_UpperCAmelCase						,							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(_UpperCAmelCase       )       )



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


             '''simple docstring'''




             UpperCAmelCase_     					=				Path(get_tests_dir("fixtures/tests_samples/COCO"       )       ) / "000000039769.png"
             UpperCAmelCase_     					=				Image.open(_UpperCAmelCase       )
             UpperCAmelCase_     					=				AgentImage(_UpperCAmelCase       )

             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(_UpperCAmelCase       )       )







       def 	lowercase__							(				self     :    Optional[Any]       )					->		Dict:


             '''simple docstring'''




             UpperCAmelCase_     					=				Path(get_tests_dir("fixtures/tests_samples/COCO"       )       ) / "000000039769.png"
             UpperCAmelCase_     					=				Image.open(_UpperCAmelCase       )
             UpperCAmelCase_     					=				AgentImage(_UpperCAmelCase       )

             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(_UpperCAmelCase       )       )



class  lowercase__  (       unittest.TestCase ):







       '''simple docstring'''


       def 	lowercase__							(				self     :    Optional[int]       )					->		Any:


             '''simple docstring'''




             UpperCAmelCase_     					=				"Hey!"
             UpperCAmelCase_     					=				AgentText(_UpperCAmelCase       )

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









"""simple docstring"""

from typing import TYPE_CHECKING

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


A_						:    Optional[Any]				=      {
    'configuration_squeezebert': [
        'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SqueezeBertConfig',
        'SqueezeBertOnnxConfig',
    ],
    'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    Tuple				=      ['SqueezeBertTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    int				=      [
							    '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

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





