

"""simple docstring"""






import argparse

import requests
import torch
from PIL import Image

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



def     lowerCAmelCase__ (		__magic_name__			)	->str:
			__lowercase						      =					CLIPSegTextConfig()
			__lowercase						      =					CLIPSegVisionConfig(patch_size=1_6			)

			__lowercase						      =					True if "refined" in model_name else False
			__lowercase						      =					1_6 if "rd16" in model_name else 6_4

			__lowercase						      =					CLIPSegConfig.from_text_vision_configs(
			    lowercase__ ,      lowercase__ ,      use_complex_transposed_convolution=lowercase__ ,      reduce_dim=lowercase__ ,      )
			return config



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

			return name



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__			)	->Union[str, Any]:
			for key in orig_state_dict.copy().keys():
						__lowercase						      =					orig_state_dict.pop(lowercase__			)

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

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

			return orig_state_dict



def     lowerCAmelCase__ (		)	->Optional[int]:
			__lowercase						      =					"http://images.cocodataset.org/val2017/000000039769.jpg"
			__lowercase						      =					Image.open(requests.get(lowercase__ ,      stream=lowercase__			).raw			)
			return image



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__			)	->Optional[int]:
			__lowercase						      =					get_clipseg_config(lowercase__			)
			__lowercase						      =					CLIPSegForImageSegmentation(lowercase__			)
			model.eval()

			__lowercase						      =					torch.load(lowercase__ ,      map_location="cpu"			)

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

    # rename some keys
			__lowercase						      =					convert_state_dict(lowercase__ ,      lowercase__			)
			__lowercase						      =					model.load_state_dict(lowercase__ ,      strict=lowercase__			)

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

			__lowercase						      =					ViTImageProcessor(size=3_5_2			)
			__lowercase						      =					CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"			)
			__lowercase						      =					CLIPSegProcessor(image_processor=lowercase__ ,      tokenizer=lowercase__			)

			__lowercase						      =					prepare_img()
			__lowercase						      =					["a glass", "something to fill", "wood", "a jar"]

			__lowercase						      =					processor(text=lowercase__ ,      images=[image] * len(lowercase__			) ,      padding="max_length" ,      return_tensors="pt"			)

			with torch.no_grad():
						__lowercase						      =					model(**lowercase__			)

			# verify values
			__lowercase						      =					torch.tensor([0.1110, -0.1882, 0.1645]			)
			__lowercase						      =					torch.tensor([0.2692, -0.7197, -0.1328]			)
			if model_name == "clipseg-rd64-refined":
						__lowercase						      =					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						      =					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						      =					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] ,      lowercase__ ,      atol=1E-3			)
			assert torch.allclose(outputs.conditional_embeddings[0, :3] ,      lowercase__ ,      atol=1E-3			)
			assert torch.allclose(outputs.pooled_output[0, :3] ,      lowercase__ ,      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(lowercase__			)
						processor.save_pretrained(lowercase__			)

			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__":
					_lowercase							 =							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.'''
					)

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







"""simple docstring"""






import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
					from .testing_utils import (
					    floats_tensor,
					    load_hf_numpy,
					    load_image,
					    load_numpy,
					    load_pt,
					    nightly,
					    parse_flag_from_env,
					    print_tensor_test,
					    require_torch_a,
					    require_torch_gpu,
					    skip_mps,
					    slow,
					    torch_all_close,
					    torch_device,
					)
					from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


_lowercase							 =							get_logger(__name__)



def     lowerCAmelCase__ (		__magic_name__			)	->Optional[int]:
			if version.parse(__magic_name__			) < version.parse(__magic_name__			):
						if "dev" in min_version:
									__lowercase						      =					(
									    "This example requires a source install from HuggingFace diffusers (see "
									    "`https://huggingface.co/docs/diffusers/installation#install-from-source`),"
									)
						else:
									__lowercase						      =					F'''This example requires a minimum version of {min_version},'''
						error_message += F''' but the version found is {__version__}.\n'''
						raise ImportError(__magic_name__			)





