import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__							)					->     List[str]:
							lowerCAmelCase		,				lowerCAmelCase		,				lowerCAmelCase     		=		pt_weights[f"{prefix}.in_proj_weight"].chunk(3       ,  dim=0							)
							lowerCAmelCase		,				lowerCAmelCase		,				lowerCAmelCase     		=		pt_weights[f"{prefix}.in_proj_bias"].chunk(3       ,  dim=0							)

							lowerCAmelCase     		=		pt_weights[f"{prefix}.out_proj.weight"]
							lowerCAmelCase     		=		pt_weights[f"{prefix}.out_proj.bias"]

							lowerCAmelCase     		=		q_proj
							lowerCAmelCase     		=		q_proj_bias

							lowerCAmelCase     		=		k_proj
							lowerCAmelCase     		=		k_proj_bias

							lowerCAmelCase     		=		v_proj
							lowerCAmelCase     		=		v_proj_bias

							lowerCAmelCase     		=		out_proj_weights
							lowerCAmelCase     		=		out_proj_bias

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__							)					->     Optional[Any]:
							copy_linear(hf_mlp.fca       ,  snake_case__       ,  f"{prefix}.c_fc"							)
							copy_linear(hf_mlp.fca       ,  snake_case__       ,  f"{prefix}.c_proj"							)

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__							)					->     Dict:
							lowerCAmelCase     		=		pt_weights[f"{prefix}.weight"].data
							lowerCAmelCase     		=		pt_weights[f"{prefix}.bias"].data

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__							)					->     List[str]:
							# copy layer norms
							copy_linear(hf_layer.layer_norma       ,  snake_case__       ,  f"{prefix}.ln_1"							)
							copy_linear(hf_layer.layer_norma       ,  snake_case__       ,  f"{prefix}.ln_2"							)

							# copy MLP
							copy_mlp(hf_layer.mlp       ,  snake_case__       ,  f"{prefix}.mlp"							)

							# copy attn
							copy_attn_layer(hf_layer.self_attn       ,  snake_case__       ,  f"{prefix}.attn"							)

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__							)					->     Union[str, Any]:
							for layer_id, hf_layer in enumerate(snake_case__							):
														copy_layer(snake_case__       ,  snake_case__       ,  f"{prefix}.{layer_id}"							)

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__							)					->     Optional[int]:
							# copy projection
							lowerCAmelCase     		=		pt_weights['''text_projection'''].data.T

							# copy text encoder
							for name, param in hf_model.text_model.named_parameters():
														lowerCAmelCase     		=		pt_weights[f"bert.{name}"].data

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__							)					->     List[Any]:
							# copy projection
							lowerCAmelCase     		=		pt_weights['''visual.proj'''].data.T

							# copy layer norms
							copy_linear(hf_model.vision_model.pre_layrnorm       ,  snake_case__       ,  '''visual.ln_pre'''							)
							copy_linear(hf_model.vision_model.post_layernorm       ,  snake_case__       ,  '''visual.ln_post'''							)

							# copy embeddings
							lowerCAmelCase     		=		pt_weights['''visual.conv1.weight'''].data
							lowerCAmelCase     		=		pt_weights['''visual.class_embedding'''].data
							lowerCAmelCase     		=		pt_weights['''visual.positional_embedding'''].data

							# copy encoder
							copy_layers(hf_model.vision_model.encoder.layers       ,  snake_case__       ,  '''visual.transformer.resblocks'''							)

@torch.no_grad()
def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__=None							)					->     List[str]:
							assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
							lowerCAmelCase     		=		ChineseCLIPConfig.from_pretrained(snake_case__							)

							lowerCAmelCase     		=		ChineseCLIPModel(snake_case__							).eval()

							lowerCAmelCase     		=		torch.load(snake_case__       ,  map_location='''cpu'''							)['''state_dict''']
							lowerCAmelCase     		=		{(name[7:] if name.startswith('''module.'''							) else name): value for name, value in pt_weights.items()}

							copy_text_model_and_projection(snake_case__       ,  snake_case__							)
							copy_vision_model_and_projection(snake_case__       ,  snake_case__							)
							lowerCAmelCase     		=		pt_weights['''logit_scale'''].data

							hf_model.save_pretrained(snake_case__							)


if __name__ == "__main__":
	lowercase__      :    Tuple          =     argparse.ArgumentParser()
	parser.add_argument(
	    '''--pytorch_dump_folder_path''',
	    default=None,
	    type=str,
	    help='''Path to the output folder storing converted hf PyTorch model.''',
	)
	parser.add_argument(
	    '''--checkpoint_path''', default=None, type=str, help='''Path to original github format ChineseCLIP checkpoint.'''
	)
	parser.add_argument(
	    '''--config_path''', default=None, required=True, type=str, help='''Path to hf config.json of model to convert.'''
	)
	lowercase__      :    List[Any]          =     parser.parse_args()

	convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
	print('''The conversion is finished!''')




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        SCREAMING_SNAKE_CASE_					(						snake_case__=None       ,  snake_case__=None							)					->     Optional[Any]:
							return field(default_factory=lambda: default       ,  metadata=snake_case__							)




@dataclass
class        lowercase_  :



	"""simple docstring"""





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

def        SCREAMING_SNAKE_CASE_					(						snake_case__							)					->     Dict:
							try:
														int(snake_case__							)
														return True
							except ValueError:
														return False

def        SCREAMING_SNAKE_CASE_					(						snake_case__							)					->     Union[str, Any]:
							try:
														float(snake_case__							)
														return True
							except ValueError:
														return False




class        lowercase_  :



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE    )						->Optional[int]:
								lowerCAmelCase     		=		args
								lowerCAmelCase     		=		defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}    )

								with open(self.args.csv_file		,      newline=''''''    ) as csv_file:
															lowerCAmelCase     		=		csv.DictReader(__SCREAMING_SNAKE_CASE    )
															for row in reader:
																						lowerCAmelCase     		=		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
																													lowerCAmelCase     		=		int(row['''result''']    )
																						elif can_convert_to_float(row['''result''']    ):
																													# value is not None
																													lowerCAmelCase     		=		float(row['''result''']    )




	def       SCREAMING_SNAKE_CASE_    (							self    )						->Tuple:
								lowerCAmelCase		,				lowerCAmelCase     		=		plt.subplots()
								lowerCAmelCase     		=		'''Time usage''' if self.args.is_time else '''Memory usage'''
								lowerCAmelCase     		=		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()    ):
															lowerCAmelCase     		=		sorted(set(self.result_dict[model_name]['''bsz''']    )    )
															lowerCAmelCase     		=		sorted(set(self.result_dict[model_name]['''seq_len''']    )    )
															lowerCAmelCase     		=		self.result_dict[model_name]['''result''']

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

															lowerCAmelCase     		=		(
															    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:
																													lowerCAmelCase     		=		np.asarray(
																													    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]		,      dtype=__SCREAMING_SNAKE_CASE		,      )
																						else:
																													lowerCAmelCase     		=		np.asarray(
																													    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]		,      dtype=np.floataa		,      )

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

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

															title_str += F" {label_model_name} vs."

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

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

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

def        SCREAMING_SNAKE_CASE_					(						)					->     List[str]:
							lowerCAmelCase     		=		HfArgumentParser(snake_case__							)
							lowerCAmelCase     		=		parser.parse_args_into_dataclasses()[0]
							lowerCAmelCase     		=		Plot(args=snake_case__							)
							plot.plot()


if __name__ == "__main__":
	main()



