
import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCamelCase_						     =					[]
for i in range(6):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append(
        (f'transformer.encoder.layers.{i}.self_attn.out_proj.weight', f'encoder.layers.{i}.self_attn.out_proj.weight')
    )
    rename_keys.append(
        (f'transformer.encoder.layers.{i}.self_attn.out_proj.bias', f'encoder.layers.{i}.self_attn.out_proj.bias')
    )
    rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'encoder.layers.{i}.fc1.weight'))
    rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'encoder.layers.{i}.fc1.bias'))
    rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'encoder.layers.{i}.fc2.weight'))
    rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'encoder.layers.{i}.fc2.bias'))
    rename_keys.append(
        (f'transformer.encoder.layers.{i}.norm1.weight', f'encoder.layers.{i}.self_attn_layer_norm.weight')
    )
    rename_keys.append((f'transformer.encoder.layers.{i}.norm1.bias', f'encoder.layers.{i}.self_attn_layer_norm.bias'))
    rename_keys.append((f'transformer.encoder.layers.{i}.norm2.weight', f'encoder.layers.{i}.final_layer_norm.weight'))
    rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'encoder.layers.{i}.final_layer_norm.bias'))
    # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.self_attn.out_proj.weight', f'decoder.layers.{i}.self_attn.out_proj.weight')
    )
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'decoder.layers.{i}.self_attn.out_proj.bias')
    )
    rename_keys.append(
        (
            f'transformer.decoder.layers.{i}.cross_attn.out_proj.weight',
            f'decoder.layers.{i}.encoder_attn.out_proj.weight',
        )
    )
    rename_keys.append(
        (
            f'transformer.decoder.layers.{i}.cross_attn.out_proj.bias',
            f'decoder.layers.{i}.encoder_attn.out_proj.bias',
        )
    )
    rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'decoder.layers.{i}.fc1.weight'))
    rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'decoder.layers.{i}.fc1.bias'))
    rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'decoder.layers.{i}.fc2.weight'))
    rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'decoder.layers.{i}.fc2.bias'))
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.norm1.weight', f'decoder.layers.{i}.self_attn_layer_norm.weight')
    )
    rename_keys.append((f'transformer.decoder.layers.{i}.norm1.bias', f'decoder.layers.{i}.self_attn_layer_norm.bias'))
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.norm2.weight', f'decoder.layers.{i}.encoder_attn_layer_norm.weight')
    )
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.norm2.bias', f'decoder.layers.{i}.encoder_attn_layer_norm.bias')
    )
    rename_keys.append((f'transformer.decoder.layers.{i}.norm3.weight', f'decoder.layers.{i}.final_layer_norm.weight'))
    rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'decoder.layers.{i}.final_layer_norm.bias'))

    # q, k, v projections in self/cross-attention in decoder for conditional DETR
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.sa_qcontent_proj.weight', f'decoder.layers.{i}.sa_qcontent_proj.weight')
    )
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.sa_kcontent_proj.weight', f'decoder.layers.{i}.sa_kcontent_proj.weight')
    )
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.sa_qpos_proj.weight', f'decoder.layers.{i}.sa_qpos_proj.weight')
    )
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.sa_kpos_proj.weight', f'decoder.layers.{i}.sa_kpos_proj.weight')
    )
    rename_keys.append((f'transformer.decoder.layers.{i}.sa_v_proj.weight', f'decoder.layers.{i}.sa_v_proj.weight'))
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.ca_qcontent_proj.weight', f'decoder.layers.{i}.ca_qcontent_proj.weight')
    )
    # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.ca_kcontent_proj.weight', f'decoder.layers.{i}.ca_kcontent_proj.weight')
    )
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.ca_kpos_proj.weight', f'decoder.layers.{i}.ca_kpos_proj.weight')
    )
    rename_keys.append((f'transformer.decoder.layers.{i}.ca_v_proj.weight', f'decoder.layers.{i}.ca_v_proj.weight'))
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight', f'decoder.layers.{i}.ca_qpos_sine_proj.weight')
    )

    rename_keys.append(
        (f'transformer.decoder.layers.{i}.sa_qcontent_proj.bias', f'decoder.layers.{i}.sa_qcontent_proj.bias')
    )
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.sa_kcontent_proj.bias', f'decoder.layers.{i}.sa_kcontent_proj.bias')
    )
    rename_keys.append((f'transformer.decoder.layers.{i}.sa_qpos_proj.bias', f'decoder.layers.{i}.sa_qpos_proj.bias'))
    rename_keys.append((f'transformer.decoder.layers.{i}.sa_kpos_proj.bias', f'decoder.layers.{i}.sa_kpos_proj.bias'))
    rename_keys.append((f'transformer.decoder.layers.{i}.sa_v_proj.bias', f'decoder.layers.{i}.sa_v_proj.bias'))
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.ca_qcontent_proj.bias', f'decoder.layers.{i}.ca_qcontent_proj.bias')
    )
    # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.ca_kcontent_proj.bias', f'decoder.layers.{i}.ca_kcontent_proj.bias')
    )
    rename_keys.append((f'transformer.decoder.layers.{i}.ca_kpos_proj.bias', f'decoder.layers.{i}.ca_kpos_proj.bias'))
    rename_keys.append((f'transformer.decoder.layers.{i}.ca_v_proj.bias', f'decoder.layers.{i}.ca_v_proj.bias'))
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias', f'decoder.layers.{i}.ca_qpos_sine_proj.bias')
    )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
        ('''transformer.decoder.ref_point_head.layers.0.weight''', '''decoder.ref_point_head.layers.0.weight'''),
        ('''transformer.decoder.ref_point_head.layers.0.bias''', '''decoder.ref_point_head.layers.0.bias'''),
        ('''transformer.decoder.ref_point_head.layers.1.weight''', '''decoder.ref_point_head.layers.1.weight'''),
        ('''transformer.decoder.ref_point_head.layers.1.bias''', '''decoder.ref_point_head.layers.1.bias'''),
        ('''transformer.decoder.query_scale.layers.0.weight''', '''decoder.query_scale.layers.0.weight'''),
        ('''transformer.decoder.query_scale.layers.0.bias''', '''decoder.query_scale.layers.0.bias'''),
        ('''transformer.decoder.query_scale.layers.1.weight''', '''decoder.query_scale.layers.1.weight'''),
        ('''transformer.decoder.query_scale.layers.1.bias''', '''decoder.query_scale.layers.1.bias'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.weight''', '''decoder.layers.0.ca_qpos_proj.weight'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.bias''', '''decoder.layers.0.ca_qpos_proj.bias'''),
    ]
)






def 						__magic_name__			(   __a      :	Dict	,		__a      :	Any	,		__a      :	Optional[Any]						):



     '''simple docstring'''




     UpperCamelCase__				=    state_dict.pop(__a						)
     UpperCamelCase__				=    val






def 						__magic_name__			(   __a      :	Optional[int]						):



     '''simple docstring'''




     UpperCamelCase__				=    OrderedDict()
     for key, value in state_dict.items():
          if "backbone.0.body" in key:
               UpperCamelCase__				=    key.replace("""backbone.0.body"""	,		"""backbone.conv_encoder.model"""						)
               UpperCamelCase__				=    value
          else:
               UpperCamelCase__				=    value

     return new_state_dict






def 						__magic_name__			(   __a      :	Any	,		__a      :	Union[str, Any]=False						):



     '''simple docstring'''




     UpperCamelCase__				=    """"""
     if is_panoptic:
          UpperCamelCase__				=    """conditional_detr."""

     # first: transformer encoder
     for i in range(6						):
          # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
          UpperCamelCase__				=    state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"						)
          UpperCamelCase__				=    state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"						)
          # next, add query, keys and values (in that order) to the state dict
          UpperCamelCase__				=    in_proj_weight[:256, :]
          UpperCamelCase__				=    in_proj_bias[:256]
          UpperCamelCase__				=    in_proj_weight[256:512, :]
          UpperCamelCase__				=    in_proj_bias[256:512]
          UpperCamelCase__				=    in_proj_weight[-256:, :]
          UpperCamelCase__				=    in_proj_bias[-256:]






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    """http://images.cocodataset.org/val2017/000000039769.jpg"""
     UpperCamelCase__				=    Image.open(requests.get(__a	,		stream=__a						).raw						)

     return im






@torch.no_grad()
def 						__magic_name__			(   __a      :	int	,		__a      :	Dict						):



     '''simple docstring'''




     UpperCamelCase__				=    ConditionalDetrConfig()
     # set backbone and dilation attributes
     if "resnet101" in model_name:
          UpperCamelCase__				=    """resnet101"""
     if "dc5" in model_name:
          UpperCamelCase__				=    True
     UpperCamelCase__				=    """panoptic""" in model_name
     if is_panoptic:
          UpperCamelCase__				=    250
     else:
          UpperCamelCase__				=    91
          UpperCamelCase__				=    """huggingface/label-files"""
          UpperCamelCase__				=    """coco-detection-id2label.json"""
          UpperCamelCase__				=    json.load(open(hf_hub_download(__a	,		__a	,		repo_type="""dataset"""						)	,		"""r"""						)						)
          UpperCamelCase__				=    {int(__a						): v for k, v in idalabel.items()}
          UpperCamelCase__				=    idalabel
          UpperCamelCase__				=    {v: k for k, v in idalabel.items()}

     # load image processor
     UpperCamelCase__				=    """coco_panoptic""" if is_panoptic else """coco_detection"""
     UpperCamelCase__				=    ConditionalDetrImageProcessor(format=__a						)

     # prepare image
     UpperCamelCase__				=    prepare_img()
     UpperCamelCase__				=    image_processor(images=__a	,		return_tensors="""pt"""						)
     UpperCamelCase__				=    encoding["""pixel_values"""]

     logger.info(f"Converting model {model_name}..."						)

     # load original model from torch hub
     UpperCamelCase__				=    torch.hub.load("""DeppMeng/ConditionalDETR"""	,		__a	,		pretrained=__a						).eval()
     UpperCamelCase__				=    conditional_detr.state_dict()
     # rename keys
     for src, dest in rename_keys:
          if is_panoptic:
               UpperCamelCase__				=    """conditional_detr.""" + src
          rename_key(__a	,		__a	,		__a						)
     UpperCamelCase__				=    rename_backbone_keys(__a						)
     # query, key and value matrices need special treatment
     read_in_q_k_v(__a	,		is_panoptic=__a						)
     # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
     UpperCamelCase__				=    """conditional_detr.model.""" if is_panoptic else """model."""
     for key in state_dict.copy().keys():
          if is_panoptic:
               if (
                   key.startswith("""conditional_detr"""						)
                   and not key.startswith("""class_labels_classifier"""						)
                   and not key.startswith("""bbox_predictor"""						)
               ):
                    UpperCamelCase__				=    state_dict.pop(__a						)
                    UpperCamelCase__				=    val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    UpperCamelCase__				=    state_dict.pop(__a						)
                    UpperCamelCase__				=    val
               elif key.startswith("""bbox_attention"""						) or key.startswith("""mask_head"""						):
                    continue
               else:
                    UpperCamelCase__				=    state_dict.pop(__a						)
                    UpperCamelCase__				=    val
          else:
               if not key.startswith("""class_labels_classifier"""						) and not key.startswith("""bbox_predictor"""						):
                    UpperCamelCase__				=    state_dict.pop(__a						)
                    UpperCamelCase__				=    val
    # finally, create HuggingFace model and load state dict
     UpperCamelCase__				=    ConditionalDetrForSegmentation(__a						) if is_panoptic else ConditionalDetrForObjectDetection(__a						)
     model.load_state_dict(__a						)
     model.eval()
     model.push_to_hub(repo_id=__a	,		organization="""DepuMeng"""	,		commit_message="""Add model"""						)
     # verify our conversion
     UpperCamelCase__				=    conditional_detr(__a						)
     UpperCamelCase__				=    model(__a						)
     assert torch.allclose(outputs.logits	,		original_outputs["""pred_logits"""]	,		atol=1E-4						)
     assert torch.allclose(outputs.pred_boxes	,		original_outputs["""pred_boxes"""]	,		atol=1E-4						)
     if is_panoptic:
          assert torch.allclose(outputs.pred_masks	,		original_outputs["""pred_masks"""]	,		atol=1E-4						)

     # Save model and image processor
     logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."						)
     Path(__a						).mkdir(exist_ok=__a						)
     model.save_pretrained(__a						)
     image_processor.save_pretrained(__a						)


if __name__ == "__main__":
    lowerCamelCase_						     =					argparse.ArgumentParser()

    parser.add_argument(
        '''--model_name''',
        default='''conditional_detr_resnet50''',
        type=str,
        help='''Name of the CONDITIONAL_DETR model you\'d like to convert.''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
    )
    lowerCamelCase_						     =					parser.parse_args()
    convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)







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:
    lowerCamelCase_						     =					False

if is_vision_available():
    from PIL import Image

    from transformers import PixaStructImageProcessor




class      __A(    unittest.TestCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=7	,      SCREAMING_SNAKE_CASE_=3	,      SCREAMING_SNAKE_CASE_=18	,      SCREAMING_SNAKE_CASE_=30	,      SCREAMING_SNAKE_CASE_=4_00	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=None	,      ):
       UpperCamelCase__				=    size if size is not None else {"""height""": 20, """width""": 20}
       UpperCamelCase__				=    parent
       UpperCamelCase__				=    batch_size
       UpperCamelCase__				=    num_channels
       UpperCamelCase__				=    image_size
       UpperCamelCase__				=    min_resolution
       UpperCamelCase__				=    max_resolution
       UpperCamelCase__				=    size
       UpperCamelCase__				=    do_normalize
       UpperCamelCase__				=    do_convert_rgb
       UpperCamelCase__				=    [5_12, 10_24, 20_48, 40_96]
       UpperCamelCase__				=    patch_size if patch_size is not None else {"""height""": 16, """width""": 16}







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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
       UpperCamelCase__				=    Image.open(requests.get(SCREAMING_SNAKE_CASE_	,      stream=SCREAMING_SNAKE_CASE_						).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(    __lowerCamelCase    ,				unittest.TestCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      PixaStructImageProcessor if is_vision_available() else None







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    PixaStructImageProcessingTester(self						)







  @property
  def 	UpperCAmelCase_ (self						):
       return self.image_processor_tester.prepare_image_processor_dict()







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_normalize"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_convert_rgb"""						)						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.image_processor_tester.prepare_dummy_image()

       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       UpperCamelCase__				=    20_48

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







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processor
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      Image.Image						)

       # Test not batched input
       UpperCamelCase__				=    (
           (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
            UpperCamelCase__				=    image_processor(
                image_inputs[0]	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (1, max_patch, expected_hidden_dim)	,      )

            # Test batched
            UpperCamelCase__				=    image_processor(
                SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,      )







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processor
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      Image.Image						)

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

       UpperCamelCase__				=    True

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

            UpperCamelCase__				=    """Hello"""

            UpperCamelCase__				=    image_processor(
                image_inputs[0]	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_	,      header_text=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (1, max_patch, expected_hidden_dim)	,      )

            # Test batched
            UpperCamelCase__				=    image_processor(
                SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_	,      header_text=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,      )







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processor
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_	,      numpify=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      np.ndarray						)

       UpperCamelCase__				=    (
           (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
            UpperCamelCase__				=    image_processor(
                image_inputs[0]	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (1, max_patch, expected_hidden_dim)	,      )

            # Test batched
            UpperCamelCase__				=    image_processor(
                SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,      )







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processor
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_	,      torchify=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      torch.Tensor						)

       # Test not batched input
       UpperCamelCase__				=    (
           (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
            UpperCamelCase__				=    image_processor(
                image_inputs[0]	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (1, max_patch, expected_hidden_dim)	,      )

            # Test batched
            UpperCamelCase__				=    image_processor(
                SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).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(    __lowerCamelCase    ,				unittest.TestCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      PixaStructImageProcessor if is_vision_available() else None







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    PixaStructImageProcessingTester(self	,      num_channels=4						)
       UpperCamelCase__				=    3







  @property
  def 	UpperCAmelCase_ (self						):
       return self.image_processor_tester.prepare_image_processor_dict()







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_normalize"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_convert_rgb"""						)						)







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processor
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      Image.Image						)

       # Test not batched input
       UpperCamelCase__				=    (
           (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
            UpperCamelCase__				=    image_processor(
                image_inputs[0]	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (1, max_patch, expected_hidden_dim)	,      )

            # Test batched
            UpperCamelCase__				=    image_processor(
                SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,      )





