



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=""  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Tuple    =    tempfile.mkdtemp()
							return os.path.join(_lowerCamelCase				, str(uuid.uuida()  ) + suffix  )



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







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    torch.rand(12,			dtype=torch.floataa) - 0.5
														_lowerCAmelCase     :							List[str]    =    AgentAudio(__a)
														_lowerCAmelCase     :							int    =    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
														_lowerCAmelCase   ,    _lowerCAmelCase     :							List[str]    =    sf.read(__a)
														self.assertTrue(torch.allclose(__a,			torch.tensor(__a),			atol=1E-4))







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    torch.rand(12,			dtype=torch.floataa) - 0.5
														_lowerCAmelCase     :							Optional[int]    =    get_new_path(suffix=".wav")
														sf.write(__a,			__a,			1_6000)

														_lowerCAmelCase     :							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):







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    torch.randint(0,			256,			(64, 64, 3))
														_lowerCAmelCase     :							str    =    AgentImage(__a)
														_lowerCAmelCase     :							Optional[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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    Path(get_tests_dir("fixtures/tests_samples/COCO")) / "000000039769.png"
														_lowerCAmelCase     :							Tuple    =    Image.open(__a)
														_lowerCAmelCase     :							Optional[Any]    =    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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    Path(get_tests_dir("fixtures/tests_samples/COCO")) / "000000039769.png"
														_lowerCAmelCase     :							Optional[Any]    =    Image.open(__a)
														_lowerCAmelCase     :							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):







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    "Hey!"
														_lowerCAmelCase     :							Any    =    AgentText(__a)

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





import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
						from ... import PreTrainedTokenizerBase, TensorType

_snake_case		=  logging.get_logger(__name__)



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		'vision-encoder-decoder'
							lowerCamelCase__   			=		True







							def __init__(   self,			**__a):

														'''simple docstring'''

														super().__init__(**__a)
														if "encoder" not in kwargs or "decoder" not in kwargs:
																					raise ValueError(
																					    f"A configuraton of type {self.model_type} cannot be instantiated because "
																					    f"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}")

														_lowerCAmelCase     :							str    =    kwargs.pop("encoder")
														_lowerCAmelCase     :							Any    =    encoder_config.pop("model_type")
														_lowerCAmelCase     :							str    =    kwargs.pop("decoder")
														_lowerCAmelCase     :							List[str]    =    decoder_config.pop("model_type")

														_lowerCAmelCase     :							Optional[Any]    =    AutoConfig.for_model(__a,			**__a)
														_lowerCAmelCase     :							Optional[Any]    =    AutoConfig.for_model(__a,			**__a)
														_lowerCAmelCase     :							Optional[int]    =    True







							@classmethod
							def    snake_case__      (   cls,			__a,			__a,			**__a):

														'''simple docstring'''

														logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config")
														_lowerCAmelCase     :							Optional[Any]    =    True
														_lowerCAmelCase     :							str    =    True

														return cls(encoder=encoder_config.to_dict(),			decoder=decoder_config.to_dict(),			**__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    copy.deepcopy(self.__dict__)
														_lowerCAmelCase     :							List[str]    =    self.encoder.to_dict()
														_lowerCAmelCase     :							List[str]    =    self.decoder.to_dict()
														_lowerCAmelCase     :							Any    =    self.__class__.model_type
														return output



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		version.parse('1.11')







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return OrderedDict(
														    [
														        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
														    ])







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return 1E-4







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}})



class   UpperCAmelCase_    (       a):







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    OrderedDict()
														_lowerCAmelCase     :							Any    =    {0: "batch", 1: "past_decoder_sequence + sequence"}
														_lowerCAmelCase     :							List[str]    =    {0: "batch", 1: "past_decoder_sequence + sequence"}
														_lowerCAmelCase     :							Optional[Any]    =    {0: "batch", 1: "encoder_sequence"}

														return common_inputs







							def    snake_case__      (   self,			__a,			__a = -1,			__a = -1,			__a = False,			__a = None,			):

														'''simple docstring'''

														import torch

														_lowerCAmelCase     :							Optional[Any]    =    OrderedDict()

														_lowerCAmelCase     :							List[str]    =    super().generate_dummy_inputs(
														    __a,			batch_size=__a,			seq_length=__a,			is_pair=__a,			framework=__a)

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[Any]    =    dummy_input["input_ids"].shape
														_lowerCAmelCase     :							str    =    (batch, encoder_sequence, self._config.encoder_hidden_size)
														_lowerCAmelCase     :							List[str]    =    dummy_input.pop("input_ids")
														_lowerCAmelCase     :							List[str]    =    dummy_input.pop("attention_mask")
														_lowerCAmelCase     :							Optional[int]    =    torch.zeros(__a)

														return common_inputs



class   UpperCAmelCase_    (       a):







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														pass







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														return VisionEncoderDecoderEncoderOnnxConfig(__a)







							def    snake_case__      (   self,			__a,			__a,			__a = "default"):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    encoder_config.hidden_size
														return VisionEncoderDecoderDecoderOnnxConfig(__a,			__a)
