



"""simple docstring"""





import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


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


if is_torch_available():
					from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

__lowerCAmelCase       :					Any      					=					25_6047
__lowerCAmelCase       :					Any      					=					25_6145



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





		"""simple docstring"""






		_lowerCamelCase			  =       NllbTokenizer
		_lowerCamelCase			  =       NllbTokenizerFast
		_lowerCamelCase			  =       True
		_lowerCamelCase			  =       True
		_lowerCamelCase			  =       {}





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







								'''simple docstring'''

								super().setUp()

								# We have a SentencePiece fixture for testing
								snake_case_   : Union[str, Any]						=			NllbTokenizer(lowerCamelCase__							,    keep_accents=lowerCamelCase__   )
								tokenizer.save_pretrained(self.tmpdirname   )





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







								'''simple docstring'''

								snake_case_   : Dict						=			NllbTokenizer(lowerCamelCase__							,    keep_accents=lowerCamelCase__   )

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

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

								snake_case_   : str						=			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_   : Any						=			tokenizer.convert_tokens_to_ids(lowerCamelCase__   )
								self.assertListEqual(
								    lowerCamelCase__							,    [
								        value + tokenizer.fairseq_offset
								        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
								    ]							,    )

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





		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								snake_case_   : Dict						=			(self.rust_tokenizer_class, "hf-internal-testing/tiny-random-nllb", {})
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'   ):
																				snake_case_   : Any						=			self.rust_tokenizer_class.from_pretrained(lowerCamelCase__							,    **lowerCamelCase__   )
																				snake_case_   : Any						=			self.tokenizer_class.from_pretrained(lowerCamelCase__							,    **lowerCamelCase__   )

																				snake_case_   : int						=			tempfile.mkdtemp()

																				snake_case_   : Tuple						=			tokenizer_r.save_pretrained(lowerCamelCase__   )
																				snake_case_   : Any						=			tokenizer_p.save_pretrained(lowerCamelCase__   )

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

																				# Checks everything loads correctly in the same way
																				snake_case_   : Tuple						=			tokenizer_r.from_pretrained(lowerCamelCase__   )
																				snake_case_   : Optional[Any]						=			tokenizer_p.from_pretrained(lowerCamelCase__   )

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

																				shutil.rmtree(lowerCamelCase__   )

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

																				snake_case_   : List[str]						=			tokenizer_r.save_pretrained(lowerCamelCase__							,    legacy_format=lowerCamelCase__   )
																				snake_case_   : List[str]						=			tokenizer_p.save_pretrained(lowerCamelCase__   )

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

																				# Checks everything loads correctly in the same way
																				snake_case_   : Union[str, Any]						=			tokenizer_r.from_pretrained(lowerCamelCase__   )
																				snake_case_   : Optional[Any]						=			tokenizer_p.from_pretrained(lowerCamelCase__   )

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

																				shutil.rmtree(lowerCamelCase__   )

																				# Save tokenizer rust, legacy_format=False
																				snake_case_   : Optional[int]						=			tempfile.mkdtemp()

																				snake_case_   : Dict						=			tokenizer_r.save_pretrained(lowerCamelCase__							,    legacy_format=lowerCamelCase__   )
																				snake_case_   : List[Any]						=			tokenizer_p.save_pretrained(lowerCamelCase__   )

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

																				# Checks everything loads correctly in the same way
																				snake_case_   : int						=			tokenizer_r.from_pretrained(lowerCamelCase__   )
																				snake_case_   : int						=			tokenizer_p.from_pretrained(lowerCamelCase__   )

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

																				shutil.rmtree(lowerCamelCase__   )





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







								'''simple docstring'''

								if not self.test_seqaseq:
														return

								snake_case_   : int						=			self.get_tokenizers()
								for tokenizer in tokenizers:
														with self.subTest(f'{tokenizer.__class__.__name__}'   ):
																				# Longer text that will definitely require truncation.
																				snake_case_   : Tuple						=			[
																				    " UN Chief Says There Is No Military Solution in Syria",
																				    " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"
																				    " Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"
																				    " will only worsen the violence and misery for millions of people.",
																				]
																				snake_case_   : Dict						=			[
																				    "Şeful ONU declară că nu există o soluţie militară în Siria",
																				    "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"
																				    " Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"
																				    " că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
																				]
																				try:
																										snake_case_   : str						=			tokenizer.prepare_seqaseq_batch(
																										    src_texts=lowerCamelCase__							,    tgt_texts=lowerCamelCase__							,    max_length=3							,    max_target_length=1_0							,    return_tensors="""pt"""							,    src_lang="""eng_Latn"""							,    tgt_lang="""ron_Latn"""							,    )
																				except NotImplementedError:
																										return
																				self.assertEqual(batch.input_ids.shape[1]							,    3   )
																				self.assertEqual(batch.labels.shape[1]							,    1_0   )
																				# max_target_length will default to max_length if not specified
																				snake_case_   : Union[str, Any]						=			tokenizer.prepare_seqaseq_batch(
																				    lowerCamelCase__							,    tgt_texts=lowerCamelCase__							,    max_length=3							,    return_tensors="""pt"""   )
																				self.assertEqual(batch.input_ids.shape[1]							,    3   )
																				self.assertEqual(batch.labels.shape[1]							,    3   )

																				snake_case_   : Union[str, Any]						=			tokenizer.prepare_seqaseq_batch(
																				    src_texts=lowerCamelCase__							,    max_length=3							,    max_target_length=1_0							,    return_tensors="""pt"""   )
																				self.assertEqual(batch_encoder_only.input_ids.shape[1]							,    3   )
																				self.assertEqual(batch_encoder_only.attention_mask.shape[1]							,    3   )
																				self.assertNotIn("""decoder_input_ids"""							,    lowerCamelCase__   )





		@unittest.skip("""Unfortunately way too slow to build a BPE with SentencePiece."""   )
		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								pass





		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'   ):
																				snake_case_   : str						=			[AddedToken("""<special>"""							,    lstrip=lowerCamelCase__   )]

																				snake_case_   : int						=			self.rust_tokenizer_class.from_pretrained(
																				    lowerCamelCase__							,    additional_special_tokens=lowerCamelCase__							,    **lowerCamelCase__   )
																				snake_case_   : str						=			tokenizer_r.encode("""Hey this is a <special> token"""   )

																				snake_case_   : str						=			tokenizer_r.encode("""<special>"""							,    add_special_tokens=lowerCamelCase__   )[0]

																				self.assertTrue(special_token_id in r_output   )

																				if self.test_slow_tokenizer:
																										snake_case_   : Optional[int]						=			self.rust_tokenizer_class.from_pretrained(
																										    lowerCamelCase__							,    additional_special_tokens=lowerCamelCase__							,    **lowerCamelCase__							,    )
																										snake_case_   : Optional[int]						=			self.tokenizer_class.from_pretrained(
																										    lowerCamelCase__							,    additional_special_tokens=lowerCamelCase__							,    **lowerCamelCase__   )

																										snake_case_   : Tuple						=			tokenizer_p.encode("""Hey this is a <special> token"""   )

																										snake_case_   : Dict						=			tokenizer_cr.encode("""Hey this is a <special> token"""   )

																										self.assertEqual(lowerCamelCase__							,    lowerCamelCase__   )
																										self.assertEqual(lowerCamelCase__							,    lowerCamelCase__   )
																										self.assertTrue(special_token_id in p_output   )
																										self.assertTrue(special_token_id in cr_output   )




@require_torch
@require_sentencepiece
@require_tokenizers
class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase			  =       "facebook/nllb-200-distilled-600M"
		_lowerCamelCase			  =       [
		    " UN Chief Says There Is No Military Solution in Syria",
		    " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
		]
		_lowerCamelCase			  =       [
		    "Şeful ONU declară că nu există o soluţie militară în Siria",
		    "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
		    " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
		    " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
		]
		_lowerCamelCase			  =       [
		    256_047,
		    16_297,
		    134_408,
		    8_165,
		    248_066,
		    14_734,
		    950,
		    1_135,
		    105_721,
		    3_573,
		    83,
		    27_352,
		    108,
		    49_486,
		    2,
		]





		@classmethod
		def    UpperCAmelCase__							(					cls   ) ->						Union[str, Any]:







								'''simple docstring'''

								snake_case_   : NllbTokenizer						=			NllbTokenizer.from_pretrained(
								    cls.checkpoint_name							,    src_lang="""eng_Latn"""							,    tgt_lang="""ron_Latn"""   )
								snake_case_   : str						=			1
								return cls





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







								'''simple docstring'''

								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Arab"""]							,    2_5_6_0_0_1   )
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Latn"""]							,    2_5_6_0_0_2   )
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""fra_Latn"""]							,    2_5_6_0_5_7   )





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







								'''simple docstring'''

								snake_case_   : Optional[int]						=			self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
								self.assertListEqual(self.expected_src_tokens							,    lowerCamelCase__   )





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







								'''simple docstring'''

								self.assertIn(lowerCamelCase__							,    self.tokenizer.all_special_ids   )
								# fmt: off
								snake_case_   : List[Any]						=			[RO_CODE, 4_2_5_4, 9_8_0_6_8, 1_1_2_9_2_3, 3_9_0_7_2, 3_9_0_9, 7_1_3, 1_0_2_7_6_7, 2_6, 1_7_3_1_4, 3_5_6_4_2, 1_4_6_8_3, 3_3_1_1_8, 2_0_2_2, 6_6_9_8_7, 2, 2_5_6_0_4_7]
								# fmt: on

								snake_case_   : Optional[Any]						=			self.tokenizer.decode(lowerCamelCase__							,    skip_special_tokens=lowerCamelCase__   )
								snake_case_   : Dict						=			self.tokenizer.decode(generated_ids[1:]							,    skip_special_tokens=lowerCamelCase__   )
								self.assertEqual(lowerCamelCase__							,    lowerCamelCase__   )
								self.assertNotIn(self.tokenizer.eos_token							,    lowerCamelCase__   )





		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								snake_case_   : Optional[int]						=			["this is gunna be a long sentence " * 2_0]
								assert isinstance(src_text[0]							,    lowerCamelCase__   )
								snake_case_   : Tuple						=			1_0
								snake_case_   : Union[str, Any]						=			self.tokenizer(lowerCamelCase__							,    max_length=lowerCamelCase__							,    truncation=lowerCamelCase__   ).input_ids[0]
								self.assertEqual(ids[-1]							,    2   )
								self.assertEqual(ids[0]							,    lowerCamelCase__   )
								self.assertEqual(len(lowerCamelCase__   )							,    lowerCamelCase__   )





		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]   )							,    [2_5_6_2_0_3, 3]   )





		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								snake_case_   : Optional[int]						=			tempfile.mkdtemp()
								snake_case_   : Any						=			self.tokenizer.fairseq_tokens_to_ids
								self.tokenizer.save_pretrained(lowerCamelCase__   )
								snake_case_   : int						=			NllbTokenizer.from_pretrained(lowerCamelCase__   )
								self.assertDictEqual(new_tok.fairseq_tokens_to_ids							,    lowerCamelCase__   )





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







								'''simple docstring'''

								snake_case_   : List[Any]						=			self.tokenizer(
								    self.src_text							,    text_target=self.tgt_text							,    padding=lowerCamelCase__							,    truncation=lowerCamelCase__							,    max_length=len(self.expected_src_tokens   )							,    return_tensors="""pt"""							,    )
								snake_case_   : Optional[int]						=			shift_tokens_right(
								    batch["""labels"""]							,    self.tokenizer.pad_token_id							,    self.tokenizer.lang_code_to_id["""ron_Latn"""]   )

								self.assertIsInstance(lowerCamelCase__							,    lowerCamelCase__   )

								self.assertEqual((2, 1_5)							,    batch.input_ids.shape   )
								self.assertEqual((2, 1_5)							,    batch.attention_mask.shape   )
								snake_case_   : str						=			batch.input_ids.tolist()[0]
								self.assertListEqual(self.expected_src_tokens							,    lowerCamelCase__   )
								self.assertEqual(lowerCamelCase__							,    batch.decoder_input_ids[0, 0]   )  # EOS
								# Test that special tokens are reset
								self.assertEqual(self.tokenizer.prefix_tokens							,    [EN_CODE]   )
								self.assertEqual(self.tokenizer.suffix_tokens							,    [self.tokenizer.eos_token_id]   )





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







								'''simple docstring'''

								snake_case_   : Optional[Any]						=			self.tokenizer(self.src_text							,    padding=lowerCamelCase__							,    truncation=lowerCamelCase__							,    max_length=3							,    return_tensors="""pt"""   )
								snake_case_   : List[Any]						=			self.tokenizer(
								    text_target=self.tgt_text							,    padding=lowerCamelCase__							,    truncation=lowerCamelCase__							,    max_length=1_0							,    return_tensors="""pt"""   )
								snake_case_   : List[Any]						=			targets["input_ids"]
								snake_case_   : Tuple						=			shift_tokens_right(
								    lowerCamelCase__							,    self.tokenizer.pad_token_id							,    decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]							,    )

								self.assertEqual(batch.input_ids.shape[1]							,    3   )
								self.assertEqual(batch.decoder_input_ids.shape[1]							,    1_0   )





		@require_torch
		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								snake_case_   : Optional[Any]						=			self.tokenizer._build_translation_inputs(
								    """A test"""							,    return_tensors="""pt"""							,    src_lang="""eng_Latn"""							,    tgt_lang="""fra_Latn"""   )

								self.assertEqual(
								    nested_simplify(lowerCamelCase__   )							,    {
								        # A, test, EOS, en_XX
								        """input_ids""": [[2_5_6_0_4_7, 7_0, 7_3_5_6, 2]],
								        """attention_mask""": [[1, 1, 1, 1]],
								        # ar_AR
								        """forced_bos_token_id""": 2_5_6_0_5_7,
								    }							,    )





		@require_torch
		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								snake_case_   : Union[str, Any]						=			True
								snake_case_   : Optional[Any]						=			self.tokenizer(
								    """UN Chief says there is no military solution in Syria"""							,    src_lang="""eng_Latn"""							,    tgt_lang="""fra_Latn"""   )
								self.assertEqual(
								    inputs.input_ids							,    [1_6_2_9_7, 1_3_4_4_0_8, 2_5_6_5_3, 6_3_7_0, 2_4_8, 2_5_4, 1_0_3_9_2_9, 9_4_9_9_5, 1_0_8, 4_9_4_8_6, 2, 2_5_6_0_4_7]   )

								snake_case_   : Union[str, Any]						=			False
								snake_case_   : Dict						=			self.tokenizer(
								    """UN Chief says there is no military solution in Syria"""							,    src_lang="""eng_Latn"""							,    tgt_lang="""fra_Latn"""   )
								self.assertEqual(
								    inputs.input_ids							,    [2_5_6_0_4_7, 1_6_2_9_7, 1_3_4_4_0_8, 2_5_6_5_3, 6_3_7_0, 2_4_8, 2_5_4, 1_0_3_9_2_9, 9_4_9_9_5, 1_0_8, 4_9_4_8_6, 2]   )










"""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)

