

"""simple docstring"""





import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig




def   _A  (  UpperCamelCase_     :       Optional[Any],			UpperCamelCase_     :       Optional[int],			UpperCamelCase_     :       int,			UpperCamelCase_     :       Optional[int])	->						List[str]:
   '''simple docstring'''


   __lowercase								=						s.rsplit(UpperCamelCase_,			UpperCamelCase_)
   return new.join(UpperCamelCase_)




def   _A  (  UpperCamelCase_     :       List[str])	->						List[str]:
   '''simple docstring'''


   return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items())




def   _A  (  UpperCamelCase_     :       str)	->						Optional[int]:
   '''simple docstring'''


   __lowercase								=						{}

   __lowercase								=						["group_1", "group_2", "group_3", "group_4"]
   for key, value in state_dict.items():
      for group_key in group_keys:
         if group_key in key:
            __lowercase								=						key.replace(F"""{group_key}.""",			F"""{group_key}.group.""")

      if "res_path" in key:
         __lowercase								=						key.replace("res_path.",			"res_path.path.")

      if key.endswith(".w"):
         __lowercase								=						rreplace(UpperCamelCase_,			".w",			".weight",			1)
      if key.endswith(".b"):
         __lowercase								=						rreplace(UpperCamelCase_,			".b",			".bias",			1)

      __lowercase								=						value.float()

   return upgrade




@torch.no_grad()
def   _A  (  UpperCamelCase_     :       Tuple,			UpperCamelCase_     :       Tuple,			UpperCamelCase_     :       Optional[int]=None,			UpperCamelCase_     :       int=True)	->						str:
   '''simple docstring'''


   from dall_e import Encoder

   __lowercase								=						Encoder()
   if os.path.exists(UpperCamelCase_):
      __lowercase								=						torch.load(UpperCamelCase_)
   else:
      __lowercase								=						torch.hub.load_state_dict_from_url(UpperCamelCase_)

   if isinstance(UpperCamelCase_,			UpperCamelCase_):
      __lowercase								=						ckpt.state_dict()
   encoder.load_state_dict(UpperCamelCase_)

   if config_path is not None:
      __lowercase								=						FlavaImageCodebookConfig.from_pretrained(UpperCamelCase_)
   else:
      __lowercase								=						FlavaImageCodebookConfig()

   __lowercase								=						FlavaImageCodebook(UpperCamelCase_).eval()
   __lowercase								=						encoder.state_dict()

   __lowercase								=						upgrade_state_dict(UpperCamelCase_)
   hf_model.load_state_dict(UpperCamelCase_)
   __lowercase								=						hf_model.state_dict()
   __lowercase								=						count_parameters(UpperCamelCase_)
   __lowercase								=						count_parameters(UpperCamelCase_)

   assert torch.allclose(UpperCamelCase_,			UpperCamelCase_,			atol=1E-3)

   if save_checkpoint:
      hf_model.save_pretrained(UpperCamelCase_)
   else:
      return hf_state_dict


if __name__ == "__main__":
   _a       =							argparse.ArgumentParser()
   parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
   parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to flava checkpoint')
   parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
   _a       =							parser.parse_args()

   convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)







"""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.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool




class 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







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

 __UpperCAmelCase	:					List[str]   		=	["audio"]
 __UpperCAmelCase	:					Tuple   		=	["text"]


 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			int	):
    return self.pre_processor(UpperCAmelCase__,					return_tensors="pt"	).input_features


 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			Optional[Any]	):
    return self.model.generate(inputs=UpperCAmelCase__	)



 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			Optional[int]	):
    return self.pre_processor.batch_decode(UpperCAmelCase__,					skip_special_tokens=UpperCAmelCase__	)[0]




