
'''simple docstring'''
import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


lowerCAmelCase					:    Union[str, Any]           = [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['memory_attention', 'encoder_attn'],
    ['attention', 'attn'],
    ['/', '.'],
    ['.LayerNorm.gamma', '_layer_norm.weight'],
    ['.LayerNorm.beta', '_layer_norm.bias'],
    ['r.layer_', 'r.layers.'],
    ['output_proj', 'out_proj'],
    ['ffn.dense_1.', 'fc2.'],
    ['ffn.dense.', 'fc1.'],
    ['ffn_layer_norm', 'final_layer_norm'],
    ['kernel', 'weight'],
    ['encoder_layer_norm.', 'encoder.layer_norm.'],
    ['decoder_layer_norm.', 'decoder.layer_norm.'],
    ['embeddings.weights', 'shared.weight'],
]







def 		A_(     A						:		Dict):
							for pegasus_name, hf_name in PATTERNS:
														UpperCamelCase   			=     k.replace(A		,     A)
							return k







def 		A_(     A						:		dict		,     A						:		dict):
							UpperCamelCase   			=     DEFAULTS.copy()
							cfg_kwargs.update(A)
							UpperCamelCase   			=     PegasusConfig(**A)
							UpperCamelCase   			=     PegasusForConditionalGeneration(A)
							UpperCamelCase   			=     torch_model.model.state_dict()
							UpperCamelCase   			=     {}
							for k, v in tf_weights.items():
														UpperCamelCase   			=     rename_state_dict_key(A)
														if new_k not in sd:
																					raise ValueError(f'''could not find new key {new_k} in state dict. (converted from {k})''')

														if "dense" in k or "proj" in new_k:
																					UpperCamelCase   			=     v.T
														UpperCamelCase   			=     torch.tensor(A		,     dtype=sd[new_k].dtype)
														assert v.shape == sd[new_k].shape, f'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
							# make sure embedding.padding_idx is respected
							UpperCamelCase   			=     torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1])
							UpperCamelCase   			=     mapping['shared.weight']
							UpperCamelCase   			=     mapping['shared.weight']
							UpperCamelCase   			=     {k: torch.zeros_like(A) for k, v in sd.items() if k.endswith('bias') and k not in mapping}
							mapping.update(**A)
							UpperCamelCase			,	UpperCamelCase   			=     torch_model.model.load_state_dict(A		,     strict=A)
							UpperCamelCase   			=     [
							    k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
							]
							assert unexpected_missing == [], f'''no matches found for the following torch keys {unexpected_missing}'''
							assert extra == [], f'''no matches found for the following tf keys {extra}'''
							return torch_model







def 		A_(     A						:		Tuple="./ckpt/aeslc/model.ckpt-32000"):
							UpperCamelCase   			=     tf.train.list_variables(A)
							UpperCamelCase   			=     {}
							UpperCamelCase   			=     ['Adafactor', 'global_step']
							for name, shape in tqdm(A		,     desc='converting tf checkpoint to dict'):
														UpperCamelCase   			=     any(pat in name for pat in ignore_name)
														if skip_key:
																					continue
														UpperCamelCase   			=     tf.train.load_variable(A		,     A)
														UpperCamelCase   			=     array
							return tf_weights







def 		A_(     A						:		str		,     A						:		str):
							# save tokenizer first
							UpperCamelCase   			=     Path(A).parent.name
							UpperCamelCase   			=     task_specific_params[f'''summarization_{dataset}''']['max_position_embeddings']
							UpperCamelCase   			=     PegasusTokenizer.from_pretrained('sshleifer/pegasus'		,     model_max_length=A)
							assert tok.model_max_length == desired_max_model_length
							tok.save_pretrained(A)

							# convert model
							UpperCamelCase   			=     get_tf_weights_as_numpy(A)
							UpperCamelCase   			=     task_specific_params[f'''summarization_{dataset}''']
							if dataset == "large":
														UpperCamelCase   			=     task_specific_params
							UpperCamelCase   			=     convert_pegasus(A		,     A)
							torch_model.save_pretrained(A)
							UpperCamelCase   			=     torch_model.state_dict()
							sd.pop('model.decoder.embed_positions.weight')
							sd.pop('model.encoder.embed_positions.weight')
							torch.save(A		,     Path(A) / 'pytorch_model.bin')


if __name__ == "__main__":
						lowerCAmelCase					:    List[str]           = argparse.ArgumentParser()
						# Required parameters
						parser.add_argument('tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
						parser.add_argument('save_dir', default=None, type=str, help='Path to the output PyTorch model.')
						lowerCAmelCase					:    Any           = parser.parse_args()
						if args.save_dir is None:
												lowerCAmelCase					:    Optional[int]           = Path(args.tf_ckpt_path).parent.name
												lowerCAmelCase					:    List[Any]           = os.path.join('pegasus', dataset)
						convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)






'''simple docstring'''
import colorsys

from PIL import Image  # type: ignore







def 		A_(     A						:		float		,     A						:		float		,     A						:		int):
							UpperCamelCase   			=     x
							UpperCamelCase   			=     y
							for step in range(A):  # noqa: B007
														UpperCamelCase   			=     a * a - b * b + x
														UpperCamelCase   			=     2 * a * b + y
														UpperCamelCase   			=     a_new

														# divergence happens for all complex number with an absolute value
														# greater than 4
														if a * a + b * b > 4:
																					break
							return step / (max_step - 1)







def 		A_(     A						:		float):
							if distance == 1:
														return (0, 0, 0)
							else:
														return (255, 255, 255)







def 		A_(     A						:		float):
							if distance == 1:
														return (0, 0, 0)
							else:
														return tuple(round(i * 255) for i in colorsys.hsv_to_rgb(A		,     1		,     1))







def 		A_(     A						:		int = 800		,     A						:		int = 600		,     A						:		float = -0.6		,     A						:		float = 0		,     A						:		float = 3.2		,     A						:		int = 50		,     A						:		bool = True		,     ):
							UpperCamelCase   			=     Image.new('RGB'		,     (image_width, image_height))
							UpperCamelCase   			=     img.load()

							# loop through the image-coordinates
							for image_x in range(A):
														for image_y in range(A):
																					# determine the figure-coordinates based on the image-coordinates
																					UpperCamelCase   			=     figure_width / image_width * image_height
																					UpperCamelCase   			=     figure_center_x + (image_x / image_width - 0.5) * figure_width
																					UpperCamelCase   			=     figure_center_y + (image_y / image_height - 0.5) * figure_height

																					UpperCamelCase   			=     get_distance(A		,     A		,     A)

																					# color the corresponding pixel based on the selected coloring-function
																					if use_distance_color_coding:
																												UpperCamelCase   			=     get_color_coded_rgb(A)
																					else:
																												UpperCamelCase   			=     get_black_and_white_rgb(A)

							return img


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						# colored version, full figure
						lowerCAmelCase					:    Any           = get_image()

						# uncomment for colored version, different section, zoomed in
						# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
						# figure_width = 0.8)

						# uncomment for black and white version, full figure
						# img = get_image(use_distance_color_coding = False)

						# uncomment to save the image
						# img.save("mandelbrot.png")

						img.show()




