
import functools
import operator

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


lowercase  :					Tuple													=       logging.get_logger(__name__)

lowercase  :					Tuple													=       {
    '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  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     'unispeech-sat'







	def __init__(					self		:Union[str, Any]     ,   a		:Tuple=3_2     ,   a		:Tuple=7_6_8     ,   a		:Optional[Any]=1_2     ,   a		:int=1_2     ,   a		:Optional[int]=3_0_7_2     ,   a		:str="gelu"     ,   a		:List[str]=0.1     ,   a		:int=0.1     ,   a		:str=0.1     ,   a		:Dict=0.0     ,   a		:List[Any]=0.0     ,   a		:Dict=0.1     ,   a		:Tuple=0.1     ,   a		:Tuple=0.02     ,   a		:Any=1E-5     ,   a		:Tuple="group"     ,   a		:Tuple="gelu"     ,   a		:Tuple=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)     ,   a		:Any=(5, 2, 2, 2, 2, 2, 2)     ,   a		:List[Any]=(1_0, 3, 3, 3, 3, 2, 2)     ,   a		:Any=False     ,   a		:Tuple=1_2_8     ,   a		:Tuple=1_6     ,   a		:List[str]=False     ,   a		:Optional[int]=True     ,   a		:Any=0.05     ,   a		:int=1_0     ,   a		:List[str]=2     ,   a		:List[Any]=0.0     ,   a		:Dict=1_0     ,   a		:str=0     ,   a		:Optional[int]=3_2_0     ,   a		:int=2     ,   a		:Optional[int]=0.1     ,   a		:List[str]=1_0_0     ,   a		:Tuple=2_5_6     ,   a		:Optional[int]=2_5_6     ,   a		:Tuple=0.1     ,   a		:Union[str, Any]="mean"     ,   a		:Dict=False     ,   a		:Optional[Any]=False     ,   a		:Any=2_5_6     ,   a		:Dict=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)     ,   a		:List[str]=(5, 3, 3, 1, 1)     ,   a		:Optional[int]=(1, 2, 3, 1, 1)     ,   a		:Optional[Any]=5_1_2     ,   a		:Union[str, Any]=0     ,   a		:int=1     ,   a		:Tuple=2     ,   a		:Optional[Any]=5_0_4     ,   **a		:Optional[Any]     ,   )		-> Optional[int]:
								super().__init__(**a     ,   pad_token_id=a     ,   bos_token_id=a     ,   eos_token_id=a						)
								__UpperCamelCase	:   str			   =					hidden_size
								__UpperCamelCase	:   Optional[Any]			   =					feat_extract_norm
								__UpperCamelCase	:   Optional[Any]			   =					feat_extract_activation
								__UpperCamelCase	:   int			   =					list(a						)
								__UpperCamelCase	:   Dict			   =					list(a						)
								__UpperCamelCase	:   Tuple			   =					list(a						)
								__UpperCamelCase	:   List[Any]			   =					conv_bias
								__UpperCamelCase	:   Union[str, Any]			   =					num_conv_pos_embeddings
								__UpperCamelCase	:   Optional[Any]			   =					num_conv_pos_embedding_groups
								__UpperCamelCase	:   List[Any]			   =					len(self.conv_dim						)
								__UpperCamelCase	:   str			   =					num_hidden_layers
								__UpperCamelCase	:   Tuple			   =					intermediate_size
								__UpperCamelCase	:   Union[str, Any]			   =					hidden_act
								__UpperCamelCase	:   List[Any]			   =					num_attention_heads
								__UpperCamelCase	:   Tuple			   =					hidden_dropout
								__UpperCamelCase	:   Tuple			   =					attention_dropout
								__UpperCamelCase	:   Union[str, Any]			   =					activation_dropout
								__UpperCamelCase	:   Optional[Any]			   =					feat_proj_dropout
								__UpperCamelCase	:   Optional[Any]			   =					final_dropout
								__UpperCamelCase	:   str			   =					layerdrop
								__UpperCamelCase	:   str			   =					layer_norm_eps
								__UpperCamelCase	:   int			   =					initializer_range
								__UpperCamelCase	:   Optional[int]			   =					vocab_size
								__UpperCamelCase	:   Optional[Any]			   =					num_clusters
								__UpperCamelCase	:   List[Any]			   =					do_stable_layer_norm
								__UpperCamelCase	:   Tuple			   =					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
								__UpperCamelCase	:   str			   =					apply_spec_augment
								__UpperCamelCase	:   Union[str, Any]			   =					mask_time_prob
								__UpperCamelCase	:   List[str]			   =					mask_time_length
								__UpperCamelCase	:   Union[str, Any]			   =					mask_time_min_masks
								__UpperCamelCase	:   Any			   =					mask_feature_prob
								__UpperCamelCase	:   Dict			   =					mask_feature_length
								__UpperCamelCase	:   Dict			   =					mask_feature_min_masks

								# parameters for pretraining with codevector quantized representations
								__UpperCamelCase	:   List[str]			   =					num_codevectors_per_group
								__UpperCamelCase	:   Any			   =					num_codevector_groups
								__UpperCamelCase	:   int			   =					contrastive_logits_temperature
								__UpperCamelCase	:   str			   =					feat_quantizer_dropout
								__UpperCamelCase	:   Tuple			   =					num_negatives
								__UpperCamelCase	:   Union[str, Any]			   =					codevector_dim
								__UpperCamelCase	:   Tuple			   =					proj_codevector_dim
								__UpperCamelCase	:   Union[str, Any]			   =					diversity_loss_weight

								# ctc loss
								__UpperCamelCase	:   Any			   =					ctc_loss_reduction
								__UpperCamelCase	:   Tuple			   =					ctc_zero_infinity

								# SequenceClassification-specific parameter. Feel free to ignore for other classes.
								__UpperCamelCase	:   List[str]			   =					classifier_proj_size

								# XVector-specific parameters. Feel free to ignore for other classes.
								__UpperCamelCase	:   Any			   =					list(a						)
								__UpperCamelCase	:   Union[str, Any]			   =					list(a						)
								__UpperCamelCase	:   Optional[int]			   =					list(a						)
								__UpperCamelCase	:   Any			   =					xvector_output_dim







	@property
	def    _lowerCamelCase				(					self		:str						)		-> Any:
								return functools.reduce(operator.mul     ,   self.conv_stride     ,   1						)

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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowercase  :					Dict													=       logging.get_logger(__name__)




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[Any]						,  _lowerCamelCase :			Union[str, Any]=False						,  _lowerCamelCase :			Optional[int]=False)					->    Any:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[int]			   =					"backbone." if is_semantic else ""

							__UpperCamelCase	:   Optional[Any]			   =					[]
							for i in range(config.num_hidden_layers):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append((F'{prefix}blocks.{i}.norm1.weight', F'beit.encoder.layer.{i}.layernorm_before.weight'))
														rename_keys.append((F'{prefix}blocks.{i}.norm1.bias', F'beit.encoder.layer.{i}.layernorm_before.bias'))
														rename_keys.append(
														    (F'{prefix}blocks.{i}.attn.proj.weight', F'beit.encoder.layer.{i}.attention.output.dense.weight'))
														rename_keys.append(
														    (F'{prefix}blocks.{i}.attn.proj.bias', F'beit.encoder.layer.{i}.attention.output.dense.bias'))
														rename_keys.append((F'{prefix}blocks.{i}.norm2.weight', F'beit.encoder.layer.{i}.layernorm_after.weight'))
														rename_keys.append((F'{prefix}blocks.{i}.norm2.bias', F'beit.encoder.layer.{i}.layernorm_after.bias'))
														rename_keys.append((F'{prefix}blocks.{i}.mlp.fc1.weight', F'beit.encoder.layer.{i}.intermediate.dense.weight'))
														rename_keys.append((F'{prefix}blocks.{i}.mlp.fc1.bias', F'beit.encoder.layer.{i}.intermediate.dense.bias'))
														rename_keys.append((F'{prefix}blocks.{i}.mlp.fc2.weight', F'beit.encoder.layer.{i}.output.dense.weight'))
														rename_keys.append((F'{prefix}blocks.{i}.mlp.fc2.bias', F'beit.encoder.layer.{i}.output.dense.bias'))

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        (F'{prefix}cls_token', "beit.embeddings.cls_token"),
							        (F'{prefix}patch_embed.proj.weight', "beit.embeddings.patch_embeddings.projection.weight"),
							        (F'{prefix}patch_embed.proj.bias', "beit.embeddings.patch_embeddings.projection.bias"),
							        (F'{prefix}pos_embed', "beit.embeddings.position_embeddings"),
							    ])

							if has_lm_head:
														# mask token + layernorm
														rename_keys.extend(
														    [
														        ("mask_token", "beit.embeddings.mask_token"),
														        ("norm.weight", "layernorm.weight"),
														        ("norm.bias", "layernorm.bias"),
														    ])
							else:
														# layernorm + classification head
														rename_keys.extend(
														    [
														        ("fc_norm.weight", "beit.pooler.layernorm.weight"),
														        ("fc_norm.bias", "beit.pooler.layernorm.bias"),
														        ("head.weight", "classifier.weight"),
														        ("head.bias", "classifier.bias"),
														    ])

							return rename_keys




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Tuple						,  _lowerCamelCase :			List[str]						,  _lowerCamelCase :			Union[str, Any]=False						,  _lowerCamelCase :			Optional[int]=False)					->    List[str]:






							'''simple docstring'''





							for i in range(config.num_hidden_layers):
														__UpperCamelCase	:   List[Any]			   =					"backbone." if is_semantic else ""
														# queries, keys and values
														__UpperCamelCase	:   Tuple			   =					state_dict.pop(F'{prefix}blocks.{i}.attn.qkv.weight')
														__UpperCamelCase	:   Optional[Any]			   =					state_dict.pop(F'{prefix}blocks.{i}.attn.q_bias')
														__UpperCamelCase	:   int			   =					state_dict.pop(F'{prefix}blocks.{i}.attn.v_bias')

														__UpperCamelCase	:   Optional[int]			   =					in_proj_weight[
														    : config.hidden_size, :
														]
														__UpperCamelCase	:   Any			   =					q_bias
														__UpperCamelCase	:   Dict			   =					in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														__UpperCamelCase	:   List[Any]			   =					in_proj_weight[
														    -config.hidden_size :, :
														]
														__UpperCamelCase	:   Tuple			   =					v_bias

														# gamma_1 and gamma_2
														# we call them lambda because otherwise they are renamed when using .from_pretrained
														__UpperCamelCase	:   Union[str, Any]			   =					state_dict.pop(F'{prefix}blocks.{i}.gamma_1')
														__UpperCamelCase	:   Optional[int]			   =					state_dict.pop(F'{prefix}blocks.{i}.gamma_2')

														__UpperCamelCase	:   Any			   =					gamma_a
														__UpperCamelCase	:   List[Any]			   =					gamma_a




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str						,  _lowerCamelCase :			List[str]						,  _lowerCamelCase :			Optional[Any])					->    Any:






							'''simple docstring'''





							__UpperCamelCase	:   str			   =					dct.pop(_lowerCamelCase)
							__UpperCamelCase	:   str			   =					val




def 	_SCREAMING_SNAKE_CASE					(		)					->    Optional[int]:






							'''simple docstring'''





							__UpperCamelCase	:   Any			   =					"http://images.cocodataset.org/val2017/000000039769.jpg"
							__UpperCamelCase	:   Dict			   =					Image.open(requests.get(_lowerCamelCase						,  stream=_lowerCamelCase).raw)
							return im




@torch.no_grad()
def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[int]						,  _lowerCamelCase :			List[str]						,  _lowerCamelCase :			Dict=False)					->    str:






							'''simple docstring'''





							__UpperCamelCase	:   str			   =					False if "rvlcdip" in checkpoint_url else True
							__UpperCamelCase	:   int			   =					BeitConfig(use_absolute_position_embeddings=_lowerCamelCase						,  use_mask_token=_lowerCamelCase)

							# size of the architecture
							if "large" in checkpoint_url or "dit-l" in checkpoint_url:
														__UpperCamelCase	:   Optional[int]			   =					1_024
														__UpperCamelCase	:   Union[str, Any]			   =					4_096
														__UpperCamelCase	:   Tuple			   =					24
														__UpperCamelCase	:   int			   =					16

							# labels
							if "rvlcdip" in checkpoint_url:
														__UpperCamelCase	:   List[str]			   =					16
														__UpperCamelCase	:   str			   =					"huggingface/label-files"
														__UpperCamelCase	:   str			   =					"rvlcdip-id2label.json"
														__UpperCamelCase	:   Tuple			   =					json.load(open(hf_hub_download(_lowerCamelCase						,  _lowerCamelCase						,  repo_type="dataset")						,  "r"))
														__UpperCamelCase	:   Dict			   =					{int(_lowerCamelCase): v for k, v in idalabel.items()}
														__UpperCamelCase	:   Optional[Any]			   =					idalabel
														__UpperCamelCase	:   List[Any]			   =					{v: k for k, v in idalabel.items()}

							# load state_dict of original model, remove and rename some keys
							__UpperCamelCase	:   str			   =					torch.hub.load_state_dict_from_url(_lowerCamelCase						,  map_location="cpu")["model"]

							__UpperCamelCase	:   Optional[Any]			   =					create_rename_keys(_lowerCamelCase						,  has_lm_head=_lowerCamelCase)
							for src, dest in rename_keys:
														rename_key(_lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase)
							read_in_q_k_v(_lowerCamelCase						,  _lowerCamelCase						,  has_lm_head=_lowerCamelCase)

							# load HuggingFace model
							__UpperCamelCase	:   List[Any]			   =					BeitForMaskedImageModeling(_lowerCamelCase) if has_lm_head else BeitForImageClassification(_lowerCamelCase)
							model.eval()
							model.load_state_dict(_lowerCamelCase)

							# Check outputs on an image
							__UpperCamelCase	:   Dict			   =					BeitImageProcessor(
							    size=config.image_size						,  resample=PILImageResampling.BILINEAR						,  do_center_crop=_lowerCamelCase)
							__UpperCamelCase	:   Union[str, Any]			   =					prepare_img()

							__UpperCamelCase	:   Union[str, Any]			   =					image_processor(images=_lowerCamelCase						,  return_tensors="pt")
							__UpperCamelCase	:   str			   =					encoding["pixel_values"]

							__UpperCamelCase	:   str			   =					model(_lowerCamelCase)
							__UpperCamelCase	:   Union[str, Any]			   =					outputs.logits

							# verify logits
							__UpperCamelCase	:   Tuple			   =					[1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8_192]
							assert logits.shape == torch.Size(_lowerCamelCase), "Shape of logits not as expected"

							Path(_lowerCamelCase).mkdir(exist_ok=_lowerCamelCase)
							print(F'Saving model to {pytorch_dump_folder_path}')
							model.save_pretrained(_lowerCamelCase)
							print(F'Saving image processor to {pytorch_dump_folder_path}')
							image_processor.save_pretrained(_lowerCamelCase)

							if push_to_hub:
														if has_lm_head:
																					__UpperCamelCase	:   List[str]			   =					"dit-base" if "base" in checkpoint_url else "dit-large"
														else:
																					__UpperCamelCase	:   Optional[Any]			   =					"dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
														image_processor.push_to_hub(
														    repo_path_or_name=Path(_lowerCamelCase						,  _lowerCamelCase)						,  organization="nielsr"						,  commit_message="Add image processor"						,  use_temp_dir=_lowerCamelCase						,  )
														model.push_to_hub(
														    repo_path_or_name=Path(_lowerCamelCase						,  _lowerCamelCase)						,  organization="nielsr"						,  commit_message="Add model"						,  use_temp_dir=_lowerCamelCase						,  )


if __name__ == "__main__":
		lowercase  :					Optional[Any]													=       argparse.ArgumentParser()

		parser.add_argument(
		    '--checkpoint_url',
		    default='https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth',
		    type=str,
		    help='URL to the original PyTorch checkpoint (.pth file).',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
		)
		parser.add_argument(
		    '--push_to_hub',
		    action='store_true',
		)
		lowercase  :					Tuple													=       parser.parse_args()
		convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)