
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


_snake_case      = [
    # 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					( _lowercase			)       ->      Tuple:
     for pegasus_name, hf_name in PATTERNS:
          UpperCamelCase						    =					k.replace(_lowercase						,     _lowercase			)
     return k

def  __lowerCamelCase					( _lowercase						,     _lowercase			)       ->      PegasusForConditionalGeneration:
     UpperCamelCase						    =					DEFAULTS.copy()
     cfg_kwargs.update(_lowercase			)
     UpperCamelCase						    =					PegasusConfig(**_lowercase			)
     UpperCamelCase						    =					PegasusForConditionalGeneration(_lowercase			)
     UpperCamelCase						    =					torch_model.model.state_dict()
     UpperCamelCase						    =					{}
     for k, v in tf_weights.items():
          UpperCamelCase						    =					rename_state_dict_key(_lowercase			)
          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						    =					v.T
          UpperCamelCase						    =					torch.tensor(_lowercase						,     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						    =					torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]			)
     UpperCamelCase						    =					mapping['shared.weight']
     UpperCamelCase						    =					mapping['shared.weight']
     UpperCamelCase						    =					{k: torch.zeros_like(_lowercase			) for k, v in sd.items() if k.endswith('bias'			) and k not in mapping}
     mapping.update(**_lowercase			)
     UpperCamelCase    ,		UpperCamelCase						    =					torch_model.model.load_state_dict(_lowercase						,     strict=_lowercase			)
     UpperCamelCase						    =					[
         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					( _lowercase="./ckpt/aeslc/model.ckpt-32000"			)       ->      Dict:
     UpperCamelCase						    =					tf.train.list_variables(_lowercase			)
     UpperCamelCase						    =					{}
     UpperCamelCase						    =					['Adafactor', 'global_step']
     for name, shape in tqdm(_lowercase						,     desc='converting tf checkpoint to dict'			):
          UpperCamelCase						    =					any(pat in name for pat in ignore_name			)
          if skip_key:
               continue
          UpperCamelCase						    =					tf.train.load_variable(_lowercase						,     _lowercase			)
          UpperCamelCase						    =					array
     return tf_weights

def  __lowerCamelCase					( _lowercase						,     _lowercase			)       ->      str:
     # save tokenizer first
     UpperCamelCase						    =					Path(_lowercase			).parent.name
     UpperCamelCase						    =					task_specific_params[F'summarization_{dataset}']['max_position_embeddings']
     UpperCamelCase						    =					PegasusTokenizer.from_pretrained('sshleifer/pegasus'						,     model_max_length=_lowercase			)
     assert tok.model_max_length == desired_max_model_length
     tok.save_pretrained(_lowercase			)

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


if __name__ == "__main__":
 _snake_case      = 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.''')
 _snake_case      = parser.parse_args()
 if args.save_dir is None:
  _snake_case      = Path(args.tf_ckpt_path).parent.name
  _snake_case      = os.path.join('''pegasus''', dataset)
 convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)




# 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.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Tuple       						="openai/whisper-base"
 SCREAMING_SNAKE_CASE_				:					List[str]       						=(
     "This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the "
     "transcribed text."
 )
 SCREAMING_SNAKE_CASE_				:					int       						="transcriber"
 SCREAMING_SNAKE_CASE_				:					Tuple       						=WhisperProcessor
 SCREAMING_SNAKE_CASE_				:					List[Any]       						=WhisperForConditionalGeneration

 SCREAMING_SNAKE_CASE_				:					Union[str, Any]       						=["audio"]
 SCREAMING_SNAKE_CASE_				:					Any       						=["text"]

 def      __lowerCAmelCase  (						self			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			List[Any]   ):






      """simple docstring"""



      return self.pre_processor(SCREAMING_SNAKE_CASE__					, return_tensors='pt'   ).input_features

 def      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]   ):






      """simple docstring"""



      return self.model.generate(inputs=SCREAMING_SNAKE_CASE__   )






 def      __lowerCAmelCase  (						self			:			List[str]					, SCREAMING_SNAKE_CASE__			:			List[str]   ):






      """simple docstring"""



      return self.pre_processor.batch_decode(SCREAMING_SNAKE_CASE__					, skip_special_tokens=SCREAMING_SNAKE_CASE__   )[0]


