


import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE      :			List[str]      				=   get_tests_dir("fixtures/test_sentencepiece.model")

@require_sentencepiece
class   A__ (   snake_case__	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		XLMProphetNetTokenizer
    __magic_name__        =		False
    __magic_name__        =		True
    def 		a_      (	self   ):
         super().setUp()

         # We have a SentencePiece fixture for testing
         snake_case					=    XLMProphetNetTokenizer(__snake_case      , keep_accents=__snake_case   )
         tokenizer.save_pretrained(self.tmpdirname   )
    def 		a_      (	self   ):
         snake_case					=    '''[PAD]'''
         snake_case					=    0

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case   )      , __snake_case   )
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case   )      , __snake_case   )
    def 		a_      (	self   ):
         snake_case					=    list(self.get_tokenizer().get_vocab().keys()   )

         self.assertEqual(vocab_keys[0]      , '''[PAD]'''   )
         self.assertEqual(vocab_keys[1]      , '''[CLS]'''   )
         self.assertEqual(vocab_keys[-1]      , '''j'''   )
         self.assertEqual(len(__snake_case   )      , 1_0_1_2   )
    def 		a_      (	self   ):
         self.assertEqual(self.get_tokenizer().vocab_size      , 1_0_1_2   )
    def 		a_      (	self   ):
         snake_case					=    XLMProphetNetTokenizer(__snake_case      , keep_accents=__snake_case   )

         snake_case					=    tokenizer.tokenize('''This is a test'''   )
         self.assertListEqual(__snake_case      , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']   )

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(__snake_case   )      , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]      , )

         snake_case					=    tokenizer.tokenize('''I was born in 92000, and this is falsé.'''   )
         self.assertListEqual(
             __snake_case      , [
                 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					=    tokenizer.convert_tokens_to_ids(__snake_case   )
         self.assertListEqual(
             __snake_case      , [
                 value + tokenizer.fairseq_offset
                 for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, -9, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, -9, 4]
             ]      , )

         snake_case					=    tokenizer.convert_ids_to_tokens(__snake_case   )
         self.assertListEqual(
             __snake_case      , [
                 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]''',
                 '''.''',
             ]      , )
    @cached_property
    def 		a_      (	self   ):
         return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased'''   )
    @slow
    def 		a_      (	self   ):
         snake_case					=    '''Hello World!'''
         snake_case					=    [3_5_3_8_9, 6_6_7_2, 4_9, 2]
         self.assertListEqual(__snake_case      , self.big_tokenizer.encode(__snake_case   )   )




    @slow
    def 		a_      (	self   ):
         # fmt: off
         snake_case					=    {'''input_ids''': [[1_1_0_7_3, 8_2_7_8_3, 1_8, 2_6, 8_2_7_8_3, 5_4_9, 5_1_5_4_0, 2_4_8, 1_7_2_0_9, 1_3_0_1, 2_1_7, 2_0, 2_1_5_1_8_6, 1_3_2_5, 1_4_7, 1_7_2_0_9, 1_3_0_1, 2_1_7, 2_0, 5_6_3_7_0, 5_3, 1_2_2_0_2_0, 2_0, 1_6_4_7_7, 2_7, 8_7_3_5_5, 4_5_4_8, 2_0, 4_7_2_8, 7_8_3_9_2, 1_7, 1_5_9_9_6_9, 1_8, 2_6, 2_4_4_9_1, 6_2_9, 1_5, 5_3_8, 2_2_7_0_4, 5_4_3_9, 1_5, 2_7_8_8, 2_4_4_9_1, 9_8_8_5, 1_5, 4_3_5_3_4, 6_0_5, 1_5, 8_1_4, 1_8_4_0_3, 3_3_2_0_0, 2_9, 1_5, 4_3_5_3_4, 2_4_4_5_8, 1_2_4_1_0, 1_1_1, 2_4_9_6_6, 8_3_6_6_9, 9_6_3_7, 1_4_4_0_6_8, 2_6, 8_5_0, 2_2_3_4_6, 2_7, 1_4_7, 2_4_9_6_6, 8_3_6_6_9, 8_3_4_9_0, 2_6, 3_9_1_1_3, 7_3_5, 2_7, 6_8_9, 6_5_6, 2_8_0_0, 1_3_3_9, 4_6_0_0, 5_3, 1_2_2_0_2_0, 1_1_5_7_8_5, 3_4, 8_1_6, 1_3_3_9, 4_6_8_8_7, 1_8, 1_4_7, 5_3_9_0_5, 1_9_5_1, 4_2_2_3_8, 4_1_1_7_0, 1_7_7_3_2, 8_3_4, 4_3_6, 1_5, 2_7_5_2_3, 9_8_7_3_3, 2_1_7, 1_4_7, 5_5_4_2, 4_9_8_1, 9_3_0, 1_7_3_4_7, 1_6, 2], [2_0_0_9_1, 6_2_9, 9_4, 8_2_7_8_6, 5_8, 4_9_0, 2_0, 1_5_2_8, 8_4, 5_3_9_0_5, 3_4_4, 8_0_5_9_2, 1_1_0_1_2_8, 1_8_8_2_2, 5_2_6_7, 1_3_0_6, 6_2, 1_5_2_5_3_7, 3_0_8, 7_9_9_7, 4_0_1, 1_2_4_4_2_7, 5_4_9, 3_5_4_4_2, 2_2_5, 1_0_9, 1_5_0_5_5, 2_5_7_4_8, 1_4_7, 7_1_1_9, 4_3_7_1_2, 3_4, 7_6_7, 1_3_5_3_6_6, 1_8, 1_6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_9_2, 6_3_7_8_4, 1_1_9_4_6_6, 1_7, 1_4_7_8_0_8, 8_8_2_1_4, 1_8, 6_5_6, 8_1, 3_2, 3_2_9_6, 1_0_2_8_0, 1_6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=__snake_case      , model_name='''microsoft/xprophetnet-large-wiki100-cased'''      , revision='''1acad1643ddd54a44df6a1b797ada8373685d90e'''      , )








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       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    []
     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       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    []
     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       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    []
     token.append((F'''cvt.encoder.stages.{idx}.cls_token''', '''stage2.cls_token''')    )
     return token
def       UpperCAmelCase__						():



     """simple docstring"""






     snake_case					=    []
     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       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    '''imagenet-1k-id2label.json'''
     snake_case					=    10_00

     snake_case					=    '''huggingface/label-files'''
     snake_case					=    num_labels
     snake_case					=    json.load(open(cached_download(hf_hub_url(UpperCamelCase_							,UpperCamelCase_							,repo_type='''dataset'''    )    )							,'''r'''    )    )
     snake_case					=    {int(UpperCamelCase_    ): v for k, v in idalabel.items()}

     snake_case					=    idalabel
     snake_case					=    {v: k for k, v in idalabel.items()}

     snake_case					=    snake_case					=    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					=    [1, 2, 10]

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

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

     snake_case					=    CvtForImageClassification(UpperCamelCase_    )
     snake_case					=    AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''    )
     snake_case					=    image_size
     snake_case					=    torch.load(UpperCamelCase_							,map_location=torch.device('''cpu'''    )    )

     snake_case					=    OrderedDict()
     snake_case					=    []

     for idx in range(len(config.depth    )    ):
          if config.cls_token[idx]:
               snake_case					=    list_of_state_dict + cls_token(UpperCamelCase_    )
          snake_case					=    list_of_state_dict + embeddings(UpperCamelCase_    )
          for cnt in range(config.depth[idx]    ):
               snake_case					=    list_of_state_dict + attention(UpperCamelCase_							,UpperCamelCase_    )

     snake_case					=    list_of_state_dict + final()
     for gg in list_of_state_dict:
          print(UpperCamelCase_    )
     for i in range(len(UpperCamelCase_    )    ):
          snake_case					=    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__":
 _SCREAMING_SNAKE_CASE      :			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=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."
 )

 _SCREAMING_SNAKE_CASE      :			Optional[int]      				=   parser.parse_args()
 convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)




