"""simple docstring"""






from __future__ import annotations

from PIL import Image

# Define glider example
_A   					=    [
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
_A   					=    [[0, 1, 0], [0, 1, 0], [0, 1, 0]]




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   list[list[int]]:
				SCREAMING_SNAKE_CASE__		  =				[]
				for i in range(len(__UpperCAmelCase       )       ):
								SCREAMING_SNAKE_CASE__		  =				[]
								for j in range(len(cells[i]       )       ):
												# Get the number of live neighbours
												SCREAMING_SNAKE_CASE__		  =				0
												if i > 0 and j > 0:
																neighbour_count += cells[i - 1][j - 1]
												if i > 0:
																neighbour_count += cells[i - 1][j]
												if i > 0 and j < len(cells[i]       ) - 1:
																neighbour_count += cells[i - 1][j + 1]
												if j > 0:
																neighbour_count += cells[i][j - 1]
												if j < len(cells[i]       ) - 1:
																neighbour_count += cells[i][j + 1]
												if i < len(__UpperCAmelCase       ) - 1 and j > 0:
																neighbour_count += cells[i + 1][j - 1]
												if i < len(__UpperCAmelCase       ) - 1:
																neighbour_count += cells[i + 1][j]
												if i < len(__UpperCAmelCase       ) - 1 and j < len(cells[i]       ) - 1:
																neighbour_count += cells[i + 1][j + 1]

												# Rules of the game of life (excerpt from Wikipedia):
												# 1. Any live cell with two or three live neighbours survives.
												# 2. Any dead cell with three live neighbours becomes a live cell.
												# 3. All other live cells die in the next generation.
												#    Similarly, all other dead cells stay dead.
												SCREAMING_SNAKE_CASE__		  =				cells[i][j] == 1
												if (
												    (alive and 2 <= neighbour_count <= 3)
												    or not alive
												    and neighbour_count == 3
												):
																next_generation_row.append(1       )
												else:
																next_generation_row.append(0       )

								next_generation.append(__UpperCAmelCase       )
				return next_generation




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   list[Image.Image]:
				SCREAMING_SNAKE_CASE__		  =				[]
				for _ in range(__UpperCAmelCase       ):
								# Create output image
								SCREAMING_SNAKE_CASE__		  =				Image.new("RGB"  ,					(len(cells[0]       ), len(__UpperCAmelCase       ))       )
								SCREAMING_SNAKE_CASE__		  =				img.load()

								# Save cells to image
								for x in range(len(__UpperCAmelCase       )       ):
												for y in range(len(cells[0]       )       ):
																SCREAMING_SNAKE_CASE__		  =				255 - cells[y][x] * 255
																SCREAMING_SNAKE_CASE__		  =				(colour, colour, colour)

        # Save image
								images.append(__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__		  =				new_generation(__UpperCAmelCase       )
				return images


if __name__ == "__main__":
						_A   					=    generate_images(GLIDER, 1_6)
						images[0].save('out.gif', save_all=True, append_images=images[1:])





"""simple docstring"""






import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


_A   					=    logging.get_logger(__name__)
logging.set_verbosity_info()




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   List[str]:
				if "xprophetnet" in prophetnet_checkpoint_path:
								SCREAMING_SNAKE_CASE__		  =				XLMProphetNetForConditionalGenerationOld.from_pretrained(__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				XLMProphetNetForConditionalGeneration.from_pretrained(
								    __UpperCAmelCase  ,					output_loading_info=__UpperCAmelCase       )
				else:
								SCREAMING_SNAKE_CASE__		  =				ProphetNetForConditionalGenerationOld.from_pretrained(__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				ProphetNetForConditionalGeneration.from_pretrained(
								    __UpperCAmelCase  ,					output_loading_info=__UpperCAmelCase       )

				SCREAMING_SNAKE_CASE__		  =				["key_proj", "value_proj", "query_proj"]

				SCREAMING_SNAKE_CASE__		  =				{
				    "self_attn": "ngram_self_attn",
				    "cross_attn": "encoder_attn",
				    "cross_attn_layer_norm": "encoder_attn_layer_norm",
				    "feed_forward_layer_norm": "final_layer_norm",
				    "feed_forward": "",
				    "intermediate": "fc1",
				    "output": "fc2",
				    "key_proj": "k_proj",
				    "query_proj": "q_proj",
				    "value_proj": "v_proj",
				    "word_embeddings": "embed_tokens",
				    "embeddings_layer_norm": "emb_layer_norm",
				    "relative_pos_embeddings": "relative_linear",
				    "ngram_embeddings": "ngram_input_embed",
				    "position_embeddings": "embed_positions",
				}

				for key in loading_info["missing_keys"]:
								SCREAMING_SNAKE_CASE__		  =				key.split("."       )

								if attributes[0] == "lm_head":
												SCREAMING_SNAKE_CASE__		  =				prophet
												SCREAMING_SNAKE_CASE__		  =				prophet_old
								else:
												SCREAMING_SNAKE_CASE__		  =				prophet.prophetnet
												SCREAMING_SNAKE_CASE__		  =				prophet_old.model

								SCREAMING_SNAKE_CASE__		  =				False
								for attribute in attributes:
												if attribute in mapping:
																SCREAMING_SNAKE_CASE__		  =				mapping[attribute]
																if not hasattr(__UpperCAmelCase  ,					__UpperCAmelCase       ) and len(__UpperCAmelCase       ) > 0:
																				SCREAMING_SNAKE_CASE__		  =				attribute
												elif hasattr(__UpperCAmelCase  ,					__UpperCAmelCase       ):
																SCREAMING_SNAKE_CASE__		  =				attribute

												if attribute == "weight":
																assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
																SCREAMING_SNAKE_CASE__		  =				old_model.weight
																logger.info(F"""{attribute} is initialized."""       )
																SCREAMING_SNAKE_CASE__		  =				True
																break
												elif attribute == "bias":
																assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																SCREAMING_SNAKE_CASE__		  =				old_model.bias
																logger.info(F"""{attribute} is initialized"""       )
																SCREAMING_SNAKE_CASE__		  =				True
																break
												elif attribute in special_keys and hasattr(__UpperCAmelCase  ,					"in_proj_weight"       ):
																SCREAMING_SNAKE_CASE__		  =				old_model.in_proj_weight.shape[0] // 3
																SCREAMING_SNAKE_CASE__		  =				getattr(__UpperCAmelCase  ,					__UpperCAmelCase       )
																param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
																param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
																if attribute == "query_proj":
																				SCREAMING_SNAKE_CASE__		  =				nn.Parameter(old_model.in_proj_weight[:embed_dim, :]       )
																				SCREAMING_SNAKE_CASE__		  =				nn.Parameter(old_model.in_proj_bias[:embed_dim]       )

																elif attribute == "key_proj":
																				SCREAMING_SNAKE_CASE__		  =				nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]       )
																				SCREAMING_SNAKE_CASE__		  =				nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]       )
																elif attribute == "value_proj":
																				SCREAMING_SNAKE_CASE__		  =				nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]       )
																				SCREAMING_SNAKE_CASE__		  =				nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]       )
																SCREAMING_SNAKE_CASE__		  =				True
																break
												elif attribute == "position_embeddings":
																assert (
																    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
																), "Hidden size has to match"
																assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
																SCREAMING_SNAKE_CASE__		  =				nn.Parameter(old_model.embed_positions.weight[:512, :]       )
																SCREAMING_SNAKE_CASE__		  =				True
																break

												if attribute.isdigit():
																SCREAMING_SNAKE_CASE__		  =				model[int(__UpperCAmelCase       )]
																SCREAMING_SNAKE_CASE__		  =				old_model[int(__UpperCAmelCase       )]
												else:
																SCREAMING_SNAKE_CASE__		  =				getattr(__UpperCAmelCase  ,					__UpperCAmelCase       )

																if old_attribute == "":
																				SCREAMING_SNAKE_CASE__		  =				old_model
																else:
																				if not hasattr(__UpperCAmelCase  ,					__UpperCAmelCase       ):
																								raise ValueError(F"""{old_model} does not have {old_attribute}"""       )
																				SCREAMING_SNAKE_CASE__		  =				getattr(__UpperCAmelCase  ,					__UpperCAmelCase       )

								if not is_key_init:
												raise ValueError(F"""{key} was not correctly initialized!"""       )

				print(F"""Saving model to {pytorch_dump_folder_path}"""       )
				prophet.save_pretrained(__UpperCAmelCase       )


if __name__ == "__main__":
						_A   					=    argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
						)
						_A   					=    parser.parse_args()
						convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)




