


import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_snake_case             =     {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}
class 	UpperCAmelCase_					(					nn.Module    ):

   '''simple docstring'''




   def __init__(		self						,				__A ):






     """simple docstring"""





     super().__init__()
     lowerCamelCase :					str    =				torchvision.models.resnetaaa(pretrained=__A )
     lowerCamelCase :					Dict    =				list(model.children() )[:-2]
     lowerCamelCase :					Optional[int]    =				nn.Sequential(*__A )
     lowerCamelCase :					Union[str, Any]    =				nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Any    =				self.pool(self.model(__A ) )
     lowerCamelCase :					Tuple    =				torch.flatten(__A						,				start_dim=2 )
     lowerCamelCase :					Optional[int]    =				out.transpose(1						,				2 ).contiguous()
     return out  # BxNx2048



class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   def __init__(		self						,				__A						,				__A						,				__A						,				__A						,				__A ):






     """simple docstring"""





     lowerCamelCase :					int    =				[json.loads(__A ) for l in open(__A )]
     lowerCamelCase :					List[str]    =				os.path.dirname(__A )
     lowerCamelCase :					List[Any]    =				tokenizer
     lowerCamelCase :					Optional[Any]    =				labels
     lowerCamelCase :					Any    =				len(__A )
     lowerCamelCase :					Union[str, Any]    =				max_seq_length

     lowerCamelCase :					Union[str, Any]    =				transforms






   def __len__(		self ):






     """simple docstring"""





     return len(self.data )






   def __getitem__(		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]						,				add_special_tokens=__A ) )
     lowerCamelCase    ,	lowerCamelCase    ,	lowerCamelCase :					List[Any]    =				sentence[0], sentence[1:-1], sentence[-1]
     lowerCamelCase :					str    =				sentence[: self.max_seq_length]

     lowerCamelCase :					Any    =				torch.zeros(self.n_classes )
     lowerCamelCase :					List[str]    =				1

     lowerCamelCase :					List[Any]    =				Image.open(os.path.join(self.data_dir						,				self.data[index]["img"] ) ).convert("RGB" )
     lowerCamelCase :					List[str]    =				self.transforms(__A )

     return {
         "image_start_token": start_token,
         "image_end_token": end_token,
         "sentence": sentence,
         "image": image,
         "label": label,
     }






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					str    =				Counter()
     for row in self.data:
       label_freqs.update(row["label"] )
     return label_freqs






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Any    =				[len(row["sentence"]	) for row in batch]
  lowerCamelCase    ,	lowerCamelCase :					Optional[Any]    =				len(SCREAMING_SNAKE_CASE_	), max(SCREAMING_SNAKE_CASE_	)

  lowerCamelCase :					List[str]    =				torch.zeros(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						dtype=torch.long	)
  lowerCamelCase :					List[str]    =				torch.zeros(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						dtype=torch.long	)

  for i_batch, (input_row, length) in enumerate(zip(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)	):
    lowerCamelCase :					Optional[Any]    =				input_row["sentence"]
    lowerCamelCase :					int    =				1

  lowerCamelCase :					Dict    =				torch.stack([row["image"] for row in batch]	)
  lowerCamelCase :					str    =				torch.stack([row["label"] for row in batch]	)
  lowerCamelCase :					Union[str, Any]    =				torch.stack([row["image_start_token"] for row in batch]	)
  lowerCamelCase :					Any    =				torch.stack([row["image_end_token"] for row in batch]	)

  return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor






def 		lowercase_(  ):


  '''simple docstring'''

  return [
      "Crime",
      "Drama",
      "Thriller",
      "Action",
      "Comedy",
      "Romance",
      "Documentary",
      "Short",
      "Mystery",
      "History",
      "Family",
      "Adventure",
      "Fantasy",
      "Sci-Fi",
      "Western",
      "Horror",
      "Sport",
      "War",
      "Music",
      "Musical",
      "Animation",
      "Biography",
      "Film-Noir",
  ]







def 		lowercase_(  ):


  '''simple docstring'''

  return transforms.Compose(
      [
          transforms.Resize(256	),
          transforms.CenterCrop(224	),
          transforms.ToTensor(),
          transforms.Normalize(
              mean=[0.46777044, 0.44531429, 0.40661017]      ,						std=[0.12221994, 0.12145835, 0.14380469]      ,						),
      ]	)







# 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 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




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

   __A :      Any     		=		["audio"]
   __A :      List[str]     		=		["text"]






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     return self.pre_processor(__A						,				return_tensors="pt" ).input_features






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     return self.model.generate(inputs=__A )






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





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



