
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 				lowercase					(		unittest.TestCase  ,   _SCREAMING_SNAKE_CASE   ):



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     load_tool('text-to-speech'       )
				self.tool.setup()



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				# SpeechT5 isn't deterministic
				torch.manual_seed(0       )
				UpperCamelCase		=     self.tool('hey'       )
				UpperCamelCase		=     result.to_raw()
				self.assertTrue(
				    torch.allclose(
				        resulting_tensor[:3]				,      torch.tensor([-0.000_5966_6688_3211_5829, -0.000_3657_6401_9079_5064, -0.0001_3439_5027_9988_3485]       )				,      )       )



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				# SpeechT5 isn't deterministic
				torch.manual_seed(0       )
				UpperCamelCase		=     self.tool('hey'       )
				UpperCamelCase		=     result.to_raw()
				self.assertTrue(
				    torch.allclose(
				        resulting_tensor[:3]				,      torch.tensor([-0.000_5966_6688_3211_5829, -0.000_3657_6401_9079_5064, -0.0001_3439_5027_9988_3485]       )				,      )       )


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase					: Union[str, Any]      =       {
    "configuration_instructblip": [
        "INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "InstructBlipConfig",
        "InstructBlipQFormerConfig",
        "InstructBlipVisionConfig",
    ],
    "processing_instructblip": ["InstructBlipProcessor"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase					: List[Any]      =       [
					    "INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "InstructBlipQFormerModel",
					    "InstructBlipPreTrainedModel",
					    "InstructBlipForConditionalGeneration",
					    "InstructBlipVisionModel",
					]

if TYPE_CHECKING:
					from .configuration_instructblip import (
					    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    InstructBlipConfig,
					    InstructBlipQFormerConfig,
					    InstructBlipVisionConfig,
					)
					from .processing_instructblip import InstructBlipProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_instructblip import (
										    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    InstructBlipForConditionalGeneration,
										    InstructBlipPreTrainedModel,
										    InstructBlipQFormerModel,
										    InstructBlipVisionModel,
										)

else:
					import sys

					_UpperCAmelCase					: Union[str, Any]      =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
