


import unittest

import numpy as np
import requests

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, prepare_image_inputs


if is_torch_available():
    import torch

    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
    UpperCamelCase					:       Dict			    =     False

if is_vision_available():
    from PIL import Image

    from transformers import PixaStructImageProcessor



class A__     ( unittest.TestCase  ):



  """simple docstring"""



  def __init__( self			:  str				,				lowerCamelCase__			:  Optional[Any]				,				lowerCamelCase__			:  str=7				,				lowerCamelCase__			:  Union[str, Any]=3				,				lowerCamelCase__			:  Optional[int]=18				,				lowerCamelCase__			:  str=30				,				lowerCamelCase__			:  List[str]=400				,				lowerCamelCase__			:  Optional[int]=None				,				lowerCamelCase__			:  Dict=True				,				lowerCamelCase__			:  int=True				,				lowerCamelCase__			:  Optional[Any]=None				,				):
        a__	:  Dict     =   size if size is not None else {"height": 20, "width": 20}
        a__	:  Optional[int]     =   parent
        a__	:  Tuple     =   batch_size
        a__	:  int     =   num_channels
        a__	:  Any     =   image_size
        a__	:  Tuple     =   min_resolution
        a__	:  Tuple     =   max_resolution
        a__	:  Union[str, Any]     =   size
        a__	:  str     =   do_normalize
        a__	:  List[Any]     =   do_convert_rgb
        a__	:  str     =   [512, 1_024, 2_048, 4_096]
        a__	:  str     =   patch_size if patch_size is not None else {"height": 16, "width": 16}

  def    _UpperCamelCase( self			:  Dict       ):
        return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}

  def    _UpperCamelCase( self			:  int       ):
        a__	:  Union[str, Any]     =   "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
        a__	:  Tuple     =   Image.open(requests.get(lowerCamelCase__				,				stream=lowerCamelCase__       ).raw       ).convert("RGB"       )
        return raw_image




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11				,		reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'				,		)
@require_torch
@require_vision
class A__     ( A__				,		unittest.TestCase  ):



  """simple docstring"""



  _lowercase									=		PixaStructImageProcessor if is_vision_available() else None

  def    _UpperCamelCase( self			:  Dict       ):
        a__	:  Optional[int]     =   PixaStructImageProcessingTester(self       )

  @property
  def    _UpperCamelCase( self			:  Any       ):
        return self.image_processor_tester.prepare_image_processor_dict()

  def    _UpperCamelCase( self			:  int       ):
        a__	:  int     =   self.image_processing_class(**self.image_processor_dict       )
        self.assertTrue(hasattr(lowerCamelCase__				,				"do_normalize"       )       )
        self.assertTrue(hasattr(lowerCamelCase__				,				"do_convert_rgb"       )       )

  def    _UpperCamelCase( self			:  List[Any]       ):
        a__	:  List[str]     =   self.image_processor_tester.prepare_dummy_image()

        a__	:  List[Any]     =   self.image_processing_class(**self.image_processor_dict       )
        a__	:  Union[str, Any]     =   2_048

        a__	:  int     =   image_processor(lowerCamelCase__				,				return_tensors="pt"				,				max_patches=lowerCamelCase__       )
        self.assertTrue(torch.allclose(inputs.flattened_patches.mean()				,				torch.tensor(0.0606       )				,				atol=1E-3				,				rtol=1E-3       )       )

  def    _UpperCamelCase( self			:  List[Any]       ):
        # Initialize image_processor
        a__	:  Optional[int]     =   self.image_processing_class(**self.image_processor_dict       )
        # create random PIL images
        a__	:  Any     =   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=lowerCamelCase__       )
        for image in image_inputs:
              self.assertIsInstance(lowerCamelCase__				,				Image.Image       )

        # Test not batched input
        a__	:  Optional[int]     =   (
            (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
            * self.image_processor_tester.num_channels
        ) + 2

        for max_patch in self.image_processor_tester.max_patches:
              # Test not batched input
              a__	:  Any     =   image_processor(
                  image_inputs[0]				,				return_tensors="pt"				,				max_patches=lowerCamelCase__       ).flattened_patches
              self.assertEqual(
                  encoded_images.shape				,				(1, max_patch, expected_hidden_dim)				,				)

              # Test batched
              a__	:  Tuple     =   image_processor(
                  lowerCamelCase__				,				return_tensors="pt"				,				max_patches=lowerCamelCase__       ).flattened_patches
              self.assertEqual(
                  encoded_images.shape				,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,				)

  def    _UpperCamelCase( self			:  Any       ):
        # Initialize image_processor
        a__	:  Tuple     =   self.image_processing_class(**self.image_processor_dict       )
        # create random PIL images
        a__	:  str     =   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=lowerCamelCase__       )
        for image in image_inputs:
              self.assertIsInstance(lowerCamelCase__				,				Image.Image       )

        # Test not batched input
        a__	:  str     =   (
            (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
            * self.image_processor_tester.num_channels
        ) + 2

        a__	:  int     =   True

        for max_patch in self.image_processor_tester.max_patches:
              # Test not batched input
              with self.assertRaises(lowerCamelCase__       ):
                    a__	:  Dict     =   image_processor(
                        image_inputs[0]				,				return_tensors="pt"				,				max_patches=lowerCamelCase__       ).flattened_patches

              a__	:  List[Any]     =   "Hello"

              a__	:  Optional[int]     =   image_processor(
                  image_inputs[0]				,				return_tensors="pt"				,				max_patches=lowerCamelCase__				,				header_text=lowerCamelCase__       ).flattened_patches
              self.assertEqual(
                  encoded_images.shape				,				(1, max_patch, expected_hidden_dim)				,				)

              # Test batched
              a__	:  str     =   image_processor(
                  lowerCamelCase__				,				return_tensors="pt"				,				max_patches=lowerCamelCase__				,				header_text=lowerCamelCase__       ).flattened_patches
              self.assertEqual(
                  encoded_images.shape				,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,				)

  def    _UpperCamelCase( self			:  Optional[Any]       ):
        # Initialize image_processor
        a__	:  Optional[int]     =   self.image_processing_class(**self.image_processor_dict       )
        # create random numpy tensors
        a__	:  Dict     =   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=lowerCamelCase__				,				numpify=lowerCamelCase__       )
        for image in image_inputs:
              self.assertIsInstance(lowerCamelCase__				,				np.ndarray       )

        a__	:  Any     =   (
            (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
            * self.image_processor_tester.num_channels
        ) + 2

        for max_patch in self.image_processor_tester.max_patches:
              # Test not batched input
              a__	:  Optional[Any]     =   image_processor(
                  image_inputs[0]				,				return_tensors="pt"				,				max_patches=lowerCamelCase__       ).flattened_patches
              self.assertEqual(
                  encoded_images.shape				,				(1, max_patch, expected_hidden_dim)				,				)

              # Test batched
              a__	:  Optional[int]     =   image_processor(
                  lowerCamelCase__				,				return_tensors="pt"				,				max_patches=lowerCamelCase__       ).flattened_patches
              self.assertEqual(
                  encoded_images.shape				,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,				)

  def    _UpperCamelCase( self			:  Tuple       ):
        # Initialize image_processor
        a__	:  Optional[Any]     =   self.image_processing_class(**self.image_processor_dict       )
        # create random PyTorch tensors
        a__	:  List[str]     =   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=lowerCamelCase__				,				torchify=lowerCamelCase__       )
        for image in image_inputs:
              self.assertIsInstance(lowerCamelCase__				,				torch.Tensor       )

        # Test not batched input
        a__	:  Optional[Any]     =   (
            (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
            * self.image_processor_tester.num_channels
        ) + 2

        for max_patch in self.image_processor_tester.max_patches:
              # Test not batched input
              a__	:  str     =   image_processor(
                  image_inputs[0]				,				return_tensors="pt"				,				max_patches=lowerCamelCase__       ).flattened_patches
              self.assertEqual(
                  encoded_images.shape				,				(1, max_patch, expected_hidden_dim)				,				)

              # Test batched
              a__	:  List[str]     =   image_processor(
                  lowerCamelCase__				,				return_tensors="pt"				,				max_patches=lowerCamelCase__       ).flattened_patches
              self.assertEqual(
                  encoded_images.shape				,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,				)




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11				,		reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'				,		)
@require_torch
@require_vision
class A__     ( A__				,		unittest.TestCase  ):



  """simple docstring"""



  _lowercase									=		PixaStructImageProcessor if is_vision_available() else None

  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        a__	:  List[str]     =   PixaStructImageProcessingTester(self				,				num_channels=4       )
        a__	:  List[str]     =   3

  @property
  def    _UpperCamelCase( self			:  List[str]       ):
        return self.image_processor_tester.prepare_image_processor_dict()

  def    _UpperCamelCase( self			:  Optional[Any]       ):
        a__	:  Union[str, Any]     =   self.image_processing_class(**self.image_processor_dict       )
        self.assertTrue(hasattr(lowerCamelCase__				,				"do_normalize"       )       )
        self.assertTrue(hasattr(lowerCamelCase__				,				"do_convert_rgb"       )       )

  def    _UpperCamelCase( self			:  str       ):
        # Initialize image_processor
        a__	:  Tuple     =   self.image_processing_class(**self.image_processor_dict       )
        # create random PIL images
        a__	:  Any     =   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=lowerCamelCase__       )
        for image in image_inputs:
              self.assertIsInstance(lowerCamelCase__				,				Image.Image       )

        # Test not batched input
        a__	:  Optional[Any]     =   (
            (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
            * (self.image_processor_tester.num_channels - 1)
        ) + 2

        for max_patch in self.image_processor_tester.max_patches:
              # Test not batched input
              a__	:  Any     =   image_processor(
                  image_inputs[0]				,				return_tensors="pt"				,				max_patches=lowerCamelCase__       ).flattened_patches
              self.assertEqual(
                  encoded_images.shape				,				(1, max_patch, expected_hidden_dim)				,				)

              # Test batched
              a__	:  str     =   image_processor(
                  lowerCamelCase__				,				return_tensors="pt"				,				max_patches=lowerCamelCase__       ).flattened_patches
              self.assertEqual(
                  encoded_images.shape				,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,				)



'''simple docstring'''
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


lowercase__						       =      logging.get_logger(__name__)

lowercase__						       =      {
    "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 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						"""yolos"""



		def __init__(      self				:			Any  ,	UpperCamelCase__				:			Optional[Any]=768  ,	UpperCamelCase__				:			Any=12  ,	UpperCamelCase__				:			Optional[int]=12  ,	UpperCamelCase__				:			Union[str, Any]=3072  ,	UpperCamelCase__				:			str="gelu"  ,	UpperCamelCase__				:			Union[str, Any]=0.0  ,	UpperCamelCase__				:			Union[str, Any]=0.0  ,	UpperCamelCase__				:			Union[str, Any]=0.02  ,	UpperCamelCase__				:			List[Any]=1e-1_2  ,	UpperCamelCase__				:			List[Any]=[512, 864]  ,	UpperCamelCase__				:			int=16  ,	UpperCamelCase__				:			Union[str, Any]=3  ,	UpperCamelCase__				:			Union[str, Any]=True  ,	UpperCamelCase__				:			int=100  ,	UpperCamelCase__				:			Optional[Any]=True  ,	UpperCamelCase__				:			int=False  ,	UpperCamelCase__				:			Dict=1  ,	UpperCamelCase__				:			Optional[int]=5  ,	UpperCamelCase__				:			List[str]=2  ,	UpperCamelCase__				:			Union[str, Any]=5  ,	UpperCamelCase__				:			Union[str, Any]=2  ,	UpperCamelCase__				:			int=0.1  ,	**UpperCamelCase__				:			Optional[Any]  ,	)						->			Any:






				"""simple docstring"""







				super().__init__(**UpperCamelCase__      )

				snake_case  :       List[str]     			=						hidden_size
				snake_case  :       Union[str, Any]     			=						num_hidden_layers
				snake_case  :       List[str]     			=						num_attention_heads
				snake_case  :       Any     			=						intermediate_size
				snake_case  :       int     			=						hidden_act
				snake_case  :       Optional[Any]     			=						hidden_dropout_prob
				snake_case  :       int     			=						attention_probs_dropout_prob
				snake_case  :       Union[str, Any]     			=						initializer_range
				snake_case  :       List[Any]     			=						layer_norm_eps
				snake_case  :       Tuple     			=						image_size
				snake_case  :       Any     			=						patch_size
				snake_case  :       str     			=						num_channels
				snake_case  :       Optional[int]     			=						qkv_bias
				snake_case  :       Optional[Any]     			=						num_detection_tokens
				snake_case  :       Dict     			=						use_mid_position_embeddings
				snake_case  :       List[Any]     			=						auxiliary_loss
				# Hungarian matcher
				snake_case  :       List[str]     			=						class_cost
				snake_case  :       Optional[Any]     			=						bbox_cost
				snake_case  :       Tuple     			=						giou_cost
				# Loss coefficients
				snake_case  :       Union[str, Any]     			=						bbox_loss_coefficient
				snake_case  :       List[Any]     			=						giou_loss_coefficient
				snake_case  :       Tuple     			=						eos_coefficient







class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						version.parse("""1.11"""					)



		@property
		def       lowerCAmelCase				(      self				:			int      )						->			Mapping[str, Mapping[int, str]]:






				"""simple docstring"""







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



		@property
		def       lowerCAmelCase				(      self				:			int      )						->			float:






				"""simple docstring"""







				return 1e-4



		@property
		def       lowerCAmelCase				(      self				:			Tuple      )						->			int:






				"""simple docstring"""







				return 12


