
import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation

UpperCAmelCase     				=			logging.get_logger(__name__)

UpperCAmelCase     				=			{'''vocab_file''': '''tokenizer.model'''}

UpperCAmelCase     				=			{
    '''vocab_file''': {
        '''hf-internal-testing/llama-tokenizer''': '''https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model''',
    },
    '''tokenizer_file''': {
        '''hf-internal-testing/llama-tokenizer''': '''https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json''',
    },
}
UpperCAmelCase     				=			{
    '''hf-internal-testing/llama-tokenizer''': 2048,
}
UpperCAmelCase     				=			'''▁'''

UpperCAmelCase				,			UpperCAmelCase     				=			'''[INST]''', '''[/INST]'''
UpperCAmelCase				,			UpperCAmelCase     				=			'''<<SYS>>\n''', '''\n<</SYS>>\n\n'''

# fmt: off
UpperCAmelCase     				=			'''You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
correct. If you don\'t know the answer to a question, please don\'t share false information.'''







class 	A_       (      __lowerCamelCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					List[str]										=	VOCAB_FILES_NAMES
	_UpperCamelCase					:					str										=	PRETRAINED_VOCAB_FILES_MAP
	_UpperCamelCase					:					int										=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_UpperCamelCase					:					Any										=	["""input_ids""", """attention_mask"""]





	def __init__(	self		,   snake_case		,   snake_case="<unk>"		,   snake_case="<s>"		,   snake_case="</s>"		,   snake_case=None		,   snake_case = None		,   snake_case=True		,   snake_case=False		,   snake_case=False		,   snake_case=True		,   **snake_case		,   ):
		lowercase					  =   {} if sp_model_kwargs is None else sp_model_kwargs
		lowercase					  =   AddedToken(snake_case		,   lstrip=snake_case		,   rstrip=snake_case	) if isinstance(snake_case		,   snake_case	) else bos_token
		lowercase					  =   AddedToken(snake_case		,   lstrip=snake_case		,   rstrip=snake_case	) if isinstance(snake_case		,   snake_case	) else eos_token
		lowercase					  =   AddedToken(snake_case		,   lstrip=snake_case		,   rstrip=snake_case	) if isinstance(snake_case		,   snake_case	) else unk_token
		lowercase					  =   AddedToken(snake_case		,   lstrip=snake_case		,   rstrip=snake_case	) if isinstance(snake_case		,   snake_case	) else pad_token
		super().__init__(
		    bos_token=snake_case		,   eos_token=snake_case		,   unk_token=snake_case		,   pad_token=snake_case		,   add_bos_token=snake_case		,   add_eos_token=snake_case		,   sp_model_kwargs=self.sp_model_kwargs		,   clean_up_tokenization_spaces=snake_case		,   legacy=snake_case		,   **snake_case		,   )
		if legacy:
			logger.warning_once(
			    F'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
			    ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'	)
		lowercase					  =   legacy
		lowercase					  =   vocab_file
		lowercase					  =   add_bos_token
		lowercase					  =   add_eos_token
		lowercase					  =   spm.SentencePieceProcessor(**self.sp_model_kwargs	)
		self.sp_model.Load(snake_case	)





	def __getstate__(	self	):
		lowercase					  =   self.__dict__.copy()
		lowercase					  =   None
		lowercase					  =   self.sp_model.serialized_model_proto()
		return state





	def __setstate__(	self		,   snake_case	):
		lowercase					  =   d
		lowercase					  =   spm.SentencePieceProcessor(**self.sp_model_kwargs	)
		self.sp_model.LoadFromSerializedProto(self.sp_model_proto	)





	@property
	def      SCREAMING_SNAKE_CASE__						(	self	):
		return self.sp_model.get_piece_size()





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   {self.convert_ids_to_tokens(snake_case	): i for i in range(self.vocab_size	)}
		vocab.update(self.added_tokens_encoder	)
		return vocab





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   **snake_case	):
		# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
		# the beginning of the text
		if not self.legacy:
			lowercase					  =   SPIECE_UNDERLINE + text.replace(snake_case		,   ' '	)
		return super().tokenize(snake_case		,   **snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		if not self.legacy:
			lowercase					  =   text.startswith(snake_case	)
			if is_first:
				lowercase					  =   text[1:]

		lowercase					  =   self.sp_model.encode(snake_case		,   out_type=snake_case	)

		if not self.legacy and not is_first and not text.startswith(' '	) and tokens[0].startswith(snake_case	):
			lowercase					  =   ([tokens[0][1:]] if len(tokens[0]	) > 1 else []) + tokens[1:]
		return tokens





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		return self.sp_model.piece_to_id(snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		lowercase					  =   self.sp_model.IdToPiece(snake_case	)
		return token





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		lowercase					  =   []
		lowercase					  =   ''
		lowercase					  =   False
		for i, token in enumerate(snake_case	):
			# make sure that special tokens are not decoded using sentencepiece model
			if token in self.all_special_tokens:
				if not prev_is_special and i != 0:
					out_string += " "
				out_string += self.sp_model.decode(snake_case	) + token
				lowercase					  =   True
				lowercase					  =   []
			else:
				current_sub_tokens.append(snake_case	)
				lowercase					  =   False
		out_string += self.sp_model.decode(snake_case	)
		return out_string





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case = None	):
		if not os.path.isdir(snake_case	):
			logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
			return
		lowercase					  =   os.path.join(
		    snake_case		,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

		if os.path.abspath(self.vocab_file	) != os.path.abspath(snake_case	) and os.path.isfile(self.vocab_file	):
			copyfile(self.vocab_file		,   snake_case	)
		elif not os.path.isfile(self.vocab_file	):
			with open(snake_case		,   'wb'	) as fi:
				lowercase					  =   self.sp_model.serialized_model_proto()
				fi.write(snake_case	)

		return (out_vocab_file,)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case=None	):
		lowercase					  =   [self.bos_token_id] if self.add_bos_token else []
		lowercase					  =   [self.eos_token_id] if self.add_eos_token else []

		lowercase					  =   bos_token_id + token_ids_a + eos_token_id

		if token_ids_a is not None:
			lowercase					  =   output + bos_token_id + token_ids_a + eos_token_id

		return output





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case = None		,   snake_case = False	):
		if already_has_special_tokens:
			return super().get_special_tokens_mask(
			    token_ids_a=snake_case		,   token_ids_a=snake_case		,   already_has_special_tokens=snake_case	)

		lowercase					  =   [1] if self.add_bos_token else []
		lowercase					  =   [1] if self.add_eos_token else []

		if token_ids_a is None:
			return bos_token_id + ([0] * len(snake_case	)) + eos_token_id
		return (
		    bos_token_id
		    + ([0] * len(snake_case	))
		    + eos_token_id
		    + bos_token_id
		    + ([0] * len(snake_case	))
		    + eos_token_id
		)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case = None	):
		lowercase					  =   [self.bos_token_id] if self.add_bos_token else []
		lowercase					  =   [self.eos_token_id] if self.add_eos_token else []

		lowercase					  =   [0] * len(bos_token_id + token_ids_a + eos_token_id	)

		if token_ids_a is not None:
			output += [1] * len(bos_token_id + token_ids_a + eos_token_id	)

		return output





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		lowercase					  =   list(conversation.iter_texts()	)
		if not all([is_user for is_user, msg in dialogue[::2]]	) or not all(
		    [not is_user for is_user, msg in dialogue[1::2]]	):
			raise ValueError(
			    'The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'	)

		lowercase					  =   []
		if len(conversation.past_user_inputs	) > 0:
			if not conversation.past_user_inputs[0].startswith(snake_case	) or E_SYS not in conversation.past_user_inputs[0]:
				lowercase					  =   (
				    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
				)
		elif not dialogue[0][1].startswith(snake_case	) or E_SYS not in dialogue[0][1]:
			lowercase					  =   (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

		dialog_tokens += sum(
		    [
		        [self.bos_token_id]
		        + self.encode(
		            F'''{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} '''		,   add_special_tokens=snake_case	)
		        + [self.eos_token_id]
		        for prompt, answer in zip(dialogue[::2]		,   dialogue[1::2]	)
		    ]		,   []		,   )
		if not (dialogue[-1][0]):
			raise ValueError(F'''Last message must be from user, got {dialogue[-1]['role']}'''	)
		dialog_tokens += [self.bos_token_id] + self.encode(
		    F'''{B_INST} {(dialogue[-1][1]).strip()} {E_INST}'''		,   add_special_tokens=snake_case	)
		return dialog_tokens



import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

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


logging.set_verbosity_info()
UpperCAmelCase     				=			logging.get_logger(__name__)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE=False			,							__SCREAMING_SNAKE_CASE=False							):
	lowercase					  =   'backbone.' if is_semantic else ''

	lowercase					  =   []
	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'),
	    ]							)

	if has_lm_head:
		# mask token + shared relative position bias + layernorm
		rename_keys.extend(
		    [
		        ('mask_token', 'beit.embeddings.mask_token'),
		        (
		            'rel_pos_bias.relative_position_bias_table',
		            'beit.encoder.relative_position_bias.relative_position_bias_table',
		        ),
		        (
		            'rel_pos_bias.relative_position_index',
		            'beit.encoder.relative_position_bias.relative_position_index',
		        ),
		        ('norm.weight', 'layernorm.weight'),
		        ('norm.bias', 'layernorm.bias'),
		    ]							)
	elif is_semantic:
		# semantic segmentation classification heads
		rename_keys.extend(
		    [
		        ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
		        ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
		        ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
		        ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.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 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE=False			,							__SCREAMING_SNAKE_CASE=False							):
	for i in range(config.num_hidden_layers							):
		lowercase					  =   'backbone.' if is_semantic else ''
		# queries, keys and values
		lowercase					  =   state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''							)
		lowercase					  =   state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''							)
		lowercase					  =   state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''							)

		lowercase					  =   in_proj_weight[
		    : config.hidden_size, :
		]
		lowercase					  =   q_bias
		lowercase					  =   in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		lowercase					  =   in_proj_weight[
		    -config.hidden_size :, :
		]
		lowercase					  =   v_bias

		# gamma_1 and gamma_2
		# we call them lambda because otherwise they are renamed when using .from_pretrained
		lowercase					  =   state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''							)
		lowercase					  =   state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''							)

		lowercase					  =   gamma_a
		lowercase					  =   gamma_a

		# relative_position bias table + index
		if not has_lm_head:
			# each layer has its own relative position bias
			lowercase					  =   state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table'''							)
			lowercase					  =   state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index'''							)

			lowercase					  =   table
			lowercase					  =   index






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   dct.pop(__SCREAMING_SNAKE_CASE							)
	lowercase					  =   val






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






@torch.no_grad()
def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   BeitConfig()
	lowercase					  =   False
	lowercase					  =   False
	lowercase					  =   'huggingface/label-files'
	# set config parameters based on URL
	if checkpoint_url[-9:-4] == "pt22k":
		# masked image modeling
		lowercase					  =   True
		lowercase					  =   True
		lowercase					  =   True
	elif checkpoint_url[-9:-4] == "ft22k":
		# intermediate fine-tuning on ImageNet-22k
		lowercase					  =   True
		lowercase					  =   2_1841
		lowercase					  =   'imagenet-22k-id2label.json'
		lowercase					  =   json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							repo_type='dataset'							)			,							'r'							)							)
		lowercase					  =   {int(__SCREAMING_SNAKE_CASE							): v for k, v in idalabel.items()}
		# this dataset contains 21843 labels but the model only has 21841
		# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
		del idalabel[9205]
		del idalabel[1_5027]
		lowercase					  =   idalabel
		lowercase					  =   {v: k for k, v in idalabel.items()}
	elif checkpoint_url[-8:-4] == "to1k":
		# fine-tuning on ImageNet-1k
		lowercase					  =   True
		lowercase					  =   1000
		lowercase					  =   'imagenet-1k-id2label.json'
		lowercase					  =   json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							repo_type='dataset'							)			,							'r'							)							)
		lowercase					  =   {int(__SCREAMING_SNAKE_CASE							): v for k, v in idalabel.items()}
		lowercase					  =   idalabel
		lowercase					  =   {v: k for k, v in idalabel.items()}
		if "384" in checkpoint_url:
			lowercase					  =   384
		if "512" in checkpoint_url:
			lowercase					  =   512
	elif "ade20k" in checkpoint_url:
		# fine-tuning
		lowercase					  =   True
		lowercase					  =   150
		lowercase					  =   'ade20k-id2label.json'
		lowercase					  =   json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							repo_type='dataset'							)			,							'r'							)							)
		lowercase					  =   {int(__SCREAMING_SNAKE_CASE							): v for k, v in idalabel.items()}
		lowercase					  =   idalabel
		lowercase					  =   {v: k for k, v in idalabel.items()}
		lowercase					  =   640
		lowercase					  =   True
	else:
		raise ValueError('Checkpoint not supported, URL should either end with \'pt22k\', \'ft22k\', \'to1k\' or \'ade20k\''							)

	# size of the architecture
	if "base" in checkpoint_url:
		pass
	elif "large" in checkpoint_url:
		lowercase					  =   1024
		lowercase					  =   4096
		lowercase					  =   24
		lowercase					  =   16
		if "ade20k" in checkpoint_url:
			lowercase					  =   640
			lowercase					  =   [7, 11, 15, 23]
	else:
		raise ValueError('Should either find \'base\' or \'large\' in checkpoint URL'							)

	# load state_dict of original model, remove and rename some keys
	lowercase					  =   torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE			,							map_location='cpu'			,							check_hash=__SCREAMING_SNAKE_CASE							)
	lowercase					  =   state_dict['model'] if 'ade20k' not in checkpoint_url else state_dict['state_dict']

	lowercase					  =   create_rename_keys(__SCREAMING_SNAKE_CASE			,							has_lm_head=__SCREAMING_SNAKE_CASE			,							is_semantic=__SCREAMING_SNAKE_CASE							)
	for src, dest in rename_keys:
		rename_key(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							)
	read_in_q_k_v(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							has_lm_head=__SCREAMING_SNAKE_CASE			,							is_semantic=__SCREAMING_SNAKE_CASE							)
	if is_semantic:
		# add prefix to decoder keys
		for key, val in state_dict.copy().items():
			lowercase					  =   state_dict.pop(__SCREAMING_SNAKE_CASE							)
			if key.startswith('backbone.fpn'							):
				lowercase					  =   key.replace('backbone.fpn'			,							'fpn'							)
			lowercase					  =   val

    # load HuggingFace model
	if checkpoint_url[-9:-4] == "pt22k":
		lowercase					  =   BeitForMaskedImageModeling(__SCREAMING_SNAKE_CASE							)
	elif "ade20k" in checkpoint_url:
		lowercase					  =   BeitForSemanticSegmentation(__SCREAMING_SNAKE_CASE							)
	else:
		lowercase					  =   BeitForImageClassification(__SCREAMING_SNAKE_CASE							)
	model.eval()
	model.load_state_dict(__SCREAMING_SNAKE_CASE							)

	# Check outputs on an image
	if is_semantic:
		lowercase					  =   BeitImageProcessor(size=config.image_size			,							do_center_crop=__SCREAMING_SNAKE_CASE							)
		lowercase					  =   load_dataset('hf-internal-testing/fixtures_ade20k'			,							split='test'							)
		lowercase					  =   Image.open(ds[0]['file']							)
	else:
		lowercase					  =   BeitImageProcessor(
		    size=config.image_size			,							resample=PILImageResampling.BILINEAR			,							do_center_crop=__SCREAMING_SNAKE_CASE							)
		lowercase					  =   prepare_img()

	lowercase					  =   image_processor(images=__SCREAMING_SNAKE_CASE			,							return_tensors='pt'							)
	lowercase					  =   encoding['pixel_values']

	lowercase					  =   model(__SCREAMING_SNAKE_CASE							)
	lowercase					  =   outputs.logits

	# verify logits
	lowercase					  =   torch.Size([1, 1000]							)
	if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'							):
		lowercase					  =   torch.Size([1, 196, 8192]							)
	elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'							):
		lowercase					  =   torch.Size([1, 196, 8192]							)
	elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'							):
		lowercase					  =   torch.Size([1, 2_1841]							)
		lowercase					  =   torch.tensor([2.22_88, 2.46_71, 0.73_95]							)
		lowercase					  =   2397
	elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'							):
		lowercase					  =   torch.Size([1, 2_1841]							)
		lowercase					  =   torch.tensor([1.68_81, -0.27_87, 0.59_01]							)
		lowercase					  =   2396
	elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'							):
		lowercase					  =   torch.tensor([0.12_41, 0.07_98, -0.65_69]							)
		lowercase					  =   285
	elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'							):
		lowercase					  =   torch.tensor([-1.23_85, -1.09_87, -1.01_08]							)
		lowercase					  =   281
	elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'							):
		lowercase					  =   torch.tensor([-1.53_03, -0.94_84, -0.31_47]							)
		lowercase					  =   761
	elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'							):
		lowercase					  =   torch.tensor([0.46_10, -0.09_28, 0.20_86]							)
		lowercase					  =   761
	elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'							):
		lowercase					  =   torch.tensor([-0.48_04, 0.62_57, -0.18_37]							)
		lowercase					  =   761
	elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'							):
		lowercase					  =   torch.tensor([[-0.51_22, 0.51_17, -0.21_13]]							)
		lowercase					  =   761
	elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'							):
		lowercase					  =   torch.tensor([-0.30_62, 0.72_61, 0.48_52]							)
		lowercase					  =   761
	elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'							):
		lowercase					  =   (1, 150, 160, 160)
		lowercase					  =   torch.tensor(
		    [
		        [[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
		        [[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
		        [[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
		    ]							)
	elif checkpoint_url[:-4].endswith('beit_large_patch16_640_pt22k_ft22ktoade20k'							):
		lowercase					  =   (1, 150, 160, 160)
		lowercase					  =   torch.tensor(
		    [
		        [[-4.33_05, -2.30_49, -3.01_61], [-2.95_91, -1.53_05, -2.22_51], [-3.41_98, -1.80_04, -2.90_62]],
		        [[-5.89_22, -3.74_35, -4.39_78], [-4.20_63, -2.78_72, -3.47_55], [-4.27_91, -3.18_74, -4.16_81]],
		        [[0.98_95, 4.34_67, 4.76_63], [4.24_76, 5.68_30, 6.15_18], [4.55_50, 6.24_95, 6.51_54]],
		    ]							)
	else:
		raise ValueError('Can\'t verify logits as model is not supported'							)

	assert logits.shape == expected_shape, "Shape of logits not as expected"
	if not has_lm_head:
		if is_semantic:
			assert torch.allclose(
			    logits[0, :3, :3, :3]			,							__SCREAMING_SNAKE_CASE			,							atol=1e-3							), "First elements of logits not as expected"
		else:
			print('Predicted class idx:'			,							logits.argmax(-1							).item()							)
			assert torch.allclose(
			    logits[0, :3]			,							__SCREAMING_SNAKE_CASE			,							atol=1e-3							), "First elements of logits not as expected"
			assert logits.argmax(-1							).item() == expected_class_idx, "Predicted class index not as expected"

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


if __name__ == "__main__":
	UpperCAmelCase     				=			argparse.ArgumentParser()

	parser.add_argument(
	    '''--checkpoint_url''',
	    default='''https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.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.'''
	)
	UpperCAmelCase     				=			parser.parse_args()
	convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)

