




import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor






def        __UpperCAmelCase    (       UpperCAmelCase       )->       List[str]:



			"""simple docstring"""







			if "cls_token" in name:
						lowercase							=		name.replace('''cls_token''',		'''vit.embeddings.cls_token'''       )
			if "mask_token" in name:
						lowercase							=		name.replace('''mask_token''',		'''decoder.mask_token'''       )
			if "decoder_pos_embed" in name:
						lowercase							=		name.replace('''decoder_pos_embed''',		'''decoder.decoder_pos_embed'''       )
			if "pos_embed" in name and "decoder" not in name:
						lowercase							=		name.replace('''pos_embed''',		'''vit.embeddings.position_embeddings'''       )
			if "patch_embed.proj" in name:
						lowercase							=		name.replace('''patch_embed.proj''',		'''vit.embeddings.patch_embeddings.projection'''       )
			if "patch_embed.norm" in name:
						lowercase							=		name.replace('''patch_embed.norm''',		'''vit.embeddings.norm'''       )
			if "decoder_blocks" in name:
						lowercase							=		name.replace('''decoder_blocks''',		'''decoder.decoder_layers'''       )
			if "blocks" in name:
						lowercase							=		name.replace('''blocks''',		'''vit.encoder.layer'''       )
			if "attn.proj" in name:
						lowercase							=		name.replace('''attn.proj''',		'''attention.output.dense'''       )
			if "attn" in name:
						lowercase							=		name.replace('''attn''',		'''attention.self'''       )
			if "norm1" in name:
						lowercase							=		name.replace('''norm1''',		'''layernorm_before'''       )
			if "norm2" in name:
						lowercase							=		name.replace('''norm2''',		'''layernorm_after'''       )
			if "mlp.fc1" in name:
						lowercase							=		name.replace('''mlp.fc1''',		'''intermediate.dense'''       )
			if "mlp.fc2" in name:
						lowercase							=		name.replace('''mlp.fc2''',		'''output.dense'''       )
			if "decoder_embed" in name:
						lowercase							=		name.replace('''decoder_embed''',		'''decoder.decoder_embed'''       )
			if "decoder_norm" in name:
						lowercase							=		name.replace('''decoder_norm''',		'''decoder.decoder_norm'''       )
			if "decoder_pred" in name:
						lowercase							=		name.replace('''decoder_pred''',		'''decoder.decoder_pred'''       )
			if "norm.weight" in name and "decoder" not in name:
						lowercase							=		name.replace('''norm.weight''',		'''vit.layernorm.weight'''       )
			if "norm.bias" in name and "decoder" not in name:
						lowercase							=		name.replace('''norm.bias''',		'''vit.layernorm.bias'''       )

			return name






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       Union[str, Any]:



			"""simple docstring"""







			for key in orig_state_dict.copy().keys():
						lowercase							=		orig_state_dict.pop(UpperCAmelCase       )

						if "qkv" in key:
									lowercase							=		key.split('''.'''       )
									lowercase							=		int(key_split[1]       )
									if "decoder_blocks" in key:
												lowercase							=		config.decoder_hidden_size
												lowercase							=		'''decoder.decoder_layers.'''
												if "weight" in key:
															lowercase							=		val[:dim, :]
															lowercase							=		val[dim : dim * 2, :]
															lowercase							=		val[-dim:, :]
												elif "bias" in key:
															lowercase							=		val[:dim]
															lowercase							=		val[dim : dim * 2]
															lowercase							=		val[-dim:]
									else:
												lowercase							=		config.hidden_size
												lowercase							=		'''vit.encoder.layer.'''
												if "weight" in key:
															lowercase							=		val[:dim, :]
															lowercase							=		val[dim : dim * 2, :]
															lowercase							=		val[-dim:, :]
												elif "bias" in key:
															lowercase							=		val[:dim]
															lowercase							=		val[dim : dim * 2]
															lowercase							=		val[-dim:]

						else:
									lowercase							=		val

			return orig_state_dict






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       List[Any]:



			"""simple docstring"""







			lowercase							=		ViTMAEConfig()
			if "large" in checkpoint_url:
						lowercase							=		1024
						lowercase							=		4096
						lowercase							=		24
						lowercase							=		16
			elif "huge" in checkpoint_url:
						lowercase							=		14
						lowercase							=		1280
						lowercase							=		5120
						lowercase							=		32
						lowercase							=		16

			lowercase							=		ViTMAEForPreTraining(UpperCAmelCase       )

			lowercase							=		torch.hub.load_state_dict_from_url(UpperCAmelCase,		map_location='''cpu'''       )['''model''']

			lowercase							=		ViTMAEImageProcessor(size=config.image_size       )

			lowercase							=		convert_state_dict(UpperCAmelCase,		UpperCAmelCase       )

			model.load_state_dict(UpperCAmelCase       )
			model.eval()

			lowercase							=		'''https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'''

			lowercase							=		Image.open(requests.get(UpperCAmelCase,		stream=UpperCAmelCase       ).raw       )
			lowercase							=		ViTMAEImageProcessor(size=config.image_size       )
			lowercase							=		image_processor(images=UpperCAmelCase,		return_tensors='''pt'''       )

			# forward pass
			torch.manual_seed(2       )
			lowercase							=		model(**UpperCAmelCase       )
			lowercase							=		outputs.logits

			if "large" in checkpoint_url:
						lowercase							=		torch.tensor(
						    [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]]       )
			elif "huge" in checkpoint_url:
						lowercase							=		torch.tensor(
						    [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]       )
			else:
						lowercase							=		torch.tensor(
						    [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]]       )

			# verify logits
			assert torch.allclose(logits[0, :3, :3],		UpperCAmelCase,		atol=1e-4       )

			print(f'Saving model 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_      =      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--checkpoint_url",
						    default="https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth",
						    type=str,
						    help="URL of the checkpoint 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_      =      parser.parse_args()
						convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






import argparse
import hashlib
import os
import urllib
import warnings

import torch
from torch import nn
from tqdm import tqdm

from transformers import WhisperConfig, WhisperForConditionalGeneration


A_      =      {
    "tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
    "tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
    "base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
    "base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
    "small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
    "small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
    "medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
    "medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
    "large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt",
    "large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
}






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Dict:



			"""simple docstring"""







			lowercase							=		['''layers''', '''blocks''']
			for k in ignore_keys:
						state_dict.pop(UpperCAmelCase,		UpperCAmelCase       )


A_      =      {
    "blocks": "layers",
    "mlp.0": "fc1",
    "mlp.2": "fc2",
    "mlp_ln": "final_layer_norm",
    ".attn.query": ".self_attn.q_proj",
    ".attn.key": ".self_attn.k_proj",
    ".attn.value": ".self_attn.v_proj",
    ".attn_ln": ".self_attn_layer_norm",
    ".attn.out": ".self_attn.out_proj",
    ".cross_attn.query": ".encoder_attn.q_proj",
    ".cross_attn.key": ".encoder_attn.k_proj",
    ".cross_attn.value": ".encoder_attn.v_proj",
    ".cross_attn_ln": ".encoder_attn_layer_norm",
    ".cross_attn.out": ".encoder_attn.out_proj",
    "decoder.ln.": "decoder.layer_norm.",
    "encoder.ln.": "encoder.layer_norm.",
    "token_embedding": "embed_tokens",
    "encoder.positional_embedding": "encoder.embed_positions.weight",
    "decoder.positional_embedding": "decoder.embed_positions.weight",
    "ln_post": "layer_norm",
}






def        __UpperCAmelCase    (       UpperCAmelCase       )->       List[str]:



			"""simple docstring"""







			lowercase							=		list(s_dict.keys()       )
			for key in keys:
						lowercase							=		key
						for k, v in WHISPER_MAPPING.items():
									if k in key:
												lowercase							=		new_key.replace(UpperCAmelCase,		UpperCAmelCase       )

						print(f'{key} -> {new_key}'       )

						lowercase							=		s_dict.pop(UpperCAmelCase       )
			return s_dict






def        __UpperCAmelCase    (       UpperCAmelCase       )->       str:



			"""simple docstring"""







			lowercase		,lowercase							=		emb.weight.shape
			lowercase							=		nn.Linear(UpperCAmelCase,		UpperCAmelCase,		bias=UpperCAmelCase       )
			lowercase							=		emb.weight.data
			return lin_layer






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       bytes:



			"""simple docstring"""







			os.makedirs(UpperCAmelCase,		exist_ok=UpperCAmelCase       )
			lowercase							=		os.path.basename(UpperCAmelCase       )

			lowercase							=		url.split('''/'''       )[-2]
			lowercase							=		os.path.join(UpperCAmelCase,		UpperCAmelCase       )

			if os.path.exists(UpperCAmelCase       ) and not os.path.isfile(UpperCAmelCase       ):
						raise RuntimeError(f'{download_target} exists and is not a regular file'       )

			if os.path.isfile(UpperCAmelCase       ):
						lowercase							=		open(UpperCAmelCase,		'''rb'''       ).read()
						if hashlib.shaaaa(UpperCAmelCase       ).hexdigest() == expected_shaaaa:
									return model_bytes
						else:
									warnings.warn(f'{download_target} exists, but the SHA256 checksum does not match; re-downloading the file'       )

			with urllib.request.urlopen(UpperCAmelCase       ) as source, open(UpperCAmelCase,		'''wb'''       ) as output:
						with tqdm(
						    total=int(source.info().get('''Content-Length'''       )       ),		ncols=80,		unit='''iB''',		unit_scale=UpperCAmelCase,		unit_divisor=1024       ) as loop:
									while True:
												lowercase							=		source.read(8192       )
												if not buffer:
															break

												output.write(UpperCAmelCase       )
												loop.update(len(UpperCAmelCase       )       )

			lowercase							=		open(UpperCAmelCase,		'''rb'''       ).read()
			if hashlib.shaaaa(UpperCAmelCase       ).hexdigest() != expected_shaaaa:
						raise RuntimeError(
						    '''Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.'''       )

			return model_bytes






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       Union[str, Any]:



			"""simple docstring"""







			if ".pt" not in checkpoint_path:
						lowercase							=		_download(_MODELS[checkpoint_path]       )
			else:
						lowercase							=		torch.load(UpperCAmelCase,		map_location='''cpu'''       )
			lowercase							=		original_checkpoint['''dims''']
			lowercase							=		original_checkpoint['''model_state_dict''']
			lowercase							=		state_dict['''decoder.token_embedding.weight''']
			remove_ignore_keys_(UpperCAmelCase       )
			rename_keys(UpperCAmelCase       )
			lowercase							=		True
			lowercase							=		state_dict['''decoder.layers.0.fc1.weight'''].shape[0]

			lowercase							=		WhisperConfig(
			    vocab_size=dimensions['''n_vocab'''],		encoder_ffn_dim=UpperCAmelCase,		decoder_ffn_dim=UpperCAmelCase,		num_mel_bins=dimensions['''n_mels'''],		d_model=dimensions['''n_audio_state'''],		max_target_positions=dimensions['''n_text_ctx'''],		encoder_layers=dimensions['''n_audio_layer'''],		encoder_attention_heads=dimensions['''n_audio_head'''],		decoder_layers=dimensions['''n_text_layer'''],		decoder_attention_heads=dimensions['''n_text_state'''],		max_source_positions=dimensions['''n_audio_ctx'''],		)

			lowercase							=		WhisperForConditionalGeneration(UpperCAmelCase       )
			lowercase		,lowercase							=		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:
						lowercase							=		make_linear_from_emb(model.model.decoder.embed_tokens       )
			else:
						lowercase							=		proj_out_weights

			model.save_pretrained(UpperCAmelCase       )


if __name__ == "__main__":
						A_      =      argparse.ArgumentParser()
						# # Required parameters
						parser.add_argument("--checkpoint_path", type=str, help="Patht to the downloaded checkpoints")
						parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
						A_      =      parser.parse_args()

						convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)





