




import gc
import unittest

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

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class      UpperCAmelCase						(			A   ,					unittest.TestCase   ):
   __SCREAMING_SNAKE_CASE		:							Tuple							=					UnCLIPPipeline
   __SCREAMING_SNAKE_CASE		:							Optional[int]							=					TEXT_TO_IMAGE_PARAMS - {
       "negative_prompt",
       "height",
       "width",
       "negative_prompt_embeds",
       "guidance_scale",
       "prompt_embeds",
       "cross_attention_kwargs",
   }
   __SCREAMING_SNAKE_CASE		:							str							=					TEXT_TO_IMAGE_BATCH_PARAMS
   __SCREAMING_SNAKE_CASE		:							List[Any]							=					[
       "generator",
       "return_dict",
       "prior_num_inference_steps",
       "decoder_num_inference_steps",
       "super_res_num_inference_steps",
   ]
   __SCREAMING_SNAKE_CASE		:							Any							=					False






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






   @property
   def  _a						(	self   :       int     )->			int:
     return 32






   @property
   def  _a						(	self   :       str     )->			List[Any]:
     return self.time_input_dim






   @property
   def  _a						(	self   :       Optional[Any]     )->			Optional[Any]:
     return self.time_input_dim * 4






   @property
   def  _a						(	self   :       int     )->			int:
     return 1_00






   @property
   def  _a						(	self   :       Any     )->			Any:
     _lowercase: Optional[Any]						     =    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )
     return tokenizer






   @property
   def  _a						(	self   :       Optional[int]     )->			List[str]:
     torch.manual_seed(0     )
     _lowercase: List[Any]						     =    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=10_00						,					)
     return CLIPTextModelWithProjection(UpperCamelCase_     )






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

     _lowercase: int						     =    {
         'num_attention_heads': 2,
         'attention_head_dim': 12,
         'embedding_dim': self.text_embedder_hidden_size,
         'num_layers': 1,
     }

     _lowercase: Tuple						     =    PriorTransformer(**UpperCamelCase_     )
     return model






   @property
   def  _a						(	self   :       str     )->			int:
     torch.manual_seed(0     )

     _lowercase: Optional[Any]						     =    {
         'clip_embeddings_dim': self.text_embedder_hidden_size,
         'time_embed_dim': self.time_embed_dim,
         'cross_attention_dim': self.cross_attention_dim,
     }

     _lowercase: Tuple						     =    UnCLIPTextProjModel(**UpperCamelCase_     )
     return model






   @property
   def  _a						(	self   :       Any     )->			str:
     torch.manual_seed(0     )

     _lowercase: List[Any]						     =    {
         'sample_size': 32,
         # RGB in channels
         'in_channels': 3,
         # Out channels is double in channels because predicts mean and variance
         'out_channels': 6,
         'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
         'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
         'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
         'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
         'layers_per_block': 1,
         'cross_attention_dim': self.cross_attention_dim,
         'attention_head_dim': 4,
         'resnet_time_scale_shift': 'scale_shift',
         'class_embed_type': 'identity',
     }

     _lowercase: Optional[int]						     =    UNetaDConditionModel(**UpperCamelCase_     )
     return model






   @property
   def  _a						(	self   :       Any     )->			List[str]:
     return {
         "sample_size": 64,
         "layers_per_block": 1,
         "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
         "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
         "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
         "in_channels": 6,
         "out_channels": 3,
     }






   @property
   def  _a						(	self   :       int     )->			Tuple:
     torch.manual_seed(0     )

     _lowercase: Dict						     =    UNetaDModel(**self.dummy_super_res_kwargs     )
     return model






   @property
   def  _a						(	self   :       Tuple     )->			str:
     # seeded differently to get different unet than `self.dummy_super_res_first`
     torch.manual_seed(1     )

     _lowercase: List[Any]						     =    UNetaDModel(**self.dummy_super_res_kwargs     )
     return model






   def  _a						(	self   :       Dict     )->			Any:
     _lowercase: str						     =    self.dummy_prior
     _lowercase: Union[str, Any]						     =    self.dummy_decoder
     _lowercase: Any						     =    self.dummy_text_proj
     _lowercase: List[str]						     =    self.dummy_text_encoder
     _lowercase: List[str]						     =    self.dummy_tokenizer
     _lowercase: List[Any]						     =    self.dummy_super_res_first
     _lowercase: Optional[Any]						     =    self.dummy_super_res_last

     _lowercase: Tuple						     =    UnCLIPScheduler(
         variance_type='fixed_small_log'						,					prediction_type='sample'						,					num_train_timesteps=10_00						,					clip_sample_range=5.0						,					)

     _lowercase: Union[str, Any]						     =    UnCLIPScheduler(
         variance_type='learned_range'						,					prediction_type='epsilon'						,					num_train_timesteps=10_00						,					)

     _lowercase: int						     =    UnCLIPScheduler(
         variance_type='fixed_small_log'						,					prediction_type='epsilon'						,					num_train_timesteps=10_00						,					)

     _lowercase: List[Any]						     =    {
         'prior': prior,
         'decoder': decoder,
         'text_proj': text_proj,
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
         'super_res_first': super_res_first,
         'super_res_last': super_res_last,
         'prior_scheduler': prior_scheduler,
         'decoder_scheduler': decoder_scheduler,
         'super_res_scheduler': super_res_scheduler,
     }

     return components






   def  _a						(	self   :       int						,					UpperCamelCase_   :       Any						,					UpperCamelCase_   :       Union[str, Any]=0     )->			int:
     if str(UpperCamelCase_     ).startswith('mps'     ):
       _lowercase: Dict						     =    torch.manual_seed(UpperCamelCase_     )
     else:
       _lowercase: Optional[Any]						     =    torch.Generator(device=UpperCamelCase_     ).manual_seed(UpperCamelCase_     )
     _lowercase: Optional[Any]						     =    {
         'prompt': 'horse',
         'generator': generator,
         'prior_num_inference_steps': 2,
         'decoder_num_inference_steps': 2,
         'super_res_num_inference_steps': 2,
         'output_type': 'numpy',
     }
     return inputs






   def  _a						(	self   :       Union[str, Any]     )->			Optional[Any]:
     _lowercase: Any						     =    'cpu'

     _lowercase: int						     =    self.get_dummy_components()

     _lowercase: Optional[Any]						     =    self.pipeline_class(**UpperCamelCase_     )
     _lowercase: str						     =    pipe.to(UpperCamelCase_     )

     pipe.set_progress_bar_config(disable=UpperCamelCase_     )

     _lowercase: Any						     =    pipe(**self.get_dummy_inputs(UpperCamelCase_     )     )
     _lowercase: Optional[Any]						     =    output.images

     _lowercase: Union[str, Any]						     =    pipe(
         **self.get_dummy_inputs(UpperCamelCase_     )						,					return_dict=UpperCamelCase_						,					)[0]

     _lowercase: Dict						     =    image[0, -3:, -3:, -1]
     _lowercase: Optional[Any]						     =    image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)

     _lowercase: List[Any]						     =    np.array(
         [
             0.9_997,
             0.9_988,
             0.0_028,
             0.9_997,
             0.9_984,
             0.9_965,
             0.0_029,
             0.9_986,
             0.0_025,
         ]     )

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






   def  _a						(	self   :       List[Any]     )->			Union[str, Any]:
     _lowercase: Optional[Any]						     =    torch.device('cpu'     )

     class      UpperCAmelCase						:
        __SCREAMING_SNAKE_CASE		:							Union[str, Any]							=					1
     _lowercase: str						     =    self.get_dummy_components()

     _lowercase: str						     =    self.pipeline_class(**UpperCamelCase_     )
     _lowercase: Any						     =    pipe.to(UpperCamelCase_     )

     _lowercase: Any						     =    components['prior']
     _lowercase: List[str]						     =    components['decoder']
     _lowercase: str						     =    components['super_res_first']
     _lowercase: List[str]						     =    components['tokenizer']
     _lowercase: List[str]						     =    components['text_encoder']

     _lowercase: Tuple						     =    torch.Generator(device=UpperCamelCase_     ).manual_seed(0     )
     _lowercase: str						     =    prior.dtype
     _lowercase: Tuple						     =    1

     _lowercase: Union[str, Any]						     =    (batch_size, prior.config.embedding_dim)
     _lowercase: Any						     =    pipe.prepare_latents(
         UpperCamelCase_						,					dtype=UpperCamelCase_						,					device=UpperCamelCase_						,					generator=UpperCamelCase_						,					latents=UpperCamelCase_						,					scheduler=DummyScheduler()     )
     _lowercase: Any						     =    (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
     _lowercase: List[str]						     =    pipe.prepare_latents(
         UpperCamelCase_						,					dtype=UpperCamelCase_						,					device=UpperCamelCase_						,					generator=UpperCamelCase_						,					latents=UpperCamelCase_						,					scheduler=DummyScheduler()     )

     _lowercase: Union[str, Any]						     =    (
         batch_size,
         super_res_first.config.in_channels // 2,
         super_res_first.config.sample_size,
         super_res_first.config.sample_size,
     )
     _lowercase: List[str]						     =    pipe.prepare_latents(
         UpperCamelCase_						,					dtype=UpperCamelCase_						,					device=UpperCamelCase_						,					generator=UpperCamelCase_						,					latents=UpperCamelCase_						,					scheduler=DummyScheduler()     )

     pipe.set_progress_bar_config(disable=UpperCamelCase_     )

     _lowercase: Optional[int]						     =    'this is a prompt example'

     _lowercase: List[Any]						     =    torch.Generator(device=UpperCamelCase_     ).manual_seed(0     )
     _lowercase: int						     =    pipe(
         [prompt]						,					generator=UpperCamelCase_						,					prior_num_inference_steps=2						,					decoder_num_inference_steps=2						,					super_res_num_inference_steps=2						,					prior_latents=UpperCamelCase_						,					decoder_latents=UpperCamelCase_						,					super_res_latents=UpperCamelCase_						,					output_type='np'						,					)
     _lowercase: List[Any]						     =    output.images

     _lowercase: int						     =    tokenizer(
         UpperCamelCase_						,					padding='max_length'						,					max_length=tokenizer.model_max_length						,					return_tensors='pt'						,					)
     _lowercase: Any						     =    text_encoder(text_inputs.input_ids     )
     _lowercase: List[Any]						     =    text_inputs.attention_mask

     _lowercase: int						     =    torch.Generator(device=UpperCamelCase_     ).manual_seed(0     )
     _lowercase: List[str]						     =    pipe(
         generator=UpperCamelCase_						,					prior_num_inference_steps=2						,					decoder_num_inference_steps=2						,					super_res_num_inference_steps=2						,					prior_latents=UpperCamelCase_						,					decoder_latents=UpperCamelCase_						,					super_res_latents=UpperCamelCase_						,					text_model_output=UpperCamelCase_						,					text_attention_mask=UpperCamelCase_						,					output_type='np'						,					)[0]

     # make sure passing text embeddings manually is identical
     assert np.abs(image - image_from_text     ).max() < 1E-4






   @skip_mps
   def  _a						(	self   :       List[Any]     )->			int:
     _lowercase: Any						     =    torch_device == 'cpu'

     self._test_attention_slicing_forward_pass(test_max_difference=UpperCamelCase_						,					expected_max_diff=0.01     )






   @skip_mps
   def  _a						(	self   :       Any     )->			Optional[int]:
     _lowercase: str						     =    torch_device == 'cpu'
     _lowercase: str						     =    True
     _lowercase: str						     =    [
         'prior_num_inference_steps',
         'decoder_num_inference_steps',
         'super_res_num_inference_steps',
     ]

     self._test_inference_batch_single_identical(
         test_max_difference=UpperCamelCase_						,					relax_max_difference=UpperCamelCase_						,					additional_params_copy_to_batched_inputs=UpperCamelCase_						,					)






   def  _a						(	self   :       Union[str, Any]     )->			List[Any]:
     _lowercase: List[str]						     =    [
         'prior_num_inference_steps',
         'decoder_num_inference_steps',
         'super_res_num_inference_steps',
     ]

     if torch_device == "mps":
       # TODO: MPS errors with larger batch sizes
       _lowercase: List[Any]						     =    [2, 3]
       self._test_inference_batch_consistent(
           batch_sizes=UpperCamelCase_						,					additional_params_copy_to_batched_inputs=UpperCamelCase_						,					)
     else:
       self._test_inference_batch_consistent(
           additional_params_copy_to_batched_inputs=UpperCamelCase_     )






   @skip_mps
   def  _a						(	self   :       List[Any]     )->			Optional[int]:
     return super().test_dict_tuple_outputs_equivalent()






   @skip_mps
   def  _a						(	self   :       Dict     )->			Dict:
     return super().test_save_load_local()






   @skip_mps
   def  _a						(	self   :       Tuple     )->			str:
     return super().test_save_load_optional_components()

@nightly
class      UpperCAmelCase						(			unittest.TestCase   ):






   def  _a						(	self   :       Tuple     )->			List[str]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






   def  _a						(	self   :       Optional[Any]     )->			Tuple:
     _lowercase: int						     =    load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/unclip/karlo_v1_alpha_horse_cpu.npy'     )

     _lowercase: int						     =    UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'     )
     pipeline.set_progress_bar_config(disable=UpperCamelCase_     )

     _lowercase: Tuple						     =    torch.manual_seed(0     )
     _lowercase: List[Any]						     =    pipeline(
         'horse'						,					num_images_per_prompt=1						,					generator=UpperCamelCase_						,					output_type='np'						,					)

     _lowercase: Union[str, Any]						     =    output.images[0]

     assert image.shape == (2_56, 2_56, 3)
     assert np.abs(expected_image - image     ).max() < 1E-1

@slow
@require_torch_gpu
class      UpperCAmelCase						(			unittest.TestCase   ):






   def  _a						(	self   :       Optional[int]     )->			List[str]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






   def  _a						(	self   :       Tuple     )->			List[Any]:
     _lowercase: Dict						     =    load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/unclip/karlo_v1_alpha_horse_fp16.npy'     )

     _lowercase: Any						     =    UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'						,					torch_dtype=torch.floataa     )
     _lowercase: Optional[int]						     =    pipeline.to(UpperCamelCase_     )
     pipeline.set_progress_bar_config(disable=UpperCamelCase_     )

     _lowercase: Any						     =    torch.Generator(device='cpu'     ).manual_seed(0     )
     _lowercase: Tuple						     =    pipeline(
         'horse'						,					generator=UpperCamelCase_						,					output_type='np'						,					)

     _lowercase: Tuple						     =    output.images[0]

     assert image.shape == (2_56, 2_56, 3)

     assert_mean_pixel_difference(UpperCamelCase_						,					UpperCamelCase_     )






   def  _a						(	self   :       List[str]     )->			int:
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     _lowercase: int						     =    UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'						,					torch_dtype=torch.floataa     )
     _lowercase: int						     =    pipe.to(UpperCamelCase_     )
     pipe.set_progress_bar_config(disable=UpperCamelCase_     )
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     _lowercase: Tuple						     =    pipe(
         'horse'						,					num_images_per_prompt=1						,					prior_num_inference_steps=2						,					decoder_num_inference_steps=2						,					super_res_num_inference_steps=2						,					output_type='np'						,					)

     _lowercase: Any						     =    torch.cuda.max_memory_allocated()
     # make sure that less than 7 GB is allocated
     assert mem_bytes < 7 * 10**9






import argparse

import requests
import torch
from PIL import Image

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


def 		_lowerCamelCase				(					UpperCamelCase							)			->		str:





  """simple docstring"""





  _lowercase: Any						     =    CLIPSegTextConfig()
  _lowercase: Optional[Any]						     =    CLIPSegVisionConfig(patch_size=16							)

  _lowercase: Union[str, Any]						     =    True if 'refined' in model_name else False
  _lowercase: str						     =    16 if 'rd16' in model_name else 64

  _lowercase: Any						     =    CLIPSegConfig.from_text_vision_configs(
      UpperCamelCase					,     UpperCamelCase					,     use_complex_transposed_convolution=UpperCamelCase					,     reduce_dim=UpperCamelCase					,     )
  return config


def 		_lowerCamelCase				(					UpperCamelCase							)			->		Any:





  """simple docstring"""





  if "clip_model" in name:
    _lowercase: Tuple						     =    name.replace('clip_model'					,     'clip'							)
  if "transformer" in name:
    if "visual" in name:
      _lowercase: Union[str, Any]						     =    name.replace('visual.transformer'					,     'vision_model'							)
    else:
      _lowercase: Optional[int]						     =    name.replace('transformer'					,     'text_model'							)
  if "resblocks" in name:
    _lowercase: List[Any]						     =    name.replace('resblocks'					,     'encoder.layers'							)
  if "ln_1" in name:
    _lowercase: Union[str, Any]						     =    name.replace('ln_1'					,     'layer_norm1'							)
  if "ln_2" in name:
    _lowercase: Union[str, Any]						     =    name.replace('ln_2'					,     'layer_norm2'							)
  if "c_fc" in name:
    _lowercase: str						     =    name.replace('c_fc'					,     'fc1'							)
  if "c_proj" in name:
    _lowercase: List[Any]						     =    name.replace('c_proj'					,     'fc2'							)
  if "attn" in name and "self" not in name:
    _lowercase: Dict						     =    name.replace('attn'					,     'self_attn'							)
  # text encoder
  if "token_embedding" in name:
    _lowercase: Optional[int]						     =    name.replace('token_embedding'					,     'text_model.embeddings.token_embedding'							)
  if "positional_embedding" in name and "visual" not in name:
    _lowercase: int						     =    name.replace('positional_embedding'					,     'text_model.embeddings.position_embedding.weight'							)
  if "ln_final" in name:
    _lowercase: Optional[Any]						     =    name.replace('ln_final'					,     'text_model.final_layer_norm'							)
  # vision encoder
  if "visual.class_embedding" in name:
    _lowercase: Union[str, Any]						     =    name.replace('visual.class_embedding'					,     'vision_model.embeddings.class_embedding'							)
  if "visual.conv1" in name:
    _lowercase: int						     =    name.replace('visual.conv1'					,     'vision_model.embeddings.patch_embedding'							)
  if "visual.positional_embedding" in name:
    _lowercase: List[Any]						     =    name.replace('visual.positional_embedding'					,     'vision_model.embeddings.position_embedding.weight'							)
  if "visual.ln_pre" in name:
    _lowercase: Dict						     =    name.replace('visual.ln_pre'					,     'vision_model.pre_layrnorm'							)
  if "visual.ln_post" in name:
    _lowercase: Dict						     =    name.replace('visual.ln_post'					,     'vision_model.post_layernorm'							)
  # projection layers
  if "visual.proj" in name:
    _lowercase: List[str]						     =    name.replace('visual.proj'					,     'visual_projection.weight'							)
  if "text_projection" in name:
    _lowercase: Any						     =    name.replace('text_projection'					,     'text_projection.weight'							)
  # decoder
  if "trans_conv" in name:
    _lowercase: List[Any]						     =    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:
    _lowercase: Any						     =    'decoder.' + name
  if "blocks" in name:
    _lowercase: Optional[int]						     =    name.replace('blocks'					,     'decoder.layers'							)
  if "linear1" in name:
    _lowercase: Optional[Any]						     =    name.replace('linear1'					,     'mlp.fc1'							)
  if "linear2" in name:
    _lowercase: int						     =    name.replace('linear2'					,     'mlp.fc2'							)
  if "norm1" in name and "layer_" not in name:
    _lowercase: Optional[int]						     =    name.replace('norm1'					,     'layer_norm1'							)
  if "norm2" in name and "layer_" not in name:
    _lowercase: Tuple						     =    name.replace('norm2'					,     'layer_norm2'							)

  return name


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		Optional[Any]:





  """simple docstring"""





  for key in orig_state_dict.copy().keys():
    _lowercase: int						     =    orig_state_dict.pop(UpperCamelCase							)

    if key.startswith('clip_model'							) and "attn.in_proj" in key:
      _lowercase: Union[str, Any]						     =    key.split('.'							)
      if "visual" in key:
        _lowercase: List[str]						     =    int(key_split[4]							)
        _lowercase: Dict						     =    config.vision_config.hidden_size
        _lowercase: Union[str, Any]						     =    'vision_model'
      else:
        _lowercase: str						     =    int(key_split[3]							)
        _lowercase: Optional[Any]						     =    config.text_config.hidden_size
        _lowercase: List[str]						     =    'text_model'

      if "weight" in key:
        _lowercase: Optional[int]						     =    val[:dim, :]
        _lowercase: List[Any]						     =    val[
            dim : dim * 2, :
        ]
        _lowercase: Tuple						     =    val[-dim:, :]
      else:
        _lowercase: Any						     =    val[:dim]
        _lowercase: List[str]						     =    val[dim : dim * 2]
        _lowercase: Optional[int]						     =    val[-dim:]
    elif "self_attn" in key and "out_proj" not in key:
      _lowercase: str						     =    key.split('.'							)
      _lowercase: Union[str, Any]						     =    int(key_split[1]							)
      _lowercase: List[str]						     =    config.reduce_dim
      if "weight" in key:
        _lowercase: Union[str, Any]						     =    val[:dim, :]
        _lowercase: Tuple						     =    val[dim : dim * 2, :]
        _lowercase: Union[str, Any]						     =    val[-dim:, :]
      else:
        _lowercase: Union[str, Any]						     =    val[:dim]
        _lowercase: Dict						     =    val[dim : dim * 2]
        _lowercase: Any						     =    val[-dim:]
    else:
      _lowercase: Dict						     =    rename_key(UpperCamelCase							)
      if "visual_projection" in new_name or "text_projection" in new_name:
        _lowercase: str						     =    val.T
      _lowercase: Tuple						     =    val

  return orig_state_dict


def 		_lowerCamelCase				(					)			->		Optional[Any]:





  """simple docstring"""





  _lowercase: Any						     =    'http://images.cocodataset.org/val2017/000000039769.jpg'
  _lowercase: str						     =    Image.open(requests.get(UpperCamelCase					,     stream=UpperCamelCase							).raw							)
  return image


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		Optional[int]:





  """simple docstring"""





  _lowercase: Optional[Any]						     =    get_clipseg_config(UpperCamelCase							)
  _lowercase: Optional[Any]						     =    CLIPSegForImageSegmentation(UpperCamelCase							)
  model.eval()

  _lowercase: Dict						     =    torch.load(UpperCamelCase					,     map_location='cpu'							)

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

    # rename some keys
  _lowercase: List[str]						     =    convert_state_dict(UpperCamelCase					,     UpperCamelCase							)
  _lowercase     ,					_lowercase: List[Any]						     =    model.load_state_dict(UpperCamelCase					,     strict=UpperCamelCase							)

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

  _lowercase: Tuple						     =    ViTImageProcessor(size=352							)
  _lowercase: Union[str, Any]						     =    CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'							)
  _lowercase: Optional[Any]						     =    CLIPSegProcessor(image_processor=UpperCamelCase					,     tokenizer=UpperCamelCase							)

  _lowercase: Union[str, Any]						     =    prepare_img()
  _lowercase: List[Any]						     =    ['a glass', 'something to fill', 'wood', 'a jar']

  _lowercase: str						     =    processor(text=UpperCamelCase					,     images=[image] * len(UpperCamelCase							)					,     padding='max_length'					,     return_tensors='pt'							)

  with torch.no_grad():
    _lowercase: Optional[Any]						     =    model(**UpperCamelCase							)

  # verify values
  _lowercase: int						     =    torch.tensor([0.1_110, -0.1_882, 0.1_645]							)
  _lowercase: Tuple						     =    torch.tensor([0.2_692, -0.7_197, -0.1_328]							)
  if model_name == "clipseg-rd64-refined":
    _lowercase: str						     =    torch.tensor(
        [[-10.0_407, -9.9_431, -10.2_646], [-9.9_751, -9.7_064, -9.9_586], [-9.6_891, -9.5_645, -9.9_618]]							)
  elif model_name == "clipseg-rd64":
    _lowercase: Dict						     =    torch.tensor(
        [[-7.2_877, -7.2_711, -7.2_463], [-7.2_652, -7.2_780, -7.2_520], [-7.2_239, -7.2_204, -7.2_001]]							)
  elif model_name == "clipseg-rd16":
    _lowercase: Optional[int]						     =    torch.tensor(
        [[-6.3_955, -6.4_055, -6.4_151], [-6.3_911, -6.4_033, -6.4_100], [-6.3_474, -6.3_702, -6.3_762]]							)
  else:
    raise ValueError(F'Model name {model_name} not supported.'							)

  assert torch.allclose(outputs.logits[0, :3, :3]					,     UpperCamelCase					,     atol=1E-3							)
  assert torch.allclose(outputs.conditional_embeddings[0, :3]					,     UpperCamelCase					,     atol=1E-3							)
  assert torch.allclose(outputs.pooled_output[0, :3]					,     UpperCamelCase					,     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(UpperCamelCase							)
    processor.save_pretrained(UpperCamelCase							)

  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__						:       int							=    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__						:       str							=    parser.parse_args()
   convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
