
"""simple docstring"""






import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class      _SCREAMING_SNAKE_CASE   (     __SCREAMING_SNAKE_CASE      ,					unittest.TestCase				):
			UpperCAmelCase_    :List[str]												=		KandinskyPriorPipeline
			UpperCAmelCase_    :List[Any]												=		["prompt"]
			UpperCAmelCase_    :Union[str, Any]												=		["prompt", "negative_prompt"]
			UpperCAmelCase_    :Any												=		[
			    "num_images_per_prompt",
			    "generator",
			    "num_inference_steps",
			    "latents",
			    "negative_prompt",
			    "guidance_scale",
			    "output_type",
			    "return_dict",
			]
			UpperCAmelCase_    :str												=		False






			@property
			def 					__lowerCAmelCase					(   self			)     ->	Union[str, Any]:
						return 32






			@property
			def 					__lowerCAmelCase					(   self			)     ->	List[str]:
						return 32






			@property
			def 					__lowerCAmelCase					(   self			)     ->	Tuple:
						return self.time_input_dim






			@property
			def 					__lowerCAmelCase					(   self			)     ->	str:
						return self.time_input_dim * 4






			@property
			def 					__lowerCAmelCase					(   self			)     ->	str:
						return 100






			@property
			def 					__lowerCAmelCase					(   self			)     ->	List[str]:
						lowerCAmelCase_       :Optional[int]     	=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)
						return tokenizer






			@property
			def 					__lowerCAmelCase					(   self			)     ->	Any:
						torch.manual_seed(0			)
						lowerCAmelCase_       :Optional[int]     	=    CLIPTextConfig(
						    bos_token_id=0			,  eos_token_id=2			,  hidden_size=self.text_embedder_hidden_size			,  projection_dim=self.text_embedder_hidden_size			,  intermediate_size=37			,  layer_norm_eps=1E-05			,  num_attention_heads=4			,  num_hidden_layers=5			,  pad_token_id=1			,  vocab_size=1000			,  )
						return CLIPTextModelWithProjection(_A			)






			@property
			def 					__lowerCAmelCase					(   self			)     ->	List[Any]:
						torch.manual_seed(0			)

						lowerCAmelCase_       :Dict     	=    {
						    """num_attention_heads""": 2,
						    """attention_head_dim""": 12,
						    """embedding_dim""": self.text_embedder_hidden_size,
						    """num_layers""": 1,
						}

						lowerCAmelCase_       :Optional[int]     	=    PriorTransformer(**_A			)
						# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
						lowerCAmelCase_       :List[Any]     	=    nn.Parameter(torch.ones(model.clip_std.shape			)			)
						return model






			@property
			def 					__lowerCAmelCase					(   self			)     ->	Union[str, Any]:
						torch.manual_seed(0			)
						lowerCAmelCase_       :List[str]     	=    CLIPVisionConfig(
						    hidden_size=self.text_embedder_hidden_size			,  image_size=224			,  projection_dim=self.text_embedder_hidden_size			,  intermediate_size=37			,  num_attention_heads=4			,  num_channels=3			,  num_hidden_layers=5			,  patch_size=14			,  )

						lowerCAmelCase_       :Optional[int]     	=    CLIPVisionModelWithProjection(_A			)
						return model






			@property
			def 					__lowerCAmelCase					(   self			)     ->	List[str]:
						lowerCAmelCase_       :int     	=    CLIPImageProcessor(
						    crop_size=224			,  do_center_crop=_A			,  do_normalize=_A			,  do_resize=_A			,  image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]			,  image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]			,  resample=3			,  size=224			,  )

						return image_processor






			def 					__lowerCAmelCase					(   self			)     ->	Optional[Any]:
						lowerCAmelCase_       :Dict     	=    self.dummy_prior
						lowerCAmelCase_       :Dict     	=    self.dummy_image_encoder
						lowerCAmelCase_       :int     	=    self.dummy_text_encoder
						lowerCAmelCase_       :List[str]     	=    self.dummy_tokenizer
						lowerCAmelCase_       :Optional[int]     	=    self.dummy_image_processor

						lowerCAmelCase_       :List[Any]     	=    UnCLIPScheduler(
						    variance_type="""fixed_small_log"""			,  prediction_type="""sample"""			,  num_train_timesteps=1000			,  clip_sample=_A			,  clip_sample_range=1_0.0			,  )

						lowerCAmelCase_       :Optional[int]     	=    {
						    """prior""": prior,
						    """image_encoder""": image_encoder,
						    """text_encoder""": text_encoder,
						    """tokenizer""": tokenizer,
						    """scheduler""": scheduler,
						    """image_processor""": image_processor,
						}

						return components






			def 					__lowerCAmelCase					(   self			,  __A			,  __A=0			)     ->	Any:
						if str(_A			).startswith("""mps"""			):
									lowerCAmelCase_       :Optional[Any]     	=    torch.manual_seed(_A			)
						else:
									lowerCAmelCase_       :int     	=    torch.Generator(device=_A			).manual_seed(_A			)
						lowerCAmelCase_       :Optional[int]     	=    {
						    """prompt""": """horse""",
						    """generator""": generator,
						    """guidance_scale""": 4.0,
						    """num_inference_steps""": 2,
						    """output_type""": """np""",
						}
						return inputs






			def 					__lowerCAmelCase					(   self			)     ->	Dict:
						lowerCAmelCase_       :Optional[Any]     	=    """cpu"""

						lowerCAmelCase_       :Optional[int]     	=    self.get_dummy_components()

						lowerCAmelCase_       :int     	=    self.pipeline_class(**_A			)
						lowerCAmelCase_       :Dict     	=    pipe.to(_A			)

						pipe.set_progress_bar_config(disable=_A			)

						lowerCAmelCase_       :Optional[int]     	=    pipe(**self.get_dummy_inputs(_A			)			)
						lowerCAmelCase_       :int     	=    output.image_embeds

						lowerCAmelCase_       :List[str]     	=    pipe(
						    **self.get_dummy_inputs(_A			)			,  return_dict=_A			,  )[0]

						lowerCAmelCase_       :List[str]     	=    image[0, -10:]
						lowerCAmelCase_       :Tuple     	=    image_from_tuple[0, -10:]

						assert image.shape == (1, 32)

						lowerCAmelCase_       :Optional[int]     	=    np.array(
						    [-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6]			)

						assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1E-2






			@skip_mps
			def 					__lowerCAmelCase					(   self			)     ->	List[str]:
						lowerCAmelCase_       :str     	=    torch_device == """cpu"""
						lowerCAmelCase_       :Any     	=    True
						lowerCAmelCase_       :List[str]     	=    False

						self._test_inference_batch_single_identical(
						    test_max_difference=_A			,  relax_max_difference=_A			,  test_mean_pixel_difference=_A			,  )






			@skip_mps
			def 					__lowerCAmelCase					(   self			)     ->	Any:
						lowerCAmelCase_       :Optional[int]     	=    torch_device == """cpu"""
						lowerCAmelCase_       :Tuple     	=    False

						self._test_attention_slicing_forward_pass(
						    test_max_difference=_A			,  test_mean_pixel_difference=_A			,  )








import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)


def  A__ (       __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      CLIPSegTextConfig()
   SCREAMING_SNAKE_CASE_	=      CLIPSegVisionConfig(patch_size=16						)

   SCREAMING_SNAKE_CASE_	=      True if '''refined''' in model_name else False
   SCREAMING_SNAKE_CASE_	=      16 if '''rd16''' in model_name else 64

   SCREAMING_SNAKE_CASE_	=      CLIPSegConfig.from_text_vision_configs(
       __lowerCamelCase,  __lowerCamelCase,  use_complex_transposed_convolution=__lowerCamelCase,  reduce_dim=__lowerCamelCase,  )
   return config


def  A__ (       __lowerCamelCase						):
   # update prefixes
   if "clip_model" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''clip_model''',  '''clip'''						)
   if "transformer" in name:
      if "visual" in name:
         SCREAMING_SNAKE_CASE_	=      name.replace('''visual.transformer''',  '''vision_model'''						)
      else:
         SCREAMING_SNAKE_CASE_	=      name.replace('''transformer''',  '''text_model'''						)
   if "resblocks" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''resblocks''',  '''encoder.layers'''						)
   if "ln_1" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''ln_1''',  '''layer_norm1'''						)
   if "ln_2" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''ln_2''',  '''layer_norm2'''						)
   if "c_fc" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''c_fc''',  '''fc1'''						)
   if "c_proj" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''c_proj''',  '''fc2'''						)
   if "attn" in name and "self" not in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''attn''',  '''self_attn'''						)
   # text encoder
   if "token_embedding" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''token_embedding''',  '''text_model.embeddings.token_embedding'''						)
   if "positional_embedding" in name and "visual" not in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''positional_embedding''',  '''text_model.embeddings.position_embedding.weight'''						)
   if "ln_final" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''ln_final''',  '''text_model.final_layer_norm'''						)
   # vision encoder
   if "visual.class_embedding" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''visual.class_embedding''',  '''vision_model.embeddings.class_embedding'''						)
   if "visual.conv1" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''visual.conv1''',  '''vision_model.embeddings.patch_embedding'''						)
   if "visual.positional_embedding" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''visual.positional_embedding''',  '''vision_model.embeddings.position_embedding.weight'''						)
   if "visual.ln_pre" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''visual.ln_pre''',  '''vision_model.pre_layrnorm'''						)
   if "visual.ln_post" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''visual.ln_post''',  '''vision_model.post_layernorm'''						)
   # projection layers
   if "visual.proj" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''visual.proj''',  '''visual_projection.weight'''						)
   if "text_projection" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''text_projection''',  '''text_projection.weight'''						)
   # decoder
   if "trans_conv" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''trans_conv''',  '''transposed_convolution'''						)
   if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
      SCREAMING_SNAKE_CASE_	=      '''decoder.''' + name
   if "blocks" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''blocks''',  '''decoder.layers'''						)
   if "linear1" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''linear1''',  '''mlp.fc1'''						)
   if "linear2" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''linear2''',  '''mlp.fc2'''						)
   if "norm1" in name and "layer_" not in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''norm1''',  '''layer_norm1'''						)
   if "norm2" in name and "layer_" not in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''norm2''',  '''layer_norm2'''						)

   return name


def  A__ (       __lowerCamelCase,  __lowerCamelCase						):
   for key in orig_state_dict.copy().keys():
      SCREAMING_SNAKE_CASE_	=      orig_state_dict.pop(__lowerCamelCase						)

      if key.startswith('''clip_model'''						) and "attn.in_proj" in key:
         SCREAMING_SNAKE_CASE_	=      key.split('''.'''						)
         if "visual" in key:
            SCREAMING_SNAKE_CASE_	=      int(key_split[4]						)
            SCREAMING_SNAKE_CASE_	=      config.vision_config.hidden_size
            SCREAMING_SNAKE_CASE_	=      '''vision_model'''
         else:
            SCREAMING_SNAKE_CASE_	=      int(key_split[3]						)
            SCREAMING_SNAKE_CASE_	=      config.text_config.hidden_size
            SCREAMING_SNAKE_CASE_	=      '''text_model'''

         if "weight" in key:
            SCREAMING_SNAKE_CASE_	=      val[:dim, :]
            SCREAMING_SNAKE_CASE_	=      val[
                dim : dim * 2, :
            ]
            SCREAMING_SNAKE_CASE_	=      val[-dim:, :]
         else:
            SCREAMING_SNAKE_CASE_	=      val[:dim]
            SCREAMING_SNAKE_CASE_	=      val[dim : dim * 2]
            SCREAMING_SNAKE_CASE_	=      val[-dim:]
      elif "self_attn" in key and "out_proj" not in key:
         SCREAMING_SNAKE_CASE_	=      key.split('''.'''						)
         SCREAMING_SNAKE_CASE_	=      int(key_split[1]						)
         SCREAMING_SNAKE_CASE_	=      config.reduce_dim
         if "weight" in key:
            SCREAMING_SNAKE_CASE_	=      val[:dim, :]
            SCREAMING_SNAKE_CASE_	=      val[dim : dim * 2, :]
            SCREAMING_SNAKE_CASE_	=      val[-dim:, :]
         else:
            SCREAMING_SNAKE_CASE_	=      val[:dim]
            SCREAMING_SNAKE_CASE_	=      val[dim : dim * 2]
            SCREAMING_SNAKE_CASE_	=      val[-dim:]
      else:
         SCREAMING_SNAKE_CASE_	=      rename_key(__lowerCamelCase						)
         if "visual_projection" in new_name or "text_projection" in new_name:
            SCREAMING_SNAKE_CASE_	=      val.T
         SCREAMING_SNAKE_CASE_	=      val

   return orig_state_dict


def  A__ (       ):
   SCREAMING_SNAKE_CASE_	=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   SCREAMING_SNAKE_CASE_	=      Image.open(requests.get(__lowerCamelCase,  stream=__lowerCamelCase						).raw						)
   return image


def  A__ (       __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      get_clipseg_config(__lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	=      CLIPSegForImageSegmentation(__lowerCamelCase						)
   model.eval()

   SCREAMING_SNAKE_CASE_	=      torch.load(__lowerCamelCase,  map_location='''cpu'''						)

   # remove some keys
   for key in state_dict.copy().keys():
      if key.startswith('''model'''						):
         state_dict.pop(__lowerCamelCase,  __lowerCamelCase						)

    # rename some keys
   SCREAMING_SNAKE_CASE_	=      convert_state_dict(__lowerCamelCase,  __lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      model.load_state_dict(__lowerCamelCase,  strict=__lowerCamelCase						)

   if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
      raise ValueError('''Missing keys that are not expected: {}'''.format(__lowerCamelCase						)						)
   if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
      raise ValueError(F'''Unexpected keys: {unexpected_keys}'''						)

   SCREAMING_SNAKE_CASE_	=      ViTImageProcessor(size=3_52						)
   SCREAMING_SNAKE_CASE_	=      CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''						)
   SCREAMING_SNAKE_CASE_	=      CLIPSegProcessor(image_processor=__lowerCamelCase,  tokenizer=__lowerCamelCase						)

   SCREAMING_SNAKE_CASE_	=      prepare_img()
   SCREAMING_SNAKE_CASE_	=      ['''a glass''', '''something to fill''', '''wood''', '''a jar''']

   SCREAMING_SNAKE_CASE_	=      processor(text=__lowerCamelCase,  images=[image] * len(__lowerCamelCase						),  padding='''max_length''',  return_tensors='''pt'''						)

   with torch.no_grad():
      SCREAMING_SNAKE_CASE_	=      model(**__lowerCamelCase						)

   # verify values
   SCREAMING_SNAKE_CASE_	=      torch.tensor([0.11_10, -0.18_82, 0.16_45]						)
   SCREAMING_SNAKE_CASE_	=      torch.tensor([0.26_92, -0.71_97, -0.13_28]						)
   if model_name == "clipseg-rd64-refined":
      SCREAMING_SNAKE_CASE_	=      torch.tensor(
          [[-10.04_07, -9.94_31, -10.26_46], [-9.97_51, -9.70_64, -9.95_86], [-9.68_91, -9.56_45, -9.96_18]]						)
   elif model_name == "clipseg-rd64":
      SCREAMING_SNAKE_CASE_	=      torch.tensor(
          [[-7.28_77, -7.27_11, -7.24_63], [-7.26_52, -7.27_80, -7.25_20], [-7.22_39, -7.22_04, -7.20_01]]						)
   elif model_name == "clipseg-rd16":
      SCREAMING_SNAKE_CASE_	=      torch.tensor(
          [[-6.39_55, -6.40_55, -6.41_51], [-6.39_11, -6.40_33, -6.41_00], [-6.34_74, -6.37_02, -6.37_62]]						)
   else:
      raise ValueError(F'''Model name {model_name} not supported.'''						)

   assert torch.allclose(outputs.logits[0, :3, :3],  __lowerCamelCase,  atol=1E-3						)
   assert torch.allclose(outputs.conditional_embeddings[0, :3],  __lowerCamelCase,  atol=1E-3						)
   assert torch.allclose(outputs.pooled_output[0, :3],  __lowerCamelCase,  atol=1E-3						)
   print('''Looks ok!'''						)

   if pytorch_dump_folder_path is not None:
      print(F'''Saving model and processor to {pytorch_dump_folder_path}'''						)
      model.save_pretrained(__lowerCamelCase						)
      processor.save_pretrained(__lowerCamelCase						)

   if push_to_hub:
      print(F'''Pushing model and processor for {model_name} to the hub'''						)
      model.push_to_hub(F'''CIDAS/{model_name}'''						)
      processor.push_to_hub(F'''CIDAS/{model_name}'''						)


if __name__ == "__main__":
   __UpperCAmelCase   						=     argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--model_name",
       default="clipseg-rd64",
       type=str,
       choices=["clipseg-rd16", "clipseg-rd64", "clipseg-rd64-refined"],
       help=(
           "Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning"
           " reduce dimension)"
       ),
   )
   parser.add_argument(
       "--checkpoint_path",
       default="/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth",
       type=str,
       help=(
           "Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and"
           " the decoder weights."
       ),
   )
   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 or not to push the converted model to the 🤗 hub."
   )

   __UpperCAmelCase   						=     parser.parse_args()
   convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)


