





"""simple docstring"""





import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel


def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		UpperCamelCase ,					UpperCamelCase ,					UpperCamelCase =						pt_attn_layer.in_proj_weight.chunk(3	,						dim=0     )
		UpperCamelCase ,					UpperCamelCase ,					UpperCamelCase =						pt_attn_layer.in_proj_bias.chunk(3	,						dim=0     )

		UpperCamelCase =						pt_attn_layer.out_proj.weight
		UpperCamelCase =						pt_attn_layer.out_proj.bias

		UpperCamelCase =						q_proj
		UpperCamelCase =						q_proj_bias

		UpperCamelCase =						k_proj
		UpperCamelCase =						k_proj_bias

		UpperCamelCase =						v_proj
		UpperCamelCase =						v_proj_bias

		UpperCamelCase =						out_proj_weights
		UpperCamelCase =						out_proj_bias


def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		copy_linear(hf_mlp.fca	,						pt_mlp.c_fc     )
		copy_linear(hf_mlp.fca	,						pt_mlp.c_proj     )


def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		UpperCamelCase =						pt_linear.weight
		UpperCamelCase =						pt_linear.bias


def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		copy_linear(hf_layer.layer_norma	,						pt_layer.ln_a     )
		copy_linear(hf_layer.layer_norma	,						pt_layer.ln_a     )

		# copy MLP
		copy_mlp(hf_layer.mlp	,						pt_layer.mlp     )

		# copy attn
		copy_attn_layer(hf_layer.self_attn	,						pt_layer.attn     )


def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		for hf_layer, pt_layer in zip(_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):
				copy_layer(_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     )


def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		UpperCamelCase =						pt_model.token_embedding.weight
		UpperCamelCase =						pt_model.positional_embedding

		# copy layer norm
		copy_linear(hf_encoder.final_layer_norm	,						pt_model.ln_final     )

		# copy hidden layers
		copy_layers(hf_encoder.encoder.layers	,						pt_model.transformer.resblocks     )


def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		UpperCamelCase =						pt_model.text_projection.data.T

		# copy text encoder
		copy_encoder(hf_model.text_model	,						_SCREAMING_SNAKE_CASE     )


def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		UpperCamelCase =						pt_model.visual.proj.data.T

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

		# copy embeds
		UpperCamelCase =						pt_model.visual.conva.weight.data
		UpperCamelCase =						pt_model.visual.class_embedding
		UpperCamelCase =						pt_model.visual.positional_embedding.data

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


@torch.no_grad()
def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE=None     ):




		"""simple docstring"""


		if config_path is not None:
				UpperCamelCase =						CLIPConfig.from_pretrained(_SCREAMING_SNAKE_CASE     )
		else:
				UpperCamelCase =						CLIPConfig(projection_dim=512	,						text_config={}	,						vision_config={}     )

		UpperCamelCase =						CLIPModel(_SCREAMING_SNAKE_CASE     ).eval()

		UpperCamelCase ,					UpperCamelCase =						load(_SCREAMING_SNAKE_CASE	,						device="cpu"	,						jit=_SCREAMING_SNAKE_CASE     )
		UpperCamelCase =						pt_model.eval()

		copy_text_model_and_projection(_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     )
		copy_vison_model_and_projection(_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     )
		UpperCamelCase =						pt_model.logit_scale

		UpperCamelCase =						torch.arange(0	,						77     ).unsqueeze(0     )
		UpperCamelCase =						torch.randn(1	,						3	,						224	,						224     )

		UpperCamelCase =						hf_model(input_ids=_SCREAMING_SNAKE_CASE	,						pixel_values=_SCREAMING_SNAKE_CASE	,						return_dict=_SCREAMING_SNAKE_CASE     )
		UpperCamelCase =						hf_outputs.logits_per_image
		UpperCamelCase =						hf_outputs.logits_per_text
		UpperCamelCase ,					UpperCamelCase =						pt_model(_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     )

		assert torch.allclose(_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						atol=1e-3     )
		assert torch.allclose(_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						atol=1e-3     )

		hf_model.save_pretrained(_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
						lowerCAmelCase__        =      argparse.ArgumentParser()
						parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
						parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
						lowerCAmelCase__        =      parser.parse_args()

						convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)













"""simple docstring"""





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__       (       _SCREAMING_SNAKE_CASE=None	,						_SCREAMING_SNAKE_CASE=None     ):




		"""simple docstring"""


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





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



def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


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


def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


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





class 							_lowerCamelCase   :


						def __init__(self ,				__a		)			-> Tuple:
								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      snake_case_ (self		)			-> Optional[int]:
								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__       (       ):




		"""simple docstring"""


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


if __name__ == "__main__":
						main()






