






import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class     UpperCamelCase_ (__a       ,		unittest.TestCase  ):
	__magic_name__			   =       GPTSanJapaneseTokenizer
	__magic_name__			   =       False
	__magic_name__			   =       {'''do_clean_text''': False, '''add_prefix_space''': False}





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    int:
				super().setUp()

				# fmt: off
				UpperCAmelCase_  :				List[str]      =      ["こん", "こんに", "にちは", "ばんは", "世界,㔺界", "、", "。", "<BR>", "<SP>", "<TAB>", "<URL>", "<EMAIL>", "<TEL>", "<DATE>", "<PRICE>", "<BLOCK>", "<KIGOU>", "<U2000U2BFF>", "<|emoji1|>", "<unk>", "<|bagoftoken|>", "<|endoftext|>"]
				# fmt: on
				UpperCAmelCase_  :				Any      =      {"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}}  # 😀
				UpperCAmelCase_  :				List[Any]      =      {"unk_token": "<unk>"}

				UpperCAmelCase_  :				Dict      =      os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES["vocab_file"]		)
				UpperCAmelCase_  :				str      =      os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES["emoji_file"]		)
				with open(self.vocab_file			,			"w"			,			encoding="utf-8"		) as vocab_writer:
							vocab_writer.write("".join([x + "\n" for x in vocab_tokens]		)		)
				with open(self.emoji_file			,			"w"		) as emoji_writer:
							emoji_writer.write(json.dumps(A__		)		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]			,			**lowerCAmelCase_      :				Optional[Any]		)		->    Union[str, Any]:
				kwargs.update(self.special_tokens_map		)
				return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname			,			**A__		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]			,			lowerCAmelCase_      :				Union[str, Any]		)		->    Any:
				UpperCAmelCase_  :				Tuple      =      "こんにちは、世界。 \nこんばんは、㔺界。😀"
				UpperCAmelCase_  :				Optional[Any]      =      "こんにちは、世界。 \nこんばんは、世界。😀"
				return input_text, output_text





	def   _SCREAMING_SNAKE_CASE   (   self      :				str			,			lowerCAmelCase_      :				List[str]		)		->    str:
				UpperCAmelCase_  ,			UpperCAmelCase_  :				Dict      =      self.get_input_output_texts(A__		)
				UpperCAmelCase_  :				Optional[int]      =      tokenizer.encode(A__			,			add_special_tokens=A__		)
				UpperCAmelCase_  :				Any      =      tokenizer.decode(A__			,			clean_up_tokenization_spaces=A__		)
				return text, ids





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    List[Any]:
				pass  # TODO add if relevant





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    int:
				pass  # TODO add if relevant





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    Union[str, Any]:
				pass  # TODO add if relevant





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    Dict:
				UpperCAmelCase_  :				List[Any]      =      self.get_tokenizer()

				# Testing tokenization
				UpperCAmelCase_  :				Any      =      "こんにちは、世界。　こんばんは、㔺界。"
				UpperCAmelCase_  :				str      =      ["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
				UpperCAmelCase_  :				int      =      tokenizer.tokenize(A__		)
				self.assertListEqual(A__			,			A__		)

				# Testing conversion to ids without special tokens
				UpperCAmelCase_  :				Optional[int]      =      [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
				UpperCAmelCase_  :				str      =      tokenizer.convert_tokens_to_ids(A__		)
				self.assertListEqual(A__			,			A__		)

				# Testing conversion to ids with special tokens
				UpperCAmelCase_  :				Tuple      =      tokens + [tokenizer.unk_token]
				UpperCAmelCase_  :				Any      =      [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
				UpperCAmelCase_  :				str      =      tokenizer.convert_tokens_to_ids(A__		)
				self.assertListEqual(A__			,			A__		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Dict:
				UpperCAmelCase_  :				List[Any]      =      self.get_tokenizer()

				# Testing tokenization
				UpperCAmelCase_  :				Union[str, Any]      =      "こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。"
				UpperCAmelCase_  :				List[str]      =      "こんにちは、、、、世界。こんばんは、、、、世界。"
				UpperCAmelCase_  :				List[str]      =      tokenizer.encode(A__		)
				UpperCAmelCase_  :				Optional[Any]      =      tokenizer.decode(A__		)
				self.assertEqual(A__			,			A__		)





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    List[Any]:
				UpperCAmelCase_  :				Optional[Any]      =      self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"		)

				# Testing tokenization
				UpperCAmelCase_  :				Union[str, Any]      =      "こんにちは、世界。"
				UpperCAmelCase_  :				Union[str, Any]      =      "こんばんは、㔺界。😀"
				UpperCAmelCase_  :				Any      =      "こんにちは、世界。こんばんは、世界。😀"
				UpperCAmelCase_  :				Tuple      =      tokenizer.encode(prefix_text + input_text		)
				UpperCAmelCase_  :				Optional[Any]      =      tokenizer.encode(""			,			prefix_text=prefix_text + input_text		)
				UpperCAmelCase_  :				Any      =      tokenizer.encode(A__			,			prefix_text=A__		)
				UpperCAmelCase_  :				Dict      =      tokenizer.decode(A__		)
				UpperCAmelCase_  :				int      =      tokenizer.decode(A__		)
				UpperCAmelCase_  :				List[str]      =      tokenizer.decode(A__		)
				self.assertEqual(A__			,			A__		)
				self.assertEqual(A__			,			A__		)
				self.assertEqual(A__			,			A__		)





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    int:
				UpperCAmelCase_  :				List[Any]      =      self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"		)

				# Testing tokenization
				UpperCAmelCase_  :				int      =      "こんにちは、世界。"
				UpperCAmelCase_  :				Any      =      "こんばんは、㔺界。😀"

				UpperCAmelCase_  :				Optional[int]      =      len(tokenizer.encode(A__		)		) - 2
				UpperCAmelCase_  :				Dict      =      len(tokenizer.encode(A__		)		) - 2

				UpperCAmelCase_  :				Tuple      =      [1] + [0] * (len_prefix + len_text + 1)
				UpperCAmelCase_  :				str      =      [1] * (len_prefix + len_text + 1) + [0]
				UpperCAmelCase_  :				Optional[Any]      =      [1] + [1] * (len_prefix) + [0] * (len_text + 1)

				UpperCAmelCase_  :				str      =      tokenizer(prefix_text + input_text		).token_type_ids
				UpperCAmelCase_  :				int      =      tokenizer(""			,			prefix_text=prefix_text + input_text		).token_type_ids
				UpperCAmelCase_  :				Tuple      =      tokenizer(A__			,			prefix_text=A__		).token_type_ids
				self.assertListEqual(A__			,			A__		)
				self.assertListEqual(A__			,			A__		)
				self.assertListEqual(A__			,			A__		)





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    Optional[Any]:
				UpperCAmelCase_  :				Tuple      =      self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"		)

				UpperCAmelCase_  :				Any      =      tokenizer.encode("あンいワ"		)
				UpperCAmelCase_  :				str      =      tokenizer.encode(""			,			prefix_text="あンいワ"		)
				UpperCAmelCase_  :				Union[str, Any]      =      tokenizer.encode("いワ"			,			prefix_text="あン"		)

				self.assertEqual(tokenizer.decode(A__		)			,			tokenizer.decode(A__		)		)
				self.assertEqual(tokenizer.decode(A__		)			,			tokenizer.decode(A__		)		)
				self.assertNotEqual(A__			,			A__		)
				self.assertNotEqual(A__			,			A__		)
				self.assertEqual(x_token_a[1]			,			x_token_a[-1]		)  # SEG token
				self.assertEqual(x_token_a[1]			,			x_token_a[3]		)  # SEG token





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    List[Any]:
				UpperCAmelCase_  :				List[Any]      =      self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"		)

				UpperCAmelCase_  :				int      =      [["武田信玄", "は、"], ["織田信長", "の配下の、"]]
				UpperCAmelCase_  :				Dict      =      tokenizer(A__			,			padding=A__		)
				UpperCAmelCase_  :				int      =      tokenizer.batch_encode_plus(A__			,			padding=A__		)

				# fmt: off
				UpperCAmelCase_  :				Any      =      [[35_993, 8_640, 25_948, 35_998, 30_647, 35_675, 35_999, 35_999], [35_993, 10_382, 9_868, 35_998, 30_646, 9_459, 30_646, 35_675]]
				UpperCAmelCase_  :				Tuple      =      [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
				UpperCAmelCase_  :				Union[str, Any]      =      [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
				# fmt: on
				self.assertListEqual(x_token.input_ids			,			A__		)
				self.assertListEqual(x_token.token_type_ids			,			A__		)
				self.assertListEqual(x_token.attention_mask			,			A__		)
				self.assertListEqual(x_token_a.input_ids			,			A__		)
				self.assertListEqual(x_token_a.token_type_ids			,			A__		)
				self.assertListEqual(x_token_a.attention_mask			,			A__		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    Optional[int]:
				# Intentionally convert some words to accommodate character fluctuations unique to Japanese
				pass





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]		)		->    Optional[int]:
				# tokenizer has no padding token
				pass











"""simple docstring"""
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        snake_case   (     A__		):
			UpperCAmelCase_  :				Tuple      =      []
			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        snake_case   (     A__			,A__		):
			UpperCAmelCase_  :				List[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        snake_case   (     A__		):
			UpperCAmelCase_  :				Union[str, Any]      =      []
			token.append((F"""cvt.encoder.stages.{idx}.cls_token""", "stage2.cls_token")		)
			return token

def        snake_case   (     ):
			UpperCAmelCase_  :				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        snake_case   (     A__			,A__			,A__			,A__		):
			UpperCAmelCase_  :				int      =      "imagenet-1k-id2label.json"
			UpperCAmelCase_  :				Union[str, Any]      =      10_00

			UpperCAmelCase_  :				Any      =      "huggingface/label-files"
			UpperCAmelCase_  :				Optional[int]      =      num_labels
			UpperCAmelCase_  :				Union[str, Any]      =      json.load(open(cached_download(hf_hub_url(A__			,A__			,repo_type="dataset"		)		)			,"r"		)		)
			UpperCAmelCase_  :				Optional[int]      =      {int(A__		): v for k, v in idalabel.items()}

			UpperCAmelCase_  :				Optional[int]      =      idalabel
			UpperCAmelCase_  :				Optional[Any]      =      {v: k for k, v in idalabel.items()}

			UpperCAmelCase_  :				str      =      CvtConfig(num_labels=A__			,idalabel=A__			,labelaid=A__		)

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

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

			# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
			else:
						UpperCAmelCase_  :				Optional[int]      =      [2, 2, 20]
						UpperCAmelCase_  :				List[str]      =      [3, 12, 16]
						UpperCAmelCase_  :				Optional[Any]      =      [1_92, 7_68, 10_24]

			UpperCAmelCase_  :				Tuple      =      CvtForImageClassification(A__		)
			UpperCAmelCase_  :				Tuple      =      AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"		)
			UpperCAmelCase_  :				int      =      image_size
			UpperCAmelCase_  :				List[str]      =      torch.load(A__			,map_location=torch.device("cpu"		)		)

			UpperCAmelCase_  :				int      =      OrderedDict()
			UpperCAmelCase_  :				Optional[Any]      =      []

			for idx in range(len(config.depth		)		):
						if config.cls_token[idx]:
									UpperCAmelCase_  :				Optional[Any]      =      list_of_state_dict + cls_token(A__		)
						UpperCAmelCase_  :				Any      =      list_of_state_dict + embeddings(A__		)
						for cnt in range(config.depth[idx]		):
									UpperCAmelCase_  :				Dict      =      list_of_state_dict + attention(A__			,A__		)

			UpperCAmelCase_  :				Union[str, Any]      =      list_of_state_dict + final()
			for gg in list_of_state_dict:
						print(A__		)
			for i in range(len(A__		)		):
						UpperCAmelCase_  :				Any      =      original_weights[list_of_state_dict[i][1]]

			model.load_state_dict(A__		)
			model.save_pretrained(A__		)
			image_processor.save_pretrained(A__		)


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

if __name__ == "__main__":
			lowerCamelCase_       			=   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.'''
			)

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



