

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


_UpperCAmelCase		:   Tuple										=       [
    # 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 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							for pegasus_name, hf_name in PATTERNS:
														snake_case_							=    k.replace(UpperCamelCase__   ,							UpperCamelCase__      )
							return k





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    DEFAULTS.copy()
							cfg_kwargs.update(UpperCamelCase__      )
							snake_case_							=    PegasusConfig(**UpperCamelCase__      )
							snake_case_							=    PegasusForConditionalGeneration(UpperCamelCase__      )
							snake_case_							=    torch_model.model.state_dict()
							snake_case_							=    {}
							for k, v in tf_weights.items():
														snake_case_							=    rename_state_dict_key(UpperCamelCase__      )
														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:
																					snake_case_							=    v.T
														snake_case_							=    torch.tensor(UpperCamelCase__   ,							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
							snake_case_							=    torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]      )
							snake_case_							=    mapping['shared.weight']
							snake_case_							=    mapping['shared.weight']
							snake_case_							=    {k: torch.zeros_like(UpperCamelCase__      ) for k, v in sd.items() if k.endswith('bias'      ) and k not in mapping}
							mapping.update(**UpperCamelCase__      )
							snake_case_				,    snake_case_							=    torch_model.model.load_state_dict(UpperCamelCase__   ,							strict=UpperCamelCase__      )
							snake_case_							=    [
							    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 					__lowerCamelCase	( UpperCamelCase__="./ckpt/aeslc/model.ckpt-32000"      ):







							'''simple docstring'''






							snake_case_							=    tf.train.list_variables(UpperCamelCase__      )
							snake_case_							=    {}
							snake_case_							=    ['Adafactor', 'global_step']
							for name, shape in tqdm(UpperCamelCase__   ,							desc='converting tf checkpoint to dict'      ):
														snake_case_							=    any(pat in name for pat in ignore_name      )
														if skip_key:
																					continue
														snake_case_							=    tf.train.load_variable(UpperCamelCase__   ,							UpperCamelCase__      )
														snake_case_							=    array
							return tf_weights





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    Path(UpperCamelCase__      ).parent.name
							snake_case_							=    task_specific_params[F'''summarization_{dataset}''']['max_position_embeddings']
							snake_case_							=    PegasusTokenizer.from_pretrained('sshleifer/pegasus'   ,							model_max_length=UpperCamelCase__      )
							assert tok.model_max_length == desired_max_model_length
							tok.save_pretrained(UpperCamelCase__      )

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


if __name__ == "__main__":
					_UpperCAmelCase		:   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.""")
					_UpperCAmelCase		:   List[str]										=       parser.parse_args()
					if args.save_dir is None:
										_UpperCAmelCase		:   str										=       Path(args.tf_ckpt_path).parent.name
										_UpperCAmelCase		:   List[Any]										=       os.path.join("""pegasus""", dataset)
					convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)







from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase		:   Any										=       logging.get_logger(__name__)


_UpperCAmelCase		:   Dict										=       {
    """nielsr/canine-s""": 2048,
}

# Unicode defines 1,114,112 total “codepoints”
_UpperCAmelCase		:   Tuple										=       111_4112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
_UpperCAmelCase		:   List[str]										=       0

_UpperCAmelCase		:   Any										=       0xE000
_UpperCAmelCase		:   Dict										=       0xE001
_UpperCAmelCase		:   Optional[int]										=       0xE002
_UpperCAmelCase		:   Tuple										=       0xE003
_UpperCAmelCase		:   Tuple										=       0xE004

# Maps special codepoints to human-readable names.
_UpperCAmelCase		:   Dict[int, str]										=       {
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
_UpperCAmelCase		:   Dict[str, int]										=       {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}







class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: str       			=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




				def __init__(					self      ,   snake_case=chr(snake_case       )      ,   snake_case=chr(snake_case       )      ,   snake_case=chr(snake_case       )      ,   snake_case=chr(snake_case       )      ,   snake_case=chr(snake_case       )      ,   snake_case=chr(snake_case       )      ,   snake_case=False      ,   snake_case=2048      ,   **snake_case      ,   ):
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else bos_token
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else eos_token
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else sep_token
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else cls_token
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else pad_token

											# Mask token behave like a normal word, i.e. include the space before it
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else mask_token

											super().__init__(
											    bos_token=snake_case      ,   eos_token=snake_case      ,   sep_token=snake_case      ,   cls_token=snake_case      ,   pad_token=snake_case      ,   mask_token=snake_case      ,   add_prefix_space=snake_case      ,   model_max_length=snake_case      ,   **snake_case      ,   )

											# Creates a mapping for looking up the IDs of special symbols.
											snake_case_							=    {}
											for codepoint, name in SPECIAL_CODEPOINTS.items():
																		snake_case_							=    codepoint

											# Creates a mapping for looking up the string forms of special symbol IDs.
											snake_case_							=    {
											    codepoint: name for name, codepoint in self._special_codepoints.items()
											}

											snake_case_							=    UNICODE_VOCAB_SIZE
											snake_case_							=    len(self._special_codepoints       )




				@property
				def     a							(					self       ):
											return self._unicode_vocab_size




				def     a							(					self      ,   snake_case       ):
											return list(snake_case       )




				def     a							(					self      ,   snake_case       ):
											try:
																		return ord(snake_case       )
											except TypeError:
																		raise ValueError(F'''invalid token: \'{token}\''''       )




				def     a							(					self      ,   snake_case       ):
											try:
																		if index in SPECIAL_CODEPOINTS:
																									return SPECIAL_CODEPOINTS[index]
																		return chr(snake_case       )
											except TypeError:
																		raise ValueError(F'''invalid id: {index}'''       )




				def     a							(					self      ,   snake_case       ):
											return "".join(snake_case       )




				def     a							(					self      ,   snake_case      ,   snake_case = None       ):
											snake_case_							=    [self.sep_token_id]
											snake_case_							=    [self.cls_token_id]

											snake_case_							=    cls + token_ids_a + sep
											if token_ids_a is not None:
																		result += token_ids_a + sep
											return result




				def     a							(					self      ,   snake_case      ,   snake_case = None      ,   snake_case = False       ):
											if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=snake_case      ,   token_ids_a=snake_case      ,   already_has_special_tokens=snake_case       )

											snake_case_							=    [1] + ([0] * len(snake_case       )) + [1]
											if token_ids_a is not None:
																		result += ([0] * len(snake_case       )) + [1]
											return result




				def     a							(					self      ,   snake_case      ,   snake_case = None       ):
											snake_case_							=    [self.sep_token_id]
											snake_case_							=    [self.cls_token_id]

											snake_case_							=    len(cls + token_ids_a + sep       ) * [0]
											if token_ids_a is not None:
																		result += len(token_ids_a + sep       ) * [1]
											return result




				def     a							(					self      ,   snake_case      ,   snake_case = None       ):
											return ()




