






"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase_       			=   get_tests_dir('''fixtures/test_sentencepiece.model''')







@require_sentencepiece
@require_tokenizers
class     UpperCamelCase_ (__A       ,		unittest.TestCase  ):
	__magic_name__			   =       XLMRobertaTokenizer
	__magic_name__			   =       XLMRobertaTokenizerFast
	__magic_name__			   =       True
	__magic_name__			   =       True





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    Union[str, Any]:
				super().setUp()

				# We have a SentencePiece fixture for testing
				UpperCAmelCase_  :				List[str]      =      XLMRobertaTokenizer(lowerCAmelCase_			,			keep_accents=lowerCAmelCase_		)
				tokenizer.save_pretrained(self.tmpdirname		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]		)		->    Union[str, Any]:
				UpperCAmelCase_  :				str      =      "<pad>"
				UpperCAmelCase_  :				int      =      1

				self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase_		)			,			lowerCAmelCase_		)
				self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase_		)			,			lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    int:
				UpperCAmelCase_  :				List[str]      =      list(self.get_tokenizer().get_vocab().keys()		)

				self.assertEqual(vocab_keys[0]			,			"<s>"		)
				self.assertEqual(vocab_keys[1]			,			"<pad>"		)
				self.assertEqual(vocab_keys[-1]			,			"<mask>"		)
				self.assertEqual(len(lowerCAmelCase_		)			,			1_002		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    int:
				self.assertEqual(self.get_tokenizer().vocab_size			,			1_002		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    str:
				UpperCAmelCase_  :				Optional[int]      =      XLMRobertaTokenizer(lowerCAmelCase_			,			keep_accents=lowerCAmelCase_		)

				UpperCAmelCase_  :				Optional[int]      =      tokenizer.tokenize("This is a test"		)
				self.assertListEqual(lowerCAmelCase_			,			["▁This", "▁is", "▁a", "▁t", "est"]		)

				self.assertListEqual(
				    tokenizer.convert_tokens_to_ids(lowerCAmelCase_		)			,			[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]			,			)

				UpperCAmelCase_  :				List[Any]      =      tokenizer.tokenize("I was born in 92000, and this is falsé."		)
				self.assertListEqual(
				    lowerCAmelCase_			,			[
				        SPIECE_UNDERLINE + "I",
				        SPIECE_UNDERLINE + "was",
				        SPIECE_UNDERLINE + "b",
				        "or",
				        "n",
				        SPIECE_UNDERLINE + "in",
				        SPIECE_UNDERLINE + "",
				        "9",
				        "2",
				        "0",
				        "0",
				        "0",
				        ",",
				        SPIECE_UNDERLINE + "and",
				        SPIECE_UNDERLINE + "this",
				        SPIECE_UNDERLINE + "is",
				        SPIECE_UNDERLINE + "f",
				        "al",
				        "s",
				        "é",
				        ".",
				    ]			,			)
				UpperCAmelCase_  :				Optional[Any]      =      tokenizer.convert_tokens_to_ids(lowerCAmelCase_		)
				self.assertListEqual(
				    lowerCAmelCase_			,			[
				        value + tokenizer.fairseq_offset
				        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
				        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
				    ]			,			)

				UpperCAmelCase_  :				Any      =      tokenizer.convert_ids_to_tokens(lowerCAmelCase_		)
				self.assertListEqual(
				    lowerCAmelCase_			,			[
				        SPIECE_UNDERLINE + "I",
				        SPIECE_UNDERLINE + "was",
				        SPIECE_UNDERLINE + "b",
				        "or",
				        "n",
				        SPIECE_UNDERLINE + "in",
				        SPIECE_UNDERLINE + "",
				        "<unk>",
				        "2",
				        "0",
				        "0",
				        "0",
				        ",",
				        SPIECE_UNDERLINE + "and",
				        SPIECE_UNDERLINE + "this",
				        SPIECE_UNDERLINE + "is",
				        SPIECE_UNDERLINE + "f",
				        "al",
				        "s",
				        "<unk>",
				        ".",
				    ]			,			)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    str:
				if not self.test_slow_tokenizer:
							# as we don't have a slow version, we can't compare the outputs between slow and fast versions
							return

				UpperCAmelCase_  :				Union[str, Any]      =      (self.rust_tokenizer_class, "hf-internal-testing/tiny-xlm-roberta", {})
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
										UpperCAmelCase_  :				Union[str, Any]      =      self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_			,			**lowerCAmelCase_		)
										UpperCAmelCase_  :				Dict      =      self.tokenizer_class.from_pretrained(lowerCAmelCase_			,			**lowerCAmelCase_		)

										UpperCAmelCase_  :				int      =      tempfile.mkdtemp()

										UpperCAmelCase_  :				str      =      tokenizer_r.save_pretrained(lowerCAmelCase_		)
										UpperCAmelCase_  :				List[Any]      =      tokenizer_p.save_pretrained(lowerCAmelCase_		)

										# Checks it save with the same files + the tokenizer.json file for the fast one
										self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files		)		)
										UpperCAmelCase_  :				Union[str, Any]      =      tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f		)
										self.assertSequenceEqual(lowerCAmelCase_			,			lowerCAmelCase_		)

										# Checks everything loads correctly in the same way
										UpperCAmelCase_  :				Tuple      =      tokenizer_r.from_pretrained(lowerCAmelCase_		)
										UpperCAmelCase_  :				List[Any]      =      tokenizer_p.from_pretrained(lowerCAmelCase_		)

										# Check special tokens are set accordingly on Rust and Python
										for key in tokenizer_pp.special_tokens_map:
													self.assertTrue(hasattr(lowerCAmelCase_			,			lowerCAmelCase_		)		)
													# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
													# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

										shutil.rmtree(lowerCAmelCase_		)

										# Save tokenizer rust, legacy_format=True
										UpperCAmelCase_  :				Optional[Any]      =      tempfile.mkdtemp()

										UpperCAmelCase_  :				Optional[int]      =      tokenizer_r.save_pretrained(lowerCAmelCase_			,			legacy_format=lowerCAmelCase_		)
										UpperCAmelCase_  :				Optional[Any]      =      tokenizer_p.save_pretrained(lowerCAmelCase_		)

										# Checks it save with the same files
										self.assertSequenceEqual(lowerCAmelCase_			,			lowerCAmelCase_		)

										# Checks everything loads correctly in the same way
										UpperCAmelCase_  :				Tuple      =      tokenizer_r.from_pretrained(lowerCAmelCase_		)
										UpperCAmelCase_  :				Any      =      tokenizer_p.from_pretrained(lowerCAmelCase_		)

										# Check special tokens are set accordingly on Rust and Python
										for key in tokenizer_pp.special_tokens_map:
													self.assertTrue(hasattr(lowerCAmelCase_			,			lowerCAmelCase_		)		)

										shutil.rmtree(lowerCAmelCase_		)

										# Save tokenizer rust, legacy_format=False
										UpperCAmelCase_  :				int      =      tempfile.mkdtemp()

										UpperCAmelCase_  :				Optional[int]      =      tokenizer_r.save_pretrained(lowerCAmelCase_			,			legacy_format=lowerCAmelCase_		)
										UpperCAmelCase_  :				List[str]      =      tokenizer_p.save_pretrained(lowerCAmelCase_		)

										# Checks it saved the tokenizer.json file
										self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files		)		)

										# Checks everything loads correctly in the same way
										UpperCAmelCase_  :				Tuple      =      tokenizer_r.from_pretrained(lowerCAmelCase_		)
										UpperCAmelCase_  :				List[str]      =      tokenizer_p.from_pretrained(lowerCAmelCase_		)

										# Check special tokens are set accordingly on Rust and Python
										for key in tokenizer_pp.special_tokens_map:
													self.assertTrue(hasattr(lowerCAmelCase_			,			lowerCAmelCase_		)		)

										shutil.rmtree(lowerCAmelCase_		)





	@cached_property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    str:
				return XLMRobertaTokenizer.from_pretrained("xlm-roberta-base"		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]		)		->    List[str]:
				with tempfile.NamedTemporaryFile() as f:
							shutil.copyfile(lowerCAmelCase_			,			f.name		)
							UpperCAmelCase_  :				Any      =      XLMRobertaTokenizer(f.name			,			keep_accents=lowerCAmelCase_		)
							UpperCAmelCase_  :				str      =      pickle.dumps(lowerCAmelCase_		)
				pickle.loads(lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    Dict:
				if not self.test_rust_tokenizer:
							return

				UpperCAmelCase_  :				Dict      =      self.get_tokenizer()
				UpperCAmelCase_  :				Union[str, Any]      =      self.get_rust_tokenizer()

				UpperCAmelCase_  :				Optional[Any]      =      "I was born in 92000, and this is falsé."

				UpperCAmelCase_  :				Optional[Any]      =      tokenizer.tokenize(lowerCAmelCase_		)
				UpperCAmelCase_  :				Optional[int]      =      rust_tokenizer.tokenize(lowerCAmelCase_		)
				self.assertListEqual(lowerCAmelCase_			,			lowerCAmelCase_		)

				UpperCAmelCase_  :				Union[str, Any]      =      tokenizer.encode(lowerCAmelCase_			,			add_special_tokens=lowerCAmelCase_		)
				UpperCAmelCase_  :				Tuple      =      rust_tokenizer.encode(lowerCAmelCase_			,			add_special_tokens=lowerCAmelCase_		)
				self.assertListEqual(lowerCAmelCase_			,			lowerCAmelCase_		)

				UpperCAmelCase_  :				Tuple      =      self.get_rust_tokenizer()
				UpperCAmelCase_  :				Optional[Any]      =      tokenizer.encode(lowerCAmelCase_		)
				UpperCAmelCase_  :				str      =      rust_tokenizer.encode(lowerCAmelCase_		)
				self.assertListEqual(lowerCAmelCase_			,			lowerCAmelCase_		)





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    List[str]:
				UpperCAmelCase_  :				Optional[Any]      =      "Hello World!"
				UpperCAmelCase_  :				Any      =      [0, 35_378, 6_661, 38, 2]
				# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
				# xlmr.eval()
				# xlmr.encode(symbols)

				self.assertListEqual(lowerCAmelCase_			,			self.big_tokenizer.encode(lowerCAmelCase_		)		)





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    List[Any]:
				UpperCAmelCase_  :				Optional[int]      =      (
				    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
				    " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
				)
				UpperCAmelCase_  :				List[Any]      =      [
				    0,
				    3_293,
				    83,
				    10,
				    4_552,
				    4_989,
				    7_986,
				    678,
				    10,
				    5_915,
				    111,
				    179_459,
				    124_850,
				    4,
				    6_044,
				    237,
				    12,
				    6,
				    5,
				    6,
				    4,
				    6_780,
				    705,
				    15,
				    1_388,
				    44,
				    378,
				    10_114,
				    711,
				    152,
				    20,
				    6,
				    5,
				    22_376,
				    642,
				    1_221,
				    15_190,
				    34_153,
				    450,
				    5_608,
				    959,
				    1_119,
				    57_702,
				    136,
				    186,
				    47,
				    1_098,
				    29_367,
				    47,
				    # 4426, # What fairseq tokenizes from "<unk>": "_<"
				    # 3678, # What fairseq tokenizes from "<unk>": "unk"
				    # 2740, # What fairseq tokenizes from "<unk>": ">"
				    3,  # What we tokenize from "<unk>": "<unk>"
				    6,  # Residue from the tokenization: an extra sentencepiece underline
				    4,
				    6_044,
				    237,
				    6_284,
				    50_901,
				    528,
				    31,
				    90,
				    34,
				    927,
				    2,
				]
				# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
				# xlmr.eval()
				# xlmr.encode(symbols)

				self.assertListEqual(lowerCAmelCase_			,			self.big_tokenizer.encode(lowerCAmelCase_		)		)





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    Any:
				# fmt: off
				UpperCAmelCase_  :				str      =      {"input_ids": [[0, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 5_428, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 12_399, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 2_789, 1_328, 4_589, 42, 122_009, 115_774, 23, 805, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [0, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
				# fmt: on

				self.tokenizer_integration_test_util(
				    expected_encoding=lowerCAmelCase_			,			model_name="xlm-roberta-base"			,			revision="d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"			,			)











"""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)



