



import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import ChineseCLIPImageProcessor

class   _A   ( unittest.TestCase	):


						'''simple docstring'''





						def __init__(	self	:							List[Any]							,      lowerCamelCase	:							Any							,      lowerCamelCase	:							Dict=7							,      lowerCamelCase	:							Tuple=3							,      lowerCamelCase	:							Dict=18							,      lowerCamelCase	:							Any=30							,      lowerCamelCase	:							List[Any]=400							,      lowerCamelCase	:							Optional[int]=True							,      lowerCamelCase	:							Optional[Any]=None							,      lowerCamelCase	:							List[Any]=True							,      lowerCamelCase	:							List[str]=None							,      lowerCamelCase	:							str=True							,      lowerCamelCase	:							Optional[Any]=[0.4814_5466, 0.457_8275, 0.4082_1073]							,      lowerCamelCase	:							List[Any]=[0.2686_2954, 0.2613_0258, 0.2757_7711]							,      lowerCamelCase	:							str=True							,      ):






							'''simple docstring'''



							__lowercase		       =			size if size is not None else {"height": 224, "width": 224}
							__lowercase		       =			crop_size if crop_size is not None else {"height": 18, "width": 18}
							__lowercase		       =			parent
							__lowercase		       =			batch_size
							__lowercase		       =			num_channels
							__lowercase		       =			image_size
							__lowercase		       =			min_resolution
							__lowercase		       =			max_resolution
							__lowercase		       =			do_resize
							__lowercase		       =			size
							__lowercase		       =			do_center_crop
							__lowercase		       =			crop_size
							__lowercase		       =			do_normalize
							__lowercase		       =			image_mean
							__lowercase		       =			image_std
							__lowercase		       =			do_convert_rgb



						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_convert_rgb": self.do_convert_rgb,
							}



						def     _snake_case	(	self	:							int							,      lowerCamelCase	:							int=False							,      lowerCamelCase	:							Dict=False							,      lowerCamelCase	:							Any=False	):






							'''simple docstring'''



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

							if equal_resolution:
								__lowercase		       =			[]
								for i in range(self.batch_size	):
									image_inputs.append(
									    np.random.randint(
									        255							,      size=(self.num_channels, self.max_resolution, self.max_resolution)							,      dtype=np.uinta	)	)
							else:
								__lowercase		       =			[]
								for i in range(self.batch_size	):
									__lowercase       ,    __lowercase		       =			np.random.choice(np.arange(self.min_resolution							,      self.max_resolution	)							,      2	)
									image_inputs.append(np.random.randint(255							,      size=(self.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(lowerCamelCase							,      0							,      -1	)	) for x in image_inputs]

							if torchify:
								__lowercase		       =			[torch.from_numpy(lowerCamelCase	) for x in image_inputs]

							return image_inputs

@require_torch
@require_vision
class   _A   ( _lowercase  ,						unittest.TestCase	):


						'''simple docstring'''





						_snake_case			:     int      							=      ChineseCLIPImageProcessor if is_vision_available() else None



						def     _snake_case	(	self	:							Dict	):






							'''simple docstring'''



							__lowercase		       =			ChineseCLIPImageProcessingTester(self							,      do_center_crop=lowerCamelCase	)



						@property
						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()



						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							__lowercase		       =			self.image_processing_class(**self.image_processor_dict	)
							self.assertTrue(hasattr(lowerCamelCase							,      "do_resize"	)	)
							self.assertTrue(hasattr(lowerCamelCase							,      "size"	)	)
							self.assertTrue(hasattr(lowerCamelCase							,      "do_center_crop"	)	)
							self.assertTrue(hasattr(lowerCamelCase							,      "center_crop"	)	)
							self.assertTrue(hasattr(lowerCamelCase							,      "do_normalize"	)	)
							self.assertTrue(hasattr(lowerCamelCase							,      "image_mean"	)	)
							self.assertTrue(hasattr(lowerCamelCase							,      "image_std"	)	)
							self.assertTrue(hasattr(lowerCamelCase							,      "do_convert_rgb"	)	)



						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							__lowercase		       =			self.image_processing_class.from_dict(self.image_processor_dict	)
							self.assertEqual(image_processor.size							,      {"height": 224, "width": 224}	)
							self.assertEqual(image_processor.crop_size							,      {"height": 18, "width": 18}	)

							__lowercase		       =			self.image_processing_class.from_dict(self.image_processor_dict							,      size=42							,      crop_size=84	)
							self.assertEqual(image_processor.size							,      {"shortest_edge": 42}	)
							self.assertEqual(image_processor.crop_size							,      {"height": 84, "width": 84}	)



						def     _snake_case	(	self	:							List[str]	):






							'''simple docstring'''



							pass



						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							__lowercase		       =			self.image_processing_class(**self.image_processor_dict	)
							# create random PIL images
							__lowercase		       =			self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase	)
							for image in image_inputs:
								self.assertIsInstance(lowerCamelCase							,      Image.Image	)

							# Test not batched input
							__lowercase		       =			image_processing(image_inputs[0]							,      return_tensors="pt"	).pixel_values
							self.assertEqual(
							    encoded_images.shape							,      (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )							,      )

							# Test batched
							__lowercase		       =			image_processing(lowerCamelCase							,      return_tensors="pt"	).pixel_values
							self.assertEqual(
							    encoded_images.shape							,      (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )							,      )



						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							__lowercase		       =			self.image_processing_class(**self.image_processor_dict	)
							# create random numpy tensors
							__lowercase		       =			self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase							,      numpify=lowerCamelCase	)
							for image in image_inputs:
								self.assertIsInstance(lowerCamelCase							,      np.ndarray	)

							# Test not batched input
							__lowercase		       =			image_processing(image_inputs[0]							,      return_tensors="pt"	).pixel_values
							self.assertEqual(
							    encoded_images.shape							,      (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )							,      )

							# Test batched
							__lowercase		       =			image_processing(lowerCamelCase							,      return_tensors="pt"	).pixel_values
							self.assertEqual(
							    encoded_images.shape							,      (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )							,      )



						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							__lowercase		       =			self.image_processing_class(**self.image_processor_dict	)
							# create random PyTorch tensors
							__lowercase		       =			self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase							,      torchify=lowerCamelCase	)
							for image in image_inputs:
								self.assertIsInstance(lowerCamelCase							,      torch.Tensor	)

							# Test not batched input
							__lowercase		       =			image_processing(image_inputs[0]							,      return_tensors="pt"	).pixel_values
							self.assertEqual(
							    encoded_images.shape							,      (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )							,      )

							# Test batched
							__lowercase		       =			image_processing(lowerCamelCase							,      return_tensors="pt"	).pixel_values
							self.assertEqual(
							    encoded_images.shape							,      (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )							,      )

@require_torch
@require_vision
class   _A   ( _lowercase  ,						unittest.TestCase	):


						'''simple docstring'''





						_snake_case			:     str      							=      ChineseCLIPImageProcessor if is_vision_available() else None



						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							__lowercase		       =			ChineseCLIPImageProcessingTester(self							,      num_channels=4							,      do_center_crop=lowerCamelCase	)
							__lowercase		       =			3



						@property
						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()



						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							__lowercase		       =			self.image_processing_class(**self.image_processor_dict	)
							self.assertTrue(hasattr(lowerCamelCase							,      "do_resize"	)	)
							self.assertTrue(hasattr(lowerCamelCase							,      "size"	)	)
							self.assertTrue(hasattr(lowerCamelCase							,      "do_center_crop"	)	)
							self.assertTrue(hasattr(lowerCamelCase							,      "center_crop"	)	)
							self.assertTrue(hasattr(lowerCamelCase							,      "do_normalize"	)	)
							self.assertTrue(hasattr(lowerCamelCase							,      "image_mean"	)	)
							self.assertTrue(hasattr(lowerCamelCase							,      "image_std"	)	)
							self.assertTrue(hasattr(lowerCamelCase							,      "do_convert_rgb"	)	)



						def     _snake_case	(	self	:							Dict	):






							'''simple docstring'''



							pass



						def     _snake_case	(	self	:							List[str]	):






							'''simple docstring'''



							__lowercase		       =			self.image_processing_class(**self.image_processor_dict	)
							# create random PIL images
							__lowercase		       =			self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase	)
							for image in image_inputs:
								self.assertIsInstance(lowerCamelCase							,      Image.Image	)

							# Test not batched input
							__lowercase		       =			image_processing(image_inputs[0]							,      return_tensors="pt"	).pixel_values
							self.assertEqual(
							    encoded_images.shape							,      (
							        1,
							        self.expected_encoded_image_num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )							,      )

							# Test batched
							__lowercase		       =			image_processing(lowerCamelCase							,      return_tensors="pt"	).pixel_values
							self.assertEqual(
							    encoded_images.shape							,      (
							        self.image_processor_tester.batch_size,
							        self.expected_encoded_image_num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )							,      )








from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case__					:     List[Any]    		=   logging.get_logger(__name__)

snake_case__					:     List[str]    		=   {
    """hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}

class   _A   ( _lowercase	):


						'''simple docstring'''





						_snake_case			:     List[Any]      							=      """yolos"""



						def __init__(	self	:							Union[str, Any]							,      lowerCamelCase	:							Union[str, Any]=768							,      lowerCamelCase	:							int=12							,      lowerCamelCase	:							Union[str, Any]=12							,      lowerCamelCase	:							Optional[Any]=3_072							,      lowerCamelCase	:							Optional[int]="gelu"							,      lowerCamelCase	:							Dict=0.0							,      lowerCamelCase	:							Optional[Any]=0.0							,      lowerCamelCase	:							Any=0.02							,      lowerCamelCase	:							Optional[Any]=1e-12							,      lowerCamelCase	:							Optional[Any]=[512, 864]							,      lowerCamelCase	:							str=16							,      lowerCamelCase	:							Dict=3							,      lowerCamelCase	:							str=True							,      lowerCamelCase	:							List[Any]=100							,      lowerCamelCase	:							Dict=True							,      lowerCamelCase	:							Dict=False							,      lowerCamelCase	:							List[str]=1							,      lowerCamelCase	:							str=5							,      lowerCamelCase	:							Any=2							,      lowerCamelCase	:							str=5							,      lowerCamelCase	:							Optional[int]=2							,      lowerCamelCase	:							List[Any]=0.1							,      **lowerCamelCase	:							List[Any]							,      ):






							'''simple docstring'''



							super().__init__(**lowerCamelCase	)

							__lowercase		       =			hidden_size
							__lowercase		       =			num_hidden_layers
							__lowercase		       =			num_attention_heads
							__lowercase		       =			intermediate_size
							__lowercase		       =			hidden_act
							__lowercase		       =			hidden_dropout_prob
							__lowercase		       =			attention_probs_dropout_prob
							__lowercase		       =			initializer_range
							__lowercase		       =			layer_norm_eps
							__lowercase		       =			image_size
							__lowercase		       =			patch_size
							__lowercase		       =			num_channels
							__lowercase		       =			qkv_bias
							__lowercase		       =			num_detection_tokens
							__lowercase		       =			use_mid_position_embeddings
							__lowercase		       =			auxiliary_loss
							# Hungarian matcher
							__lowercase		       =			class_cost
							__lowercase		       =			bbox_cost
							__lowercase		       =			giou_cost
							# Loss coefficients
							__lowercase		       =			bbox_loss_coefficient
							__lowercase		       =			giou_loss_coefficient
							__lowercase		       =			eos_coefficient

class   _A   ( _lowercase	):


						'''simple docstring'''





						_snake_case			:     Dict      							=      version.parse("""1.11"""	)



						@property
						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



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



						@property
						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



							return 1e-4



						@property
						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							return 12



