




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       :       int        =    get_tests_dir('fixtures/test_sentencepiece.model')




@require_sentencepiece
@require_tokenizers
class 						__lowercase		(__UpperCamelCase     ,		unittest.TestCase							):




      """simple docstring"""





      _snake_case							     =						XLNetTokenizer
      _snake_case							     =						XLNetTokenizerFast
      _snake_case							     =						True
      _snake_case							     =						True

      def 	UpperCAmelCase      (      self	)		->				List[Any]:
         super().setUp()

         # We have a SentencePiece fixture for testing
         snake_case      :				Optional[Any]				=       XLNetTokenizer(A				,      keep_accents=A	)
         tokenizer.sanitize_special_tokens()
         tokenizer.save_pretrained(self.tmpdirname	)

      def 	UpperCAmelCase      (      self	)		->				int:
         snake_case      :				Tuple				=       '<s>'
         snake_case      :				int				=       1

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

      def 	UpperCAmelCase      (      self	)		->				Any:
         snake_case      :				int				=       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(A	)				,      1_0_0_6	)

      def 	UpperCAmelCase      (      self	)		->				Union[str, Any]:
         self.assertEqual(self.get_tokenizer().vocab_size				,      1_0_0_0	)

      def 	UpperCAmelCase      (      self	)		->				Tuple:
         snake_case      :				Optional[int]				=       XLNetTokenizer(A				,      keep_accents=A	)

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

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

         snake_case      :				Union[str, Any]				=       tokenizer.tokenize("""I was born in 92000, and this is falsé."""	)
         self.assertListEqual(
             A				,      [
                 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      :				Optional[int]				=       tokenizer.convert_tokens_to_ids(A	)
         self.assertListEqual(A				,      [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      :				List[Any]				=       tokenizer.convert_ids_to_tokens(A	)
         self.assertListEqual(
             A				,      [
                 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	)		->				Any:
         snake_case      :				str				=       XLNetTokenizer(A				,      do_lower_case=A	)
         snake_case      :				Tuple				=       tokenizer.tokenize("""I was born in 92000, and this is falsé."""	)
         self.assertListEqual(
             A				,      [
                 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	)		->				int:
         snake_case      :				str				=       XLNetTokenizer(A				,      do_lower_case=A	)
         snake_case      :				Any				=       tokenizer.tokenize("""I was born in 92000, and this is falsé."""	)
         self.assertListEqual(
             A				,      [
                 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	)		->				Optional[int]:
         snake_case      :				List[str]				=       XLNetTokenizer.from_pretrained("""xlnet-base-cased"""	)

         snake_case      :				List[Any]				=       tokenizer.encode("""sequence builders"""				,      add_special_tokens=A	)
         snake_case      :				Dict				=       tokenizer.encode("""multi-sequence build"""				,      add_special_tokens=A	)

         snake_case      :				str				=       tokenizer.build_inputs_with_special_tokens(A	)
         snake_case      :				Any				=       tokenizer.build_inputs_with_special_tokens(A				,      A	)

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

      @slow
      def 	UpperCAmelCase      (      self	)		->				List[str]:
         # fmt: off
         snake_case      :				Tuple				=       {'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=A				,      model_name="""xlnet-base-cased"""				,      revision="""c841166438c31ec7ca9a106dee7bb312b73ae511"""				,      )












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        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     int:
    lowerCamelCase__    :      Optional[int]					 =		[]
    embed.append(
        (
            F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
            F"""stage{idx}.patch_embed.proj.weight""",
        ) )
    embed.append(
        (
            F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
            F"""stage{idx}.patch_embed.proj.bias""",
        ) )
    embed.append(
        (
            F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
            F"""stage{idx}.patch_embed.norm.weight""",
        ) )
    embed.append(
        (
            F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
            F"""stage{idx}.patch_embed.norm.bias""",
        ) )
    return embed


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     Tuple:
    lowerCamelCase__    :      Tuple					 =		[]
    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        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     Tuple:
    lowerCamelCase__    :      Union[str, Any]					 =		[]
    token.append((F"""cvt.encoder.stages.{idx}.cls_token""", 'stage2.cls_token') )
    return token


def        SCREAMING_SNAKE_CASE      (       )			->     str:
    lowerCamelCase__    :      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        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     Optional[int]:
    lowerCamelCase__    :      Tuple					 =		'imagenet-1k-id2label.json'
    lowerCamelCase__    :      Union[str, Any]					 =		1000

    lowerCamelCase__    :      Optional[Any]					 =		'huggingface/label-files'
    lowerCamelCase__    :      Any					 =		num_labels
    lowerCamelCase__    :      Dict					 =		json.load(open(cached_download(hf_hub_url(_UpperCAmelCase						,	_UpperCAmelCase						,	repo_type='dataset' ) )						,	'r' ) )
    lowerCamelCase__    :      int					 =		{int(_UpperCAmelCase ): v for k, v in idalabel.items()}

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

    lowerCamelCase__    :      List[str]					 =		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":
        lowerCamelCase__    :      List[Any]					 =		[1, 2, 10]

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

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

    lowerCamelCase__    :      Any					 =		CvtForImageClassification(_UpperCAmelCase )
    lowerCamelCase__    :      Optional[Any]					 =		AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
    lowerCamelCase__    :      Tuple					 =		image_size
    lowerCamelCase__    :      List[str]					 =		torch.load(_UpperCAmelCase						,	map_location=torch.device('cpu' ) )

    lowerCamelCase__    :      Optional[int]					 =		OrderedDict()
    lowerCamelCase__    :      Tuple					 =		[]

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

    lowerCamelCase__    :      int					 =		list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(_UpperCAmelCase )
    for i in range(len(_UpperCAmelCase ) ):
        lowerCamelCase__    :      str					 =		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__":
    _UpperCAmelCase				:       List[str]	   =				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=3_84,
        type=int,
        help="""Input Image Size""",
    )
    parser.add_argument(
        """--cvt_file_name""",
        default=R"""cvtmodels\CvT-w24-384x384-IN-22k.pth""",
        type=str,
        help="""Input Image Size""",
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
    )

    _UpperCAmelCase				:       List[str]	   =				parser.parse_args()
    convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)





