

import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
a_    :						Optional[int]    		=      logging.get_logger(__name__)


def        __lowerCAmelCase    (       _UpperCamelCase		:  Optional[int]		,							_UpperCamelCase		:  int=False	)		->							Any:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			[]
 for i in range(config.num_hidden_layers	):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""deit.encoder.layer.{i}.layernorm_before.weight""")	)
  rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""deit.encoder.layer.{i}.layernorm_before.bias""")	)
  rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""deit.encoder.layer.{i}.attention.output.dense.weight""")	)
  rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""deit.encoder.layer.{i}.attention.output.dense.bias""")	)
  rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""deit.encoder.layer.{i}.layernorm_after.weight""")	)
  rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""deit.encoder.layer.{i}.layernorm_after.bias""")	)
  rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""deit.encoder.layer.{i}.intermediate.dense.weight""")	)
  rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""deit.encoder.layer.{i}.intermediate.dense.bias""")	)
  rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""deit.encoder.layer.{i}.output.dense.weight""")	)
  rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""deit.encoder.layer.{i}.output.dense.bias""")	)

 # projection layer + position embeddings
 rename_keys.extend(
     [
         ('cls_token', 'deit.embeddings.cls_token'),
         ('dist_token', 'deit.embeddings.distillation_token'),
         ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
         ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
         ('pos_embed', 'deit.embeddings.position_embeddings'),
     ]	)

 if base_model:
  # layernorm + pooler
  rename_keys.extend(
      [
          ('norm.weight', 'layernorm.weight'),
          ('norm.bias', 'layernorm.bias'),
          ('pre_logits.fc.weight', 'pooler.dense.weight'),
          ('pre_logits.fc.bias', 'pooler.dense.bias'),
      ]	)

  # if just the base model, we should remove "deit" from all keys that start with "deit"
  SCREAMING_SNAKE_CASE								=			[(pair[0], pair[1][4:]) if pair[1].startswith('deit'	) else pair for pair in rename_keys]
 else:
  # layernorm + classification heads
  rename_keys.extend(
      [
          ('norm.weight', 'deit.layernorm.weight'),
          ('norm.bias', 'deit.layernorm.bias'),
          ('head.weight', 'cls_classifier.weight'),
          ('head.bias', 'cls_classifier.bias'),
          ('head_dist.weight', 'distillation_classifier.weight'),
          ('head_dist.bias', 'distillation_classifier.bias'),
      ]	)

 return rename_keys


def        __lowerCAmelCase    (       _UpperCamelCase		:  Dict		,							_UpperCamelCase		:  str		,							_UpperCamelCase		:  Tuple=False	)		->							Optional[int]:
 '''simple docstring'''


 for i in range(config.num_hidden_layers	):
  if base_model:
   SCREAMING_SNAKE_CASE								=			''
  else:
   SCREAMING_SNAKE_CASE								=			'deit.'
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  SCREAMING_SNAKE_CASE								=			state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""	)
  SCREAMING_SNAKE_CASE								=			state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""	)
  # next, add query, keys and values (in that order) to the state dict
  SCREAMING_SNAKE_CASE								=			in_proj_weight[
      : config.hidden_size, :
  ]
  SCREAMING_SNAKE_CASE								=			in_proj_bias[: config.hidden_size]
  SCREAMING_SNAKE_CASE								=			in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  SCREAMING_SNAKE_CASE								=			in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  SCREAMING_SNAKE_CASE								=			in_proj_weight[
      -config.hidden_size :, :
  ]
  SCREAMING_SNAKE_CASE								=			in_proj_bias[-config.hidden_size :]


def        __lowerCAmelCase    (       _UpperCamelCase		:  Tuple		,							_UpperCamelCase		:  Optional[int]		,							_UpperCamelCase		:  Union[str, Any]	)		->							List[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			dct.pop(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			val


def        __lowerCAmelCase    (       )		->							Tuple:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			'http://images.cocodataset.org/val2017/000000039769.jpg'
 SCREAMING_SNAKE_CASE								=			Image.open(requests.get(_UpperCamelCase		,							stream=_UpperCamelCase	).raw	)
 return im


@torch.no_grad()
def        __lowerCAmelCase    (       _UpperCamelCase		:  Dict		,							_UpperCamelCase		:  Union[str, Any]	)		->							Any:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			DeiTConfig()
 # all deit models have fine-tuned heads
 SCREAMING_SNAKE_CASE								=			False
 # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
 SCREAMING_SNAKE_CASE								=			10_00
 SCREAMING_SNAKE_CASE								=			'huggingface/label-files'
 SCREAMING_SNAKE_CASE								=			'imagenet-1k-id2label.json'
 SCREAMING_SNAKE_CASE								=			json.load(open(hf_hub_download(_UpperCamelCase		,							_UpperCamelCase		,							repo_type='dataset'	)		,							'r'	)	)
 SCREAMING_SNAKE_CASE								=			{int(_UpperCamelCase	): v for k, v in idalabel.items()}
 SCREAMING_SNAKE_CASE								=			idalabel
 SCREAMING_SNAKE_CASE								=			{v: k for k, v in idalabel.items()}
 SCREAMING_SNAKE_CASE								=			int(deit_name[-6:-4]	)
 SCREAMING_SNAKE_CASE								=			int(deit_name[-3:]	)
 # size of the architecture
 if deit_name[9:].startswith('tiny'	):
  SCREAMING_SNAKE_CASE								=			1_92
  SCREAMING_SNAKE_CASE								=			7_68
  SCREAMING_SNAKE_CASE								=			12
  SCREAMING_SNAKE_CASE								=			3
 elif deit_name[9:].startswith('small'	):
  SCREAMING_SNAKE_CASE								=			3_84
  SCREAMING_SNAKE_CASE								=			15_36
  SCREAMING_SNAKE_CASE								=			12
  SCREAMING_SNAKE_CASE								=			6
 if deit_name[9:].startswith('base'	):
  pass
 elif deit_name[4:].startswith('large'	):
  SCREAMING_SNAKE_CASE								=			10_24
  SCREAMING_SNAKE_CASE								=			40_96
  SCREAMING_SNAKE_CASE								=			24
  SCREAMING_SNAKE_CASE								=			16

 # load original model from timm
 SCREAMING_SNAKE_CASE								=			timm.create_model(_UpperCamelCase		,							pretrained=_UpperCamelCase	)
 timm_model.eval()

 # load state_dict of original model, remove and rename some keys
 SCREAMING_SNAKE_CASE								=			timm_model.state_dict()
 SCREAMING_SNAKE_CASE								=			create_rename_keys(_UpperCamelCase		,							_UpperCamelCase	)
 for src, dest in rename_keys:
  rename_key(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase	)
 read_in_q_k_v(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase	)

 # load HuggingFace model
 SCREAMING_SNAKE_CASE								=			DeiTForImageClassificationWithTeacher(_UpperCamelCase	).eval()
 model.load_state_dict(_UpperCamelCase	)

 # Check outputs on an image, prepared by DeiTImageProcessor
 SCREAMING_SNAKE_CASE								=			int(
     (2_56 / 2_24) * config.image_size	)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
 SCREAMING_SNAKE_CASE								=			DeiTImageProcessor(size=_UpperCamelCase		,							crop_size=config.image_size	)
 SCREAMING_SNAKE_CASE								=			image_processor(images=prepare_img()		,							return_tensors='pt'	)
 SCREAMING_SNAKE_CASE								=			encoding['pixel_values']
 SCREAMING_SNAKE_CASE								=			model(_UpperCamelCase	)

 SCREAMING_SNAKE_CASE								=			timm_model(_UpperCamelCase	)
 assert timm_logits.shape == outputs.logits.shape
 assert torch.allclose(_UpperCamelCase		,							outputs.logits		,							atol=1e-3	)

 Path(_UpperCamelCase	).mkdir(exist_ok=_UpperCamelCase	)
 print(f"""Saving model {deit_name} to {pytorch_dump_folder_path}"""	)
 model.save_pretrained(_UpperCamelCase	)
 print(f"""Saving image processor to {pytorch_dump_folder_path}"""	)
 image_processor.save_pretrained(_UpperCamelCase	)


if __name__ == "__main__":
      a_    :						str    		=      argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--deit_name",
          default="vit_deit_base_distilled_patch16_224",
          type=str,
          help="Name of the DeiT timm model you'd like to convert.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
      )

      a_    :						Optional[Any]    		=      parser.parse_args()
      convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)




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

    __UpperCamelCase         =["audio"]
    __UpperCamelCase         =["text"]

    def UpperCamelCase			(			self :      Dict							,    snake_case__ :      Tuple    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      Tuple    ):

     """simple docstring"""






     return self.model.generate(inputs=snake_case__    )




    def UpperCamelCase			(			self :      str							,    snake_case__ :      Union[str, Any]    ):

     """simple docstring"""






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

