
import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
						import numpy as np
						import torch

if is_vision_available():
						from PIL import Image

def    __snake_case       (  __magic_name__		,						__magic_name__=False		,						__magic_name__=False		,						__magic_name__=False	):







				'''simple docstring'''


				assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

				lowercase						    =			[]
				for i in range(image_processor_tester.batch_size	):
								if equal_resolution:
												lowercase						    =			lowercase						    =			image_processor_tester.max_resolution
								else:
												# To avoid getting image width/height 0
												lowercase						    =			image_processor_tester.min_resolution
												if getattr(__magic_name__		,						"size_divisor"		,						__magic_name__	):
																# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
																lowercase						    =			max(image_processor_tester.size_divisor		,						__magic_name__	)
												lowercase    ,  lowercase						    =			np.random.choice(np.arange(__magic_name__		,						image_processor_tester.max_resolution	)		,						2	)
								image_inputs.append(
								    np.random.randint(255		,						size=(image_processor_tester.num_channels, width, height)		,						dtype=np.uinta	)	)

				if not numpify and not torchify:
								# PIL expects the channel dimension as last dimension
								lowercase						    =			[Image.fromarray(np.moveaxis(__magic_name__		,						0		,						-1	)	) for image in image_inputs]

				if torchify:
								lowercase						    =			[torch.from_numpy(__magic_name__	) for image in image_inputs]

				return image_inputs

def    __snake_case       (  __magic_name__		,						__magic_name__=10		,						__magic_name__=10		,						__magic_name__=False		,						__magic_name__=False	):







				'''simple docstring'''


				lowercase						    =			[]
				for i in range(image_processor_tester.num_frames	):
								video.append(np.random.randint(255		,						size=(image_processor_tester.num_channels, width, height)		,						dtype=np.uinta	)	)

				if not numpify and not torchify:
								# PIL expects the channel dimension as last dimension
								lowercase						    =			[Image.fromarray(np.moveaxis(__magic_name__		,						0		,						-1	)	) for frame in video]

				if torchify:
								lowercase						    =			[torch.from_numpy(__magic_name__	) for frame in video]

				return video

def    __snake_case       (  __magic_name__		,						__magic_name__=False		,						__magic_name__=False		,						__magic_name__=False	):







				'''simple docstring'''


				assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

				lowercase						    =			[]
				for i in range(image_processor_tester.batch_size	):
								if equal_resolution:
												lowercase						    =			lowercase						    =			image_processor_tester.max_resolution
								else:
												lowercase    ,  lowercase						    =			np.random.choice(
												    np.arange(image_processor_tester.min_resolution		,						image_processor_tester.max_resolution	)		,						2	)
												lowercase						    =			prepare_video(
												    image_processor_tester=__magic_name__		,						width=__magic_name__		,						height=__magic_name__		,						numpify=__magic_name__		,						torchify=__magic_name__		,						)
								video_inputs.append(__magic_name__	)

				return video_inputs





class      UpperCamelCase_			:







						'''simple docstring'''



						UpperCamelCase			:     List[str]							      =				None





						def 					SCREAMING_SNAKE_CASE( self       :Any    )     ->Optional[Any]:
										lowercase						    =			self.image_processing_class(**self.image_processor_dict    )
										lowercase						    =			json.loads(image_processor.to_json_string()    )
										for key, value in self.image_processor_dict.items():
														self.assertEqual(obj[key]		,						lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :Optional[Any]    )     ->List[str]:
										lowercase						    =			self.image_processing_class(**self.image_processor_dict    )

										with tempfile.TemporaryDirectory() as tmpdirname:
														lowercase						    =			os.path.join(lowerCAmelCase__		,						"image_processor.json"    )
														image_processor_first.to_json_file(lowerCAmelCase__    )
														lowercase						    =			self.image_processing_class.from_json_file(lowerCAmelCase__    )

										self.assertEqual(image_processor_second.to_dict()		,						image_processor_first.to_dict()    )





						def 					SCREAMING_SNAKE_CASE( self       :List[str]    )     ->Optional[int]:
										lowercase						    =			self.image_processing_class(**self.image_processor_dict    )

										with tempfile.TemporaryDirectory() as tmpdirname:
														lowercase						    =			image_processor_first.save_pretrained(lowerCAmelCase__    )[0]
														check_json_file_has_correct_format(lowerCAmelCase__    )
														lowercase						    =			self.image_processing_class.from_pretrained(lowerCAmelCase__    )

										self.assertEqual(image_processor_second.to_dict()		,						image_processor_first.to_dict()    )





						def 					SCREAMING_SNAKE_CASE( self       :Any    )     ->Dict:
										lowercase						    =			self.image_processing_class()
										self.assertIsNotNone(lowerCAmelCase__    )





						@require_torch
						@require_vision
						def 					SCREAMING_SNAKE_CASE( self       :int    )     ->int:
										if self.test_cast_dtype is not None:
														# Initialize image_processor
														lowercase						    =			self.image_processing_class(**self.image_processor_dict    )

														# create random PyTorch tensors
														lowercase						    =			prepare_image_inputs(self.image_processor_tester		,						equal_resolution=lowerCAmelCase__		,						torchify=lowerCAmelCase__    )

														lowercase						    =			image_processor(lowerCAmelCase__		,						return_tensors="pt"    )
														# for layoutLM compatiblity
														self.assertEqual(encoding.pixel_values.device		,						torch.device("cpu"    )    )
														self.assertEqual(encoding.pixel_values.dtype		,						torch.floataa    )

														lowercase						    =			image_processor(lowerCAmelCase__		,						return_tensors="pt"    ).to(torch.floataa    )
														self.assertEqual(encoding.pixel_values.device		,						torch.device("cpu"    )    )
														self.assertEqual(encoding.pixel_values.dtype		,						torch.floataa    )

														lowercase						    =			image_processor(lowerCAmelCase__		,						return_tensors="pt"    ).to("cpu"		,						torch.bfloataa    )
														self.assertEqual(encoding.pixel_values.device		,						torch.device("cpu"    )    )
														self.assertEqual(encoding.pixel_values.dtype		,						torch.bfloataa    )

														with self.assertRaises(lowerCAmelCase__    ):
																		lowercase						    =			image_processor(lowerCAmelCase__		,						return_tensors="pt"    ).to(torch.bfloataa		,						"cpu"    )

														# Try with text + image feature
														lowercase						    =			image_processor(lowerCAmelCase__		,						return_tensors="pt"    )
														encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]]    )}    )
														lowercase						    =			encoding.to(torch.floataa    )

														self.assertEqual(encoding.pixel_values.device		,						torch.device("cpu"    )    )
														self.assertEqual(encoding.pixel_values.dtype		,						torch.floataa    )
														self.assertEqual(encoding.input_ids.dtype		,						torch.long    )


from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				if not inplace:
								lowercase						    =			ma + ma
				else:
								ma += ma

				return ma

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			-1 * len(__magic_name__	)
				lowercase						    =			list(range(len(tensor.shape[:zero_index]	)	)	)
				return tensor.permute(first_inds + [zero_index + i for i in inds]	)

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				return t.reshape(t.shape[:-no_dims] + (-1,)	)

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__		,						__magic_name__ = 1e-4	):







				'''simple docstring'''


				lowercase						    =			mask.expand(*value.shape	)
				return torch.sum(mask * value		,						dim=__magic_name__	) / (eps + torch.sum(__magic_name__		,						dim=__magic_name__	))

def    __snake_case       (  __magic_name__		,						__magic_name__ = 2.3_1_2_5		,						__magic_name__ = 2_1.6_8_7_5		,						__magic_name__ = 64	):







				'''simple docstring'''


				lowercase						    =			torch.linspace(__magic_name__		,						__magic_name__		,						no_bins - 1		,						device=pts.device	)
				lowercase						    =			torch.sqrt(torch.sum((pts.unsqueeze(-2	) - pts.unsqueeze(-3	)) ** 2		,						dim=-1	)	)
				return torch.bucketize(__magic_name__		,						__magic_name__	)

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			dicts[0]
				lowercase						    =			{}
				for k, v in first.items():
								lowercase						    =			[d[k] for d in dicts]
								if isinstance(__magic_name__		,						__magic_name__	):
												lowercase						    =			dict_multimap(__magic_name__		,						__magic_name__	)
								else:
												lowercase						    =			fn(__magic_name__	)

				return new_dict

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			v_bins.view(((1,) * len(x.shape	)) + (len(__magic_name__	),)	)
				lowercase						    =			x[..., None] - reshaped_bins
				lowercase						    =			torch.argmin(torch.abs(__magic_name__	)		,						dim=-1	)
				return nn.functional.one_hot(__magic_name__		,						num_classes=len(__magic_name__	)	).float()

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__ = 0		,						__magic_name__ = 0	):







				'''simple docstring'''


				lowercase						    =			[]
				for i, s in enumerate(data.shape[:no_batch_dims]	):
								lowercase						    =			torch.arange(__magic_name__	)
								lowercase						    =			r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape	) - i - 1)))	)
								ranges.append(__magic_name__	)

				lowercase						    =			[slice(__magic_name__	) for _ in range(len(data.shape	) - no_batch_dims	)]
				lowercase						    =			inds
				ranges.extend(__magic_name__	)
				# Matt note: Editing this to get around the behaviour of using a list as an array index changing
				# in recent Numpy versions
				return data[tuple(__magic_name__	)]


_snake_case			:       List[Any]      =						TypeVar("T")

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			{}
				for k, v in dic.items():
								if isinstance(__magic_name__		,						__magic_name__	):
												lowercase						    =			dict_map(__magic_name__		,						__magic_name__		,						__magic_name__	)
								else:
												lowercase						    =			tree_map(__magic_name__		,						__magic_name__		,						__magic_name__	)

				return new_dict

@overload
def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				...

@overload
def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				...

@overload
def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				...

@overload
def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				...

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				if isinstance(__magic_name__		,						__magic_name__	):
								return dict_map(__magic_name__		,						__magic_name__		,						__magic_name__	)
				elif isinstance(__magic_name__		,						__magic_name__	):
								return [tree_map(__magic_name__		,						__magic_name__		,						__magic_name__	) for x in tree]
				elif isinstance(__magic_name__		,						__magic_name__	):
								return tuple(tree_map(__magic_name__		,						__magic_name__		,						__magic_name__	) for x in tree	)
				elif isinstance(__magic_name__		,						__magic_name__	):
								return fn(__magic_name__	)
				else:
								print(type(__magic_name__	)	)
								raise ValueError("Not supported"	)




_snake_case			:       Union[str, Any]      =						partial(tree_map, leaf_type=torch.Tensor)
