
'''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 transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase					:    int           = logging.get_logger(__name__)

lowerCAmelCase					:    Any           = torch.device('cpu')







def 		A_(     ):
							UpperCamelCase   			=     'http://images.cocodataset.org/val2017/000000039769.jpg'
							UpperCamelCase   			=     Image.open(requests.get(A		,     stream=A).raw)
							return im







def 		A_(     A						:		Optional[int]):
							if swiftformer_name == "swiftformer_xs":
														return torch.tensor([-2.1703E00, 2.1107E00, -2.0811E00, 8.8685E-01, 2.4360E-01])

							elif swiftformer_name == "swiftformer_s":
														return torch.tensor([3.9636E-01, 2.3478E-01, -1.6963E00, -1.7381E00, -8.6337E-01])

							elif swiftformer_name == "swiftformer_l1":
														return torch.tensor([-4.2768E-01, -4.7429E-01, -1.0897E00, -1.0248E00, 3.5523E-02])

							elif swiftformer_name == "swiftformer_l3":
														return torch.tensor([-2.5330E-01, 2.4211E-01, -6.0185E-01, -8.2789E-01, -6.0446E-02])







def 		A_(     A						:		str		,     A						:		List[Any]		,     A						:		int):
							UpperCamelCase   			=     dct.pop(A)
							UpperCamelCase   			=     val







def 		A_(     A						:		int):
							UpperCamelCase   			=     []
							for k in state_dict.keys():
														UpperCamelCase   			=     k
														if ".pwconv" in k:
																					UpperCamelCase   			=     k_new.replace('.pwconv'		,     '.point_wise_conv')
														if ".dwconv" in k:
																					UpperCamelCase   			=     k_new.replace('.dwconv'		,     '.depth_wise_conv')
														if ".Proj." in k:
																					UpperCamelCase   			=     k_new.replace('.Proj.'		,     '.proj.')
														if "patch_embed" in k_new:
																					UpperCamelCase   			=     k_new.replace('patch_embed'		,     'swiftformer.patch_embed.patch_embedding')
														if "network" in k_new:
																					UpperCamelCase   			=     k_new.split('.')
																					if ls[2].isdigit():
																												UpperCamelCase   			=     'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:])
																					else:
																												UpperCamelCase   			=     k_new.replace('network'		,     'swiftformer.encoder.network')
														rename_keys.append((k, k_new))
							return rename_keys







@torch.no_grad()
def 		A_(     A						:		List[str]		,     A						:		Optional[int]		,     A						:		Tuple):
							UpperCamelCase   			=     SwiftFormerConfig()

							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							UpperCamelCase   			=     1000
							UpperCamelCase   			=     'huggingface/label-files'
							UpperCamelCase   			=     'imagenet-1k-id2label.json'
							UpperCamelCase   			=     json.load(open(hf_hub_download(A		,     A		,     repo_type='dataset')		,     'r'))
							UpperCamelCase   			=     {int(A): v for k, v in idalabel.items()}
							UpperCamelCase   			=     idalabel
							UpperCamelCase   			=     {v: k for k, v in idalabel.items()}

							# size of the architecture
							if swiftformer_name == "swiftformer_xs":
														UpperCamelCase   			=     [3, 3, 6, 4]
														UpperCamelCase   			=     [48, 56, 112, 220]

							elif swiftformer_name == "swiftformer_s":
														UpperCamelCase   			=     [3, 3, 9, 6]
														UpperCamelCase   			=     [48, 64, 168, 224]

							elif swiftformer_name == "swiftformer_l1":
														UpperCamelCase   			=     [4, 3, 10, 5]
														UpperCamelCase   			=     [48, 96, 192, 384]

							elif swiftformer_name == "swiftformer_l3":
														UpperCamelCase   			=     [4, 4, 12, 6]
														UpperCamelCase   			=     [64, 128, 320, 512]

							# load state_dict of original model, remove and rename some keys
							if original_ckpt:
														if original_ckpt.startswith('https'):
																					UpperCamelCase   			=     torch.hub.load_state_dict_from_url(A		,     map_location='cpu'		,     check_hash=A)
														else:
																					UpperCamelCase   			=     torch.load(A		,     map_location='cpu')
							UpperCamelCase   			=     checkpoint

							UpperCamelCase   			=     create_rename_keys(A)
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(A		,     A		,     A)

							# load HuggingFace model
							UpperCamelCase   			=     SwiftFormerForImageClassification(A).eval()
							hf_model.load_state_dict(A)

							# prepare test inputs
							UpperCamelCase   			=     prepare_img()
							UpperCamelCase   			=     ViTImageProcessor.from_pretrained('preprocessor_config')
							UpperCamelCase   			=     processor(images=A		,     return_tensors='pt')

							# compare outputs from both models
							UpperCamelCase   			=     get_expected_output(A)
							UpperCamelCase   			=     hf_model(inputs['pixel_values']).logits

							assert hf_logits.shape == torch.Size([1, 1000])
							assert torch.allclose(hf_logits[0, 0:5]		,     A		,     atol=1E-3)

							Path(A).mkdir(exist_ok=A)
							print(f'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}''')
							hf_model.save_pretrained(A)


if __name__ == "__main__":
						lowerCAmelCase					:    Union[str, Any]           = argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--swiftformer_name',
						    default='swiftformer_xs',
						    choices=['swiftformer_xs', 'swiftformer_s', 'swiftformer_l1', 'swiftformer_l3'],
						    type=str,
						    help='Name of the SwiftFormer model you\'d like to convert.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path',
						    default='./converted_outputs/',
						    type=str,
						    help='Path to the output PyTorch model directory.',
						)
						parser.add_argument('--original_ckpt', default=None, type=str, help='Path to the original model checkpoint.')

						lowerCAmelCase					:    List[Any]           = parser.parse_args()
						convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)






'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging


lowerCAmelCase					:    Optional[Any]           = logging.get_logger(__name__)

lowerCAmelCase					:    Dict           = {
    'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json',
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""mctct"""







				def __init__(       self						,				A_=8065						,				A_=1536						,				A_=36						,				A_=6144						,				A_=4						,				A_=384						,				A_=920						,				A_=1e-5						,				A_=0.3						,				A_="relu"						,				A_=0.02						,				A_=0.3						,				A_=0.3						,				A_=1						,				A_=0						,				A_=2						,				A_=1						,				A_=0.3						,				A_=1						,				A_=(7,)						,				A_=(3,)						,				A_=80						,				A_=1						,				A_=None						,				A_="sum"						,				A_=False						,				**A_						,				)->		str:







											'''simple docstring'''






											super().__init__(**A_						,				pad_token_id=A_						,				bos_token_id=A_						,				eos_token_id=A_     )
											UpperCamelCase   			=     vocab_size
											UpperCamelCase   			=     hidden_size
											UpperCamelCase   			=     num_hidden_layers
											UpperCamelCase   			=     intermediate_size
											UpperCamelCase   			=     num_attention_heads
											UpperCamelCase   			=     attention_head_dim
											UpperCamelCase   			=     max_position_embeddings
											UpperCamelCase   			=     layer_norm_eps
											UpperCamelCase   			=     layerdrop
											UpperCamelCase   			=     hidden_act
											UpperCamelCase   			=     initializer_range
											UpperCamelCase   			=     hidden_dropout_prob
											UpperCamelCase   			=     attention_probs_dropout_prob
											UpperCamelCase   			=     pad_token_id
											UpperCamelCase   			=     bos_token_id
											UpperCamelCase   			=     eos_token_id
											UpperCamelCase   			=     conv_glu_dim
											UpperCamelCase   			=     conv_dropout
											UpperCamelCase   			=     num_conv_layers
											UpperCamelCase   			=     input_feat_per_channel
											UpperCamelCase   			=     input_channels
											UpperCamelCase   			=     conv_channels
											UpperCamelCase   			=     ctc_loss_reduction
											UpperCamelCase   			=     ctc_zero_infinity

											# prevents config testing fail with exporting to json
											UpperCamelCase   			=     list(A_     )
											UpperCamelCase   			=     list(A_     )

											if len(self.conv_kernel     ) != self.num_conv_layers:
																		raise ValueError(
																		    'Configuration for convolutional module is incorrect. '
																		    'It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '
																		    F'''but is `len(config.conv_kernel) = {len(self.conv_kernel     )}`, '''
																		    F'''`config.num_conv_layers = {self.num_conv_layers}`.'''     )




