

import argparse

import requests
import torch
from PIL import Image

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




def        _lowercase  (					a_					:       Any					)					->						Union[str, Any]:




					'''simple docstring'''







					__magic_name__				=     CLIPSegTextConfig()
					__magic_name__				=     CLIPSegVisionConfig(patch_size=1_6					)

					__magic_name__				=     True if 'refined' in model_name else False
					__magic_name__				=     1_6 if 'rd16' in model_name else 6_4

					__magic_name__				=     CLIPSegConfig.from_text_vision_configs(
					    a_       ,a_       ,use_complex_transposed_convolution=a_       ,reduce_dim=a_       ,)
					return config




def        _lowercase  (					a_					:       Dict					)					->						List[Any]:




					'''simple docstring'''







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

					return name




def        _lowercase  (					a_					:       str       ,a_					:       Union[str, Any]					)					->						Any:




					'''simple docstring'''







					for key in orig_state_dict.copy().keys():
										__magic_name__				=     orig_state_dict.pop(a_					)

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

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

					return orig_state_dict




def        _lowercase  (					)					->						Optional[int]:




					'''simple docstring'''







					__magic_name__				=     'http://images.cocodataset.org/val2017/000000039769.jpg'
					__magic_name__				=     Image.open(requests.get(a_       ,stream=a_					).raw					)
					return image




def        _lowercase  (					a_					:       Optional[int]       ,a_					:       str       ,a_					:       str       ,a_					:       Optional[int]					)					->						Optional[int]:




					'''simple docstring'''







					__magic_name__				=     get_clipseg_config(a_					)
					__magic_name__				=     CLIPSegForImageSegmentation(a_					)
					model.eval()

					__magic_name__				=     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
					__magic_name__				=     convert_state_dict(a_       ,a_					)
					__magic_name__,						__magic_name__				=     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}'					)

					__magic_name__				=     ViTImageProcessor(size=3_5_2					)
					__magic_name__				=     CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'					)
					__magic_name__				=     CLIPSegProcessor(image_processor=a_       ,tokenizer=a_					)

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

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

					with torch.no_grad():
										__magic_name__				=     model(**a_					)

					# verify values
					__magic_name__				=     torch.tensor([0.1110, -0.1882, 0.1645]					)
					__magic_name__				=     torch.tensor([0.2692, -0.7197, -0.1328]					)
					if model_name == "clipseg-rd64-refined":
										__magic_name__				=     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":
										__magic_name__				=     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":
										__magic_name__				=     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__":
						A__							=				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."
						)

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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A__							=				{"configuration_pegasus": ["PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusConfig"]}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__							=				["PegasusTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__							=				["PegasusTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__							=				[
						    "PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "PegasusForCausalLM",
						    "PegasusForConditionalGeneration",
						    "PegasusModel",
						    "PegasusPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__							=				[
						    "TFPegasusForConditionalGeneration",
						    "TFPegasusModel",
						    "TFPegasusPreTrainedModel",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__							=				[
						    "FlaxPegasusForConditionalGeneration",
						    "FlaxPegasusModel",
						    "FlaxPegasusPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_pegasus import PegasusTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_pegasus_fast import PegasusTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_pegasus import (
												    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
												    PegasusForCausalLM,
												    PegasusForConditionalGeneration,
												    PegasusModel,
												    PegasusPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_pegasus import (
												    FlaxPegasusForConditionalGeneration,
												    FlaxPegasusModel,
												    FlaxPegasusPreTrainedModel,
												)

else:
						import sys

						A__							=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




