




'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
a  :				List[Any]									=  logging.get_logger(__name__)


def 		__magic_name__    (	__UpperCAmelCase	)		->					Optional[int]:

					'''simple docstring'''

					snake_case_									=				'''huggingface/label-files'''
					snake_case_									=				'''imagenet-1k-id2label.json'''
					snake_case_									=				json.load(open(hf_hub_download(__UpperCAmelCase,      __UpperCAmelCase,      repo_type='''dataset'''	),      '''r'''	)	)
					snake_case_									=				{int(__UpperCAmelCase	): v for k, v in idalabel.items()}
					snake_case_									=				{v: k for k, v in idalabel.items()}

					snake_case_									=				'''std_conv''' if '''bit''' in model_name else False

					# note that when using BiT as backbone for ViT-hybrid checkpoints,
					# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
					# config.conv_layer = "std_conv_same"
					snake_case_									=				BitConfig(
					    conv_layer=__UpperCAmelCase,      num_labels=1000,      idalabel=__UpperCAmelCase,      labelaid=__UpperCAmelCase,      )

					return config


def 		__magic_name__    (	__UpperCAmelCase	)		->					Optional[int]:

					'''simple docstring'''

					if "stem.conv" in name:
										snake_case_									=				name.replace('''stem.conv''',      '''bit.embedder.convolution'''	)
					if "blocks" in name:
										snake_case_									=				name.replace('''blocks''',      '''layers'''	)
					if "head.fc" in name:
										snake_case_									=				name.replace('''head.fc''',      '''classifier.1'''	)
					if name.startswith('''norm'''	):
										snake_case_									=				'''bit.''' + name
					if "bit" not in name and "classifier" not in name:
										snake_case_									=				'''bit.encoder.''' + name

					return name


def 		__magic_name__    (	)		->					Tuple:

					'''simple docstring'''

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


@torch.no_grad()
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase=False	)		->					str:

					'''simple docstring'''

					snake_case_									=				get_config(__UpperCAmelCase	)

					# load original model from timm
					snake_case_									=				create_model(__UpperCAmelCase,      pretrained=__UpperCAmelCase	)
					timm_model.eval()

					# load state_dict of original model
					snake_case_									=				timm_model.state_dict()
					for key in state_dict.copy().keys():
										snake_case_									=				state_dict.pop(__UpperCAmelCase	)
										snake_case_									=				val.squeeze() if '''head''' in key else val

					# load HuggingFace model
					snake_case_									=				BitForImageClassification(__UpperCAmelCase	)
					model.eval()
					model.load_state_dict(__UpperCAmelCase	)

					# create image processor
					snake_case_									=				create_transform(**resolve_data_config({},      model=__UpperCAmelCase	)	)
					snake_case_									=				transform.transforms

					snake_case_									=				{
					    '''bilinear''': PILImageResampling.BILINEAR,
					    '''bicubic''': PILImageResampling.BICUBIC,
					    '''nearest''': PILImageResampling.NEAREST,
					}

					snake_case_									=				BitImageProcessor(
					    do_resize=__UpperCAmelCase,      size={'''shortest_edge''': timm_transforms[0].size},      resample=pillow_resamplings[timm_transforms[0].interpolation.value],      do_center_crop=__UpperCAmelCase,      crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]},      do_normalize=__UpperCAmelCase,      image_mean=timm_transforms[-1].mean.tolist(),      image_std=timm_transforms[-1].std.tolist(),      )

					snake_case_									=				prepare_img()
					snake_case_									=				transform(__UpperCAmelCase	).unsqueeze(0	)
					snake_case_									=				processor(__UpperCAmelCase,      return_tensors='''pt'''	).pixel_values

					# verify pixel values
					assert torch.allclose(__UpperCAmelCase,      __UpperCAmelCase	)

					# verify logits
					with torch.no_grad():
										snake_case_									=				model(__UpperCAmelCase	)
										snake_case_									=				outputs.logits

					print('''Logits:''',      logits[0, :3]	)
					print('''Predicted class:''',      model.config.idalabel[logits.argmax(-1	).item()]	)
					snake_case_									=				timm_model(__UpperCAmelCase	)
					assert timm_logits.shape == outputs.logits.shape
					assert torch.allclose(__UpperCAmelCase,      outputs.logits,      atol=1e-3	)
					print('''Looks ok!'''	)

					if pytorch_dump_folder_path is not None:
										Path(__UpperCAmelCase	).mkdir(exist_ok=__UpperCAmelCase	)
										print(F"Saving model {model_name} and processor to {pytorch_dump_folder_path}"	)
										model.save_pretrained(__UpperCAmelCase	)
										processor.save_pretrained(__UpperCAmelCase	)

					if push_to_hub:
										print(F"Pushing model {model_name} and processor to the hub"	)
										model.push_to_hub(F"ybelkada/{model_name}"	)
										processor.push_to_hub(F"ybelkada/{model_name}"	)


if __name__ == "__main__":
							a  :				Optional[Any]									=  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--model_name',
							    default='resnetv2_50x1_bitm',
							    type=str,
							    help='Name of the BiT 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.'
							)
							parser.add_argument(
							    '--push_to_hub',
							    action='store_true',
							    help='Whether to push the model to the hub.',
							)

							a  :				Any									=  parser.parse_args()
							convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def 		__magic_name__    (	__UpperCAmelCase	)		->					str:

					'''simple docstring'''

					snake_case_									=				[
					    '''encoder.version''',
					    '''decoder.version''',
					    '''model.encoder.version''',
					    '''model.decoder.version''',
					    '''decoder.output_projection.weight''',
					    '''_float_tensor''',
					    '''encoder.embed_positions._float_tensor''',
					    '''decoder.embed_positions._float_tensor''',
					]
					for k in ignore_keys:
										state_dict.pop(__UpperCAmelCase,      __UpperCAmelCase	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					Optional[int]:

					'''simple docstring'''

					snake_case_									=				list(s_dict.keys()	)
					for key in keys:
										if "transformer_layers" in key:
															snake_case_									=				s_dict.pop(__UpperCAmelCase	)
										elif "subsample" in key:
															snake_case_									=				s_dict.pop(__UpperCAmelCase	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					int:

					'''simple docstring'''

					snake_case_			,snake_case_									=				emb.weight.shape
					snake_case_									=				nn.Linear(__UpperCAmelCase,      __UpperCAmelCase,      bias=__UpperCAmelCase	)
					snake_case_									=				emb.weight.data
					return lin_layer


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Dict:

					'''simple docstring'''

					snake_case_									=				torch.load(__UpperCAmelCase,      map_location='''cpu'''	)
					snake_case_									=				mam_aaa['''args''']
					snake_case_									=				mam_aaa['''model''']
					snake_case_									=				state_dict['''decoder.output_projection.weight''']

					remove_ignore_keys_(__UpperCAmelCase	)
					rename_keys(__UpperCAmelCase	)

					snake_case_									=				state_dict['''decoder.embed_tokens.weight'''].shape[0]

					snake_case_									=				args.share_decoder_input_output_embed

					snake_case_									=				[int(__UpperCAmelCase	) for i in args.conv_kernel_sizes.split(''','''	)]
					snake_case_									=				SpeechaTextConfig(
					    vocab_size=__UpperCAmelCase,      max_source_positions=args.max_source_positions,      max_target_positions=args.max_target_positions,      encoder_layers=args.encoder_layers,      decoder_layers=args.decoder_layers,      encoder_attention_heads=args.encoder_attention_heads,      decoder_attention_heads=args.decoder_attention_heads,      encoder_ffn_dim=args.encoder_ffn_embed_dim,      decoder_ffn_dim=args.decoder_ffn_embed_dim,      d_model=args.encoder_embed_dim,      dropout=args.dropout,      attention_dropout=args.attention_dropout,      activation_dropout=args.activation_dropout,      activation_function='''relu''',      num_conv_layers=len(__UpperCAmelCase	),      conv_channels=args.conv_channels,      conv_kernel_sizes=__UpperCAmelCase,      input_feat_per_channel=args.input_feat_per_channel,      input_channels=args.input_channels,      tie_word_embeddings=__UpperCAmelCase,      num_beams=5,      max_length=200,      use_cache=__UpperCAmelCase,      decoder_start_token_id=2,      early_stopping=__UpperCAmelCase,      )

					snake_case_									=				SpeechaTextForConditionalGeneration(__UpperCAmelCase	)
					snake_case_			,snake_case_									=				model.model.load_state_dict(__UpperCAmelCase,      strict=__UpperCAmelCase	)
					if len(__UpperCAmelCase	) > 0 and not set(__UpperCAmelCase	) <= {
					    "encoder.embed_positions.weights",
					    "decoder.embed_positions.weights",
					}:
										raise ValueError(
										    '''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'''
										    F" but all the following weights are missing {missing}"	)

					if tie_embeds:
										snake_case_									=				make_linear_from_emb(model.model.decoder.embed_tokens	)
					else:
										snake_case_									=				lm_head_weights

					model.save_pretrained(__UpperCAmelCase	)


if __name__ == "__main__":
							a  :				Any									=  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
							parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
							a  :				List[Any]									=  parser.parse_args()
							convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)

