





from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


_SCREAMING_SNAKE_CASE      	=		[
    """Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"""
    """ final seconds on board Flight 9525. The Germanwings co-pilot says he had a \"previous episode of severe"""
    """ depression\" German airline confirms it knew of Andreas Lubitz's depression years before he took control.""",
    """The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal"""
    """ accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC's"""
    """ founding Rome Statute in January. Israel and the United States opposed the Palestinians' efforts to join the"""
    """ body.""",
    """Amnesty International releases its annual report on the death penalty. The report catalogs the use of"""
    """ state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the"""
    """ world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital"""
    """ punishment.""",
]

_SCREAMING_SNAKE_CASE      	=		[
    """Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports ."""
    """ Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz"""
    """ had informed his Lufthansa training school of an episode of severe depression, airline says .""",
    """Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June ."""
    """ Israel and the United States opposed the move, which could open the door to war crimes investigations against"""
    """ Israelis .""",
    """Amnesty's annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to"""
    """ death . Organization claims that governments around the world are using the threat of terrorism to advance"""
    """ executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death"""
    """ sentences up by 28% .""",
]


def 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_		:			List[Any]											=	calculate_rouge(__a   , __a   , bootstrap_aggregation=__a   , rouge_keys=['rouge2', 'rougeL']   )
	assert isinstance(__a   , __a   )
	snake_case_		:			List[Any]											=	calculate_rouge(__a   , __a   , bootstrap_aggregation=__a   , rouge_keys=['rouge2']   )
	assert (
	    pd.DataFrame(no_aggregation['rouge2']   ).fmeasure.mean()
	    == pd.DataFrame(no_aggregation_just_ra['rouge2']   ).fmeasure.mean()
	)


def 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_		:			Any											=	'rougeLsum'
	snake_case_		:			str											=	calculate_rouge(__a   , __a   , newline_sep=__a   , rouge_keys=[k]   )[k]
	snake_case_		:			Optional[int]											=	calculate_rouge(__a   , __a   , newline_sep=__a   , rouge_keys=[k]   )[k]
	assert score > score_no_sep


def 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_		:			Union[str, Any]											=	['rouge1', 'rouge2', 'rougeL']
	snake_case_		:			List[Any]											=	calculate_rouge(__a   , __a   , newline_sep=__a   , rouge_keys=__a   )
	snake_case_		:			List[str]											=	calculate_rouge(__a   , __a   , newline_sep=__a   , rouge_keys=__a   )
	assert score_sep == score_no_sep


def 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_		:			Optional[int]											=	[
	    'Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.',
	    'Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .',
	]
	snake_case_		:			str											=	[
	    'Margot Frank, died in 1945, a month earlier than previously thought.',
	    'Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of'
	    ' the final seconds on board Flight 9525.',
	]
	assert calculate_rouge(__a   , __a   , newline_sep=__a   ) == calculate_rouge(__a   , __a   , newline_sep=__a   )


def 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_		:			List[str]											=	[
	    '" "a person who has such a video needs to immediately give it to the investigators," prosecutor says .<n> "it is a very disturbing scene," editor-in-chief of bild online tells "erin burnett: outfront" '
	]
	snake_case_		:			Optional[int]											=	[
	    ' Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports . Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says .'
	]

	snake_case_		:			int											=	calculate_rouge(__a   , __a   , rouge_keys=['rougeLsum']   , newline_sep=__a   )['rougeLsum']
	snake_case_		:			Optional[Any]											=	calculate_rouge(__a   , __a   , rouge_keys=['rougeLsum']   )['rougeLsum']
	assert new_score > prev_score


def 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_		:			str											=	Path('examples/seq2seq/test_data/wmt_en_ro'   )
	snake_case_		:			Optional[Any]											=	calculate_rouge_path(data_dir.joinpath('test.source'   )   , data_dir.joinpath('test.target'   )   )
	assert isinstance(__a   , __a   )
	snake_case_		:			Optional[int]											=	calculate_rouge_path(
	    data_dir.joinpath('test.source'   )   , data_dir.joinpath('test.target'   )   , bootstrap_aggregation=__a   )
	assert isinstance(__a   , __a   )








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


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


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	if "xprophetnet" in prophetnet_checkpoint_path:
		snake_case_		:			int											=	XLMProphetNetForConditionalGenerationOld.from_pretrained(__a   )
		snake_case_						,snake_case_		:			List[Any]											=	XLMProphetNetForConditionalGeneration.from_pretrained(
		    __a   , output_loading_info=__a   )
	else:
		snake_case_		:			List[str]											=	ProphetNetForConditionalGenerationOld.from_pretrained(__a   )
		snake_case_						,snake_case_		:			Dict											=	ProphetNetForConditionalGeneration.from_pretrained(
		    __a   , output_loading_info=__a   )

	snake_case_		:			str											=	['key_proj', 'value_proj', 'query_proj']

	snake_case_		:			List[str]											=	{
	    '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"]:
		snake_case_		:			Union[str, Any]											=	key.split('.'   )

		if attributes[0] == "lm_head":
			snake_case_		:			Optional[Any]											=	prophet
			snake_case_		:			Any											=	prophet_old
		else:
			snake_case_		:			Optional[int]											=	prophet.prophetnet
			snake_case_		:			str											=	prophet_old.model

		snake_case_		:			Union[str, Any]											=	False
		for attribute in attributes:
			if attribute in mapping:
				snake_case_		:			Optional[Any]											=	mapping[attribute]
				if not hasattr(__a   , __a   ) and len(__a   ) > 0:
					snake_case_		:			List[Any]											=	attribute
			elif hasattr(__a   , __a   ):
				snake_case_		:			List[Any]											=	attribute

			if attribute == "weight":
				assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
				snake_case_		:			int											=	old_model.weight
				logger.info(f"""{attribute} is initialized."""   )
				snake_case_		:			Optional[int]											=	True
				break
			elif attribute == "bias":
				assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
				snake_case_		:			List[str]											=	old_model.bias
				logger.info(f"""{attribute} is initialized"""   )
				snake_case_		:			int											=	True
				break
			elif attribute in special_keys and hasattr(__a   , 'in_proj_weight'   ):
				snake_case_		:			Optional[Any]											=	old_model.in_proj_weight.shape[0] // 3
				snake_case_		:			List[Any]											=	getattr(__a   , __a   )
				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":
					snake_case_		:			Tuple											=	nn.Parameter(old_model.in_proj_weight[:embed_dim, :]   )
					snake_case_		:			Optional[int]											=	nn.Parameter(old_model.in_proj_bias[:embed_dim]   )

				elif attribute == "key_proj":
					snake_case_		:			int											=	nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]   )
					snake_case_		:			Any											=	nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]   )
				elif attribute == "value_proj":
					snake_case_		:			List[Any]											=	nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]   )
					snake_case_		:			str											=	nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]   )
				snake_case_		:			Any											=	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] == 5_12, "We want 512 position_embeddings."
				snake_case_		:			Any											=	nn.Parameter(old_model.embed_positions.weight[:5_12, :]   )
				snake_case_		:			List[Any]											=	True
				break

			if attribute.isdigit():
				snake_case_		:			Any											=	model[int(__a   )]
				snake_case_		:			Any											=	old_model[int(__a   )]
			else:
				snake_case_		:			str											=	getattr(__a   , __a   )

				if old_attribute == "":
					snake_case_		:			Tuple											=	old_model
				else:
					if not hasattr(__a   , __a   ):
						raise ValueError(f"""{old_model} does not have {old_attribute}"""   )
					snake_case_		:			List[str]											=	getattr(__a   , __a   )

		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(__a   )


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		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."""
						)
						_SCREAMING_SNAKE_CASE      	=		parser.parse_args()
						convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)

