


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


A							:					str     						=     logging.get_logger(__name__)





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	List[Any]:


						'''simple docstring'''






						__snake_case				=		b.T
						__snake_case				=		np.sum(np.square(_lowerCAmelCase )      ,	axis=1 )
						__snake_case				=		np.sum(np.square(_lowerCAmelCase )      ,	axis=0 )
						__snake_case				=		np.matmul(_lowerCAmelCase      ,	_lowerCAmelCase )
						__snake_case				=		aa[:, None] - 2 * ab + ba[None, :]
						return d





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	Dict:


						'''simple docstring'''






						__snake_case				=		x.reshape(-1      ,	3 )
						__snake_case				=		squared_euclidean_distance(_lowerCAmelCase      ,	_lowerCAmelCase )
						return np.argmin(_lowerCAmelCase      ,	axis=1 )






class     UpperCamelCase(  _a   ):
					snake_case_		:				List[str]										=	["""pixel_values"""]


					def __init__(						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[List[List[int]], np.ndarray]] = None     ,    SCREAMING_SNAKE_CASE	:		bool = True     ,    SCREAMING_SNAKE_CASE	:		Dict[str, int] = None     ,    SCREAMING_SNAKE_CASE	:		PILImageResampling = PILImageResampling.BILINEAR     ,    SCREAMING_SNAKE_CASE	:		bool = True     ,    SCREAMING_SNAKE_CASE	:		bool = True     ,    **SCREAMING_SNAKE_CASE	:		str     ,    )			->	None:




											'''simple docstring'''

											super().__init__(**SCREAMING_SNAKE_CASE						)
											__snake_case				=		size if size is not None else {"height": 2_5_6, "width": 2_5_6}
											__snake_case				=		get_size_dict(SCREAMING_SNAKE_CASE						)
											__snake_case				=		np.array(SCREAMING_SNAKE_CASE						) if clusters is not None else None
											__snake_case				=		do_resize
											__snake_case				=		size
											__snake_case				=		resample
											__snake_case				=		do_normalize
											__snake_case				=		do_color_quantize


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		np.ndarray     ,    SCREAMING_SNAKE_CASE	:		Dict[str, int]     ,    SCREAMING_SNAKE_CASE	:		PILImageResampling = PILImageResampling.BILINEAR     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[str, ChannelDimension]] = None     ,    **SCREAMING_SNAKE_CASE	:		str     ,    )			->	np.ndarray:




											'''simple docstring'''

											__snake_case				=		get_size_dict(SCREAMING_SNAKE_CASE						)
											if "height" not in size or "width" not in size:
																	raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}'''						)
											return resize(
											    SCREAMING_SNAKE_CASE     ,    size=(size["height"], size["width"])     ,    resample=SCREAMING_SNAKE_CASE     ,    data_format=SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		np.ndarray     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[str, ChannelDimension]] = None     ,    )			->	np.ndarray:




											'''simple docstring'''

											__snake_case				=		rescale(image=SCREAMING_SNAKE_CASE     ,    scale=1 / 127.5     ,    data_format=SCREAMING_SNAKE_CASE						)
											__snake_case				=		image - 1
											return image






					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		ImageInput     ,    SCREAMING_SNAKE_CASE	:		bool = None     ,    SCREAMING_SNAKE_CASE	:		Dict[str, int] = None     ,    SCREAMING_SNAKE_CASE	:		PILImageResampling = None     ,    SCREAMING_SNAKE_CASE	:		bool = None     ,    SCREAMING_SNAKE_CASE	:		Optional[bool] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[List[List[int]], np.ndarray]] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[str, TensorType]] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST     ,    **SCREAMING_SNAKE_CASE	:		List[Any]     ,    )			->	PIL.Image.Image:




											'''simple docstring'''

											__snake_case				=		do_resize if do_resize is not None else self.do_resize
											__snake_case				=		size if size is not None else self.size
											__snake_case				=		get_size_dict(SCREAMING_SNAKE_CASE						)
											__snake_case				=		resample if resample is not None else self.resample
											__snake_case				=		do_normalize if do_normalize is not None else self.do_normalize
											__snake_case				=		do_color_quantize if do_color_quantize is not None else self.do_color_quantize
											__snake_case				=		clusters if clusters is not None else self.clusters
											__snake_case				=		np.array(SCREAMING_SNAKE_CASE						)

											__snake_case				=		make_list_of_images(SCREAMING_SNAKE_CASE						)

											if not valid_images(SCREAMING_SNAKE_CASE						):
																	raise ValueError(
																	    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
																	    "torch.Tensor, tf.Tensor or jax.ndarray."						)

											if do_resize and size is None or resample is None:
																	raise ValueError("Size and resample must be specified if do_resize is True."						)

											if do_color_quantize and clusters is None:
																	raise ValueError("Clusters must be specified if do_color_quantize is True."						)

											# All transformations expect numpy arrays.
											__snake_case				=		[to_numpy_array(SCREAMING_SNAKE_CASE						) for image in images]

											if do_resize:
																	__snake_case				=		[self.resize(image=SCREAMING_SNAKE_CASE     ,    size=SCREAMING_SNAKE_CASE     ,    resample=SCREAMING_SNAKE_CASE						) for image in images]

											if do_normalize:
																	__snake_case				=		[self.normalize(image=SCREAMING_SNAKE_CASE						) for image in images]

											if do_color_quantize:
																	__snake_case				=		[to_channel_dimension_format(SCREAMING_SNAKE_CASE     ,    ChannelDimension.LAST						) for image in images]
																	# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
																	__snake_case				=		np.array(SCREAMING_SNAKE_CASE						)
																	__snake_case				=		color_quantize(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						).reshape(images.shape[:-1]						)

																	# flatten to (batch_size, height*width)
																	__snake_case				=		images.shape[0]
																	__snake_case				=		images.reshape(SCREAMING_SNAKE_CASE     ,    -1						)

																	# We need to convert back to a list of images to keep consistent behaviour across processors.
																	__snake_case				=		list(SCREAMING_SNAKE_CASE						)
											else:
																	__snake_case				=		[to_channel_dimension_format(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						) for image in images]

											__snake_case				=		{"input_ids": images}
											return BatchFeature(data=SCREAMING_SNAKE_CASE     ,    tensor_type=SCREAMING_SNAKE_CASE						)









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       (    _lowerCAmelCase="" )						->	str:


						'''simple docstring'''






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






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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	Optional[int]:




											'''simple docstring'''

											__snake_case				=		torch.rand(1_2     ,    dtype=torch.floataa						) - 0.5
											__snake_case				=		AgentAudio(SCREAMING_SNAKE_CASE						)
											__snake_case				=		str(agent_type.to_string()						)

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

											del agent_type

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

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






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	int:




											'''simple docstring'''

											__snake_case				=		torch.rand(1_2     ,    dtype=torch.floataa						) - 0.5
											__snake_case				=		get_new_path(suffix=".wav"						)
											sf.write(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    1_6_0_0_0						)

											__snake_case				=		AgentAudio(SCREAMING_SNAKE_CASE						)

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






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


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




											'''simple docstring'''

											__snake_case				=		torch.randint(0     ,    2_5_6     ,    (6_4, 6_4, 3)						)
											__snake_case				=		AgentImage(SCREAMING_SNAKE_CASE						)
											__snake_case				=		str(agent_type.to_string()						)

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	Any:




											'''simple docstring'''

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

											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(SCREAMING_SNAKE_CASE						)						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	Union[str, Any]:




											'''simple docstring'''

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

											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(SCREAMING_SNAKE_CASE						)						)






class     UpperCamelCase(  unittest.TestCase   ):


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Any:




											'''simple docstring'''

											__snake_case				=		"Hey!"
											__snake_case				=		AgentText(SCREAMING_SNAKE_CASE						)

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





