import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_=0  ):



			"""simple docstring"""





			if name is None:
						_snake_case              =      None
			else:
						_snake_case              =      "." * max(0							,	spaces - 2  ) + "# {:" + str(50 - spaces  ) + "s}"
						_snake_case              =      fmt.format(lowerCamelCase_  )

			# Print and recurse (if needed).
			if isinstance(lowerCamelCase_							,	lowerCamelCase_  ):
						if msg is not None:
									print(lowerCamelCase_  )
						for k in val.keys():
									recursive_print(lowerCamelCase_							,	val[k]							,	spaces + 2  )
			elif isinstance(lowerCamelCase_							,	torch.Tensor  ):
						print(lowerCamelCase_							,	":"							,	val.size()  )
			else:
						print(lowerCamelCase_							,	":"							,	lowerCamelCase_  )






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      param.size()
			# other versions store [num_heads * num_splits * hidden_size, :]
			_snake_case              =      (num_heads, num_splits, hidden_size) + input_shape[1:]
			_snake_case              =      param.view(*lowerCamelCase_  )
			_snake_case              =      param.transpose(0							,	1  ).contiguous()
			_snake_case              =      param.view(*lowerCamelCase_  )
			return param






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      config.n_positions
			_snake_case              =      config.n_layer
			_snake_case              =      config.vocab_size
			_snake_case              =      config.n_head
			_snake_case              =      config.n_embd // config.n_head

			_snake_case              =      sd_megatron["model.language_model.embedding.word_embeddings.weight"][:vocab_size, :]
			_snake_case              =      {
			    "transformer.wte.weight": word_embeddings,
			    "transformer.wpe.weight": sd_megatron["model.language_model.embedding.position_embeddings.weight"],
			    "transformer.ln_f.weight": sd_megatron["model.language_model.encoder.final_layernorm.weight"],
			    "transformer.ln_f.bias": sd_megatron["model.language_model.encoder.final_layernorm.bias"],
			}

			_snake_case              =      "model.language_model.encoder.layers."
			for i in range(lowerCamelCase_  ):
						_snake_case              =      torch.tril(torch.ones((n_positions, n_positions)							,	dtype=torch.bool  )  )
						_snake_case              =      causal_mask.view(1							,	1							,	lowerCamelCase_							,	lowerCamelCase_  )
						_snake_case              =      causal_mask
						_snake_case              =      torch.tensor(-1E4							,	dtype=torch.bfloataa  )

						_snake_case              =      sd_megatron[f'{pf}{i}.input_layernorm.weight']
						_snake_case              =      sd_megatron[f'{pf}{i}.input_layernorm.bias']

						_snake_case              =      sd_megatron[f'{pf}{i}.self_attention.query_key_value.weight']
						_snake_case              =      fix_query_key_value_ordering(lowerCamelCase_							,	3							,	lowerCamelCase_							,	lowerCamelCase_  )
						_snake_case              =      vala.transpose(0							,	1  ).contiguous()

						_snake_case              =      sd_megatron[f'{pf}{i}.self_attention.query_key_value.bias']
						_snake_case              =      fix_query_key_value_ordering(lowerCamelCase_							,	3							,	lowerCamelCase_							,	lowerCamelCase_  )
						_snake_case              =      vala

						_snake_case              =      sd_megatron[f'{pf}{i}.self_attention.dense.weight'].transpose(
						    0							,	1  )
						_snake_case              =      sd_megatron[f'{pf}{i}.self_attention.dense.bias']
						_snake_case              =      sd_megatron[f'{pf}{i}.post_attention_layernorm.weight']
						_snake_case              =      sd_megatron[f'{pf}{i}.post_attention_layernorm.bias']
						_snake_case              =      sd_megatron[f'{pf}{i}.mlp.dense_h_to_4h.weight'].transpose(0							,	1  )
						_snake_case              =      sd_megatron[f'{pf}{i}.mlp.dense_h_to_4h.bias']
						_snake_case              =      sd_megatron[f'{pf}{i}.mlp.dense_4h_to_h.weight'].transpose(
						    0							,	1  )
						_snake_case              =      sd_megatron[f'{pf}{i}.mlp.dense_4h_to_h.bias']

			# For LM head, transformers' wants the matrix to weight embeddings.
			_snake_case              =      word_embeddings

			return sd_hf






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      64000
			_snake_case              =      config_megatron["encoder_seq_length"]
			_snake_case              =      config_megatron["hidden_size"]
			_snake_case              =      config_megatron["num_layers"]
			_snake_case              =      config_megatron["num_attention_heads"]
			_snake_case              =      config_megatron["ffn_hidden_size"]
			_snake_case              =      "gelu"
			_snake_case              =      0.1
			_snake_case              =      0.1
			_snake_case              =      0.1
			_snake_case              =      config_megatron["layernorm_epsilon"]  # 1e-5
			_snake_case              =      config_megatron["init_method_std"]  # 0.02
			_snake_case              =      config_megatron["apply_query_key_layer_scaling"]  # True
			_snake_case              =      True
			_snake_case              =      True

			# This identifies the 6.7B (7B) model which uses a different tokenizer
			if config_megatron["hidden_size"] == 4096:
						_snake_case              =      1  # <|endoftext|>
						_snake_case              =      1  # <|endoftext|>
						_snake_case              =      0  # <unk>
			else:
						_snake_case              =      2  # <s>
						_snake_case              =      3  # <|endoftext|>
						_snake_case              =      0  # <pad>

			return config_hf






def    UpperCAmelCase__(						lowerCamelCase_  ):



			"""simple docstring"""





			print(lowerCamelCase_  )

			_snake_case              =      args.checkpoint_path
			_snake_case              =      args.save_path
			if isfile(lowerCamelCase_  ):
						raise FileNotFoundError(f'ERROR! could not find file {checkpoint_path}'  )

			# Load the model.
			_snake_case              =      torch.load(lowerCamelCase_							,	map_location="cpu"  )

			# Load the config.
			_snake_case              =      checkpoint["hyper_parameters"]["cfg"]
			_snake_case              =      GPTaConfig()
			_snake_case              =      copy_config(config_hf=lowerCamelCase_							,	config_megatron=lowerCamelCase_  )
			_snake_case              =      ["GPT2LMHeadModel"]

			_snake_case              =      checkpoint["state_dict"]

			# Convert.
			print("Converting"  )
			_snake_case              =      convert_megatron_checkpoint(lowerCamelCase_							,	lowerCamelCase_  )

			# Print the structure of converted state dict.
			if args.print_checkpoint_structure:
						recursive_print(lowerCamelCase_							,	lowerCamelCase_  )

			_snake_case              =      "GPTSw3Tokenizer"

			# Store the config to file.
			print("Saving config"  )
			config_hf.save_pretrained(lowerCamelCase_  )

			# Store the state_dict to file.
			_snake_case              =      os.path.join(lowerCamelCase_							,	"pytorch_model.bin"  )
			print(f'Saving checkpoint to "{output_checkpoint_file}"'  )
			torch.save(lowerCamelCase_							,	lowerCamelCase_  )


if __name__ == "__main__":
			_SCREAMING_SNAKE_CASE:						Dict    	=       argparse.ArgumentParser()
			parser.add_argument(
			    '''--checkpoint_path''',
			    type=str,
			    required=True,
			    help='''e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000''',
			)
			parser.add_argument('''--save_path''', type=str, required=True, help='''e.g. /home/user/gpt-sw3/hf''')
			parser.add_argument('''--print-checkpoint-structure''', action='''store_true''')
			_SCREAMING_SNAKE_CASE:						Any    	=       parser.parse_args()
			main(_args)


import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey

class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		def __init__(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					_snake_case              =      0
					# max greyscale value for #FFFFFF
					_snake_case              =      int(self.get_greyscale(255  ,					255  ,					255   )   )

					if not self.min_threshold < threshold < self.max_threshold:
								_snake_case              =      F'Factor value should be from 0 to {self.max_threshold}'
								raise ValueError(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      input_img
					_snake_case              =      threshold
					_snake_case       , _snake_case              =      self.input_img.shape[1], self.input_img.shape[0]

					# error table size (+4 columns and +1 row) greater than input image because of
					# lack of if statements
					_snake_case              =      [
					    [0 for _ in range(self.height + 4   )] for __ in range(self.width + 1   )
					]
					_snake_case              =      np.ones((self.width, self.height, 3)  ,					np.uinta   ) * 255



		@classmethod
		def 				_a				(			cls    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					return 0.1_1_4 * blue + 0.5_8_7 * green + 0.2_1_2_6 * red







		def 				_a				(			self    :				Union[str, Any]   ):






					"""simple docstring"""
					for y in range(self.height   ):
								for x in range(self.width   ):
											_snake_case              =      int(self.get_greyscale(*self.input_img[y][x]   )   )
											if self.threshold > greyscale + self.error_table[y][x]:
														_snake_case              =      (0, 0, 0)
														_snake_case              =      greyscale + self.error_table[x][y]
											else:
														_snake_case              =      (255, 255, 255)
														_snake_case              =      greyscale + self.error_table[x][y] - 255
											self.error_table[y][x + 1] += int(8 / 32 * current_error   )
											self.error_table[y][x + 2] += int(4 / 32 * current_error   )
											self.error_table[y + 1][x] += int(8 / 32 * current_error   )
											self.error_table[y + 1][x + 1] += int(4 / 32 * current_error   )
											self.error_table[y + 1][x + 2] += int(2 / 32 * current_error   )
											self.error_table[y + 1][x - 1] += int(4 / 32 * current_error   )
											self.error_table[y + 1][x - 2] += int(2 / 32 * current_error   )


if __name__ == "__main__":
			# create Burke's instances with original images in greyscale
			_SCREAMING_SNAKE_CASE:						Tuple    	=       [
			    Burkes(imread('''image_data/lena.jpg''', 1), threshold)
			    for threshold in (1, 126, 130, 140)
			]

			for burkes in burkes_instances:
						burkes.process()

			for burkes in burkes_instances:
						imshow(
						    F"Original image with dithering threshold: {burkes.threshold}",
						    burkes.output_img,
						)

			waitKey(0)
			destroyAllWindows()

