
import argparse

import requests
import torch
from PIL import Image

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




def 				__magic_name__				(     __lowerCAmelCase			:		Union[str, Any]							)    ->     Any:
				__lowerCamelCase					      =			CLIPSegTextConfig()
				__lowerCamelCase					      =			CLIPSegVisionConfig(patch_size=16							)

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

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




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

				return name




def 				__magic_name__				(     __lowerCAmelCase			:		Union[str, Any]	,						__lowerCAmelCase			:		Optional[Any]							)    ->     Union[str, Any]:
				for key in orig_state_dict.copy().keys():
								__lowerCamelCase					      =			orig_state_dict.pop(__lowerCAmelCase							)

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

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

				return orig_state_dict




def 				__magic_name__				(     )    ->     str:
				__lowerCamelCase					      =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__lowerCamelCase					      =			Image.open(requests.get(__lowerCAmelCase	,						stream=__lowerCAmelCase							).raw							)
				return image




def 				__magic_name__				(     __lowerCAmelCase			:		List[Any]	,						__lowerCAmelCase			:		str	,						__lowerCAmelCase			:		Dict	,						__lowerCAmelCase			:		List[str]							)    ->     Union[str, Any]:
				__lowerCamelCase					      =			get_clipseg_config(__lowerCAmelCase							)
				__lowerCamelCase					      =			CLIPSegForImageSegmentation(__lowerCAmelCase							)
				model.eval()

				__lowerCamelCase					      =			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
				__lowerCamelCase					      =			convert_state_dict(__lowerCAmelCase	,						__lowerCAmelCase							)
				__lowerCamelCase      ,					__lowerCamelCase					      =			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}'''							)

				__lowerCamelCase					      =			ViTImageProcessor(size=352							)
				__lowerCamelCase					      =			CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''							)
				__lowerCamelCase					      =			CLIPSegProcessor(image_processor=__lowerCAmelCase	,						tokenizer=__lowerCAmelCase							)

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

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

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

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

					SCREAMING_SNAKE_CASE__     :    Optional[Any]      							=					parser.parse_args()
					convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


SCREAMING_SNAKE_CASE__     :    int      							=					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :    List[str]      							=					{
    "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json",
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}

class      lowerCAmelCase__  (			__lowercase    ):
		a__ :		Union[str, Any]   						=	"""m2m_100"""
		a__ :		Optional[int]   						=	["""past_key_values"""]
		a__ :		List[Any]   						=	{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






		def __init__(	self	:							int							,    SCREAMING_SNAKE_CASE__	:							Tuple=12_81_12							,    SCREAMING_SNAKE_CASE__	:							Any=10_24							,    SCREAMING_SNAKE_CASE__	:							int=12							,    SCREAMING_SNAKE_CASE__	:							List[str]=40_96							,    SCREAMING_SNAKE_CASE__	:							Optional[int]=16							,    SCREAMING_SNAKE_CASE__	:							Optional[int]=12							,    SCREAMING_SNAKE_CASE__	:							int=40_96							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=16							,    SCREAMING_SNAKE_CASE__	:							Dict=0.05							,    SCREAMING_SNAKE_CASE__	:							List[Any]=0.05							,    SCREAMING_SNAKE_CASE__	:							Dict=True							,    SCREAMING_SNAKE_CASE__	:							Tuple=True							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]="relu"							,    SCREAMING_SNAKE_CASE__	:							Dict=10_24							,    SCREAMING_SNAKE_CASE__	:							Optional[int]=0.1							,    SCREAMING_SNAKE_CASE__	:							Tuple=0.1							,    SCREAMING_SNAKE_CASE__	:							str=0.0							,    SCREAMING_SNAKE_CASE__	:							Any=0.02							,    SCREAMING_SNAKE_CASE__	:							int=2							,    SCREAMING_SNAKE_CASE__	:							List[Any]=True							,    SCREAMING_SNAKE_CASE__	:							Any=1							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=0							,    SCREAMING_SNAKE_CASE__	:							str=2							,    **SCREAMING_SNAKE_CASE__	:							Tuple							,    )  ->							Tuple:
						__lowerCamelCase					      =			vocab_size
						__lowerCamelCase					      =			max_position_embeddings
						__lowerCamelCase					      =			d_model
						__lowerCamelCase					      =			encoder_ffn_dim
						__lowerCamelCase					      =			encoder_layers
						__lowerCamelCase					      =			encoder_attention_heads
						__lowerCamelCase					      =			decoder_ffn_dim
						__lowerCamelCase					      =			decoder_layers
						__lowerCamelCase					      =			decoder_attention_heads
						__lowerCamelCase					      =			dropout
						__lowerCamelCase					      =			attention_dropout
						__lowerCamelCase					      =			activation_dropout
						__lowerCamelCase					      =			activation_function
						__lowerCamelCase					      =			init_std
						__lowerCamelCase					      =			encoder_layerdrop
						__lowerCamelCase					      =			decoder_layerdrop
						__lowerCamelCase					      =			use_cache
						__lowerCamelCase					      =			encoder_layers
						__lowerCamelCase					      =			scale_embedding  # scale factor will be sqrt(d_model) if True

						super().__init__(
						    pad_token_id=SCREAMING_SNAKE_CASE__							,    bos_token_id=SCREAMING_SNAKE_CASE__							,    eos_token_id=SCREAMING_SNAKE_CASE__							,    is_encoder_decoder=SCREAMING_SNAKE_CASE__							,    decoder_start_token_id=SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__							,    )





class      lowerCAmelCase__  (			__lowercase    ):






		@property
		def 				__A (	self	:							List[Any]						)  ->							Mapping[str, Mapping[int, str]]:
						__lowerCamelCase					      =			OrderedDict(
						    [
						        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
						        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
						    ]						)

						if self.use_past:
										__lowerCamelCase					      =			{0: '''batch'''}
										__lowerCamelCase					      =			{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
						else:
										__lowerCamelCase					      =			{0: '''batch''', 1: '''decoder_sequence'''}
										__lowerCamelCase					      =			{0: '''batch''', 1: '''decoder_sequence'''}

						if self.use_past:
										self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE__							,    direction='''inputs'''						)
						return common_inputs






		def 				__A (	self	:							Any							,    SCREAMING_SNAKE_CASE__	:							PreTrainedTokenizer							,    SCREAMING_SNAKE_CASE__	:							int = -1							,    SCREAMING_SNAKE_CASE__	:							int = -1							,    SCREAMING_SNAKE_CASE__	:							bool = False							,    SCREAMING_SNAKE_CASE__	:							Optional[TensorType] = None							,    )  ->							Mapping[str, Any]:
						# Copied from OnnxConfig.generate_dummy_inputs
						# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
						# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
						__lowerCamelCase					      =			compute_effective_axis_dimension(
						    SCREAMING_SNAKE_CASE__							,    fixed_dimension=OnnxConfig.default_fixed_batch							,    num_token_to_add=0						)

						# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
						__lowerCamelCase					      =			tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			compute_effective_axis_dimension(
						    SCREAMING_SNAKE_CASE__							,    fixed_dimension=OnnxConfig.default_fixed_sequence							,    num_token_to_add=SCREAMING_SNAKE_CASE__						)

						# Generate dummy inputs according to compute batch and sequence
						__lowerCamelCase					      =			[''' '''.join([tokenizer.unk_token]						) * seq_length] * batch_size
						__lowerCamelCase					      =			dict(tokenizer(SCREAMING_SNAKE_CASE__							,    return_tensors=SCREAMING_SNAKE_CASE__						)						)
						return common_inputs






		def 				__A (	self	:							Union[str, Any]							,    SCREAMING_SNAKE_CASE__	:							PreTrainedTokenizer							,    SCREAMING_SNAKE_CASE__	:							int = -1							,    SCREAMING_SNAKE_CASE__	:							int = -1							,    SCREAMING_SNAKE_CASE__	:							bool = False							,    SCREAMING_SNAKE_CASE__	:							Optional[TensorType] = None							,    )  ->							Mapping[str, Any]:
						__lowerCamelCase					      =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)

						# Generate decoder inputs
						__lowerCamelCase					      =			seq_length if not self.use_past else 1
						__lowerCamelCase					      =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
						__lowerCamelCase					      =			dict(**SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)

						if self.use_past:
										if not is_torch_available():
														raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''						)
										else:
														import torch
										__lowerCamelCase      ,					__lowerCamelCase					      =			common_inputs['''input_ids'''].shape
										__lowerCamelCase					      =			common_inputs['''decoder_input_ids'''].shape[1]
										__lowerCamelCase      ,					__lowerCamelCase					      =			self.num_attention_heads
										__lowerCamelCase					      =			(
										    batch,
										    num_encoder_attention_heads,
										    encoder_seq_length,
										    self._config.hidden_size // num_encoder_attention_heads,
										)
										__lowerCamelCase					      =			decoder_seq_length + 3
										__lowerCamelCase					      =			(
										    batch,
										    num_decoder_attention_heads,
										    decoder_past_length,
										    self._config.hidden_size // num_decoder_attention_heads,
										)

										__lowerCamelCase					      =			torch.cat(
										    [common_inputs['''decoder_attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)]							,    dim=1						)

										__lowerCamelCase					      =			[]
										# If the number of encoder and decoder layers are present in the model configuration, both are considered
										__lowerCamelCase      ,					__lowerCamelCase					      =			self.num_layers
										__lowerCamelCase					      =			min(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)
										__lowerCamelCase					      =			max(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						) - min_num_layers
										__lowerCamelCase					      =			'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

										for _ in range(SCREAMING_SNAKE_CASE__						):
														common_inputs["past_key_values"].append(
														    (
														        torch.zeros(SCREAMING_SNAKE_CASE__						),
														        torch.zeros(SCREAMING_SNAKE_CASE__						),
														        torch.zeros(SCREAMING_SNAKE_CASE__						),
														        torch.zeros(SCREAMING_SNAKE_CASE__						),
														    )						)
										# TODO: test this.
										__lowerCamelCase					      =			encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
										for _ in range(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						):
														common_inputs["past_key_values"].append((torch.zeros(SCREAMING_SNAKE_CASE__						), torch.zeros(SCREAMING_SNAKE_CASE__						))						)
						return common_inputs







		a__ :		Dict   						=	_generate_dummy_inputs_for_default_and_seqaseq_lm



