


'''simple docstring'''







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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE							: str										=			logging.get_logger(__name__)



def     _UpperCamelCase				(			UpperCamelCase__  , UpperCamelCase__=False		):






				"""simple docstring"""



				__magic_name__					:       Optional[int]    							=							[]
				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"""vit.encoder.layer.{i}.layernorm_before.weight""")		)
								rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""")		)
								rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""")		)
								rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""")		)
								rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""")		)
								rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""")		)
								rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""")		)
								rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""")		)
								rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""")		)
								rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""")		)

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        ("cls_token", "vit.embeddings.cls_token"),
				        ("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
				        ("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
				        ("pos_embed", "vit.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 "vit" from all keys that start with "vit"
								__magic_name__					:       Dict    							=							[(pair[0], pair[1][4:]) if pair[1].startswith("vit"		) else pair for pair in rename_keys]
				else:
								# layernorm + classification head
								rename_keys.extend(
								    [
								        ("norm.weight", "vit.layernorm.weight"),
								        ("norm.bias", "vit.layernorm.bias"),
								        ("head.weight", "classifier.weight"),
								        ("head.bias", "classifier.bias"),
								    ]		)

				return rename_keys



def     _UpperCamelCase				(			UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__=False		):






				"""simple docstring"""



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



def     _UpperCamelCase				(			UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__					:       Tuple    							=							["head.weight", "head.bias"]
				for k in ignore_keys:
								state_dict.pop(_UpperCamelCase  , _UpperCamelCase		)



def     _UpperCamelCase				(			UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__					:       str    							=							dct.pop(_UpperCamelCase		)
				__magic_name__					:       Optional[int]    							=							val



def     _UpperCamelCase				(			):






				"""simple docstring"""



				__magic_name__					:       Dict    							=							"http://images.cocodataset.org/val2017/000000039769.jpg"
				__magic_name__					:       Union[str, Any]    							=							Image.open(requests.get(_UpperCamelCase  , stream=_UpperCamelCase		).raw		)
				return im



@torch.no_grad()
def     _UpperCamelCase				(			UpperCamelCase__  , UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__					:       List[Any]    							=							ViTConfig()
				__magic_name__					:       Any    							=							False
				# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
				if vit_name[-5:] == "in21k":
								__magic_name__					:       Optional[Any]    							=							True
								__magic_name__					:       Optional[int]    							=							int(vit_name[-12:-10]		)
								__magic_name__					:       Optional[Any]    							=							int(vit_name[-9:-6]		)
				else:
								__magic_name__					:       Union[str, Any]    							=							1000
								__magic_name__					:       Dict    							=							"huggingface/label-files"
								__magic_name__					:       Optional[int]    							=							"imagenet-1k-id2label.json"
								__magic_name__					:       Tuple    							=							json.load(open(hf_hub_download(_UpperCamelCase  , _UpperCamelCase  , repo_type="dataset"		)  , "r"		)		)
								__magic_name__					:       int    							=							{int(_UpperCamelCase		): v for k, v in idalabel.items()}
								__magic_name__					:       Optional[int]    							=							idalabel
								__magic_name__					:       int    							=							{v: k for k, v in idalabel.items()}
								__magic_name__					:       Union[str, Any]    							=							int(vit_name[-6:-4]		)
								__magic_name__					:       Any    							=							int(vit_name[-3:]		)
				# size of the architecture
				if "deit" in vit_name:
								if vit_name[9:].startswith("tiny"		):
												__magic_name__					:       List[str]    							=							192
												__magic_name__					:       List[str]    							=							768
												__magic_name__					:       List[Any]    							=							12
												__magic_name__					:       Union[str, Any]    							=							3
								elif vit_name[9:].startswith("small"		):
												__magic_name__					:       Dict    							=							384
												__magic_name__					:       str    							=							1536
												__magic_name__					:       Union[str, Any]    							=							12
												__magic_name__					:       str    							=							6
								else:
												pass
				else:
								if vit_name[4:].startswith("small"		):
												__magic_name__					:       List[Any]    							=							768
												__magic_name__					:       Tuple    							=							2304
												__magic_name__					:       str    							=							8
												__magic_name__					:       int    							=							8
								elif vit_name[4:].startswith("base"		):
												pass
								elif vit_name[4:].startswith("large"		):
												__magic_name__					:       str    							=							1024
												__magic_name__					:       List[str]    							=							4096
												__magic_name__					:       Union[str, Any]    							=							24
												__magic_name__					:       Optional[Any]    							=							16
								elif vit_name[4:].startswith("huge"		):
												__magic_name__					:       Dict    							=							1280
												__magic_name__					:       Optional[int]    							=							5120
												__magic_name__					:       List[str]    							=							32
												__magic_name__					:       List[str]    							=							16

    # load original model from timm
				__magic_name__					:       Optional[int]    							=							timm.create_model(_UpperCamelCase  , pretrained=_UpperCamelCase		)
				timm_model.eval()

				# load state_dict of original model, remove and rename some keys
				__magic_name__					:       Optional[Any]    							=							timm_model.state_dict()
				if base_model:
								remove_classification_head_(_UpperCamelCase		)
				__magic_name__					:       Any    							=							create_rename_keys(_UpperCamelCase  , _UpperCamelCase		)
				for src, dest in rename_keys:
								rename_key(_UpperCamelCase  , _UpperCamelCase  , _UpperCamelCase		)
				read_in_q_k_v(_UpperCamelCase  , _UpperCamelCase  , _UpperCamelCase		)

				# load HuggingFace model
				if vit_name[-5:] == "in21k":
								__magic_name__					:       Tuple    							=							ViTModel(_UpperCamelCase		).eval()
				else:
								__magic_name__					:       Optional[Any]    							=							ViTForImageClassification(_UpperCamelCase		).eval()
				model.load_state_dict(_UpperCamelCase		)

				# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
				if "deit" in vit_name:
								__magic_name__					:       str    							=							DeiTImageProcessor(size=config.image_size		)
				else:
								__magic_name__					:       Tuple    							=							ViTImageProcessor(size=config.image_size		)
				__magic_name__					:       str    							=							image_processor(images=prepare_img()  , return_tensors="pt"		)
				__magic_name__					:       Dict    							=							encoding["pixel_values"]
				__magic_name__					:       str    							=							model(_UpperCamelCase		)

				if base_model:
								__magic_name__					:       List[Any]    							=							timm_model.forward_features(_UpperCamelCase		)
								assert timm_pooled_output.shape == outputs.pooler_output.shape
								assert torch.allclose(_UpperCamelCase  , outputs.pooler_output  , atol=1E-3		)
				else:
								__magic_name__					:       List[str]    							=							timm_model(_UpperCamelCase		)
								assert timm_logits.shape == outputs.logits.shape
								assert torch.allclose(_UpperCamelCase  , outputs.logits  , atol=1E-3		)

				Path(_UpperCamelCase		).mkdir(exist_ok=_UpperCamelCase		)
				print(F"""Saving model {vit_name} 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__":
		_SCREAMING_SNAKE_CASE							: str										=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--vit_name",
		    default="vit_base_patch16_224",
		    type=str,
		    help="Name of the ViT 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."
		)

		_SCREAMING_SNAKE_CASE							: Tuple										=			parser.parse_args()
		convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)





'''simple docstring'''





import functools
import operator

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


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

UpperCamelCase_		:     Any          =		{
    '''microsoft/unispeech-sat-base-100h-libri-ft''': (
        '''https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'''
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}

class   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       str       							=		"""unispeech-sat"""






     def __init__(					self   ,_SCREAMING_SNAKE_CASE=32   ,_SCREAMING_SNAKE_CASE=768   ,_SCREAMING_SNAKE_CASE=12   ,_SCREAMING_SNAKE_CASE=12   ,_SCREAMING_SNAKE_CASE=3_072   ,_SCREAMING_SNAKE_CASE="gelu"   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=0.0   ,_SCREAMING_SNAKE_CASE=0.0   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=0.0_2   ,_SCREAMING_SNAKE_CASE=1e-5   ,_SCREAMING_SNAKE_CASE="group"   ,_SCREAMING_SNAKE_CASE="gelu"   ,_SCREAMING_SNAKE_CASE=(512, 512, 512, 512, 512, 512, 512)   ,_SCREAMING_SNAKE_CASE=(5, 2, 2, 2, 2, 2, 2)   ,_SCREAMING_SNAKE_CASE=(10, 3, 3, 3, 3, 2, 2)   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=128   ,_SCREAMING_SNAKE_CASE=16   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=True   ,_SCREAMING_SNAKE_CASE=0.0_5   ,_SCREAMING_SNAKE_CASE=10   ,_SCREAMING_SNAKE_CASE=2   ,_SCREAMING_SNAKE_CASE=0.0   ,_SCREAMING_SNAKE_CASE=10   ,_SCREAMING_SNAKE_CASE=0   ,_SCREAMING_SNAKE_CASE=320   ,_SCREAMING_SNAKE_CASE=2   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=100   ,_SCREAMING_SNAKE_CASE=256   ,_SCREAMING_SNAKE_CASE=256   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE="mean"   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=256   ,_SCREAMING_SNAKE_CASE=(512, 512, 512, 512, 1_500)   ,_SCREAMING_SNAKE_CASE=(5, 3, 3, 1, 1)   ,_SCREAMING_SNAKE_CASE=(1, 2, 3, 1, 1)   ,_SCREAMING_SNAKE_CASE=512   ,_SCREAMING_SNAKE_CASE=0   ,_SCREAMING_SNAKE_CASE=1   ,_SCREAMING_SNAKE_CASE=2   ,_SCREAMING_SNAKE_CASE=504   ,**_SCREAMING_SNAKE_CASE   ,)				->     Any:
          super().__init__(**_SCREAMING_SNAKE_CASE   ,pad_token_id=_SCREAMING_SNAKE_CASE   ,bos_token_id=_SCREAMING_SNAKE_CASE   ,eos_token_id=_SCREAMING_SNAKE_CASE       )
          _snake_case				= hidden_size
          _snake_case				= feat_extract_norm
          _snake_case				= feat_extract_activation
          _snake_case				= list(_SCREAMING_SNAKE_CASE       )
          _snake_case				= list(_SCREAMING_SNAKE_CASE       )
          _snake_case				= list(_SCREAMING_SNAKE_CASE       )
          _snake_case				= conv_bias
          _snake_case				= num_conv_pos_embeddings
          _snake_case				= num_conv_pos_embedding_groups
          _snake_case				= len(self.conv_dim       )
          _snake_case				= num_hidden_layers
          _snake_case				= intermediate_size
          _snake_case				= hidden_act
          _snake_case				= num_attention_heads
          _snake_case				= hidden_dropout
          _snake_case				= attention_dropout
          _snake_case				= activation_dropout
          _snake_case				= feat_proj_dropout
          _snake_case				= final_dropout
          _snake_case				= layerdrop
          _snake_case				= layer_norm_eps
          _snake_case				= initializer_range
          _snake_case				= vocab_size
          _snake_case				= num_clusters
          _snake_case				= do_stable_layer_norm
          _snake_case				= use_weighted_layer_sum

          if (
              (len(self.conv_stride       ) != self.num_feat_extract_layers)
              or (len(self.conv_kernel       ) != self.num_feat_extract_layers)
              or (len(self.conv_dim       ) != self.num_feat_extract_layers)
          ):
               raise ValueError(
                   "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
                   " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
                   f""" {len(self.conv_dim       )}`, `len(config.conv_stride) = {len(self.conv_stride       )}`,"""
                   f""" `len(config.conv_kernel) = {len(self.conv_kernel       )}`."""       )

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          _snake_case				= apply_spec_augment
          _snake_case				= mask_time_prob
          _snake_case				= mask_time_length
          _snake_case				= mask_time_min_masks
          _snake_case				= mask_feature_prob
          _snake_case				= mask_feature_length
          _snake_case				= mask_feature_min_masks

          # parameters for pretraining with codevector quantized representations
          _snake_case				= num_codevectors_per_group
          _snake_case				= num_codevector_groups
          _snake_case				= contrastive_logits_temperature
          _snake_case				= feat_quantizer_dropout
          _snake_case				= num_negatives
          _snake_case				= codevector_dim
          _snake_case				= proj_codevector_dim
          _snake_case				= diversity_loss_weight

          # ctc loss
          _snake_case				= ctc_loss_reduction
          _snake_case				= ctc_zero_infinity

          # SequenceClassification-specific parameter. Feel free to ignore for other classes.
          _snake_case				= classifier_proj_size

          # XVector-specific parameters. Feel free to ignore for other classes.
          _snake_case				= list(_SCREAMING_SNAKE_CASE       )
          _snake_case				= list(_SCREAMING_SNAKE_CASE       )
          _snake_case				= list(_SCREAMING_SNAKE_CASE       )
          _snake_case				= xvector_output_dim






     @property
     def 							_lowercase      (					self       )				->     List[str]:
          return functools.reduce(operator.mul   ,self.conv_stride   ,1       )


