"""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_     =	[
    # 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_(__SCREAMING_SNAKE_CASE  )->    int:




							for pegasus_name, hf_name in PATTERNS:
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    k.replace(__A     ,	__A  )
							return k



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    PegasusForConditionalGeneration:
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    DEFAULTS.copy()
							cfg_kwargs.update(__A  )
							_SCREAMING_SNAKE_CASE		:						Dict			  =    PegasusConfig(**__A  )
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    PegasusForConditionalGeneration(__A  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    torch_model.model.state_dict()
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    {}
							for k, v in tf_weights.items():
														_SCREAMING_SNAKE_CASE		:						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:
																					_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    v.T
														_SCREAMING_SNAKE_CASE		:						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
							_SCREAMING_SNAKE_CASE		:						int			  =    torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1]  )
							_SCREAMING_SNAKE_CASE		:						Dict			  =    mapping['''shared.weight''']
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    mapping['''shared.weight''']
							_SCREAMING_SNAKE_CASE		:						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  )
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    torch_model.model.load_state_dict(__A     ,	strict=__A  )
							_SCREAMING_SNAKE_CASE		:						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      lowerCamelCase_(__SCREAMING_SNAKE_CASE="./ckpt/aeslc/model.ckpt-32000"  )->    Dict:
							_SCREAMING_SNAKE_CASE		:						Dict			  =    tf.train.list_variables(__A  )
							_SCREAMING_SNAKE_CASE		:						str			  =    {}
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    ['''Adafactor''', '''global_step''']
							for name, shape in tqdm(__A     ,	desc="""converting tf checkpoint to dict"""  ):
														_SCREAMING_SNAKE_CASE		:						int			  =    any(pat in name for pat in ignore_name  )
														if skip_key:
																					continue
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    tf.train.load_variable(__A     ,	__A  )
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    array
							return tf_weights



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Dict:
							_SCREAMING_SNAKE_CASE		:						Any			  =    Path(__A  ).parent.name
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    task_specific_params[F"""summarization_{dataset}"""]['''max_position_embeddings''']
							_SCREAMING_SNAKE_CASE		:						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
							_SCREAMING_SNAKE_CASE		:						str			  =    get_tf_weights_as_numpy(__A  )
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    task_specific_params[F"""summarization_{dataset}"""]
							if dataset == "large":
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    task_specific_params
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    convert_pegasus(__A     ,	__A  )
							torch_model.save_pretrained(__A  )
							_SCREAMING_SNAKE_CASE		:						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_     =	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_     =	parser.parse_args()
			if args.save_dir is None:
						lowerCAmelCase_     =	Path(args.tf_ckpt_path).parent.name
						lowerCAmelCase_     =	os.path.join('''pegasus''', dataset)
			convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)



"""simple docstring"""





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						"facebook/bart-large-mnli"
				a			=						(
				    "This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
				    "should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
				    "It returns the most likely label in the list of provided `labels` for the input text."
				)
				a			=						"text_classifier"
				a			=						AutoTokenizer
				a			=						AutoModelForSequenceClassification

				a			=						["text", ["text"]]
				a			=						["text"]





				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											super().setup()
											_SCREAMING_SNAKE_CASE		:						Any			  =    self.model.config
											_SCREAMING_SNAKE_CASE		:						Any			  =    -1
											for idx, label in config.idalabel.items():
																		if label.lower().startswith("""entail"""):
																									_SCREAMING_SNAKE_CASE		:						List[Any]			  =    int(_A)
											if self.entailment_id == -1:
																		raise ValueError("""Could not determine the entailment ID from the model config, please pass it at init.""")





				def 		_lowerCAmelCase		(      self							:							Optional[Any]      ,    _A							:							Tuple      ,    _A							:							List[str]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    labels
											return self.pre_processor(
											    [text] * len(_A)      ,    [f"""This example is {label}""" for label in labels]      ,    return_tensors="""pt"""      ,    padding="""max_length"""      ,    )





				def 		_lowerCAmelCase		(      self							:							Tuple      ,    _A							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    outputs.logits
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    torch.argmax(logits[:, 2]).item()
											return self._labels[label_id]


