
'''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_	:		List[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							(     UpperCAmelCase					)     ->			int:




						'''simple docstring'''




						for pegasus_name, hf_name in PATTERNS:
												_UpperCamelCase			:			Optional[int]   			=						k.replace(__A     ,__A					)
						return k
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase					)     ->			PegasusForConditionalGeneration:




						'''simple docstring'''
						_UpperCamelCase			:			List[str]   			=						DEFAULTS.copy()
						cfg_kwargs.update(__A					)
						_UpperCamelCase			:			Dict   			=						PegasusConfig(**__A					)
						_UpperCamelCase			:			Optional[int]   			=						PegasusForConditionalGeneration(__A					)
						_UpperCamelCase			:			Union[str, Any]   			=						torch_model.model.state_dict()
						_UpperCamelCase			:			Union[str, Any]   			=						{}
						for k, v in tf_weights.items():
												_UpperCamelCase			:			Dict   			=						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			:			Union[str, Any]   			=						v.T
												_UpperCamelCase			:			Any   			=						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			:			int   			=						torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]					)
						_UpperCamelCase			:			Dict   			=						mapping['''shared.weight''']
						_UpperCamelCase			:			List[str]   			=						mapping['''shared.weight''']
						_UpperCamelCase			:			Optional[int]   			=						{k: torch.zeros_like(__A					) for k, v in sd.items() if k.endswith("bias"					) and k not in mapping}
						mapping.update(**__A					)
						_UpperCamelCase			:			List[str]   			=						torch_model.model.load_state_dict(__A     ,strict=__A					)
						_UpperCamelCase			:			Optional[int]   			=						[
						    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							(     UpperCAmelCase="./ckpt/aeslc/model.ckpt-32000"					)     ->			Dict:




						'''simple docstring'''
						_UpperCamelCase			:			Dict   			=						tf.train.list_variables(__A					)
						_UpperCamelCase			:			str   			=						{}
						_UpperCamelCase			:			Union[str, Any]   			=						['''Adafactor''', '''global_step''']
						for name, shape in tqdm(__A     ,desc="converting tf checkpoint to dict"					):
												_UpperCamelCase			:			int   			=						any(pat in name for pat in ignore_name					)
												if skip_key:
																		continue
												_UpperCamelCase			:			List[Any]   			=						tf.train.load_variable(__A     ,__A					)
												_UpperCamelCase			:			Tuple   			=						array
						return tf_weights
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase					)     ->			Dict:




						'''simple docstring'''
						_UpperCamelCase			:			Any   			=						Path(__A					).parent.name
						_UpperCamelCase			:			List[Any]   			=						task_specific_params[f'''summarization_{dataset}''']['''max_position_embeddings''']
						_UpperCamelCase			:			List[str]   			=						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			:			str   			=						get_tf_weights_as_numpy(__A					)
						_UpperCamelCase			:			List[Any]   			=						task_specific_params[f'''summarization_{dataset}''']
						if dataset == "large":
												_UpperCamelCase			:			Tuple   			=						task_specific_params
						_UpperCamelCase			:			Optional[Any]   			=						convert_pegasus(__A     ,__A					)
						torch_model.save_pretrained(__A					)
						_UpperCamelCase			:			int   			=						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_	:		Optional[Any]			=     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_	:		List[Any]			=     Path(args.tf_ckpt_path).parent.name
						lowerCAmelCase_	:		Any			=     os.path.join("""pegasus""", dataset)
			convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)



'''simple docstring'''





import re
from pathlib import Path
from unittest import TestCase

import pytest




@pytest.mark.integration
class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				def  snake_case__				(						self					:						Dict       ,		lowercase__					:						str					)							->Tuple:


										'''simple docstring'''




										with open(lowercase__       ,		encoding="utf-8"					) as input_file:
																_UpperCamelCase			:			Optional[int]   			=						re.compile(r"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)"					)
																_UpperCamelCase			:			Dict   			=						input_file.read()
																_UpperCamelCase			:			Dict   			=						regexp.search(lowercase__					)

										return match





				def  snake_case__				(						self					:						str       ,		lowercase__					:						str					)							->Tuple:


										'''simple docstring'''




										with open(lowercase__       ,		encoding="utf-8"					) as input_file:
																_UpperCamelCase			:			str   			=						re.compile(r"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()"       ,		re.DOTALL					)
																_UpperCamelCase			:			Optional[Any]   			=						input_file.read()
																# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
																_UpperCamelCase			:			List[str]   			=						regexp.finditer(lowercase__					)

										_UpperCamelCase			:			Any   			=						[match for match in matches if match is not None and match.group(1					) is not None]
										return matches[0] if matches else None





				def  snake_case__				(						self					:						Optional[int]					)							->int:


										'''simple docstring'''




										_UpperCamelCase			:			Any   			=						Path("./datasets"					)
										_UpperCamelCase			:			List[Any]   			=						list(dataset_paths.absolute().glob("**/*.py"					)					)

										for dataset in dataset_files:
																if self._no_encoding_on_file_open(str(lowercase__					)					):
																						raise AssertionError(f'''open(...) must use utf-8 encoding in {dataset}'''					)







				def  snake_case__				(						self					:						Optional[Any]					)							->Union[str, Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Any   			=						Path("./datasets"					)
										_UpperCamelCase			:			Any   			=						list(dataset_paths.absolute().glob("**/*.py"					)					)

										for dataset in dataset_files:
																if self._no_print_statements(str(lowercase__					)					):
																						raise AssertionError(f'''print statement found in {dataset}. Use datasets.logger/logging instead.'''					)


