import argparse

import requests
import torch
from PIL import Image

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

def      lowercase_							(     _lowerCamelCase				:			Optional[Any]):
    lowercase__					:  Optional[int]          =							CLIPSegTextConfig()
    lowercase__					:  int          =							CLIPSegVisionConfig(patch_size=16)

    lowercase__					:  Optional[Any]          =							True if 'refined' in model_name else False
    lowercase__					:  Any          =							16 if 'rd16' in model_name else 64

    lowercase__					:  List[Any]          =							CLIPSegConfig.from_text_vision_configs(
        _A						,      _A						,      use_complex_transposed_convolution=_A						,      reduce_dim=_A						,      )
    return config

def      lowercase_							(     _lowerCamelCase				:			str):





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

    return name

def      lowercase_							(     _lowerCamelCase				:			Any						,      _lowerCamelCase				:			List[str]):





    for key in orig_state_dict.copy().keys():
        lowercase__					:  int          =							orig_state_dict.pop(_A)

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

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

    return orig_state_dict

def      lowercase_							(     ):
    lowercase__					:  List[str]          =							'http://images.cocodataset.org/val2017/000000039769.jpg'
    lowercase__					:  Dict          =							Image.open(requests.get(_A						,      stream=_A).raw)
    return image

def      lowercase_							(     _lowerCamelCase				:			Dict						,      _lowerCamelCase				:			Dict						,      _lowerCamelCase				:			int						,      _lowerCamelCase				:			Dict):
    lowercase__					:  List[str]          =							get_clipseg_config(_A)
    lowercase__					:  str          =							CLIPSegForImageSegmentation(_A)
    model.eval()

    lowercase__					:  Tuple          =							torch.load(_A						,      map_location="cpu")

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

    # rename some keys
    lowercase__					:  Any          =							convert_state_dict(_A						,      _A)
    lowercase__					:  Any          =							model.load_state_dict(_A						,      strict=_A)

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

    lowercase__					:  Tuple          =							ViTImageProcessor(size=352)
    lowercase__					:  Any          =							CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
    lowercase__					:  List[Any]          =							CLIPSegProcessor(image_processor=_A						,      tokenizer=_A)

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

    lowercase__					:  Optional[int]          =							processor(text=_A						,      images=[image] * len(_A)						,      padding="max_length"						,      return_tensors="pt")

    with torch.no_grad():
        lowercase__					:  int          =							model(**_A)

    # verify values
    lowercase__					:  str          =							torch.tensor([0.1110, -0.1882, 0.1645])
    lowercase__					:  str          =							torch.tensor([0.2692, -0.7197, -0.1328])
    if model_name == "clipseg-rd64-refined":
        lowercase__					:  List[str]          =							torch.tensor(
            [[-10.0407, -9.9431, -10.2646], [-9.9751, -9.7064, -9.9586], [-9.6891, -9.5645, -9.9618]])
    elif model_name == "clipseg-rd64":
        lowercase__					:  Dict          =							torch.tensor(
            [[-7.2877, -7.2711, -7.2463], [-7.2652, -7.2780, -7.2520], [-7.2239, -7.2204, -7.2001]])
    elif model_name == "clipseg-rd16":
        lowercase__					:  int          =							torch.tensor(
            [[-6.3955, -6.4055, -6.4151], [-6.3911, -6.4033, -6.4100], [-6.3474, -6.3702, -6.3762]])
    else:
        raise ValueError(f'''Model name {model_name} not supported.''')

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

    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)







'''simple docstring'''




import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
				import jax



@require_flax
class 			lowerCAmelCase					:



	def 					lowercase  (       self     ):
		lowerCAmelCase				, lowerCAmelCase       :     Union[str, Any]   				=			self.prepare_init_args_and_inputs_for_common()

		lowerCAmelCase       :     Any   				=			self.model_class(**snake_case__     )
		lowerCAmelCase       :     int   				=			model.init(inputs_dict['prng_key']			,       inputs_dict['sample']     )
		jax.lax.stop_gradient(snake_case__     )

		lowerCAmelCase       :     Tuple   				=			model.apply(snake_case__			,       inputs_dict['sample']     )

		if isinstance(snake_case__			,       snake_case__     ):
			lowerCAmelCase       :     int   				=			output.sample

		self.assertIsNotNone(snake_case__     )
		lowerCAmelCase       :     List[str]   				=			inputs_dict['sample'].shape
		self.assertEqual(output.shape			,       snake_case__			,       'Input and output shapes do not match'     )



	def 					lowercase  (       self     ):
		lowerCAmelCase				, lowerCAmelCase       :     Optional[int]   				=			self.prepare_init_args_and_inputs_for_common()

		lowerCAmelCase       :     Tuple   				=			16
		lowerCAmelCase       :     Dict   				=			(16, 32)

		lowerCAmelCase       :     Dict   				=			self.model_class(**snake_case__     )
		lowerCAmelCase       :     int   				=			model.init(inputs_dict['prng_key']			,       inputs_dict['sample']     )
		jax.lax.stop_gradient(snake_case__     )

		lowerCAmelCase       :     int   				=			model.apply(snake_case__			,       inputs_dict['sample']     )

		if isinstance(snake_case__			,       snake_case__     ):
			lowerCAmelCase       :     Dict   				=			output.sample

		self.assertIsNotNone(snake_case__     )
		lowerCAmelCase       :     Union[str, Any]   				=			inputs_dict['sample'].shape
		self.assertEqual(output.shape			,       snake_case__			,       'Input and output shapes do not match'     )



	def 					lowercase  (       self     ):
		lowerCAmelCase       :     Any   				=			'kwargs' in inspect.signature(self.model_class.__init__     ).parameters
		lowerCAmelCase       :     Optional[Any]   				=			len(self.model_class._deprecated_kwargs     ) > 0

		if has_kwarg_in_model_class and not has_deprecated_kwarg:
			raise ValueError(
			    f"{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs"
			    ' under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are'
			    ' no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ='
			    ' [<deprecated_argument>]`'     )

		if not has_kwarg_in_model_class and has_deprecated_kwarg:
			raise ValueError(
			    f"{self.model_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated kwargs"
			    ' under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to'
			    f" {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument"
			    ' from `_deprecated_kwargs = [<deprecated_argument>]`'     )
