



"""simple docstring"""





import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


sys.path.append(str(Path(__file__).parent.parent / '''utils'''))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
					from test_module.custom_tokenization_fast import CustomTokenizerFast



class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								snake_case_   : int						=			mock.Mock()
								snake_case_   : Tuple						=			5_0_0
								snake_case_   : Dict						=			{}
								snake_case_   : Dict						=			HTTPError
								snake_case_   : Optional[Any]						=			{}

								# Download this model to make sure it's in the cache.
								snake_case_   : List[Any]						=			BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bert"""   )

								# Under the mock environment we get a 500 error when trying to reach the tokenizer.
								with mock.patch("""requests.Session.request"""							,    return_value=lowerCamelCase__   ) as mock_head:
														snake_case_   : Any						=			BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bert"""   )
														# This check we did call the fake head request
														mock_head.assert_called()





		@require_tokenizers
		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								snake_case_   : Union[str, Any]						=			mock.Mock()
								snake_case_   : Any						=			5_0_0
								snake_case_   : str						=			{}
								snake_case_   : Optional[Any]						=			HTTPError
								snake_case_   : Tuple						=			{}

								# Download this model to make sure it's in the cache.
								snake_case_   : Optional[Any]						=			GPTaTokenizerFast.from_pretrained("""gpt2"""   )

								# Under the mock environment we get a 500 error when trying to reach the tokenizer.
								with mock.patch("""requests.Session.request"""							,    return_value=lowerCamelCase__   ) as mock_head:
														snake_case_   : str						=			GPTaTokenizerFast.from_pretrained("""gpt2"""   )
														# This check we did call the fake head request
														mock_head.assert_called()





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







								'''simple docstring'''

								try:
														snake_case_   : Dict						=			tempfile.mktemp()
														with open(lowerCamelCase__							,    """wb"""   ) as f:
																				http_get("""https://huggingface.co/albert-base-v1/resolve/main/spiece.model"""							,    lowerCamelCase__   )

														snake_case_   : Dict						=			AlbertTokenizer.from_pretrained(lowerCamelCase__   )
								finally:
														os.remove(lowerCamelCase__   )

								# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
								# the current folder and have the right name.
								if os.path.isfile("""tokenizer.json"""   ):
														# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
														return
								try:
														with open("""tokenizer.json"""							,    """wb"""   ) as f:
																				http_get("""https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json"""							,    lowerCamelCase__   )
														snake_case_   : Union[str, Any]						=			AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""   )
														# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
														self.assertEqual(tokenizer.vocab_size							,    1_0_0_0   )
														# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

								finally:
														os.remove("""tokenizer.json"""   )





		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								snake_case_   : Any						=			AlbertTokenizer.from_pretrained("""https://huggingface.co/albert-base-v1/resolve/main/spiece.model"""   )




@is_staging_test
class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase			  =       ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]





		@classmethod
		def    UpperCAmelCase__							(					cls   ) ->						int:







								'''simple docstring'''

								snake_case_   : List[str]						=			TOKEN
								HfFolder.save_token(lowerCamelCase__   )





		@classmethod
		def    UpperCAmelCase__							(					cls   ) ->						Optional[Any]:







								'''simple docstring'''

								try:
														delete_repo(token=cls._token							,    repo_id="""test-tokenizer"""   )
								except HTTPError:
														pass

								try:
														delete_repo(token=cls._token							,    repo_id="""valid_org/test-tokenizer-org"""   )
								except HTTPError:
														pass

								try:
														delete_repo(token=cls._token							,    repo_id="""test-dynamic-tokenizer"""   )
								except HTTPError:
														pass





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







								'''simple docstring'''

								with tempfile.TemporaryDirectory() as tmp_dir:
														snake_case_   : int						=			os.path.join(lowerCamelCase__							,    """vocab.txt"""   )
														with open(lowerCamelCase__							,    """w"""							,    encoding="""utf-8"""   ) as vocab_writer:
																				vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]   )   )
														snake_case_   : Tuple						=			BertTokenizer(lowerCamelCase__   )

								tokenizer.push_to_hub("""test-tokenizer"""							,    use_auth_token=self._token   )
								snake_case_   : Union[str, Any]						=			BertTokenizer.from_pretrained(f'{USER}/test-tokenizer'   )
								self.assertDictEqual(new_tokenizer.vocab							,    tokenizer.vocab   )

								# Reset repo
								delete_repo(token=self._token							,    repo_id="""test-tokenizer"""   )

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
														tokenizer.save_pretrained(lowerCamelCase__							,    repo_id="""test-tokenizer"""							,    push_to_hub=lowerCamelCase__							,    use_auth_token=self._token   )

								snake_case_   : str						=			BertTokenizer.from_pretrained(f'{USER}/test-tokenizer'   )
								self.assertDictEqual(new_tokenizer.vocab							,    tokenizer.vocab   )





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







								'''simple docstring'''

								with tempfile.TemporaryDirectory() as tmp_dir:
														snake_case_   : Dict						=			os.path.join(lowerCamelCase__							,    """vocab.txt"""   )
														with open(lowerCamelCase__							,    """w"""							,    encoding="""utf-8"""   ) as vocab_writer:
																				vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]   )   )
														snake_case_   : List[str]						=			BertTokenizer(lowerCamelCase__   )

								tokenizer.push_to_hub("""valid_org/test-tokenizer-org"""							,    use_auth_token=self._token   )
								snake_case_   : Optional[Any]						=			BertTokenizer.from_pretrained("""valid_org/test-tokenizer-org"""   )
								self.assertDictEqual(new_tokenizer.vocab							,    tokenizer.vocab   )

								# Reset repo
								delete_repo(token=self._token							,    repo_id="""valid_org/test-tokenizer-org"""   )

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
														tokenizer.save_pretrained(
														    lowerCamelCase__							,    repo_id="""valid_org/test-tokenizer-org"""							,    push_to_hub=lowerCamelCase__							,    use_auth_token=self._token   )

								snake_case_   : Union[str, Any]						=			BertTokenizer.from_pretrained("""valid_org/test-tokenizer-org"""   )
								self.assertDictEqual(new_tokenizer.vocab							,    tokenizer.vocab   )





		@require_tokenizers
		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								CustomTokenizer.register_for_auto_class()
								with tempfile.TemporaryDirectory() as tmp_dir:
														snake_case_   : List[Any]						=			os.path.join(lowerCamelCase__							,    """vocab.txt"""   )
														with open(lowerCamelCase__							,    """w"""							,    encoding="""utf-8"""   ) as vocab_writer:
																				vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]   )   )
														snake_case_   : Any						=			CustomTokenizer(lowerCamelCase__   )

								# No fast custom tokenizer
								tokenizer.push_to_hub("""test-dynamic-tokenizer"""							,    use_auth_token=self._token   )

								snake_case_   : str						=			AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer'							,    trust_remote_code=lowerCamelCase__   )
								# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
								self.assertEqual(tokenizer.__class__.__name__							,    """CustomTokenizer"""   )

								# Fast and slow custom tokenizer
								CustomTokenizerFast.register_for_auto_class()
								with tempfile.TemporaryDirectory() as tmp_dir:
														snake_case_   : List[Any]						=			os.path.join(lowerCamelCase__							,    """vocab.txt"""   )
														with open(lowerCamelCase__							,    """w"""							,    encoding="""utf-8"""   ) as vocab_writer:
																				vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]   )   )

														snake_case_   : List[Any]						=			BertTokenizerFast.from_pretrained(lowerCamelCase__   )
														bert_tokenizer.save_pretrained(lowerCamelCase__   )
														snake_case_   : Optional[int]						=			CustomTokenizerFast.from_pretrained(lowerCamelCase__   )

								tokenizer.push_to_hub("""test-dynamic-tokenizer"""							,    use_auth_token=self._token   )

								snake_case_   : str						=			AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer'							,    trust_remote_code=lowerCamelCase__   )
								# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
								self.assertEqual(tokenizer.__class__.__name__							,    """CustomTokenizerFast"""   )
								snake_case_   : int						=			AutoTokenizer.from_pretrained(
								    f'{USER}/test-dynamic-tokenizer'							,    use_fast=lowerCamelCase__							,    trust_remote_code=lowerCamelCase__   )
								# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
								self.assertEqual(tokenizer.__class__.__name__							,    """CustomTokenizer"""   )




class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								snake_case_   : Any						=			Trie()
								trie.add("""Hello 友達"""   )
								self.assertEqual(trie.data							,    {"""H""": {"""e""": {"""l""": {"""l""": {"""o""": {""" """: {"""友""": {"""達""": {"""""": 1}}}}}}}}}   )
								trie.add("""Hello"""   )
								trie.data
								self.assertEqual(trie.data							,    {"""H""": {"""e""": {"""l""": {"""l""": {"""o""": {"""""": 1, """ """: {"""友""": {"""達""": {"""""": 1}}}}}}}}}   )





		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								snake_case_   : Dict						=			Trie()
								self.assertEqual(trie.split("""[CLS] This is a extra_id_100"""   )							,    ["""[CLS] This is a extra_id_100"""]   )
								trie.add("""[CLS]"""   )
								trie.add("""extra_id_1"""   )
								trie.add("""extra_id_100"""   )
								self.assertEqual(trie.split("""[CLS] This is a extra_id_100"""   )							,    ["""[CLS]""", """ This is a """, """extra_id_100"""]   )





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







								'''simple docstring'''

								snake_case_   : int						=			Trie()
								trie.add("""A"""   )
								self.assertEqual(trie.split("""ABC"""   )							,    ["""A""", """BC"""]   )
								self.assertEqual(trie.split("""BCA"""   )							,    ["""BC""", """A"""]   )





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







								'''simple docstring'''

								snake_case_   : Any						=			Trie()
								trie.add("""TOKEN]"""   )
								trie.add("""[SPECIAL_TOKEN]"""   )
								self.assertEqual(trie.split("""This is something [SPECIAL_TOKEN]"""   )							,    ["""This is something """, """[SPECIAL_TOKEN]"""]   )





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







								'''simple docstring'''

								snake_case_   : List[Any]						=			Trie()
								trie.add("""A"""   )
								trie.add("""P"""   )
								trie.add("""[SPECIAL_TOKEN]"""   )
								self.assertEqual(trie.split("""This is something [SPECIAL_TOKEN]"""   )							,    ["""This is something """, """[SPECIAL_TOKEN]"""]   )





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







								'''simple docstring'''

								snake_case_   : List[Any]						=			Trie()
								trie.add("""AB"""   )
								trie.add("""B"""   )
								trie.add("""C"""   )
								self.assertEqual(trie.split("""ABC"""   )							,    ["""AB""", """C"""]   )





		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								snake_case_   : int						=			Trie()
								trie.add("""ABC"""   )
								trie.add("""B"""   )
								trie.add("""CD"""   )
								self.assertEqual(trie.split("""ABCD"""   )							,    ["""ABC""", """D"""]   )





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







								'''simple docstring'''

								snake_case_   : Dict						=			Trie()
								snake_case_   : Optional[Any]						=			trie.cut_text("""ABC"""							,    [0, 0, 2, 1, 2, 3]   )
								self.assertEqual(lowerCamelCase__							,    ["""AB""", """C"""]   )










"""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)

