
import argparse

import requests
import torch
from PIL import Image

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


def  A			(   lowercase					)  ->    Optional[Any]:
		'''simple docstring'''

		UpperCamelCase            =	CLIPSegTextConfig()
		UpperCamelCase            =	CLIPSegVisionConfig(patch_size=16					)

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

		UpperCamelCase            =	CLIPSegConfig.from_text_vision_configs(
		    lowercase       ,			lowercase       ,			use_complex_transposed_convolution=lowercase       ,			reduce_dim=lowercase       ,			)
		return config


def  A			(   lowercase					)  ->    Any:
		'''simple docstring'''

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

		return name


def  A			(   lowercase       ,			lowercase					)  ->    Union[str, Any]:
		'''simple docstring'''

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

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

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

		return orig_state_dict


def  A			(   )  ->    List[Any]:
		'''simple docstring'''

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


def  A			(   lowercase       ,			lowercase       ,			lowercase       ,			lowercase					)  ->    int:
		'''simple docstring'''

		UpperCamelCase            =	get_clipseg_config(lowercase					)
		UpperCamelCase            =	CLIPSegForImageSegmentation(lowercase					)
		model.eval()

		UpperCamelCase            =	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
		UpperCamelCase            =	convert_state_dict(lowercase       ,			lowercase					)
		UpperCamelCase      ,    UpperCamelCase            =	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}'''					)

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

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

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

		with torch.no_grad():
				UpperCamelCase            =	model(**lowercase					)

		# verify values
		UpperCamelCase            =	torch.tensor([0.1_1_1_0, -0.1_8_8_2, 0.1_6_4_5]					)
		UpperCamelCase            =	torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8]					)
		if model_name == "clipseg-rd64-refined":
				UpperCamelCase            =	torch.tensor(
				    [[-1_0.0_4_0_7, -9.9_4_3_1, -1_0.2_6_4_6], [-9.9_7_5_1, -9.7_0_6_4, -9.9_5_8_6], [-9.6_8_9_1, -9.5_6_4_5, -9.9_6_1_8]]					)
		elif model_name == "clipseg-rd64":
				UpperCamelCase            =	torch.tensor(
				    [[-7.2_8_7_7, -7.2_7_1_1, -7.2_4_6_3], [-7.2_6_5_2, -7.2_7_8_0, -7.2_5_2_0], [-7.2_2_3_9, -7.2_2_0_4, -7.2_0_0_1]]					)
		elif model_name == "clipseg-rd16":
				UpperCamelCase            =	torch.tensor(
				    [[-6.3_9_5_5, -6.4_0_5_5, -6.4_1_5_1], [-6.3_9_1_1, -6.4_0_3_3, -6.4_1_0_0], [-6.3_4_7_4, -6.3_7_0_2, -6.3_7_6_2]]					)
		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__":
					_UpperCAmelCase   :					str    =			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   :					Dict    =			parser.parse_args()
					convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)


import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser


def  A			(   lowercase=None       ,			lowercase=None					)  ->    Tuple:
		'''simple docstring'''

		return field(default_factory=lambda: default       ,			metadata=lowercase					)


@dataclass
class 							lowercase   :
		__lowercase			:			str      	=							field(
		    metadata={"help": "The csv file to plot."}    ,  )
		__lowercase			:			bool      	=							field(
		    default=_SCREAMING_SNAKE_CASE    ,  metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}    ,  )
		__lowercase			:			bool      	=							field(
		    default=_SCREAMING_SNAKE_CASE    ,  metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}    ,  )
		__lowercase			:			bool      	=							field(
		    default=_SCREAMING_SNAKE_CASE    ,  metadata={"help": "Disable logarithmic scale when plotting"}    ,  )
		__lowercase			:			bool      	=							field(
		    default=_SCREAMING_SNAKE_CASE    ,  metadata={
		        "help": "Whether the csv file has training results or inference results. Defaults to inference results."
		    }    ,  )
		__lowercase			:			Optional[str]      	=							field(
		    default=_SCREAMING_SNAKE_CASE    ,  metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}    ,  )
		__lowercase			:			Optional[List[str]]      	=							list_field(
		    default=_SCREAMING_SNAKE_CASE    ,  metadata={"help": "List of model names that are used instead of the ones in the csv file."}  )






def  A			(   lowercase					)  ->    List[Any]:
		'''simple docstring'''

		try:
				int(lowercase					)
				return True
		except ValueError:
				return False


def  A			(   lowercase					)  ->    Optional[Any]:
		'''simple docstring'''

		try:
				float(lowercase					)
				return True
		except ValueError:
				return False


class 							lowercase   :



		def __init__(					self							, A_			)    ->   List[str]:
				"""simple docstring"""
				UpperCamelCase            =	args
				UpperCamelCase            =	defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}			)

				with open(self.args.csv_file							, newline=''			) as csv_file:
						UpperCamelCase            =	csv.DictReader(A_			)
						for row in reader:
								UpperCamelCase            =	row['model']
								self.result_dict[model_name]["bsz"].append(int(row['batch_size']			)			)
								self.result_dict[model_name]["seq_len"].append(int(row['sequence_length']			)			)
								if can_convert_to_int(row['result']			):
										# value is not None
										UpperCamelCase            =	int(row['result']			)
								elif can_convert_to_float(row['result']			):
										# value is not None
										UpperCamelCase            =	float(row['result']			)



		def __UpperCamelCase					(					self			)    ->   Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase      ,    UpperCamelCase            =	plt.subplots()
				UpperCamelCase            =	'Time usage' if self.args.is_time else 'Memory usage'
				UpperCamelCase            =	title_str + ' for training' if self.args.is_train else title_str + ' for inference'

				if not self.args.no_log_scale:
						# set logarithm scales
						ax.set_xscale('log'			)
						ax.set_yscale('log'			)

				for axis in [ax.xaxis, ax.yaxis]:
						axis.set_major_formatter(ScalarFormatter()			)

				for model_name_idx, model_name in enumerate(self.result_dict.keys()			):
						UpperCamelCase            =	sorted(set(self.result_dict[model_name]['bsz']			)			)
						UpperCamelCase            =	sorted(set(self.result_dict[model_name]['seq_len']			)			)
						UpperCamelCase            =	self.result_dict[model_name]['result']

						((UpperCamelCase)      ,    (UpperCamelCase))            =	(
						    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
						)

						UpperCamelCase            =	(
						    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
						)

						for inner_loop_value in inner_loop_array:
								if self.args.plot_along_batch:
										UpperCamelCase            =	np.asarray(
										    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]							, dtype=A_							, )
								else:
										UpperCamelCase            =	np.asarray(
										    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]							, dtype=np.floataa							, )

								((UpperCamelCase)      ,    (UpperCamelCase))            =	(
								    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
								)

								UpperCamelCase            =	np.asarray(A_							, A_			)[: len(A_			)]
								plt.scatter(
								    A_							, A_							, label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''			)
								plt.plot(A_							, A_							, '--'			)

						title_str += F''' {label_model_name} vs.'''

				UpperCamelCase            =	title_str[:-4]
				UpperCamelCase            =	'Time in s' if self.args.is_time else 'Memory in MB'

				# plot
				plt.title(A_			)
				plt.xlabel(A_			)
				plt.ylabel(A_			)
				plt.legend()

				if self.args.figure_png_file is not None:
						plt.savefig(self.args.figure_png_file			)
				else:
						plt.show()






def  A			(   )  ->    Dict:
		'''simple docstring'''

		UpperCamelCase            =	HfArgumentParser(lowercase					)
		UpperCamelCase            =	parser.parse_args_into_dataclasses()[0]
		UpperCamelCase            =	Plot(args=lowercase					)
		plot.plot()


if __name__ == "__main__":
					main()
