






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__						(     _a							:      Any=""     ):
		snake_case_					:				Dict    			=							tempfile.mkdtemp()
		return os.path.join(_a	,   str(uuid.uuida()     ) + suffix     )




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


						'''simple docstring'''




						def    _lowerCAmelCase							(						self					)			->					Dict:
								snake_case_					:				Optional[int]    			=							torch.rand(12		,					dtype=torch.floataa					) - 0.5
								snake_case_					:				Tuple    			=							AgentAudio(UpperCamelCase__					)
								snake_case_					:				List[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.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
								snake_case_					:				List[Any]    			=							sf.read(UpperCamelCase__					)
								self.assertTrue(torch.allclose(UpperCamelCase__		,					torch.tensor(UpperCamelCase__					)		,					atol=1e-4					)					)






						def    _lowerCAmelCase							(						self					)			->					str:
								snake_case_					:				Any    			=							torch.rand(12		,					dtype=torch.floataa					) - 0.5
								snake_case_					:				Union[str, Any]    			=							get_new_path(suffix=".wav"					)
								sf.write(UpperCamelCase__		,					UpperCamelCase__		,					1_6000					)

								snake_case_					:				Optional[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 		UpperCAmelCase_    (						unittest.TestCase       ):


						'''simple docstring'''




						def    _lowerCAmelCase							(						self					)			->					int:
								snake_case_					:				Union[str, Any]    			=							torch.randint(0		,					256		,					(64, 64, 3)					)
								snake_case_					:				int    			=							AgentImage(UpperCamelCase__					)
								snake_case_					:				Tuple    			=							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    _lowerCAmelCase							(						self					)			->					Optional[int]:
								snake_case_					:				Dict    			=							Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"
								snake_case_					:				str    			=							Image.open(UpperCamelCase__					)
								snake_case_					:				int    			=							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    _lowerCAmelCase							(						self					)			->					Any:
								snake_case_					:				Any    			=							Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"
								snake_case_					:				Dict    			=							Image.open(UpperCamelCase__					)
								snake_case_					:				Optional[Any]    			=							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 		UpperCAmelCase_    (						unittest.TestCase       ):


						'''simple docstring'''




						def    _lowerCAmelCase							(						self					)			->					Optional[int]:
								snake_case_					:				Tuple    			=							"Hey!"
								snake_case_					:				Optional[int]    			=							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_torch_available


__snake_case       =       {
    """configuration_time_series_transformer""": [
        """TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TimeSeriesTransformerConfig""",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case       =       [
      """TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TimeSeriesTransformerForPrediction""",
      """TimeSeriesTransformerModel""",
      """TimeSeriesTransformerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_time_series_transformer import (
      TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TimeSeriesTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_time_series_transformer import (
        TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimeSeriesTransformerForPrediction,
        TimeSeriesTransformerModel,
        TimeSeriesTransformerPreTrainedModel,
    )

else:
  import sys

  __snake_case       =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





