
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()
lowercase						:      Any									= logging.get_logger(__name__)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str		,						_lowerCamelCase				:    Dict=False)      ->					str:






							'''simple docstring'''





							__UpperCamelCase			:     Union[str, Any]         =      []
							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"
														__UpperCamelCase			:     List[Any]         =      [(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 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict		,						_lowerCamelCase				:    Optional[Any]		,						_lowerCamelCase				:    Tuple=False)      ->					Optional[int]:






							'''simple docstring'''





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




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[int]		,						_lowerCamelCase				:    List[str]		,						_lowerCamelCase				:    List[str])      ->					str:






							'''simple docstring'''





							__UpperCamelCase			:     Union[str, Any]         =      dct.pop(a_)
							__UpperCamelCase			:     str         =      val




def 			_SCREAMING_SNAKE_CASE      (	)      ->					List[str]:






							'''simple docstring'''





							__UpperCamelCase			:     Optional[Any]         =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
							__UpperCamelCase			:     str         =      Image.open(requests.get(a_		,						stream=a_).raw)
							return im




@torch.no_grad()
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    int)      ->					List[Any]:






							'''simple docstring'''





							__UpperCamelCase			:     Union[str, Any]         =      DeiTConfig()
							# all deit models have fine-tuned heads
							__UpperCamelCase			:     Tuple         =      False
							# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
							__UpperCamelCase			:     List[Any]         =      1_000
							__UpperCamelCase			:     Tuple         =      '''huggingface/label-files'''
							__UpperCamelCase			:     Union[str, Any]         =      '''imagenet-1k-id2label.json'''
							__UpperCamelCase			:     Optional[Any]         =      json.load(open(hf_hub_download(a_		,						a_		,						repo_type="dataset")		,						"r"))
							__UpperCamelCase			:     Dict         =      {int(a_): v for k, v in idalabel.items()}
							__UpperCamelCase			:     Optional[Any]         =      idalabel
							__UpperCamelCase			:     Optional[int]         =      {v: k for k, v in idalabel.items()}
							__UpperCamelCase			:     List[str]         =      int(deit_name[-6:-4])
							__UpperCamelCase			:     Optional[Any]         =      int(deit_name[-3:])
							# size of the architecture
							if deit_name[9:].startswith("tiny"):
														__UpperCamelCase			:     Optional[Any]         =      192
														__UpperCamelCase			:     Optional[Any]         =      768
														__UpperCamelCase			:     Optional[Any]         =      12
														__UpperCamelCase			:     Dict         =      3
							elif deit_name[9:].startswith("small"):
														__UpperCamelCase			:     List[str]         =      384
														__UpperCamelCase			:     Tuple         =      1_536
														__UpperCamelCase			:     List[str]         =      12
														__UpperCamelCase			:     Optional[Any]         =      6
							if deit_name[9:].startswith("base"):
														pass
							elif deit_name[4:].startswith("large"):
														__UpperCamelCase			:     int         =      1_024
														__UpperCamelCase			:     List[Any]         =      4_096
														__UpperCamelCase			:     Optional[int]         =      24
														__UpperCamelCase			:     Optional[int]         =      16

							# load original model from timm
							__UpperCamelCase			:     Any         =      timm.create_model(a_		,						pretrained=a_)
							timm_model.eval()

							# load state_dict of original model, remove and rename some keys
							__UpperCamelCase			:     str         =      timm_model.state_dict()
							__UpperCamelCase			:     Optional[Any]         =      create_rename_keys(a_		,						a_)
							for src, dest in rename_keys:
														rename_key(a_		,						a_		,						a_)
							read_in_q_k_v(a_		,						a_		,						a_)

							# load HuggingFace model
							__UpperCamelCase			:     int         =      DeiTForImageClassificationWithTeacher(a_).eval()
							model.load_state_dict(a_)

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

							__UpperCamelCase			:     Dict         =      timm_model(a_)
							assert timm_logits.shape == outputs.logits.shape
							assert torch.allclose(a_		,						outputs.logits		,						atol=1e-3)

							Path(a_).mkdir(exist_ok=a_)
							print(F'Saving model {deit_name} to {pytorch_dump_folder_path}')
							model.save_pretrained(a_)
							print(F'Saving image processor to {pytorch_dump_folder_path}')
							image_processor.save_pretrained(a_)


if __name__ == "__main__":
		lowercase						:      List[Any]									= 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.'
		)

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

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase						:      Dict									= logging.get_logger(__name__)

lowercase						:      Optional[Any]									= {
    'facebook/s2t-wav2vec2-large-en-de': (
        'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  'speech_to_text_2'
	_A			=  ['past_key_values']
	_A			=  {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}







	def __init__(      self	:Dict  ,     a	:Tuple=1_0_0_0_0  ,     a	:Optional[int]=6  ,     a	:List[str]=2_0_4_8  ,     a	:Tuple=4  ,     a	:List[Any]=0.0  ,     a	:str=True  ,     a	:Any="relu"  ,     a	:Any=2_5_6  ,     a	:Optional[int]=0.1  ,     a	:Any=0.0  ,     a	:int=0.0  ,     a	:int=0.02  ,     a	:List[Any]=2  ,     a	:Tuple=True  ,     a	:str=1  ,     a	:Optional[int]=0  ,     a	:List[Any]=2  ,     a	:Any=1_0_2_4  ,     **a	:str  ,     )	->				int:
								__UpperCamelCase			:     List[str]         =      vocab_size
								__UpperCamelCase			:     int         =      d_model
								__UpperCamelCase			:     Optional[int]         =      decoder_ffn_dim
								__UpperCamelCase			:     Any         =      decoder_layers
								__UpperCamelCase			:     Any         =      decoder_attention_heads
								__UpperCamelCase			:     Tuple         =      dropout
								__UpperCamelCase			:     Any         =      attention_dropout
								__UpperCamelCase			:     Any         =      activation_dropout
								__UpperCamelCase			:     Dict         =      activation_function
								__UpperCamelCase			:     int         =      init_std
								__UpperCamelCase			:     List[str]         =      decoder_layerdrop
								__UpperCamelCase			:     Optional[Any]         =      use_cache
								__UpperCamelCase			:     int         =      decoder_layers
								__UpperCamelCase			:     Union[str, Any]         =      scale_embedding  # scale factor will be sqrt(d_model) if True
								__UpperCamelCase			:     Dict         =      max_target_positions

								super().__init__(
								    pad_token_id=a  ,     bos_token_id=a  ,     eos_token_id=a  ,     decoder_start_token_id=a  ,     **a  ,     )