
"""simple docstring"""

import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		lowerCamelCase					:      Dict          =   old_name

		if "patch_embed" in old_name:
				lowerCamelCase					:      Dict          =   old_name.split('.'      )

				if layer == "0":
						lowerCamelCase					:      int          =   old_name.replace('0',						'convolution1'      )
				elif layer == "1":
						lowerCamelCase					:      int          =   old_name.replace('1',						'batchnorm_before'      )
				elif layer == "3":
						lowerCamelCase					:      str          =   old_name.replace('3',						'convolution2'      )
				else:
						lowerCamelCase					:      str          =   old_name.replace('4',						'batchnorm_after'      )

		if "network" in old_name and re.search(r'\d\.\d',						a_      ):
				lowerCamelCase					:      Dict          =   r'\b\d{2}\b'
				if bool(re.search(a_,						a_      )      ):
						lowerCamelCase					:      str          =   re.search(r'\d\.\d\d.',						a_      ).group()
				else:
						lowerCamelCase					:      str          =   re.search(r'\d\.\d.',						a_      ).group()
				if int(match[0]      ) < 6:
						lowerCamelCase					:      Any          =   old_name.replace(a_,						''      )
						lowerCamelCase					:      Dict          =   trimmed_name.replace('network',						match[0] + '.meta4D_layers.blocks.' + match[2:-1]      )
						lowerCamelCase					:      List[str]          =   'intermediate_stages.' + trimmed_name
				else:
						lowerCamelCase					:      Union[str, Any]          =   old_name.replace(a_,						''      )
						if int(match[2]      ) < num_meta4D_last_stage:
								lowerCamelCase					:      Tuple          =   trimmed_name.replace('network',						'meta4D_layers.blocks.' + match[2]      )
						else:
								lowerCamelCase					:      List[Any]          =   str(int(match[2]      ) - num_meta4D_last_stage      )
								lowerCamelCase					:      Tuple          =   trimmed_name.replace('network',						'meta3D_layers.blocks.' + layer_index      )
								if "norm1" in old_name:
										lowerCamelCase					:      str          =   trimmed_name.replace('norm1',						'layernorm1'      )
								elif "norm2" in old_name:
										lowerCamelCase					:      Dict          =   trimmed_name.replace('norm2',						'layernorm2'      )
								elif "fc1" in old_name:
										lowerCamelCase					:      Tuple          =   trimmed_name.replace('fc1',						'linear_in'      )
								elif "fc2" in old_name:
										lowerCamelCase					:      int          =   trimmed_name.replace('fc2',						'linear_out'      )

						lowerCamelCase					:      List[Any]          =   'last_stage.' + trimmed_name

		elif "network" in old_name and re.search(r'.\d.',						a_      ):
				lowerCamelCase					:      Optional[int]          =   old_name.replace('network',						'intermediate_stages'      )

		if "fc" in new_name:
				lowerCamelCase					:      str          =   new_name.replace('fc',						'convolution'      )
		elif ("norm1" in new_name) and ("layernorm1" not in new_name):
				lowerCamelCase					:      List[str]          =   new_name.replace('norm1',						'batchnorm_before'      )
		elif ("norm2" in new_name) and ("layernorm2" not in new_name):
				lowerCamelCase					:      List[str]          =   new_name.replace('norm2',						'batchnorm_after'      )
		if "proj" in new_name:
				lowerCamelCase					:      int          =   new_name.replace('proj',						'projection'      )
		if "dist_head" in new_name:
				lowerCamelCase					:      Optional[int]          =   new_name.replace('dist_head',						'distillation_classifier'      )
		elif "head" in new_name:
				lowerCamelCase					:      str          =   new_name.replace('head',						'classifier'      )
		elif "patch_embed" in new_name:
				lowerCamelCase					:      str          =   'efficientformer.' + new_name
		elif new_name == "norm.weight" or new_name == "norm.bias":
				lowerCamelCase					:      Tuple          =   new_name.replace('norm',						'layernorm'      )
				lowerCamelCase					:      Tuple          =   'efficientformer.' + new_name
		else:
				lowerCamelCase					:      List[Any]          =   'efficientformer.encoder.' + new_name

		return new_name
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		for key in checkpoint.copy().keys():
				lowerCamelCase					:      Dict          =   checkpoint.pop(a_      )
				lowerCamelCase					:      str          =   val

		return checkpoint
def 		UpperCAmelCase (		):





		'''simple docstring'''


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

		return image
def 		UpperCAmelCase (		a_,						a_,						a_,						a_      ):





		'''simple docstring'''


		lowerCamelCase					:      Any          =   torch.load(a_,						map_location='cpu'      )['model']
		lowerCamelCase					:      int          =   EfficientFormerConfig.from_json_file(a_      )
		lowerCamelCase					:      int          =   EfficientFormerForImageClassificationWithTeacher(a_      )
		lowerCamelCase					:      List[Any]          =   '_'.join(checkpoint_path.split('/'      )[-1].split('.'      )[0].split('_'      )[:-1]      )

		lowerCamelCase					:      List[str]          =   config.depths[-1] - config.num_metaad_blocks + 1
		lowerCamelCase					:      Optional[int]          =   convert_torch_checkpoint(a_,						a_      )

		model.load_state_dict(a_      )
		model.eval()

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

		# prepare image
		lowerCamelCase					:      List[Any]          =   prepare_img()
		lowerCamelCase					:      Optional[Any]          =   256
		lowerCamelCase					:      Optional[Any]          =   224
		lowerCamelCase					:      List[str]          =   EfficientFormerImageProcessor(
		    size={'shortest_edge': image_size},						crop_size={'height': crop_size, 'width': crop_size},						resample=pillow_resamplings['bicubic'],						)
		lowerCamelCase					:      Any          =   processor(images=a_,						return_tensors='pt'      ).pixel_values

		# original processing pipeline
		lowerCamelCase					:      Optional[int]          =   Compose(
		    [
		        Resize(a_,						interpolation=pillow_resamplings['bicubic']      ),
		        CenterCrop(a_      ),
		        ToTensor(),
		        Normalize(a_,						a_      ),
		    ]      )
		lowerCamelCase					:      Tuple          =   image_transforms(a_      ).unsqueeze(0      )

		assert torch.allclose(a_,						a_      )

		lowerCamelCase					:      Tuple          =   model(a_      )
		lowerCamelCase					:      int          =   outputs.logits

		lowerCamelCase					:      int          =   (1, 1000)

		if "l1" in model_name:
				lowerCamelCase					:      Any          =   torch.Tensor(
				    [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8]      )
				assert torch.allclose(logits[0, :10],						a_,						atol=1E-3      )
				assert logits.shape == expected_shape
		elif "l3" in model_name:
				lowerCamelCase					:      Dict          =   torch.Tensor(
				    [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7]      )
				assert torch.allclose(logits[0, :10],						a_,						atol=1E-3      )
				assert logits.shape == expected_shape
		elif "l7" in model_name:
				lowerCamelCase					:      Optional[int]          =   torch.Tensor(
				    [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8]      )
				assert logits.shape == expected_shape
		else:
				raise ValueError(
				    F"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"""      )

		# Save Checkpoints
		Path(a_      ).mkdir(exist_ok=a_      )
		model.save_pretrained(a_      )
		print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""      )
		processor.save_pretrained(a_      )
		print(F"""Processor successfuly saved at {pytorch_dump_path}"""      )

		if push_to_hub:
				print('Pushing model to the hub...'      )

				model.push_to_hub(
				    repo_id=F"""Bearnardd/{pytorch_dump_path}""",						commit_message='Add model',						use_temp_dir=a_,						)
				processor.push_to_hub(
				    repo_id=F"""Bearnardd/{pytorch_dump_path}""",						commit_message='Add image processor',						use_temp_dir=a_,						)


if __name__ == "__main__":
					_A						=			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path',
					    default=None,
					    type=str,
					    required=True,
					    help='Path to EfficientFormer pytorch checkpoint.',
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for EfficientFormer model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)

					parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
					parser.add_argument(
					    '--no-push_to_hub',
					    dest='push_to_hub',
					    action='store_false',
					    help='Do not push model and image processor to the hub',
					)
					parser.set_defaults(push_to_hub=True)

					_A						=			parser.parse_args()
					convert_efficientformer_checkpoint(
					    checkpoint_path=args.pytorch_model_path,
					    efficientformer_config_file=args.config_file,
					    pytorch_dump_path=args.pytorch_dump_path,
					    push_to_hub=args.push_to_hub,
					)







"""simple docstring"""

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 (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      str          =   ['layers', 'blocks']
		for k in ignore_keys:
				state_dict.pop(a_,						a_      )


_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 (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      Tuple          =   list(s_dict.keys()      )
		for key in keys:
				lowerCamelCase					:      List[Any]          =   key
				for k, v in WHISPER_MAPPING.items():
						if k in key:
								lowerCamelCase					:      Optional[int]          =   new_key.replace(a_,						a_      )

				print(F"""{key} -> {new_key}"""      )

				lowerCamelCase					:      Any          =   s_dict.pop(a_      )
		return s_dict
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase     ,   lowerCamelCase					:      int          =   emb.weight.shape
		lowerCamelCase					:      Dict          =   nn.Linear(a_,						a_,						bias=a_      )
		lowerCamelCase					:      Union[str, Any]          =   emb.weight.data
		return lin_layer
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		os.makedirs(a_,						exist_ok=a_      )
		lowerCamelCase					:      Union[str, Any]          =   os.path.basename(a_      )

		lowerCamelCase					:      Any          =   url.split('/'      )[-2]
		lowerCamelCase					:      Tuple          =   os.path.join(a_,						a_      )

		if os.path.exists(a_      ) and not os.path.isfile(a_      ):
				raise RuntimeError(F"""{download_target} exists and is not a regular file"""      )

		if os.path.isfile(a_      ):
				lowerCamelCase					:      Union[str, Any]          =   open(a_,						'rb'      ).read()
				if hashlib.shaaaa(a_      ).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(a_      ) as source, open(a_,						'wb'      ) as output:
				with tqdm(
				    total=int(source.info().get('Content-Length'      )      ),						ncols=80,						unit='iB',						unit_scale=a_,						unit_divisor=1024      ) as loop:
						while True:
								lowerCamelCase					:      Union[str, Any]          =   source.read(8192      )
								if not buffer:
										break

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

		lowerCamelCase					:      int          =   open(a_,						'rb'      ).read()
		if hashlib.shaaaa(a_      ).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 (		a_,						a_      ):





		'''simple docstring'''


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

		lowerCamelCase					:      Optional[int]          =   WhisperConfig(
		    vocab_size=dimensions['n_vocab'],						encoder_ffn_dim=a_,						decoder_ffn_dim=a_,						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'],						)

		lowerCamelCase					:      Union[str, Any]          =   WhisperForConditionalGeneration(a_      )
		lowerCamelCase     ,   lowerCamelCase					:      Optional[int]          =   model.model.load_state_dict(a_,						strict=a_      )
		if len(a_      ) > 0 and not set(a_      ) <= {
		    "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:
				lowerCamelCase					:      List[Any]          =   make_linear_from_emb(model.model.decoder.embed_tokens      )
		else:
				lowerCamelCase					:      Tuple          =   proj_out_weights

		model.save_pretrained(a_      )


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)






