


import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

__lowerCAmelCase			=							[
    # (stable-diffusion, HF Diffusers)
    ('''time_embed.0.weight''', '''time_embedding.linear_1.weight'''),
    ('''time_embed.0.bias''', '''time_embedding.linear_1.bias'''),
    ('''time_embed.2.weight''', '''time_embedding.linear_2.weight'''),
    ('''time_embed.2.bias''', '''time_embedding.linear_2.bias'''),
    ('''input_blocks.0.0.weight''', '''conv_in.weight'''),
    ('''input_blocks.0.0.bias''', '''conv_in.bias'''),
    ('''out.0.weight''', '''conv_norm_out.weight'''),
    ('''out.0.bias''', '''conv_norm_out.bias'''),
    ('''out.2.weight''', '''conv_out.weight'''),
    ('''out.2.bias''', '''conv_out.bias'''),
]

__lowerCAmelCase			=							[
    # (stable-diffusion, HF Diffusers)
    ('''in_layers.0''', '''norm1'''),
    ('''in_layers.2''', '''conv1'''),
    ('''out_layers.0''', '''norm2'''),
    ('''out_layers.3''', '''conv2'''),
    ('''emb_layers.1''', '''time_emb_proj'''),
    ('''skip_connection''', '''conv_shortcut'''),
]

__lowerCAmelCase			=							[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
 # loop over downblocks/upblocks

 for j in range(2):
  # loop over resnets/attentions for downblocks
  __lowerCAmelCase			=							F'''down_blocks.{i}.resnets.{j}.'''
  __lowerCAmelCase			=							F'''input_blocks.{3*i + j + 1}.0.'''
  unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

  if i < 3:
   # no attention layers in down_blocks.3
   __lowerCAmelCase			=							F'''down_blocks.{i}.attentions.{j}.'''
   __lowerCAmelCase			=							F'''input_blocks.{3*i + j + 1}.1.'''
   unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

 for j in range(3):
  # loop over resnets/attentions for upblocks
  __lowerCAmelCase			=							F'''up_blocks.{i}.resnets.{j}.'''
  __lowerCAmelCase			=							F'''output_blocks.{3*i + j}.0.'''
  unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

  if i > 0:
   # no attention layers in up_blocks.0
   __lowerCAmelCase			=							F'''up_blocks.{i}.attentions.{j}.'''
   __lowerCAmelCase			=							F'''output_blocks.{3*i + j}.1.'''
   unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

 if i < 3:
  # no downsample in down_blocks.3
  __lowerCAmelCase			=							F'''down_blocks.{i}.downsamplers.0.conv.'''
  __lowerCAmelCase			=							F'''input_blocks.{3*(i+1)}.0.op.'''
  unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

  # no upsample in up_blocks.3
  __lowerCAmelCase			=							F'''up_blocks.{i}.upsamplers.0.'''
  __lowerCAmelCase			=							F'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
  unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

__lowerCAmelCase			=							'''mid_block.attentions.0.'''
__lowerCAmelCase			=							'''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
 __lowerCAmelCase			=							F'''mid_block.resnets.{j}.'''
 __lowerCAmelCase			=							F'''middle_block.{2*j}.'''
 unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))




def        snake_case_					(  snake_case					)  ->    Union[str, Any]:
       # buyer beware: this is a *brittle* function,
       # and correct output requires that all of these pieces interact in
       # the exact order in which I have arranged them.
       lowercase__:       Optional[Any]        =       {k: k for k in unet_state_dict.keys()}
       for sd_name, hf_name in unet_conversion_map:
              lowercase__:       Any        =       sd_name
       for k, v in mapping.items():
              if "resnets" in k:
                     for sd_part, hf_part in unet_conversion_map_resnet:
                            lowercase__:       Optional[Any]        =       v.replace(snake_case      ,							snake_case					)
                     lowercase__:       Optional[int]        =       v
       for k, v in mapping.items():
              for sd_part, hf_part in unet_conversion_map_layer:
                     lowercase__:       int        =       v.replace(snake_case      ,							snake_case					)
              lowercase__:       List[Any]        =       v
       lowercase__:       Union[str, Any]        =       {v: unet_state_dict[k] for k, v in mapping.items()}
       return new_state_dict


# ================#
# VAE Conversion #
# ================#

__lowerCAmelCase			=							[
    # (stable-diffusion, HF Diffusers)
    ('''nin_shortcut''', '''conv_shortcut'''),
    ('''norm_out''', '''conv_norm_out'''),
    ('''mid.attn_1.''', '''mid_block.attentions.0.'''),
]

for i in range(4):
 # down_blocks have two resnets
 for j in range(2):
  __lowerCAmelCase			=							F'''encoder.down_blocks.{i}.resnets.{j}.'''
  __lowerCAmelCase			=							F'''encoder.down.{i}.block.{j}.'''
  vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

 if i < 3:
  __lowerCAmelCase			=							F'''down_blocks.{i}.downsamplers.0.'''
  __lowerCAmelCase			=							F'''down.{i}.downsample.'''
  vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

  __lowerCAmelCase			=							F'''up_blocks.{i}.upsamplers.0.'''
  __lowerCAmelCase			=							F'''up.{3-i}.upsample.'''
  vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

 # up_blocks have three resnets
 # also, up blocks in hf are numbered in reverse from sd
 for j in range(3):
  __lowerCAmelCase			=							F'''decoder.up_blocks.{i}.resnets.{j}.'''
  __lowerCAmelCase			=							F'''decoder.up.{3-i}.block.{j}.'''
  vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
 __lowerCAmelCase			=							F'''mid_block.resnets.{i}.'''
 __lowerCAmelCase			=							F'''mid.block_{i+1}.'''
 vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


__lowerCAmelCase			=							[
    # (stable-diffusion, HF Diffusers)
    ('''norm.''', '''group_norm.'''),
    ('''q.''', '''query.'''),
    ('''k.''', '''key.'''),
    ('''v.''', '''value.'''),
    ('''proj_out.''', '''proj_attn.'''),
]




def        snake_case_					(  snake_case					)  ->    Optional[Any]:
       # convert HF linear weights to SD conv2d weights
       return w.reshape(*w.shape      ,							1      ,							1					)




def        snake_case_					(  snake_case					)  ->    int:
       lowercase__:       int        =       {k: k for k in vae_state_dict.keys()}
       for k, v in mapping.items():
              for sd_part, hf_part in vae_conversion_map:
                     lowercase__:       Union[str, Any]        =       v.replace(snake_case      ,							snake_case					)
              lowercase__:       List[str]        =       v
       for k, v in mapping.items():
              if "attentions" in k:
                     for sd_part, hf_part in vae_conversion_map_attn:
                            lowercase__:       Dict        =       v.replace(snake_case      ,							snake_case					)
                     lowercase__:       List[str]        =       v
       lowercase__:       List[str]        =       {v: vae_state_dict[k] for k, v in mapping.items()}
       lowercase__:       Dict        =       ['q', 'k', 'v', 'proj_out']
       for k, v in new_state_dict.items():
              for weight_name in weights_to_convert:
                     if f'mid.attn_1.{weight_name}.weight' in k:
                            print(f'Reshaping {k} for SD format'					)
                            lowercase__:       Any        =       reshape_weight_for_sd(snake_case					)
       return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


__lowerCAmelCase			=							[
    # (stable-diffusion, HF Diffusers)
    ('''resblocks.''', '''text_model.encoder.layers.'''),
    ('''ln_1''', '''layer_norm1'''),
    ('''ln_2''', '''layer_norm2'''),
    ('''.c_fc.''', '''.fc1.'''),
    ('''.c_proj.''', '''.fc2.'''),
    ('''.attn''', '''.self_attn'''),
    ('''ln_final.''', '''transformer.text_model.final_layer_norm.'''),
    ('''token_embedding.weight''', '''transformer.text_model.embeddings.token_embedding.weight'''),
    ('''positional_embedding''', '''transformer.text_model.embeddings.position_embedding.weight'''),
]
__lowerCAmelCase			=							{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
__lowerCAmelCase			=							re.compile('''|'''.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
__lowerCAmelCase			=							{'''q''': 0, '''k''': 1, '''v''': 2}




def        snake_case_					(  snake_case					)  ->    Optional[int]:
       lowercase__:       Union[str, Any]        =       {}
       lowercase__:       Optional[Any]        =       {}
       lowercase__:       str        =       {}
       for k, v in text_enc_dict.items():
              if (
                  k.endswith('.self_attn.q_proj.weight'					)
                  or k.endswith('.self_attn.k_proj.weight'					)
                  or k.endswith('.self_attn.v_proj.weight'					)
              ):
                     lowercase__:       int        =       k[: -len('.q_proj.weight'					)]
                     lowercase__:       int        =       k[-len('q_proj.weight'					)]
                     if k_pre not in capture_qkv_weight:
                            lowercase__:       Dict        =       [None, None, None]
                     lowercase__:       Dict        =       v
                     continue

              if (
                  k.endswith('.self_attn.q_proj.bias'					)
                  or k.endswith('.self_attn.k_proj.bias'					)
                  or k.endswith('.self_attn.v_proj.bias'					)
              ):
                     lowercase__:       Optional[int]        =       k[: -len('.q_proj.bias'					)]
                     lowercase__:       Optional[int]        =       k[-len('q_proj.bias'					)]
                     if k_pre not in capture_qkv_bias:
                            lowercase__:       Any        =       [None, None, None]
                     lowercase__:       List[Any]        =       v
                     continue

              lowercase__:       Any        =       textenc_pattern.sub(lambda snake_case					: protected[re.escape(m.group(0					)					)]      ,							snake_case					)
              lowercase__:       Any        =       v

       for k_pre, tensors in capture_qkv_weight.items():
              if None in tensors:
                     raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'					)
              lowercase__:       Dict        =       textenc_pattern.sub(lambda snake_case					: protected[re.escape(m.group(0					)					)]      ,							snake_case					)
              lowercase__:       Optional[int]        =       torch.cat(snake_case					)

       for k_pre, tensors in capture_qkv_bias.items():
              if None in tensors:
                     raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'					)
              lowercase__:       Optional[Any]        =       textenc_pattern.sub(lambda snake_case					: protected[re.escape(m.group(0					)					)]      ,							snake_case					)
              lowercase__:       Optional[Any]        =       torch.cat(snake_case					)

       return new_state_dict




def        snake_case_					(  snake_case					)  ->    int:
       return text_enc_dict


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

 parser.add_argument('''--model_path''', default=None, type=str, required=True, help='''Path to the model to convert.''')
 parser.add_argument('''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output model.''')
 parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
 parser.add_argument(
     '''--use_safetensors''', action='''store_true''', help='''Save weights use safetensors, default is ckpt.'''
 )

 __lowerCAmelCase			=							parser.parse_args()

 assert args.model_path is not None, "Must provide a model path!"

 assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

 # Path for safetensors
 __lowerCAmelCase			=							osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
 __lowerCAmelCase			=							osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
 __lowerCAmelCase			=							osp.join(args.model_path, '''text_encoder''', '''model.safetensors''')

 # Load models from safetensors if it exists, if it doesn't pytorch
 if osp.exists(unet_path):
  __lowerCAmelCase			=							load_file(unet_path, device='''cpu''')
 else:
  __lowerCAmelCase			=							osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
  __lowerCAmelCase			=							torch.load(unet_path, map_location='''cpu''')

 if osp.exists(vae_path):
  __lowerCAmelCase			=							load_file(vae_path, device='''cpu''')
 else:
  __lowerCAmelCase			=							osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.bin''')
  __lowerCAmelCase			=							torch.load(vae_path, map_location='''cpu''')

 if osp.exists(text_enc_path):
  __lowerCAmelCase			=							load_file(text_enc_path, device='''cpu''')
 else:
  __lowerCAmelCase			=							osp.join(args.model_path, '''text_encoder''', '''pytorch_model.bin''')
  __lowerCAmelCase			=							torch.load(text_enc_path, map_location='''cpu''')

 # Convert the UNet model
 __lowerCAmelCase			=							convert_unet_state_dict(unet_state_dict)
 __lowerCAmelCase			=							{'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}

 # Convert the VAE model
 __lowerCAmelCase			=							convert_vae_state_dict(vae_state_dict)
 __lowerCAmelCase			=							{'''first_stage_model.''' + k: v for k, v in vae_state_dict.items()}

 # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
 __lowerCAmelCase			=							'''text_model.encoder.layers.22.layer_norm2.bias''' in text_enc_dict

 if is_vaa_model:
  # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
  __lowerCAmelCase			=							{'''transformer.''' + k: v for k, v in text_enc_dict.items()}
  __lowerCAmelCase			=							convert_text_enc_state_dict_vaa(text_enc_dict)
  __lowerCAmelCase			=							{'''cond_stage_model.model.''' + k: v for k, v in text_enc_dict.items()}
 else:
  __lowerCAmelCase			=							convert_text_enc_state_dict(text_enc_dict)
  __lowerCAmelCase			=							{'''cond_stage_model.transformer.''' + k: v for k, v in text_enc_dict.items()}

 # Put together new checkpoint
 __lowerCAmelCase			=							{**unet_state_dict, **vae_state_dict, **text_enc_dict}
 if args.half:
  __lowerCAmelCase			=							{k: v.half() for k, v in state_dict.items()}

 if args.use_safetensors:
  save_file(state_dict, args.checkpoint_path)
 else:
  __lowerCAmelCase			=							{'''state_dict''': state_dict}
  torch.save(state_dict, args.checkpoint_path)






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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

if is_vision_available():
 from PIL import Image

 from transformers import DetrImageProcessor





class   __a      (   unittest.TestCase				):
 def __init__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=7    ,			lowerCAmelCase__=3    ,			lowerCAmelCase__=30    ,			lowerCAmelCase__=400    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=None    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=1 / 255    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=[0.5, 0.5, 0.5]    ,			lowerCAmelCase__=[0.5, 0.5, 0.5]    ,			lowerCAmelCase__=True    ,			) ->    List[str]:




        '''simple docstring'''
        # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
        lowercase__:       Dict        =       size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
        lowercase__:       Tuple        =       parent
        lowercase__:       Optional[Any]        =       batch_size
        lowercase__:       Any        =       num_channels
        lowercase__:       str        =       min_resolution
        lowercase__:       Dict        =       max_resolution
        lowercase__:       Any        =       do_resize
        lowercase__:       str        =       size
        lowercase__:       Any        =       do_rescale
        lowercase__:       Union[str, Any]        =       rescale_factor
        lowercase__:       Optional[int]        =       do_normalize
        lowercase__:       Union[str, Any]        =       image_mean
        lowercase__:       List[str]        =       image_std
        lowercase__:       Optional[Any]        =       do_pad
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_pad": self.do_pad,
        }
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=False			) ->    int:




        '''simple docstring'''
        if not batched:
               lowercase__:       List[Any]        =       image_inputs[0]
               if isinstance(lowerCAmelCase__    ,			Image.Image			):
                      lowercase__      ,						lowercase__:       List[str]        =       image.size
               else:
                      lowercase__      ,						lowercase__:       str        =       image.shape[1], image.shape[2]
               if w < h:
                      lowercase__:       Optional[int]        =       int(self.size['shortest_edge'] * h / w			)
                      lowercase__:       int        =       self.size['shortest_edge']
               elif w > h:
                      lowercase__:       Tuple        =       self.size['shortest_edge']
                      lowercase__:       int        =       int(self.size['shortest_edge'] * w / h			)
               else:
                      lowercase__:       Tuple        =       self.size['shortest_edge']
                      lowercase__:       Optional[Any]        =       self.size['shortest_edge']

        else:
               lowercase__:       str        =       []
               for image in image_inputs:
                      lowercase__      ,						lowercase__:       Any        =       self.get_expected_values([image]			)
                      expected_values.append((expected_height, expected_width)			)
               lowercase__:       Union[str, Any]        =       max(lowerCAmelCase__    ,			key=lambda lowerCAmelCase__			: item[0]			)[0]
               lowercase__:       Any        =       max(lowerCAmelCase__    ,			key=lambda lowerCAmelCase__			: item[1]			)[1]

        return expected_height, expected_width





@require_torch
@require_vision
class   __a      (   __UpperCamelCase	,	unittest.TestCase				):
 __lowercase			:							Tuple       					=			DetrImageProcessor if is_vision_available() else None
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        lowercase__:       Optional[int]        =       DetrImageProcessingTester(self			)
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        return self.image_processor_tester.prepare_image_processor_dict()
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        lowercase__:       Dict        =       self.image_processing_class(**self.image_processor_dict			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'image_mean'			)			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'image_std'			)			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'do_normalize'			)			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'do_rescale'			)			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'rescale_factor'			)			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'do_resize'			)			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'size'			)			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'do_pad'			)			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        lowercase__:       int        =       self.image_processing_class.from_dict(self.image_processor_dict			)
        self.assertEqual(image_processor.size    ,			{'shortest_edge': 18, 'longest_edge': 1_333}			)
        self.assertEqual(image_processor.do_pad    ,			lowerCAmelCase__			)

        lowercase__:       str        =       self.image_processing_class.from_dict(
            self.image_processor_dict    ,			size=42    ,			max_size=84    ,			pad_and_return_pixel_mask=lowerCAmelCase__			)
        self.assertEqual(image_processor.size    ,			{'shortest_edge': 42, 'longest_edge': 84}			)
        self.assertEqual(image_processor.do_pad    ,			lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        pass
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        # Initialize image_processing
        lowercase__:       Dict        =       self.image_processing_class(**self.image_processor_dict			)
        # create random PIL images
        lowercase__:       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
        lowercase__:       Optional[Any]        =       image_processing(image_inputs[0]    ,			return_tensors='pt'			).pixel_values

        lowercase__      ,						lowercase__:       Tuple        =       self.image_processor_tester.get_expected_values(lowerCAmelCase__			)

        self.assertEqual(
            encoded_images.shape    ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,			)

        # Test batched
        lowercase__      ,						lowercase__:       Union[str, Any]        =       self.image_processor_tester.get_expected_values(lowerCAmelCase__    ,			batched=lowerCAmelCase__			)

        lowercase__:       Dict        =       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,
                expected_height,
                expected_width,
            )    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        # Initialize image_processing
        lowercase__:       Union[str, Any]        =       self.image_processing_class(**self.image_processor_dict			)
        # create random numpy tensors
        lowercase__:       Optional[int]        =       prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=lowerCAmelCase__    ,			numpify=lowerCAmelCase__			)
        for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase__    ,			np.ndarray			)

        # Test not batched input
        lowercase__:       Union[str, Any]        =       image_processing(image_inputs[0]    ,			return_tensors='pt'			).pixel_values

        lowercase__      ,						lowercase__:       Optional[int]        =       self.image_processor_tester.get_expected_values(lowerCAmelCase__			)

        self.assertEqual(
            encoded_images.shape    ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,			)

        # Test batched
        lowercase__:       Union[str, Any]        =       image_processing(lowerCAmelCase__    ,			return_tensors='pt'			).pixel_values

        lowercase__      ,						lowercase__:       Dict        =       self.image_processor_tester.get_expected_values(lowerCAmelCase__    ,			batched=lowerCAmelCase__			)

        self.assertEqual(
            encoded_images.shape    ,			(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        # Initialize image_processing
        lowercase__:       Tuple        =       self.image_processing_class(**self.image_processor_dict			)
        # create random PyTorch tensors
        lowercase__:       Union[str, 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
        lowercase__:       Optional[int]        =       image_processing(image_inputs[0]    ,			return_tensors='pt'			).pixel_values

        lowercase__      ,						lowercase__:       Dict        =       self.image_processor_tester.get_expected_values(lowerCAmelCase__			)

        self.assertEqual(
            encoded_images.shape    ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,			)

        # Test batched
        lowercase__:       Optional[Any]        =       image_processing(lowerCAmelCase__    ,			return_tensors='pt'			).pixel_values

        lowercase__      ,						lowercase__:       Optional[Any]        =       self.image_processor_tester.get_expected_values(lowerCAmelCase__    ,			batched=lowerCAmelCase__			)

        self.assertEqual(
            encoded_images.shape    ,			(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )    ,			)
 @slow
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        # prepare image and target
        lowercase__:       Any        =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
        with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'    ,			'r'			) as f:
               lowercase__:       Optional[int]        =       json.loads(f.read()			)

        lowercase__:       Optional[Any]        =       {'image_id': 39_769, 'annotations': target}

        # encode them
        lowercase__:       Optional[Any]        =       DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'			)
        lowercase__:       List[Any]        =       image_processing(images=lowerCAmelCase__    ,			annotations=lowerCAmelCase__    ,			return_tensors='pt'			)

        # verify pixel values
        lowercase__:       Optional[int]        =       torch.Size([1, 3, 800, 1_066]			)
        self.assertEqual(encoding['pixel_values'].shape    ,			lowerCAmelCase__			)

        lowercase__:       Optional[Any]        =       torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]			)
        self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,			lowerCAmelCase__    ,			atol=1E-4			)			)

        # verify area
        lowercase__:       List[Any]        =       torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,			lowerCAmelCase__			)			)
        # verify boxes
        lowercase__:       Union[str, Any]        =       torch.Size([6, 4]			)
        self.assertEqual(encoding['labels'][0]['boxes'].shape    ,			lowerCAmelCase__			)
        lowercase__:       int        =       torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,			lowerCAmelCase__    ,			atol=1E-3			)			)
        # verify image_id
        lowercase__:       List[Any]        =       torch.tensor([39_769]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,			lowerCAmelCase__			)			)
        # verify is_crowd
        lowercase__:       Union[str, Any]        =       torch.tensor([0, 0, 0, 0, 0, 0]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,			lowerCAmelCase__			)			)
        # verify class_labels
        lowercase__:       List[Any]        =       torch.tensor([75, 75, 63, 65, 17, 17]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,			lowerCAmelCase__			)			)
        # verify orig_size
        lowercase__:       Tuple        =       torch.tensor([480, 640]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,			lowerCAmelCase__			)			)
        # verify size
        lowercase__:       Tuple        =       torch.tensor([800, 1_066]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,			lowerCAmelCase__			)			)
 @slow
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        # prepare image, target and masks_path
        lowercase__:       List[str]        =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
        with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'    ,			'r'			) as f:
               lowercase__:       Tuple        =       json.loads(f.read()			)

        lowercase__:       Tuple        =       {'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

        lowercase__:       List[str]        =       pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'			)

        # encode them
        lowercase__:       Dict        =       DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'			)
        lowercase__:       Dict        =       image_processing(images=lowerCAmelCase__    ,			annotations=lowerCAmelCase__    ,			masks_path=lowerCAmelCase__    ,			return_tensors='pt'			)

        # verify pixel values
        lowercase__:       Any        =       torch.Size([1, 3, 800, 1_066]			)
        self.assertEqual(encoding['pixel_values'].shape    ,			lowerCAmelCase__			)

        lowercase__:       Any        =       torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]			)
        self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,			lowerCAmelCase__    ,			atol=1E-4			)			)

        # verify area
        lowercase__:       str        =       torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,			lowerCAmelCase__			)			)
        # verify boxes
        lowercase__:       Dict        =       torch.Size([6, 4]			)
        self.assertEqual(encoding['labels'][0]['boxes'].shape    ,			lowerCAmelCase__			)
        lowercase__:       str        =       torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,			lowerCAmelCase__    ,			atol=1E-3			)			)
        # verify image_id
        lowercase__:       Optional[int]        =       torch.tensor([39_769]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,			lowerCAmelCase__			)			)
        # verify is_crowd
        lowercase__:       List[Any]        =       torch.tensor([0, 0, 0, 0, 0, 0]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,			lowerCAmelCase__			)			)
        # verify class_labels
        lowercase__:       Optional[int]        =       torch.tensor([17, 17, 63, 75, 75, 93]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,			lowerCAmelCase__			)			)
        # verify masks
        lowercase__:       str        =       822_873
        self.assertEqual(encoding['labels'][0]['masks'].sum().item()    ,			lowerCAmelCase__			)
        # verify orig_size
        lowercase__:       Optional[int]        =       torch.tensor([480, 640]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,			lowerCAmelCase__			)			)
        # verify size
        lowercase__:       Optional[int]        =       torch.tensor([800, 1_066]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,			lowerCAmelCase__			)			)


