



"""simple docstring"""





import unittest

from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCAmelCase       :					Any      					=					get_tests_dir('''fixtures/test_sentencepiece.model''')



@require_sentencepiece
@require_tokenizers
class    _lowerCAmelCase    (				_A  ,				unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase			  =       XLNetTokenizer
		_lowerCamelCase			  =       XLNetTokenizerFast
		_lowerCamelCase			  =       True
		_lowerCamelCase			  =       True





		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								super().setUp()

								# We have a SentencePiece fixture for testing
								snake_case_   : Dict						=			XLNetTokenizer(__lowerCamelCase							,    keep_accents=__lowerCamelCase   )
								tokenizer.sanitize_special_tokens()
								tokenizer.save_pretrained(self.tmpdirname   )





		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								snake_case_   : List[Any]						=			"<s>"
								snake_case_   : List[str]						=			1

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





		def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







								'''simple docstring'''

								snake_case_   : List[str]						=			list(self.get_tokenizer().get_vocab().keys()   )

								self.assertEqual(vocab_keys[0]							,    """<unk>"""   )
								self.assertEqual(vocab_keys[1]							,    """<s>"""   )
								self.assertEqual(vocab_keys[-1]							,    """<eod>"""   )
								self.assertEqual(len(__lowerCamelCase   )							,    1_0_0_6   )





		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								self.assertEqual(self.get_tokenizer().vocab_size							,    1_0_0_0   )





		def    UpperCAmelCase__							(					self   ) ->						Optional[Any]:







								'''simple docstring'''

								snake_case_   : List[str]						=			XLNetTokenizer(__lowerCamelCase							,    keep_accents=__lowerCamelCase   )

								snake_case_   : Optional[Any]						=			tokenizer.tokenize("""This is a test"""   )
								self.assertListEqual(__lowerCamelCase							,    ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]   )

								self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase   )							,    [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]   )

								snake_case_   : Optional[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""",
								        """.""",
								    ]							,    )
								snake_case_   : Tuple						=			tokenizer.convert_tokens_to_ids(__lowerCamelCase   )
								self.assertListEqual(__lowerCamelCase							,    [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]   )

								snake_case_   : Dict						=			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    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								snake_case_   : Dict						=			XLNetTokenizer(__lowerCamelCase							,    do_lower_case=__lowerCamelCase   )
								snake_case_   : 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""",
								        """se""",
								        """.""",
								    ]							,    )
								self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""   )							,    ["""▁he""", """ll""", """o"""]   )





		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								snake_case_   : int						=			XLNetTokenizer(__lowerCamelCase							,    do_lower_case=__lowerCamelCase   )
								snake_case_   : 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""",
								        """se""",
								        """.""",
								    ]							,    )





		@slow
		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								snake_case_   : List[Any]						=			XLNetTokenizer.from_pretrained("""xlnet-base-cased"""   )

								snake_case_   : Tuple						=			tokenizer.encode("""sequence builders"""							,    add_special_tokens=__lowerCamelCase   )
								snake_case_   : List[Any]						=			tokenizer.encode("""multi-sequence build"""							,    add_special_tokens=__lowerCamelCase   )

								snake_case_   : Any						=			tokenizer.build_inputs_with_special_tokens(__lowerCamelCase   )
								snake_case_   : List[Any]						=			tokenizer.build_inputs_with_special_tokens(__lowerCamelCase							,    __lowerCamelCase   )

								assert encoded_sentence == text + [4, 3]
								assert encoded_pair == text + [4] + text_a + [4, 3]





		@slow
		def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







								'''simple docstring'''

								snake_case_   : str						=			{"input_ids": [[1_7, 2_1_4_4_2, 2_7_0, 1_7, 1_0, 1_4_6_4_5, 3_1_8, 3_4, 1_7, 4_5_4_6, 3_1_4_5, 7_8_7, 1_3, 7_7_5_2, 2_2_0_1_8, 2_3, 2_1, 1_7, 4_5_4_6, 3_1_4_5, 7_8_7, 1_3, 3_3_5_2, 1_4_4_3_1, 1_3, 5_5_0_0, 1_1, 1_1_7_6, 5_8_0, 1_3, 1_6_8_1_9, 4_7_9_7, 2_3, 1_7, 1_0, 1_7_1_3_5, 6_5_8, 1_9, 4_5_7, 7_9_3_2, 1_3, 1_8_4, 1_9, 3_1_5_4, 1_7_1_3_5, 6_4_6_8, 1_9, 1_4_0_4, 1_2_2_6_9, 1_9, 4_2_2_9, 5_3_5_6, 1_6_2_6_4, 4_6, 1_9, 1_7, 2_0_5_4_5, 1_0_3_9_5, 9, 9, 9, 1_1, 2_8, 6_4_2_1, 9_5_3_1, 2_0_7_2_9, 1_7, 1_0, 3_5_3, 1_7_0_2_2, 1_1, 2_1, 6_4_2_1, 9_5_3_1, 1_6_9_4_9, 1_7, 1_0, 1_1_5_0_9, 7_5_3, 1_1, 3_3, 9_5, 2_4_2_1, 7_3_8_5, 9_5_6, 1_4_4_3_1, 2_6_2_6, 2_5, 8_4_2, 7_3_8_5, 4_8_3_6, 2_1, 1_4_2_9, 2_2_7_2, 9_8_5_5, 3_1_2_0, 1_6_1, 2_4_7_3_8, 1_9, 1_3_2_0_3, 6_5_8, 2_1_8, 7_8_7, 2_1, 4_3_0, 1_8_4_8_2, 8_4_7, 2_6_3_7, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_2_2, 2_2_1_7_8, 2_7, 1_0_6_4, 2_2, 9_5_6, 1_3, 1_1_1_0_1, 1_4_2_9, 5_8_5_4, 2_4_3_1_3, 1_8_9_5_3, 4_0, 4_2_2, 2_4_3_6_6, 6_8, 1_7_5_8, 3_7, 1_0_4_8_3, 1_4_2_5_7, 3_1, 2_0_7, 2_6_3, 2_1, 2_0_3, 3_7_7_3, 2_5, 7_1, 9_7_3_5, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_2, 2_0_4_9, 3_4_4_2, 1_7, 1_3_8_9_4, 3_3_8_0, 2_3, 9_5, 1_8, 1_7_6_3_4, 2_2_8_8, 9, 4, 3]], "token_type_ids": [[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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 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, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], "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], [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, 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, 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]]}  # noqa: E501
								# fmt: on

								self.tokenizer_integration_test_util(
								    expected_encoding=__lowerCamelCase							,    model_name="""xlnet-base-cased"""							,    revision="""c841166438c31ec7ca9a106dee7bb312b73ae511"""							,    )










"""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 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]    ):


						'''simple docstring'''






						snake_case_   : List[str]						=			[]
						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 	__lowerCAmelCase						(		__UpperCamelCase      :			str					, __UpperCamelCase      :			int    ):


						'''simple docstring'''






						snake_case_   : str						=			[]
						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 	__lowerCAmelCase						(		__UpperCamelCase      :			Tuple    ):


						'''simple docstring'''






						snake_case_   : int						=			[]
						token.append((F'cvt.encoder.stages.{idx}.cls_token', """stage2.cls_token""")    )
						return token
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						snake_case_   : Union[str, Any]						=			[]
						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 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Optional[Any]					, __UpperCamelCase      :			List[Any]    ):


						'''simple docstring'''






						snake_case_   : Union[str, Any]						=			"""imagenet-1k-id2label.json"""
						snake_case_   : Optional[Any]						=			1_0_0_0

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

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

						snake_case_   : Dict						=			CvtConfig(num_labels=__UpperCamelCase					, idalabel=__UpperCamelCase					, labelaid=__UpperCamelCase    )

						# For depth size 13 (13 = 1+2+10)
						if cvt_model.rsplit("""/"""					, 1    )[-1][4:6] == "13":
												snake_case_   : Any						=			[1, 2, 1_0]

						# For depth size 21 (21 = 1+4+16)
						elif cvt_model.rsplit("""/"""					, 1    )[-1][4:6] == "21":
												snake_case_   : Any						=			[1, 4, 1_6]

						# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
						else:
												snake_case_   : Optional[int]						=			[2, 2, 2_0]
												snake_case_   : str						=			[3, 1_2, 1_6]
												snake_case_   : Any						=			[1_9_2, 7_6_8, 1_0_2_4]

						snake_case_   : Union[str, Any]						=			CvtForImageClassification(__UpperCamelCase    )
						snake_case_   : str						=			AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""    )
						snake_case_   : List[Any]						=			image_size
						snake_case_   : str						=			torch.load(__UpperCamelCase					, map_location=torch.device("""cpu"""    )    )

						snake_case_   : Any						=			OrderedDict()
						snake_case_   : Tuple						=			[]

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

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

						model.load_state_dict(__UpperCamelCase    )
						model.save_pretrained(__UpperCamelCase    )
						image_processor.save_pretrained(__UpperCamelCase    )


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

if __name__ == "__main__":
					__lowerCAmelCase       :					Optional[int]      					=					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       :					Dict      					=					parser.parse_args()
					convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)

