

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 					__lowercase      (     _A=""			)     ->						str:
						SCREAMING_SNAKE_CASE :      Dict       =   tempfile.mkdtemp()
						return os.path.join(_A						,    str(uuid.uuida()			) + suffix			)







@require_soundfile
@require_torch
class        a__       (   unittest.TestCase       ):







		"""simple docstring"""




		def  _lowercase      (			self    : Any		)					->Any:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      Any       =   torch.rand(1_2				,				dtype=torch.floataa		) - 0.5
								SCREAMING_SNAKE_CASE :      List[Any]       =   AgentAudio(UpperCAmelCase__		)
								SCREAMING_SNAKE_CASE :      List[str]       =   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
								SCREAMING_SNAKE_CASE :      str       =   sf.read(UpperCAmelCase__		)
								self.assertTrue(torch.allclose(UpperCAmelCase__				,				torch.tensor(UpperCAmelCase__		)				,				atol=1e-4		)		)




		def  _lowercase      (			self    : Optional[Any]		)					->Tuple:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      Optional[int]       =   torch.rand(1_2				,				dtype=torch.floataa		) - 0.5
								SCREAMING_SNAKE_CASE :      Tuple       =   get_new_path(suffix=""".wav"""		)
								sf.write(UpperCAmelCase__				,				UpperCAmelCase__				,				1_6_0_0_0		)

								SCREAMING_SNAKE_CASE :      int       =   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        a__       (   unittest.TestCase       ):







		"""simple docstring"""




		def  _lowercase      (			self    : Dict		)					->Any:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      Any       =   torch.randint(0				,				2_5_6				,				(6_4, 6_4, 3)		)
								SCREAMING_SNAKE_CASE :      List[str]       =   AgentImage(UpperCAmelCase__		)
								SCREAMING_SNAKE_CASE :      Optional[Any]       =   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    : int		)					->int:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      Optional[Any]       =   Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""
								SCREAMING_SNAKE_CASE :      str       =   Image.open(UpperCAmelCase__		)
								SCREAMING_SNAKE_CASE :      List[str]       =   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    : Dict		)					->List[Any]:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      str       =   Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""
								SCREAMING_SNAKE_CASE :      Optional[int]       =   Image.open(UpperCAmelCase__		)
								SCREAMING_SNAKE_CASE :      Optional[int]       =   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        a__       (   unittest.TestCase       ):







		"""simple docstring"""




		def  _lowercase      (			self    : Tuple		)					->Any:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      List[str]       =   """Hey!"""
								SCREAMING_SNAKE_CASE :      Optional[int]       =   AgentText(UpperCAmelCase__		)

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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase__ :     List[Any]				    =    {"""configuration_vit""": ["""VIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTConfig""", """ViTOnnxConfig"""]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     Union[str, Any]				    =    ["""ViTFeatureExtractor"""]
  UpperCAmelCase__ :     List[Any]				    =    ["""ViTImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     List[Any]				    =    [
      """VIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """ViTForImageClassification""",
      """ViTForMaskedImageModeling""",
      """ViTModel""",
      """ViTPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     Tuple				    =    [
      """TFViTForImageClassification""",
      """TFViTModel""",
      """TFViTPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     str				    =    [
      """FlaxViTForImageClassification""",
      """FlaxViTModel""",
      """FlaxViTPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_vit import ViTFeatureExtractor
    from .image_processing_vit import ViTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit import (
        VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTForImageClassification,
        ViTForMaskedImageModeling,
        ViTModel,
        ViTPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
  import sys

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




