



import re
import time
from typing import Optional

import IPython.display as disp

from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length


def 			UpperCamelCase(  __UpperCamelCase							:		Optional[int] ):
		lowerCAmelCase_			:     Union[str, Any]									= int(__UpperCamelCase )
		lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[Any]									= t // 3600, (t // 60) % 60, t % 60
		return f"""{h}:{m:02d}:{s:02d}""" if h != 0 else f"""{m:02d}:{s:02d}"""


def 			UpperCamelCase(  __UpperCamelCase							:		Dict     ,__UpperCamelCase							:		Tuple     ,__UpperCamelCase							:		List[str]     ,__UpperCamelCase							:		Optional[int]     ,__UpperCamelCase							:		int=300 ):
		# docstyle-ignore
		return f"""
    <div>
      {prefix}
      <progress value='{value}' max='{total}' style='width:{width}px; height:20px; vertical-align: middle;'></progress>
      {label}
    </div>
    """


def 			UpperCamelCase(  __UpperCamelCase							:		int ):
		lowerCAmelCase_			:     str									= '''<table border="1" class="dataframe">\n'''
		html_code += """  <thead>\n <tr style="text-align: left;">\n"""
		for i in items[0]:
				html_code += f"""      <th>{i}</th>\n"""
		html_code += "    </tr>\n  </thead>\n  <tbody>\n"
		for line in items[1:]:
				html_code += "    <tr>\n"
				for elt in line:
						lowerCAmelCase_			:     Optional[Any]									= f"""{elt:.6f}""" if isinstance(__UpperCamelCase     ,__UpperCamelCase ) else str(__UpperCamelCase )
						html_code += f"""      <td>{elt}</td>\n"""
				html_code += "    </tr>\n"
		html_code += "  </tbody>\n</table><p>"
		return html_code



class 				__snake_case						:
							_a			      = 5
							_a			      = 0.2




							def __init__(       self     :       Optional[Any]				,				A_     :       int				,				A_     :       Optional[str] = None				,				A_     :       bool = True				,				A_     :       Optional["NotebookTrainingTracker"] = None				,				A_     :       int = 3_0_0				,				):
									lowerCAmelCase_			:     Union[str, Any]									= total
									lowerCAmelCase_			:     int									= '''''' if prefix is None else prefix
									lowerCAmelCase_			:     Optional[Any]									= leave
									lowerCAmelCase_			:     Union[str, Any]									= parent
									lowerCAmelCase_			:     Any									= width
									lowerCAmelCase_			:     Optional[Any]									= None
									lowerCAmelCase_			:     Tuple									= None
									lowerCAmelCase_			:     str									= None




							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       int				,				A_     :       bool = False				,				A_     :       str = None):
									lowerCAmelCase_			:     Dict									= value
									if comment is not None:
											lowerCAmelCase_			:     List[str]									= comment
									if self.last_value is None:
											lowerCAmelCase_			:     List[str]									= time.time()
											lowerCAmelCase_			:     Dict									= value
											lowerCAmelCase_			:     Union[str, Any]									= None
											lowerCAmelCase_			:     str									= self.warmup
											lowerCAmelCase_			:     List[Any]									= 1
											self.update_bar(A_)
									elif value <= self.last_value and not force_update:
											return
									elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for				,				self.total):
											if self.first_calls > 0:
													self.first_calls -= 1
											lowerCAmelCase_			:     Tuple									= time.time()
											lowerCAmelCase_			:     List[str]									= current_time - self.start_time
											# We could have value = self.start_value if the update is called twixe with the same start value.
											if value > self.start_value:
													lowerCAmelCase_			:     int									= self.elapsed_time / (value - self.start_value)
											else:
													lowerCAmelCase_			:     str									= None
											if value >= self.total:
													lowerCAmelCase_			:     int									= self.total
													lowerCAmelCase_			:     List[str]									= None
													if not self.leave:
															self.close()
											elif self.average_time_per_item is not None:
													lowerCAmelCase_			:     List[str]									= self.average_time_per_item * (self.total - value)
											self.update_bar(A_)
											lowerCAmelCase_			:     Tuple									= value
											lowerCAmelCase_			:     Optional[int]									= current_time
											if self.average_time_per_item is None:
													lowerCAmelCase_			:     List[Any]									= 1
											else:
													lowerCAmelCase_			:     Any									= max(int(self.update_every / self.average_time_per_item)				,				1)




							def    UpperCAmelCase__			(       self     :       int				,				A_     :       Optional[int]				,				A_     :       List[str]=None):
									lowerCAmelCase_			:     str									= ''' ''' * (len(str(self.total)) - len(str(A_))) + str(A_)
									if self.elapsed_time is None:
											lowerCAmelCase_			:     str									= F"""[{spaced_value}/{self.total} : < :"""
									elif self.predicted_remaining is None:
											lowerCAmelCase_			:     Tuple									= F"""[{spaced_value}/{self.total} {format_time(self.elapsed_time)}"""
									else:
											lowerCAmelCase_			:     Any									= (
											    F"""[{spaced_value}/{self.total} {format_time(self.elapsed_time)} <"""
											    F""" {format_time(self.predicted_remaining)}"""
											)
											self.label += F""", {1/self.average_time_per_item:.2f} it/s"""
									self.label += "]" if self.comment is None or len(self.comment) == 0 else F""", {self.comment}]"""
									self.display()




							def    UpperCAmelCase__			(       self     :       Tuple):
									lowerCAmelCase_			:     Union[str, Any]									= html_progress_bar(self.value				,				self.total				,				self.prefix				,				self.label				,				self.width)
									if self.parent is not None:
											# If this is a child bar, the parent will take care of the display.
											self.parent.display()
											return
									if self.output is None:
											lowerCAmelCase_			:     int									= disp.display(disp.HTML(self.html_code)				,				display_id=A_)
									else:
											self.output.update(disp.HTML(self.html_code))







							def    UpperCAmelCase__			(       self     :       Optional[int]):
									if self.parent is None and self.output is not None:
											self.output.update(disp.HTML(''''''))





class 				__snake_case						(     UpperCamelCase_						):




							def __init__(       self     :       Union[str, Any]				,				A_     :       Optional[int]				,				A_     :       Dict=None):
									super().__init__(A_)
									lowerCAmelCase_			:     int									= None if column_names is None else [column_names]
									lowerCAmelCase_			:     Optional[int]									= None




							def    UpperCAmelCase__			(       self     :       List[str]):
									lowerCAmelCase_			:     Optional[Any]									= html_progress_bar(self.value				,				self.total				,				self.prefix				,				self.label				,				self.width)
									if self.inner_table is not None:
											self.html_code += text_to_html_table(self.inner_table)
									if self.child_bar is not None:
											self.html_code += self.child_bar.html_code
									if self.output is None:
											lowerCAmelCase_			:     Optional[Any]									= disp.display(disp.HTML(self.html_code)				,				display_id=A_)
									else:
											self.output.update(disp.HTML(self.html_code))




							def    UpperCAmelCase__			(       self     :       List[str]				,				A_     :       Optional[int]):
									if self.inner_table is None:
											lowerCAmelCase_			:     List[Any]									= [list(values.keys()), list(values.values())]
									else:
											lowerCAmelCase_			:     List[Any]									= self.inner_table[0]
											if len(self.inner_table) == 1:
													# We give a chance to update the column names at the first iteration
													for key in values.keys():
															if key not in columns:
																	columns.append(A_)
													lowerCAmelCase_			:     str									= columns
											self.inner_table.append([values[c] for c in columns])




							def    UpperCAmelCase__			(       self     :       Optional[Any]				,				A_     :       Optional[Any]				,				A_     :       Any=None				,				A_     :       Optional[int]=3_0_0):
									lowerCAmelCase_			:     Any									= NotebookProgressBar(A_				,				prefix=A_				,				parent=self				,				width=A_)
									return self.child_bar







							def    UpperCAmelCase__			(       self     :       List[str]):
									lowerCAmelCase_			:     Dict									= None
									self.display()





class 				__snake_case						(     UpperCamelCase_						):




							def __init__(       self     :       Dict):
									lowerCAmelCase_			:     str									= None
									lowerCAmelCase_			:     List[str]									= None
									lowerCAmelCase_			:     Tuple									= False




							def    UpperCAmelCase__			(       self     :       int				,				A_     :       Optional[Any]				,				A_     :       List[Any]				,				A_     :       Union[str, Any]				,				**A_     :       str):
									lowerCAmelCase_			:     str									= '''Epoch''' if args.evaluation_strategy == IntervalStrategy.EPOCH else '''Step'''
									lowerCAmelCase_			:     Optional[int]									= 0
									lowerCAmelCase_			:     Any									= 0
									lowerCAmelCase_			:     List[str]									= [self.first_column] + ['''Training Loss''']
									if args.evaluation_strategy != IntervalStrategy.NO:
											column_names.append('''Validation Loss''')
									lowerCAmelCase_			:     str									= NotebookTrainingTracker(state.max_steps				,				A_)




							def    UpperCAmelCase__			(       self     :       Optional[int]				,				A_     :       Optional[Any]				,				A_     :       Optional[int]				,				A_     :       List[str]				,				**A_     :       List[Any]):
									lowerCAmelCase_			:     int									= int(state.epoch) if int(state.epoch) == state.epoch else F"""{state.epoch:.2f}"""
									self.training_tracker.update(
									    state.global_step + 1				,				comment=F"""Epoch {epoch}/{state.num_train_epochs}"""				,				force_update=self._force_next_update				,				)
									lowerCAmelCase_			:     Dict									= False




							def    UpperCAmelCase__			(       self     :       Optional[Any]				,				A_     :       List[str]				,				A_     :       Optional[Any]				,				A_     :       int				,				A_     :       List[str]=None				,				**A_     :       List[str]):
									if not has_length(A_):
											return
									if self.prediction_bar is None:
											if self.training_tracker is not None:
													lowerCAmelCase_			:     int									= self.training_tracker.add_child(len(A_))
											else:
													lowerCAmelCase_			:     List[str]									= NotebookProgressBar(len(A_))
											self.prediction_bar.update(1)
									else:
											self.prediction_bar.update(self.prediction_bar.value + 1)




							def    UpperCAmelCase__			(       self     :       List[Any]				,				A_     :       Optional[Any]				,				A_     :       List[str]				,				A_     :       Dict				,				**A_     :       Tuple):
									if self.prediction_bar is not None:
											self.prediction_bar.close()
									lowerCAmelCase_			:     List[Any]									= None




							def    UpperCAmelCase__			(       self     :       List[Any]				,				A_     :       Optional[Any]				,				A_     :       str				,				A_     :       Any				,				A_     :       Tuple=None				,				**A_     :       str):
									# Only for when there is no evaluation
									if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
											lowerCAmelCase_			:     List[str]									= {'''Training Loss''': logs['''loss''']}
											# First column is necessarily Step sine we're not in epoch eval strategy
											lowerCAmelCase_			:     int									= state.global_step
											self.training_tracker.write_line(A_)




							def    UpperCAmelCase__			(       self     :       int				,				A_     :       Union[str, Any]				,				A_     :       Tuple				,				A_     :       Tuple				,				A_     :       Optional[Any]=None				,				**A_     :       Optional[Any]):
									if self.training_tracker is not None:
											lowerCAmelCase_			:     Any									= {'''Training Loss''': '''No log''', '''Validation Loss''': '''No log'''}
											for log in reversed(state.log_history):
													if "loss" in log:
															lowerCAmelCase_			:     Dict									= log['''loss''']
															break

											if self.first_column == "Epoch":
													lowerCAmelCase_			:     str									= int(state.epoch)
											else:
													lowerCAmelCase_			:     Optional[int]									= state.global_step
											lowerCAmelCase_			:     Optional[Any]									= '''eval'''
											for k in metrics:
													if k.endswith('''_loss'''):
															lowerCAmelCase_			:     str									= re.sub(r'''\_loss$'''				,				''''''				,				A_)
											lowerCAmelCase_			:     Any									= metrics.pop('''total_flos'''				,				A_)
											lowerCAmelCase_			:     List[Any]									= metrics.pop('''epoch'''				,				A_)
											lowerCAmelCase_			:     Optional[Any]									= metrics.pop(F"""{metric_key_prefix}_runtime"""				,				A_)
											lowerCAmelCase_			:     List[Any]									= metrics.pop(F"""{metric_key_prefix}_samples_per_second"""				,				A_)
											lowerCAmelCase_			:     List[Any]									= metrics.pop(F"""{metric_key_prefix}_steps_per_second"""				,				A_)
											lowerCAmelCase_			:     Tuple									= metrics.pop(F"""{metric_key_prefix}_jit_compilation_time"""				,				A_)
											for k, v in metrics.items():
													if k == F"""{metric_key_prefix}_loss""":
															lowerCAmelCase_			:     Optional[int]									= v
													else:
															lowerCAmelCase_			:     str									= k.split('''_''')
															lowerCAmelCase_			:     List[str]									= ''' '''.join([part.capitalize() for part in splits[1:]])
															lowerCAmelCase_			:     List[Any]									= v
											self.training_tracker.write_line(A_)
											self.training_tracker.remove_child()
											lowerCAmelCase_			:     Any									= None
											# Evaluation takes a long time so we should force the next update.
											lowerCAmelCase_			:     str									= True







							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       Optional[int]				,				A_     :       Union[str, Any]				,				A_     :       Any				,				**A_     :       int):
									self.training_tracker.update(
									    state.global_step				,				comment=F"""Epoch {int(state.epoch)}/{state.num_train_epochs}"""				,				force_update=A_)
									lowerCAmelCase_			:     str									= None






import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification

def 			lowercase_				(			_lowerCamelCase							:       int):
    lowercase__ :   int    					=				[]
    embed.append(
        (
            f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
            f'''stage{idx}.patch_embed.proj.weight''',
        ))
    embed.append(
        (
            f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
            f'''stage{idx}.patch_embed.proj.bias''',
        ))
    embed.append(
        (
            f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
            f'''stage{idx}.patch_embed.norm.weight''',
        ))
    embed.append(
        (
            f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
            f'''stage{idx}.patch_embed.norm.bias''',
        ))
    return embed

def 			lowercase_				(			_lowerCamelCase							:       Optional[int]		,	_lowerCamelCase							:       int):
    lowercase__ :   Optional[Any]    					=				[]
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
            f'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
            f'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
            f'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
            f'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
        ))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.weight'''))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.bias'''))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.weight'''))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.bias'''))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', f'''stage{idx}.blocks.{cnt}.norm1.weight'''))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', f'''stage{idx}.blocks.{cnt}.norm1.bias'''))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', f'''stage{idx}.blocks.{cnt}.norm2.weight'''))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', f'''stage{idx}.blocks.{cnt}.norm2.bias'''))
    return attention_weights

def 			lowercase_				(			_lowerCamelCase							:       Optional[int]):
    lowercase__ :   Tuple    					=				[]
    token.append((f'''cvt.encoder.stages.{idx}.cls_token''', "stage2.cls_token"))
    return token

def 			lowercase_				(			):
    lowercase__ :   List[str]    					=				[]
    head.append(("layernorm.weight", "norm.weight"))
    head.append(("layernorm.bias", "norm.bias"))
    head.append(("classifier.weight", "head.weight"))
    head.append(("classifier.bias", "head.bias"))
    return head

def 			lowercase_				(			_lowerCamelCase							:       Union[str, Any]		,	_lowerCamelCase							:       Optional[int]		,	_lowerCamelCase							:       Optional[int]		,	_lowerCamelCase							:       Union[str, Any]):
    lowercase__ :   Optional[Any]    					=				"imagenet-1k-id2label.json"
    lowercase__ :   List[str]    					=				1000

    lowercase__ :   Dict    					=				"huggingface/label-files"
    lowercase__ :   List[Any]    					=				num_labels
    lowercase__ :   Tuple    					=				json.load(open(cached_download(hf_hub_url(_lowerCamelCase		,	_lowerCamelCase		,	repo_type="dataset"))		,	"r"))
    lowercase__ :   Tuple    					=				{int(_lowerCamelCase): v for k, v in idalabel.items()}

    lowercase__ :   Any    					=				idalabel
    lowercase__ :   List[Any]    					=				{v: k for k, v in idalabel.items()}

    lowercase__ :   Optional[int]    					=				CvtConfig(num_labels=_lowerCamelCase		,	idalabel=_lowerCamelCase		,	labelaid=_lowerCamelCase)

    # For depth size 13 (13 = 1+2+10)
    if cvt_model.rsplit("/"		,	1)[-1][4:6] == "13":
        lowercase__ :   Any    					=				[1, 2, 10]

    # For depth size 21 (21 = 1+4+16)
    elif cvt_model.rsplit("/"		,	1)[-1][4:6] == "21":
        lowercase__ :   Tuple    					=				[1, 4, 16]

    # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
    else:
        lowercase__ :   Union[str, Any]    					=				[2, 2, 20]
        lowercase__ :   Optional[Any]    					=				[3, 12, 16]
        lowercase__ :   Optional[Any]    					=				[192, 768, 1024]

    lowercase__ :   Union[str, Any]    					=				CvtForImageClassification(_lowerCamelCase)
    lowercase__ :   Tuple    					=				AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k")
    lowercase__ :   int    					=				image_size
    lowercase__ :   Dict    					=				torch.load(_lowerCamelCase		,	map_location=torch.device("cpu"))

    lowercase__ :   Any    					=				OrderedDict()
    lowercase__ :   int    					=				[]

    for idx in range(len(config.depth)):
        if config.cls_token[idx]:
            lowercase__ :   Dict    					=				list_of_state_dict + cls_token(_lowerCamelCase)
        lowercase__ :   List[str]    					=				list_of_state_dict + embeddings(_lowerCamelCase)
        for cnt in range(config.depth[idx]):
            lowercase__ :   Any    					=				list_of_state_dict + attention(_lowerCamelCase		,	_lowerCamelCase)

    lowercase__ :   List[str]    					=				list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(_lowerCamelCase)
    for i in range(len(_lowerCamelCase)):
        lowercase__ :   Dict    					=				original_weights[list_of_state_dict[i][1]]

    model.load_state_dict(_lowerCamelCase)
    model.save_pretrained(_lowerCamelCase)
    image_processor.save_pretrained(_lowerCamelCase)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
      UpperCamelCase  							=						argparse.ArgumentParser()
      parser.add_argument(
          '''--cvt_model''',
          default='''cvt-w24''',
          type=str,
          help='''Name of the cvt model you\'d like to convert.''',
      )
      parser.add_argument(
          '''--image_size''',
          default=384,
          type=int,
          help='''Input Image Size''',
      )
      parser.add_argument(
          '''--cvt_file_name''',
          default=R'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
          type=str,
          help='''Input Image Size''',
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
      )

      UpperCamelCase  							=						parser.parse_args()
      convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)



