




"""simple docstring"""






import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__     						=						logging.get_logger(__name__)







def      _snake_case			(				lowercase__							,    lowercase__=False		):
				_lowerCamelCase     :			Optional[int]    =    []

				# fmt: off
				# stem:
				rename_keys.append(('cls_token', 'vit.embeddings.cls_token')		)
				rename_keys.append(('pos_embed', 'vit.embeddings.position_embeddings')		)

				rename_keys.append(('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight')		)
				rename_keys.append(('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias')		)

				# backbone
				rename_keys.append(('patch_embed.backbone.stem.conv.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight')		)
				rename_keys.append(('patch_embed.backbone.stem.norm.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight')		)
				rename_keys.append(('patch_embed.backbone.stem.norm.bias', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias')		)

				for stage_idx in range(len(config.backbone_config.depths		)		):
								for layer_idx in range(config.backbone_config.depths[stage_idx]		):
												rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight''')		)
												rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight''')		)
												rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias''')		)
												rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight''')		)
												rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight''')		)
												rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias''')		)
												rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight''')		)
												rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight''')		)
												rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias''')		)

								rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight''')		)
								rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight''')		)
								rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias''')		)

				# transformer encoder
				for i in range(config.num_hidden_layers		):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append((f'''blocks.{i}.norm1.weight''', f'''vit.encoder.layer.{i}.layernorm_before.weight''')		)
								rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''vit.encoder.layer.{i}.layernorm_before.bias''')		)
								rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''vit.encoder.layer.{i}.attention.output.dense.weight''')		)
								rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''vit.encoder.layer.{i}.attention.output.dense.bias''')		)
								rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''vit.encoder.layer.{i}.layernorm_after.weight''')		)
								rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''vit.encoder.layer.{i}.layernorm_after.bias''')		)
								rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''vit.encoder.layer.{i}.intermediate.dense.weight''')		)
								rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''vit.encoder.layer.{i}.intermediate.dense.bias''')		)
								rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''vit.encoder.layer.{i}.output.dense.weight''')		)
								rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''vit.encoder.layer.{i}.output.dense.bias''')		)

				if base_model:
								# layernorm + pooler
								rename_keys.extend(
								    [
								        ('norm.weight', 'layernorm.weight'),
								        ('norm.bias', 'layernorm.bias'),
								        ('pre_logits.fc.weight', 'pooler.dense.weight'),
								        ('pre_logits.fc.bias', 'pooler.dense.bias'),
								    ]		)

								# if just the base model, we should remove "vit" from all keys that start with "vit"
								_lowerCamelCase     :			Union[str, Any]    =    [(pair[0], pair[1][4:]) if pair[1].startswith('vit'		) else pair for pair in rename_keys]
				else:
								# layernorm + classification head
								rename_keys.extend(
								    [
								        ('norm.weight', 'vit.layernorm.weight'),
								        ('norm.bias', 'vit.layernorm.bias'),
								        ('head.weight', 'classifier.weight'),
								        ('head.bias', 'classifier.bias'),
								    ]		)
				# fmt: on

				return rename_keys







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__=False		):







				for i in range(config.num_hidden_layers		):
								if base_model:
												_lowerCamelCase     :			List[str]    =    ''''''
								else:
												_lowerCamelCase     :			str    =    '''vit.'''
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								_lowerCamelCase     :			Tuple    =    state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''		)
								_lowerCamelCase     :			Dict    =    state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''		)
								# next, add query, keys and values (in that order) to the state dict
								_lowerCamelCase     :			str    =    in_proj_weight[
								    : config.hidden_size, :
								]
								_lowerCamelCase     :			List[str]    =    in_proj_bias[: config.hidden_size]
								_lowerCamelCase     :			str    =    in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								_lowerCamelCase     :			Dict    =    in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								_lowerCamelCase     :			List[str]    =    in_proj_weight[
								    -config.hidden_size :, :
								]
								_lowerCamelCase     :			List[Any]    =    in_proj_bias[-config.hidden_size :]







def      _snake_case			(				lowercase__		):
				_lowerCamelCase     :			Optional[Any]    =    ['''head.weight''', '''head.bias''']
				for k in ignore_keys:
								state_dict.pop(snake_case_							,    snake_case_		)







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__		):
				_lowerCamelCase     :			Union[str, Any]    =    dct.pop(snake_case_		)
				_lowerCamelCase     :			Union[str, Any]    =    val







def      _snake_case			(				):
				_lowerCamelCase     :			Tuple    =    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				_lowerCamelCase     :			Union[str, Any]    =    Image.open(requests.get(snake_case_							,    stream=snake_case_		).raw		)
				return im







@torch.no_grad()
def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__=False		):
				_lowerCamelCase     :			Dict    =    BitConfig(
				    global_padding='same'							,    layer_type='bottleneck'							,    depths=(3, 4, 9)							,    out_features=['stage3']							,    embedding_dynamic_padding=snake_case_							,    )
				_lowerCamelCase     :			Dict    =    ViTHybridConfig(backbone_config=snake_case_							,    image_size=384							,    num_labels=1000		)
				_lowerCamelCase     :			Optional[Any]    =    False

				# load original model from timm
				_lowerCamelCase     :			Dict    =    timm.create_model(snake_case_							,    pretrained=snake_case_		)
				timm_model.eval()

				# load state_dict of original model, remove and rename some keys
				_lowerCamelCase     :			int    =    timm_model.state_dict()
				if base_model:
								remove_classification_head_(snake_case_		)
				_lowerCamelCase     :			Any    =    create_rename_keys(snake_case_							,    snake_case_		)
				for src, dest in rename_keys:
								rename_key(snake_case_							,    snake_case_							,    snake_case_		)
				read_in_q_k_v(snake_case_							,    snake_case_							,    snake_case_		)

				_lowerCamelCase     :			List[Any]    =    '''huggingface/label-files'''
				_lowerCamelCase     :			Tuple    =    '''imagenet-1k-id2label.json'''
				_lowerCamelCase     :			Union[str, Any]    =    json.load(open(hf_hub_download(snake_case_							,    snake_case_							,    repo_type='dataset'		)							,    'r'		)		)
				_lowerCamelCase     :			Any    =    {int(snake_case_		): v for k, v in idalabel.items()}
				_lowerCamelCase     :			List[Any]    =    idalabel
				_lowerCamelCase     :			int    =    {v: k for k, v in idalabel.items()}

				# load HuggingFace model
				if vit_name[-5:] == "in21k":
								_lowerCamelCase     :			Optional[int]    =    ViTHybridModel(snake_case_		).eval()
				else:
								_lowerCamelCase     :			Any    =    ViTHybridForImageClassification(snake_case_		).eval()
				model.load_state_dict(snake_case_		)

				# create image processor
				_lowerCamelCase     :			Dict    =    create_transform(**resolve_data_config({}							,    model=snake_case_		)		)
				_lowerCamelCase     :			Dict    =    transform.transforms

				_lowerCamelCase     :			Dict    =    {
				    '''bilinear''': PILImageResampling.BILINEAR,
				    '''bicubic''': PILImageResampling.BICUBIC,
				    '''nearest''': PILImageResampling.NEAREST,
				}

				_lowerCamelCase     :			Optional[Any]    =    ViTHybridImageProcessor(
				    do_resize=snake_case_							,    size={'shortest_edge': timm_transforms[0].size}							,    resample=pillow_resamplings[timm_transforms[0].interpolation.value]							,    do_center_crop=snake_case_							,    crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]}							,    do_normalize=snake_case_							,    image_mean=timm_transforms[-1].mean.tolist()							,    image_std=timm_transforms[-1].std.tolist()							,    )

				_lowerCamelCase     :			Optional[Any]    =    prepare_img()
				_lowerCamelCase     :			str    =    transform(snake_case_		).unsqueeze(0		)
				_lowerCamelCase     :			str    =    processor(snake_case_							,    return_tensors='pt'		).pixel_values

				# verify pixel values
				assert torch.allclose(snake_case_							,    snake_case_		)

				# verify logits
				with torch.no_grad():
								_lowerCamelCase     :			Dict    =    model(snake_case_		)
								_lowerCamelCase     :			Union[str, Any]    =    outputs.logits

				print('Predicted class:'							,    logits.argmax(-1		).item()		)
				if base_model:
								_lowerCamelCase     :			List[str]    =    timm_model.forward_features(snake_case_		)
								assert timm_pooled_output.shape == outputs.pooler_output.shape
								assert torch.allclose(snake_case_							,    outputs.pooler_output							,    atol=1E-3		)
				else:
								_lowerCamelCase     :			Tuple    =    timm_model(snake_case_		)
								assert timm_logits.shape == outputs.logits.shape
								assert torch.allclose(snake_case_							,    outputs.logits							,    atol=1E-3		)
				print('Looks ok!'		)

				if pytorch_dump_folder_path is not None:
								Path(snake_case_		).mkdir(exist_ok=snake_case_		)
								print(f'''Saving model {vit_name} to {pytorch_dump_folder_path}'''		)
								model.save_pretrained(snake_case_		)
								print(f'''Saving processor to {pytorch_dump_folder_path}'''		)
								processor.save_pretrained(snake_case_		)

				if push_to_hub:
								print(f'''Pushing model and processor to the hub {vit_name}'''		)
								model.push_to_hub(f'''ybelkada/{vit_name}'''		)
								processor.push_to_hub(f'''ybelkada/{vit_name}'''		)


if __name__ == "__main__":
			lowercase__     						=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--vit_name""",
			    default="""vit_base_r50_s16_384""",
			    type=str,
			    help="""Name of the hybrid ViT timm model you'd like to convert.""",
			)
			parser.add_argument(
			    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
			)
			parser.add_argument(
			    """--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
			)

			lowercase__     						=						parser.parse_args()
			convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""



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:
       a_ =			False

if is_vision_available():
       from PIL import Image

       from transformers import PixaStructImageProcessor






class 		__snake_case					(		unittest.TestCase     ):





 """simple docstring"""


 def __init__(    self					,						__lowerCamelCase					,						__lowerCamelCase=7					,						__lowerCamelCase=3					,						__lowerCamelCase=18					,						__lowerCamelCase=30					,						__lowerCamelCase=400					,						__lowerCamelCase=None					,						__lowerCamelCase=True					,						__lowerCamelCase=True					,						__lowerCamelCase=None					,						):



     '''simple docstring'''




     __A    :						str						 =     size if size is not None else {'''height''': 20, '''width''': 20}
     __A    :						Dict						 =     parent
     __A    :						Union[str, Any]						 =     batch_size
     __A    :						List[Any]						 =     num_channels
     __A    :						Union[str, Any]						 =     image_size
     __A    :						Any						 =     min_resolution
     __A    :						str						 =     max_resolution
     __A    :						Any						 =     size
     __A    :						Dict						 =     do_normalize
     __A    :						str						 =     do_convert_rgb
     __A    :						List[Any]						 =     [512, 1024, 2048, 4096]
     __A    :						Dict						 =     patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[str]						 =     '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
     __A    :						Optional[Any]						 =     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 		__snake_case					(		SCREAMING_SNAKE_CASE__							,							unittest.TestCase     ):





 """simple docstring"""


 _lowerCamelCase        =     PixaStructImageProcessor if is_vision_available() else None

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						str						 =     PixaStructImageProcessingTester(self  )

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return self.image_processor_tester.prepare_image_processor_dict()

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						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  ):



     '''simple docstring'''




     __A    :						Tuple						 =     self.image_processor_tester.prepare_dummy_image()

     __A    :						Optional[int]						 =     self.image_processing_class(**self.image_processor_dict  )
     __A    :						Dict						 =     2048

     __A    :						Any						 =     image_processor(__lowerCamelCase					,						return_tensors='''pt'''					,						max_patches=__lowerCamelCase  )
     self.assertTrue(torch.allclose(inputs.flattened_patches.mean()					,						torch.tensor(0.0_6_0_6  )					,						atol=1e-3					,						rtol=1e-3  )  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[Any]						 =     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    :						List[str]						 =     (
         (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    :						Dict						 =     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  ):



     '''simple docstring'''




     __A    :						List[str]						 =     self.image_processing_class(**self.image_processor_dict  )
     # create random PIL images
     __A    :						Union[str, 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    :						Tuple						 =     (
         (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
         * self.image_processor_tester.num_channels
     ) + 2

     __A    :						str						 =     True

     for max_patch in self.image_processor_tester.max_patches:
         # Test not batched input
         with self.assertRaises(__lowerCamelCase  ):
             __A    :						List[Any]						 =     image_processor(
                 image_inputs[0]					,						return_tensors='''pt'''					,						max_patches=__lowerCamelCase  ).flattened_patches

         __A    :						Any						 =     '''Hello'''

         __A    :						Tuple						 =     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    :						Union[str, Any]						 =     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  ):



     '''simple docstring'''




     __A    :						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    :						List[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    :						List[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    :						Union[str, Any]						 =     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  ):



     '''simple docstring'''




     __A    :						int						 =     self.image_processing_class(**self.image_processor_dict  )
     # create random PyTorch tensors
     __A    :						Optional[Any]						 =     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    :						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    :						List[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    :						Dict						 =     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 		__snake_case					(		SCREAMING_SNAKE_CASE__							,							unittest.TestCase     ):





 """simple docstring"""


 _lowerCamelCase        =     PixaStructImageProcessor if is_vision_available() else None

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						int						 =     PixaStructImageProcessingTester(self					,						num_channels=4  )
     __A    :						Any						 =     3

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return self.image_processor_tester.prepare_image_processor_dict()

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __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  ):



     '''simple docstring'''




     __A    :						Optional[int]						 =     self.image_processing_class(**self.image_processor_dict  )
     # create random PIL images
     __A    :						Dict						 =     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    :						List[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    :						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)					,						)




