



import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def 		UpperCamelCase	(   snake_case__       :    List[Any]	)	->     Union[str, Any]:
   # vision encoder
   if "img_encoder.pos_embed" in name:
      UpperCamelCase    :  Union[str, Any]							=	name.replace('img_encoder.pos_embed'       ,     'vision_model.embeddings.position_embeddings'	)
   if "img_encoder.patch_embed.proj" in name:
      UpperCamelCase    :  Optional[Any]							=	name.replace('img_encoder.patch_embed.proj'       ,     'vision_model.embeddings.patch_embeddings.projection'	)
   if "img_encoder.patch_embed.norm" in name:
      UpperCamelCase    :  int							=	name.replace('img_encoder.patch_embed.norm'       ,     'vision_model.embeddings.layernorm'	)
   if "img_encoder.layers" in name:
      UpperCamelCase    :  Optional[Any]							=	name.replace('img_encoder.layers'       ,     'vision_model.encoder.stages'	)
   if "blocks" in name and "res" not in name:
      UpperCamelCase    :  str							=	name.replace('blocks'       ,     'layers'	)
   if "attn" in name and "pre_assign" not in name:
      UpperCamelCase    :  Union[str, Any]							=	name.replace('attn'       ,     'self_attn'	)
   if "proj" in name and "self_attn" in name and "text" not in name:
      UpperCamelCase    :  Optional[Any]							=	name.replace('proj'       ,     'out_proj'	)
   if "pre_assign_attn.attn.proj" in name:
      UpperCamelCase    :  Tuple							=	name.replace('pre_assign_attn.attn.proj'       ,     'pre_assign_attn.attn.out_proj'	)
   if "norm1" in name:
      UpperCamelCase    :  int							=	name.replace('norm1'       ,     'layer_norm1'	)
   if "norm2" in name and "pre_assign" not in name:
      UpperCamelCase    :  Tuple							=	name.replace('norm2'       ,     'layer_norm2'	)
   if "img_encoder.norm" in name:
      UpperCamelCase    :  Dict							=	name.replace('img_encoder.norm'       ,     'vision_model.layernorm'	)
   # text encoder
   if "text_encoder.token_embedding" in name:
      UpperCamelCase    :  List[Any]							=	name.replace('text_encoder.token_embedding'       ,     'text_model.embeddings.token_embedding'	)
   if "text_encoder.positional_embedding" in name:
      UpperCamelCase    :  Tuple							=	name.replace('text_encoder.positional_embedding'       ,     'text_model.embeddings.position_embedding.weight'	)
   if "text_encoder.transformer.resblocks." in name:
      UpperCamelCase    :  Any							=	name.replace('text_encoder.transformer.resblocks.'       ,     'text_model.encoder.layers.'	)
   if "ln_1" in name:
      UpperCamelCase    :  Optional[Any]							=	name.replace('ln_1'       ,     'layer_norm1'	)
   if "ln_2" in name:
      UpperCamelCase    :  Any							=	name.replace('ln_2'       ,     'layer_norm2'	)
   if "c_fc" in name:
      UpperCamelCase    :  Union[str, Any]							=	name.replace('c_fc'       ,     'fc1'	)
   if "c_proj" in name:
      UpperCamelCase    :  Optional[Any]							=	name.replace('c_proj'       ,     'fc2'	)
   if "text_encoder" in name:
      UpperCamelCase    :  Dict							=	name.replace('text_encoder'       ,     'text_model'	)
   if "ln_final" in name:
      UpperCamelCase    :  Optional[Any]							=	name.replace('ln_final'       ,     'final_layer_norm'	)
   # projection layers
   if "img_projector.linear_hidden." in name:
      UpperCamelCase    :  Union[str, Any]							=	name.replace('img_projector.linear_hidden.'       ,     'visual_projection.'	)
   if "img_projector.linear_out." in name:
      UpperCamelCase    :  Any							=	name.replace('img_projector.linear_out.'       ,     'visual_projection.3.'	)
   if "text_projector.linear_hidden" in name:
      UpperCamelCase    :  Optional[int]							=	name.replace('text_projector.linear_hidden'       ,     'text_projection'	)
   if "text_projector.linear_out" in name:
      UpperCamelCase    :  Any							=	name.replace('text_projector.linear_out'       ,     'text_projection.3'	)

   return name
def 		UpperCamelCase	(   snake_case__       :    Optional[Any]       ,     snake_case__       :    Optional[int]	)	->     Dict:
   for key in orig_state_dict.copy().keys():
      UpperCamelCase    :  Optional[int]							=	orig_state_dict.pop(snake_case__	)

      if "qkv" in key:
         # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
         # we need to split them up into separate matrices/vectors
         UpperCamelCase    :  Dict							=	key.split('.'	)
         UpperCamelCase	,     UpperCamelCase    :  int							=	int(key_split[2]	), int(key_split[4]	)
         UpperCamelCase    :  Union[str, Any]							=	config.vision_config.hidden_size
         if "weight" in key:
            UpperCamelCase    :  Optional[Any]							=	val[:dim, :]
            UpperCamelCase    :  List[str]							=	val[dim : dim * 2, :]
            UpperCamelCase    :  Dict							=	val[-dim:, :]
         else:
            UpperCamelCase    :  Tuple							=	val[:dim]
            UpperCamelCase    :  List[Any]							=	val[dim : dim * 2]
            UpperCamelCase    :  int							=	val[-dim:]
      elif "in_proj" in key:
         # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
         # we need to split them up into separate matrices/vectors
         UpperCamelCase    :  int							=	key.split('.'	)
         UpperCamelCase    :  int							=	int(key_split[3]	)
         UpperCamelCase    :  Union[str, Any]							=	config.text_config.hidden_size
         if "weight" in key:
            UpperCamelCase    :  List[Any]							=	val[:dim, :]
            UpperCamelCase    :  Any							=	val[
                dim : dim * 2, :
            ]
            UpperCamelCase    :  Optional[Any]							=	val[-dim:, :]
         else:
            UpperCamelCase    :  Dict							=	val[:dim]
            UpperCamelCase    :  Any							=	val[dim : dim * 2]
            UpperCamelCase    :  Optional[Any]							=	val[-dim:]
      else:
         UpperCamelCase    :  List[str]							=	rename_key(snake_case__	)
         # squeeze if necessary
         if (
             "text_projection.0" in new_name
             or "text_projection.3" in new_name
             or "visual_projection.0" in new_name
             or "visual_projection.3" in new_name
         ):
            UpperCamelCase    :  List[Any]							=	val.squeeze_()
         else:
            UpperCamelCase    :  Tuple							=	val

   return orig_state_dict
def 		UpperCamelCase	(   )	->     List[Any]:
   UpperCamelCase    :  Any							=	'http://images.cocodataset.org/val2017/000000039769.jpg'
   UpperCamelCase    :  str							=	Image.open(requests.get(snake_case__       ,     stream=snake_case__	).raw	)
   return im
@torch.no_grad()
def 		UpperCamelCase	(   snake_case__       :    Optional[Any]       ,     snake_case__       :    Any       ,     snake_case__       :    Tuple="groupvit-gcc-yfcc"       ,     snake_case__       :    List[Any]=False	)	->     Any:
   UpperCamelCase    :  List[str]							=	GroupViTConfig()
   UpperCamelCase    :  Union[str, Any]							=	GroupViTModel(snake_case__	).eval()

   UpperCamelCase    :  Tuple							=	torch.load(snake_case__       ,     map_location='cpu'	)['model']
   UpperCamelCase    :  List[str]							=	convert_state_dict(snake_case__       ,     snake_case__	)
   UpperCamelCase	,     UpperCamelCase    :  Union[str, Any]							=	model.load_state_dict(snake_case__       ,     strict=snake_case__	)
   assert missing_keys == ["text_model.embeddings.position_ids"]
   assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(snake_case__	) == 0)

   # verify result
   UpperCamelCase    :  Tuple							=	CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32'	)
   UpperCamelCase    :  Any							=	prepare_img()
   UpperCamelCase    :  Dict							=	processor(text=['a photo of a cat', 'a photo of a dog']       ,     images=snake_case__       ,     padding=snake_case__       ,     return_tensors='pt'	)

   with torch.no_grad():
      UpperCamelCase    :  Optional[int]							=	model(**snake_case__	)

   if model_name == "groupvit-gcc-yfcc":
      UpperCamelCase    :  Optional[Any]							=	torch.tensor([[13.3523, 6.3629]]	)
   elif model_name == "groupvit-gcc-redcaps":
      UpperCamelCase    :  Tuple							=	torch.tensor([[16.1873, 8.6230]]	)
   else:
      raise ValueError(F"""Model name {model_name} not supported."""	)
   assert torch.allclose(outputs.logits_per_image       ,     snake_case__       ,     atol=1E-3	)

   processor.save_pretrained(snake_case__	)
   model.save_pretrained(snake_case__	)
   print('Successfully saved processor and model to'       ,     snake_case__	)

   if push_to_hub:
      print('Pushing to the hub...'	)
      processor.push_to_hub(snake_case__       ,     organization='nielsr'	)
      model.push_to_hub(snake_case__       ,     organization='nielsr'	)


if __name__ == "__main__":
    __UpperCAmelCase          =       argparse.ArgumentParser()
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to dump the processor and PyTorch model.'''
    )
    parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to GroupViT checkpoint''')
    parser.add_argument(
        '''--model_name''',
        default='''groupvit-gccy-fcc''',
        type=str,
        help='''Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'''',
    )
    parser.add_argument(
        '''--push_to_hub''',
        action='''store_true''',
        help='''Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.''',
    )
    __UpperCAmelCase          =       parser.parse_args()

    convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)








import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
def 		UpperCamelCase	(   snake_case__       :    Tuple	)	->     List[str]:
   UpperCamelCase    :  Optional[Any]							=	[tensor.shape for tensor in tensor_list]
   return all(shape == shapes[0] for shape in shapes[1:]	)



class 				lowerCAmelCase_		(  a__       ,    a__       ,    a__       ,    unittest.TestCase							):
       UpperCAmelCase__				:					str        =  StableDiffusionLatentUpscalePipeline
       UpperCAmelCase__				:					Optional[Any]        =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
           "height",
           "width",
           "cross_attention_kwargs",
           "negative_prompt_embeds",
           "prompt_embeds",
       }
       UpperCAmelCase__				:					str        =  PipelineTesterMixin.required_optional_params - {"num_images_per_prompt"}
       UpperCAmelCase__				:					Any        =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       UpperCAmelCase__				:					List[str]        =  frozenset(
           []							)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       UpperCAmelCase__				:					Union[str, Any]        =  frozenset([]							)

       UpperCAmelCase__				:					Optional[int]        =  True
       @property
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          UpperCamelCase    :  List[Any]							=	1
          UpperCamelCase    :  List[str]							=	4
          UpperCamelCase    :  List[str]							=	(16, 16)

          UpperCamelCase    :  Optional[int]							=	floats_tensor((batch_size, num_channels) + sizes,			rng=random.Random(0       )       ).to(SCREAMING_SNAKE_CASE_       )
          return image
       def 					snake_case_       (			self       )     ->		int:
          torch.manual_seed(0       )
          UpperCamelCase    :  int							=	UNetaDConditionModel(
              act_fn='gelu',			attention_head_dim=8,			norm_num_groups=SCREAMING_SNAKE_CASE_,			block_out_channels=[32, 32, 64, 64],			time_cond_proj_dim=160,			conv_in_kernel=1,			conv_out_kernel=1,			cross_attention_dim=32,			down_block_types=(
                  'KDownBlock2D',
                  'KCrossAttnDownBlock2D',
                  'KCrossAttnDownBlock2D',
                  'KCrossAttnDownBlock2D',
              ),			in_channels=8,			mid_block_type=SCREAMING_SNAKE_CASE_,			only_cross_attention=SCREAMING_SNAKE_CASE_,			out_channels=5,			resnet_time_scale_shift='scale_shift',			time_embedding_type='fourier',			timestep_post_act='gelu',			up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D'),			)
          UpperCamelCase    :  Tuple							=	AutoencoderKL(
              block_out_channels=[32, 32, 64, 64],			in_channels=3,			out_channels=3,			down_block_types=[
                  'DownEncoderBlock2D',
                  'DownEncoderBlock2D',
                  'DownEncoderBlock2D',
                  'DownEncoderBlock2D',
              ],			up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D'],			latent_channels=4,			)
          UpperCamelCase    :  Tuple							=	EulerDiscreteScheduler(prediction_type='sample'       )
          UpperCamelCase    :  Optional[int]							=	CLIPTextConfig(
              bos_token_id=0,			eos_token_id=2,			hidden_size=32,			intermediate_size=37,			layer_norm_eps=1e-05,			num_attention_heads=4,			num_hidden_layers=5,			pad_token_id=1,			vocab_size=1000,			hidden_act='quick_gelu',			projection_dim=512,			)
          UpperCamelCase    :  Any							=	CLIPTextModel(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Union[str, Any]							=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )

          UpperCamelCase    :  Optional[int]							=	{
              'unet': model.eval(),
              'vae': vae.eval(),
              'scheduler': scheduler,
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
          }

          return components
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=0       )     ->		List[str]:
          if str(SCREAMING_SNAKE_CASE_       ).startswith('mps'       ):
             UpperCamelCase    :  List[str]							=	torch.manual_seed(SCREAMING_SNAKE_CASE_       )
          else:
             UpperCamelCase    :  Optional[Any]							=	torch.Generator(device=SCREAMING_SNAKE_CASE_       ).manual_seed(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  List[str]							=	{
              'prompt': 'A painting of a squirrel eating a burger',
              'image': self.dummy_image.cpu(),
              'generator': generator,
              'num_inference_steps': 2,
              'output_type': 'numpy',
          }
          return inputs
       def 					snake_case_       (			self       )     ->		List[Any]:
          UpperCamelCase    :  str							=	'cpu'

          UpperCamelCase    :  Tuple							=	self.get_dummy_components()
          UpperCamelCase    :  Dict							=	self.pipeline_class(**SCREAMING_SNAKE_CASE_       )
          pipe.to(SCREAMING_SNAKE_CASE_       )
          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Union[str, Any]							=	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  List[Any]							=	pipe(**SCREAMING_SNAKE_CASE_       ).images
          UpperCamelCase    :  str							=	image[0, -3:, -3:, -1]

          self.assertEqual(image.shape,			(1, 256, 256, 3)       )
          UpperCamelCase    :  List[Any]							=	np.array(
              [0.47_22_24_12, 0.41_92_16_33, 0.44_71_74_34, 0.46_87_41_92, 0.42_58_82_58, 0.46_15_07_26, 0.4_67_75_34, 0.45_58_38_32, 0.48_57_90_55]       )
          UpperCamelCase    :  Dict							=	np.abs(image_slice.flatten() - expected_slice       ).max()
          self.assertLessEqual(SCREAMING_SNAKE_CASE_,			1e-3       )
       def 					snake_case_       (			self       )     ->		List[Any]:
          super().test_attention_slicing_forward_pass(expected_max_diff=7e-3       )
       def 					snake_case_       (			self       )     ->		Tuple:
          super().test_cpu_offload_forward_pass(expected_max_diff=3e-3       )
       def 					snake_case_       (			self       )     ->		Dict:
          super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3       )
       def 					snake_case_       (			self       )     ->		Any:
          super().test_inference_batch_single_identical(expected_max_diff=7e-3       )
       def 					snake_case_       (			self       )     ->		List[str]:
          super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3       )
       def 					snake_case_       (			self       )     ->		Optional[int]:
          super().test_save_load_local(expected_max_difference=3e-3       )
       def 					snake_case_       (			self       )     ->		int:
          super().test_save_load_optional_components(expected_max_difference=3e-3       )






       def 					snake_case_       (			self       )     ->		Optional[Any]:
          UpperCamelCase    :  Optional[Any]							=	[
              'DDIMScheduler',
              'DDPMScheduler',
              'PNDMScheduler',
              'HeunDiscreteScheduler',
              'EulerAncestralDiscreteScheduler',
              'KDPM2DiscreteScheduler',
              'KDPM2AncestralDiscreteScheduler',
              'DPMSolverSDEScheduler',
          ]
          UpperCamelCase    :  Tuple							=	self.get_dummy_components()
          UpperCamelCase    :  Tuple							=	self.pipeline_class(**SCREAMING_SNAKE_CASE_       )

          # make sure that PNDM does not need warm-up
          pipe.scheduler.register_to_config(skip_prk_steps=SCREAMING_SNAKE_CASE_       )

          pipe.to(SCREAMING_SNAKE_CASE_       )
          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  int							=	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Optional[Any]							=	2

          UpperCamelCase    :  Optional[Any]							=	[]
          for scheduler_enum in KarrasDiffusionSchedulers:
             if scheduler_enum.name in skip_schedulers:
                # no sigma schedulers are not supported
                # no schedulers
                continue

             UpperCamelCase    :  List[Any]							=	getattr(SCREAMING_SNAKE_CASE_,			scheduler_enum.name       )
             UpperCamelCase    :  List[str]							=	scheduler_cls.from_config(pipe.scheduler.config       )
             UpperCamelCase    :  List[str]							=	pipe(**SCREAMING_SNAKE_CASE_       )[0]
             outputs.append(SCREAMING_SNAKE_CASE_       )

          assert check_same_shape(SCREAMING_SNAKE_CASE_       )






@require_torch_gpu
@slow
class 				lowerCAmelCase_		(  unittest.TestCase							):
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
       def 					snake_case_       (			self       )     ->		List[Any]:
          UpperCamelCase    :  Dict							=	torch.manual_seed(33       )

          UpperCamelCase    :  Union[str, Any]							=	StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4',			torch_dtype=torch.floataa       )
          pipe.to('cuda'       )

          UpperCamelCase    :  Tuple							=	StableDiffusionLatentUpscalePipeline.from_pretrained(
              'stabilityai/sd-x2-latent-upscaler',			torch_dtype=torch.floataa       )
          upscaler.to('cuda'       )

          UpperCamelCase    :  Union[str, Any]							=	'a photo of an astronaut high resolution, unreal engine, ultra realistic'

          UpperCamelCase    :  int							=	pipe(SCREAMING_SNAKE_CASE_,			generator=SCREAMING_SNAKE_CASE_,			output_type='latent'       ).images

          UpperCamelCase    :  List[str]							=	upscaler(
              prompt=SCREAMING_SNAKE_CASE_,			image=SCREAMING_SNAKE_CASE_,			num_inference_steps=20,			guidance_scale=0,			generator=SCREAMING_SNAKE_CASE_,			output_type='np',			).images[0]

          UpperCamelCase    :  Optional[int]							=	load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'       )
          assert np.abs((expected_image - image).mean()       ) < 5e-2






       def 					snake_case_       (			self       )     ->		int:
          UpperCamelCase    :  List[Any]							=	torch.manual_seed(33       )

          UpperCamelCase    :  Union[str, Any]							=	StableDiffusionLatentUpscalePipeline.from_pretrained(
              'stabilityai/sd-x2-latent-upscaler',			torch_dtype=torch.floataa       )
          upscaler.to('cuda'       )

          UpperCamelCase    :  Dict							=	'the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'

          UpperCamelCase    :  List[str]							=	load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'       )

          UpperCamelCase    :  str							=	upscaler(
              prompt=SCREAMING_SNAKE_CASE_,			image=SCREAMING_SNAKE_CASE_,			num_inference_steps=20,			guidance_scale=0,			generator=SCREAMING_SNAKE_CASE_,			output_type='np',			).images[0]

          UpperCamelCase    :  Optional[Any]							=	load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'       )
          assert np.abs((expected_image - image).max()       ) < 5e-2



