





'''simple docstring'''




import gc
import unittest

from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_gpu,
    slow,
)

from .test_pipelines_common import ANY
@is_pipeline_test
class 			_lowerCAmelCase		(			unittest.TestCase	):






     """simple docstring"""






     snake_case_						      =   MODEL_FOR_MASKED_LM_MAPPING
     snake_case_						      =   TF_MODEL_FOR_MASKED_LM_MAPPING



     def      lowerCAmelCase			(   self							:			List[str]						)->	List[str]:
        super().tearDown()
        # clean-up as much as possible GPU memory occupied by PyTorch
        gc.collect()
        if is_torch_available():
           import torch

           torch.cuda.empty_cache()



     @require_tf
     def      lowerCAmelCase			(   self							:			Optional[Any]						)->	Union[str, Any]:
        snake_case 						=   pipeline(task="""fill-mask"""   ,       model="""sshleifer/tiny-distilroberta-base"""   ,       top_k=2   ,       framework="""tf"""						)
        snake_case 						=   unmasker("""My name is <mask>"""						)
        self.assertEqual(
            nested_simplify(__snake_case   ,       decimals=6						)   ,       [
                {"""sequence""": """My name is grouped""", """score""": 2.1e-05, """token""": 3_80_15, """token_str""": """ grouped"""},
                {"""sequence""": """My name is accuser""", """score""": 2.1e-05, """token""": 2_55_06, """token_str""": """ accuser"""},
            ]   ,       )

        snake_case 						=   unmasker("""The largest city in France is <mask>"""						)
        self.assertEqual(
            nested_simplify(__snake_case   ,       decimals=6						)   ,       [
                {
                    """sequence""": """The largest city in France is grouped""",
                    """score""": 2.1e-05,
                    """token""": 3_80_15,
                    """token_str""": """ grouped""",
                },
                {
                    """sequence""": """The largest city in France is accuser""",
                    """score""": 2.1e-05,
                    """token""": 2_55_06,
                    """token_str""": """ accuser""",
                },
            ]   ,       )

        snake_case 						=   unmasker("""My name is <mask>"""   ,       targets=[""" Patrick""", """ Clara""", """ Teven"""]   ,       top_k=3						)
        self.assertEqual(
            nested_simplify(__snake_case   ,       decimals=6						)   ,       [
                {"""sequence""": """My name is Clara""", """score""": 2e-05, """token""": 1_36_06, """token_str""": """ Clara"""},
                {"""sequence""": """My name is Patrick""", """score""": 2e-05, """token""": 34_99, """token_str""": """ Patrick"""},
                {"""sequence""": """My name is Te""", """score""": 1.9e-05, """token""": 29_41, """token_str""": """ Te"""},
            ]   ,       )



     @require_torch
     def      lowerCAmelCase			(   self							:			int						)->	str:
        snake_case 						=   pipeline(task="""fill-mask"""   ,       model="""sshleifer/tiny-distilroberta-base"""   ,       top_k=2   ,       framework="""pt"""						)

        snake_case 						=   unmasker("""My name is <mask>"""						)
        self.assertEqual(
            nested_simplify(__snake_case   ,       decimals=6						)   ,       [
                {"""sequence""": """My name is Maul""", """score""": 2.2e-05, """token""": 3_56_76, """token_str""": """ Maul"""},
                {"""sequence""": """My name isELS""", """score""": 2.2e-05, """token""": 1_64_16, """token_str""": """ELS"""},
            ]   ,       )

        snake_case 						=   unmasker("""The largest city in France is <mask>"""						)
        self.assertEqual(
            nested_simplify(__snake_case   ,       decimals=6						)   ,       [
                {
                    """sequence""": """The largest city in France is Maul""",
                    """score""": 2.2e-05,
                    """token""": 3_56_76,
                    """token_str""": """ Maul""",
                },
                {"""sequence""": """The largest city in France isELS""", """score""": 2.2e-05, """token""": 1_64_16, """token_str""": """ELS"""},
            ]   ,       )

        snake_case 						=   unmasker("""My name is <mask>"""   ,       targets=[""" Patrick""", """ Clara""", """ Teven"""]   ,       top_k=3						)
        self.assertEqual(
            nested_simplify(__snake_case   ,       decimals=6						)   ,       [
                {"""sequence""": """My name is Patrick""", """score""": 2.1e-05, """token""": 34_99, """token_str""": """ Patrick"""},
                {"""sequence""": """My name is Te""", """score""": 2e-05, """token""": 29_41, """token_str""": """ Te"""},
                {"""sequence""": """My name is Clara""", """score""": 2e-05, """token""": 1_36_06, """token_str""": """ Clara"""},
            ]   ,       )

        snake_case 						=   unmasker("""My name is <mask> <mask>"""   ,       top_k=2						)

        self.assertEqual(
            nested_simplify(__snake_case   ,       decimals=6						)   ,       [
                [
                    {
                        """score""": 2.2e-05,
                        """token""": 3_56_76,
                        """token_str""": """ Maul""",
                        """sequence""": """<s>My name is Maul<mask></s>""",
                    },
                    {"""score""": 2.2e-05, """token""": 1_64_16, """token_str""": """ELS""", """sequence""": """<s>My name isELS<mask></s>"""},
                ],
                [
                    {
                        """score""": 2.2e-05,
                        """token""": 3_56_76,
                        """token_str""": """ Maul""",
                        """sequence""": """<s>My name is<mask> Maul</s>""",
                    },
                    {"""score""": 2.2e-05, """token""": 1_64_16, """token_str""": """ELS""", """sequence""": """<s>My name is<mask>ELS</s>"""},
                ],
            ]   ,       )



     @require_torch_gpu
     def      lowerCAmelCase			(   self							:			Optional[Any]						)->	List[str]:
        snake_case 						=   pipeline("""fill-mask"""   ,       model="""hf-internal-testing/tiny-random-distilbert"""   ,       device=0   ,       framework="""pt"""						)

        # convert model to fp16
        pipe.model.half()

        snake_case 						=   pipe("""Paris is the [MASK] of France."""						)
        # We actually don't care about the result, we just want to make sure
        # it works, meaning the float16 tensor got casted back to float32
        # for postprocessing.
        self.assertIsInstance(__snake_case   ,       __snake_case						)



     @slow
     @require_torch
     def      lowerCAmelCase			(   self							:			Optional[Any]						)->	Optional[int]:
        snake_case 						=   pipeline(task="""fill-mask"""   ,       model="""distilroberta-base"""   ,       top_k=2   ,       framework="""pt"""						)
        self.run_large_test(__snake_case						)



     @slow
     @require_tf
     def      lowerCAmelCase			(   self							:			int						)->	List[str]:
        snake_case 						=   pipeline(task="""fill-mask"""   ,       model="""distilroberta-base"""   ,       top_k=2   ,       framework="""tf"""						)
        self.run_large_test(__snake_case						)



     def      lowerCAmelCase			(   self							:			Optional[Any]   ,       __snake_case							:			str						)->	Dict:
        snake_case 						=   unmasker("""My name is <mask>"""						)
        self.assertEqual(
            nested_simplify(__snake_case						)   ,       [
                {"""sequence""": """My name is John""", """score""": 0.0_08, """token""": 6_10, """token_str""": """ John"""},
                {"""sequence""": """My name is Chris""", """score""": 0.0_07, """token""": 15_73, """token_str""": """ Chris"""},
            ]   ,       )
        snake_case 						=   unmasker("""The largest city in France is <mask>"""						)
        self.assertEqual(
            nested_simplify(__snake_case						)   ,       [
                {
                    """sequence""": """The largest city in France is Paris""",
                    """score""": 0.2_51,
                    """token""": 22_01,
                    """token_str""": """ Paris""",
                },
                {
                    """sequence""": """The largest city in France is Lyon""",
                    """score""": 0.2_14,
                    """token""": 1_27_90,
                    """token_str""": """ Lyon""",
                },
            ]   ,       )

        snake_case 						=   unmasker("""My name is <mask>"""   ,       targets=[""" Patrick""", """ Clara""", """ Teven"""]   ,       top_k=3						)
        self.assertEqual(
            nested_simplify(__snake_case						)   ,       [
                {"""sequence""": """My name is Patrick""", """score""": 0.0_05, """token""": 34_99, """token_str""": """ Patrick"""},
                {"""sequence""": """My name is Clara""", """score""": 0.0_00, """token""": 1_36_06, """token_str""": """ Clara"""},
                {"""sequence""": """My name is Te""", """score""": 0.0_00, """token""": 29_41, """token_str""": """ Te"""},
            ]   ,       )



     @require_torch
     def      lowerCAmelCase			(   self							:			Optional[Any]						)->	Any:
        snake_case 						=   pipeline(task="""fill-mask"""   ,       model="""sshleifer/tiny-distilroberta-base"""   ,       framework="""pt"""						)
        snake_case 						=   None
        snake_case 						=   None
        self.run_pipeline_test(__snake_case   ,       []						)



     @require_tf
     def      lowerCAmelCase			(   self							:			int						)->	Optional[int]:
        snake_case 						=   pipeline(task="""fill-mask"""   ,       model="""sshleifer/tiny-distilroberta-base"""   ,       framework="""tf"""						)
        snake_case 						=   None
        snake_case 						=   None
        self.run_pipeline_test(__snake_case   ,       []						)



     def      lowerCAmelCase			(   self							:			List[Any]   ,       __snake_case							:			Union[str, Any]   ,       __snake_case							:			Tuple   ,       __snake_case							:			Dict						)->	List[str]:
        if tokenizer is None or tokenizer.mask_token_id is None:
           self.skipTest("""The provided tokenizer has no mask token, (probably reformer or wav2vec2)"""						)

        snake_case 						=   FillMaskPipeline(model=__snake_case   ,       tokenizer=__snake_case						)
        snake_case 						=   [
            f'''This is another {tokenizer.mask_token} test''',
        ]
        return fill_masker, examples



     def      lowerCAmelCase			(   self							:			str   ,       __snake_case							:			List[str]   ,       __snake_case							:			List[Any]						)->	Any:
        snake_case 						=   fill_masker.tokenizer
        snake_case 						=   fill_masker.model

        snake_case 						=   fill_masker(
            f'''This is a {tokenizer.mask_token}'''   ,       )
        self.assertEqual(
            __snake_case   ,       [
                {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
                {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
                {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
                {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
                {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
            ]   ,       )

        snake_case 						=   fill_masker([f'''This is a {tokenizer.mask_token}''']						)
        self.assertEqual(
            __snake_case   ,       [
                {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
                {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
                {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
                {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
                {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
            ]   ,       )

        snake_case 						=   fill_masker([f'''This is a {tokenizer.mask_token}''', f'''Another {tokenizer.mask_token} great test.''']						)
        self.assertEqual(
            __snake_case   ,       [
                [
                    {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
                    {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
                    {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
                    {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
                    {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
                ],
                [
                    {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
                    {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
                    {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
                    {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
                    {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
                ],
            ]   ,       )

        with self.assertRaises(__snake_case						):
           fill_masker([None]						)
        # No mask_token is not supported
        with self.assertRaises(__snake_case						):
           fill_masker("""This is"""						)

        self.run_test_top_k(__snake_case   ,       __snake_case						)
        self.run_test_targets(__snake_case   ,       __snake_case						)
        self.run_test_top_k_targets(__snake_case   ,       __snake_case						)
        self.fill_mask_with_duplicate_targets_and_top_k(__snake_case   ,       __snake_case						)
        self.fill_mask_with_multiple_masks(__snake_case   ,       __snake_case						)



     def      lowerCAmelCase			(   self							:			Union[str, Any]   ,       __snake_case							:			Any   ,       __snake_case							:			Union[str, Any]						)->	Any:
        snake_case 						=   tokenizer.get_vocab()
        snake_case 						=   sorted(vocab.keys()						)[:2]
        # Pipeline argument
        snake_case 						=   FillMaskPipeline(model=__snake_case   ,       tokenizer=__snake_case   ,       targets=__snake_case						)
        snake_case 						=   fill_masker(f'''This is a {tokenizer.mask_token}'''						)
        self.assertEqual(
            __snake_case   ,       [
                {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
                {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
            ]   ,       )
        snake_case 						=   {vocab[el] for el in targets}
        self.assertEqual({el["""token"""] for el in outputs}   ,       __snake_case						)
        snake_case 						=   [tokenizer.decode([x]						) for x in target_ids]
        self.assertEqual({el["""token_str"""] for el in outputs}   ,       set(__snake_case						)						)

        # Call argument
        snake_case 						=   FillMaskPipeline(model=__snake_case   ,       tokenizer=__snake_case						)
        snake_case 						=   fill_masker(f'''This is a {tokenizer.mask_token}'''   ,       targets=__snake_case						)
        self.assertEqual(
            __snake_case   ,       [
                {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
                {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
            ]   ,       )
        snake_case 						=   {vocab[el] for el in targets}
        self.assertEqual({el["""token"""] for el in outputs}   ,       __snake_case						)
        snake_case 						=   [tokenizer.decode([x]						) for x in target_ids]
        self.assertEqual({el["""token_str"""] for el in outputs}   ,       set(__snake_case						)						)

        # Score equivalence
        snake_case 						=   fill_masker(f'''This is a {tokenizer.mask_token}'''   ,       targets=__snake_case						)
        snake_case 						=   [top_mask["""token_str"""] for top_mask in outputs]
        snake_case 						=   [top_mask["""score"""] for top_mask in outputs]

        # For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
        if set(__snake_case						) == set(__snake_case						):
           snake_case 						=   fill_masker(f'''This is a {tokenizer.mask_token}'''   ,       targets=__snake_case						)
           snake_case 						=   [top_mask["""score"""] for top_mask in unmasked_targets]
           self.assertEqual(nested_simplify(__snake_case						)   ,       nested_simplify(__snake_case						)						)

        # Raises with invalid
        with self.assertRaises(__snake_case						):
           snake_case 						=   fill_masker(f'''This is a {tokenizer.mask_token}'''   ,       targets=[]						)
        # For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
        if "" not in tokenizer.get_vocab():
           with self.assertRaises(__snake_case						):
              snake_case 						=   fill_masker(f'''This is a {tokenizer.mask_token}'''   ,       targets=[""""""]						)
           with self.assertRaises(__snake_case						):
              snake_case 						=   fill_masker(f'''This is a {tokenizer.mask_token}'''   ,       targets=""""""						)



     def      lowerCAmelCase			(   self							:			int   ,       __snake_case							:			Union[str, Any]   ,       __snake_case							:			Dict						)->	Union[str, Any]:
        snake_case 						=   FillMaskPipeline(model=__snake_case   ,       tokenizer=__snake_case   ,       top_k=2						)
        snake_case 						=   fill_masker(f'''This is a {tokenizer.mask_token}'''						)
        self.assertEqual(
            __snake_case   ,       [
                {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
                {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
            ]   ,       )

        snake_case 						=   FillMaskPipeline(model=__snake_case   ,       tokenizer=__snake_case						)
        snake_case 						=   fill_masker(f'''This is a {tokenizer.mask_token}'''   ,       top_k=2						)
        self.assertEqual(
            __snake_case   ,       [
                {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
                {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
            ]   ,       )
        self.assertEqual(nested_simplify(__snake_case						)   ,       nested_simplify(__snake_case						)						)



     def      lowerCAmelCase			(   self							:			int   ,       __snake_case							:			Dict   ,       __snake_case							:			Any						)->	Union[str, Any]:
        snake_case 						=   tokenizer.get_vocab()
        snake_case 						=   FillMaskPipeline(model=__snake_case   ,       tokenizer=__snake_case						)

        # top_k=2, ntargets=3
        snake_case 						=   sorted(vocab.keys()						)[:3]
        snake_case 						=   fill_masker(f'''This is a {tokenizer.mask_token}'''   ,       top_k=2   ,       targets=__snake_case						)

        # If we use the most probably targets, and filter differently, we should still
        # have the same results
        snake_case 						=   [el["""token_str"""] for el in sorted(__snake_case   ,       key=lambda __snake_case						: x["score"]   ,       reverse=__snake_case						)]
        # For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
        if set(__snake_case						).issubset(__snake_case						):
           snake_case 						=   fill_masker(f'''This is a {tokenizer.mask_token}'''   ,       top_k=3   ,       targets=__snake_case						)
           # They should yield exactly the same result
           self.assertEqual(nested_simplify(__snake_case						)   ,       nested_simplify(__snake_case						)						)



     def      lowerCAmelCase			(   self							:			Optional[int]   ,       __snake_case							:			List[str]   ,       __snake_case							:			int						)->	Optional[Any]:
        snake_case 						=   FillMaskPipeline(model=__snake_case   ,       tokenizer=__snake_case						)
        snake_case 						=   tokenizer.get_vocab()
        # String duplicates + id duplicates
        snake_case 						=   sorted(vocab.keys()						)[:3]
        snake_case 						=   [targets[0], targets[1], targets[0], targets[2], targets[1]]
        snake_case 						=   fill_masker(f'''My name is {tokenizer.mask_token}'''   ,       targets=__snake_case   ,       top_k=10						)

        # The target list contains duplicates, so we can't output more
        # than them
        self.assertEqual(len(__snake_case						)   ,       3						)



     def      lowerCAmelCase			(   self							:			Optional[int]   ,       __snake_case							:			Optional[int]   ,       __snake_case							:			Any						)->	int:
        snake_case 						=   FillMaskPipeline(model=__snake_case   ,       tokenizer=__snake_case						)

        snake_case 						=   fill_masker(
            f'''This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}'''   ,       top_k=2						)
        self.assertEqual(
            __snake_case   ,       [
                [
                    {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
                    {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
                ],
                [
                    {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
                    {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
                ],
                [
                    {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
                    {"""sequence""": ANY(__snake_case						), """score""": ANY(__snake_case						), """token""": ANY(__snake_case						), """token_str""": ANY(__snake_case						)},
                ],
            ]   ,       )








'''simple docstring'''




import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE							     =			logging.get_logger()





def 	__lowerCamelCase			(      __lowerCAmelCase       :							int      ,    __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							LevitConfig      ,    __lowerCAmelCase       :							Path      ,    __lowerCAmelCase       :							bool = True		)		->  int:
   print(F'''Converting {name}...'''		)

   with torch.no_grad():
      if hidden_sizes == 1_28:
         if name[-1] == "S":
            snake_case 						=   timm.create_model("""levit_128s"""      ,    pretrained=__lowerCAmelCase		)
         else:
            snake_case 						=   timm.create_model("""levit_128"""      ,    pretrained=__lowerCAmelCase		)
      if hidden_sizes == 1_92:
         snake_case 						=   timm.create_model("""levit_192"""      ,    pretrained=__lowerCAmelCase		)
      if hidden_sizes == 2_56:
         snake_case 						=   timm.create_model("""levit_256"""      ,    pretrained=__lowerCAmelCase		)
      if hidden_sizes == 3_84:
         snake_case 						=   timm.create_model("""levit_384"""      ,    pretrained=__lowerCAmelCase		)

      from_model.eval()
      snake_case 						=   LevitForImageClassificationWithTeacher(__lowerCAmelCase		).eval()
      snake_case 						=   OrderedDict()

      snake_case 						=   from_model.state_dict()
      snake_case 						=   list(from_model.state_dict().keys()		)
      snake_case 						=   list(our_model.state_dict().keys()		)
      print(len(__lowerCAmelCase		)      ,    len(__lowerCAmelCase		)		)
      for i in range(len(__lowerCAmelCase		)		):
         snake_case 						=   weights[og_keys[i]]
      our_model.load_state_dict(__lowerCAmelCase		)

      snake_case 						=   torch.randn((2, 3, 2_24, 2_24)		)
      snake_case 						=   from_model(__lowerCAmelCase		)
      snake_case 						=   our_model(__lowerCAmelCase		).logits

   assert torch.allclose(__lowerCAmelCase      ,    __lowerCAmelCase		), "The model logits don't match the original one."

   snake_case 						=   name
   print(__lowerCAmelCase		)

   if push_to_hub:
      our_model.save_pretrained(save_directory / checkpoint_name		)
      snake_case 						=   LevitImageProcessor()
      image_processor.save_pretrained(save_directory / checkpoint_name		)

      print(F'''Pushed {checkpoint_name}'''		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Path      ,    __lowerCAmelCase       :							str = None      ,    __lowerCAmelCase       :							bool = True		)		->  List[Any]:
   snake_case 						=   """imagenet-1k-id2label.json"""
   snake_case 						=   10_00
   snake_case 						=   (1, num_labels)

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

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

   snake_case 						=   partial(__lowerCAmelCase      ,    num_labels=__lowerCAmelCase      ,    idalabel=__lowerCAmelCase      ,    labelaid=__lowerCAmelCase		)

   snake_case 						=   {
       """levit-128S""": 1_28,
       """levit-128""": 1_28,
       """levit-192""": 1_92,
       """levit-256""": 2_56,
       """levit-384""": 3_84,
   }

   snake_case 						=   {
       """levit-128S""": ImageNetPreTrainedConfig(
           hidden_sizes=[1_28, 2_56, 3_84]      ,    num_attention_heads=[4, 6, 8]      ,    depths=[2, 3, 4]      ,    key_dim=[16, 16, 16]      ,    drop_path_rate=0      ,    ),
       """levit-128""": ImageNetPreTrainedConfig(
           hidden_sizes=[1_28, 2_56, 3_84]      ,    num_attention_heads=[4, 8, 12]      ,    depths=[4, 4, 4]      ,    key_dim=[16, 16, 16]      ,    drop_path_rate=0      ,    ),
       """levit-192""": ImageNetPreTrainedConfig(
           hidden_sizes=[1_92, 2_88, 3_84]      ,    num_attention_heads=[3, 5, 6]      ,    depths=[4, 4, 4]      ,    key_dim=[32, 32, 32]      ,    drop_path_rate=0      ,    ),
       """levit-256""": ImageNetPreTrainedConfig(
           hidden_sizes=[2_56, 3_84, 5_12]      ,    num_attention_heads=[4, 6, 8]      ,    depths=[4, 4, 4]      ,    key_dim=[32, 32, 32]      ,    drop_path_rate=0      ,    ),
       """levit-384""": ImageNetPreTrainedConfig(
           hidden_sizes=[3_84, 5_12, 7_68]      ,    num_attention_heads=[6, 9, 12]      ,    depths=[4, 4, 4]      ,    key_dim=[32, 32, 32]      ,    drop_path_rate=0.1      ,    ),
   }

   if model_name:
      convert_weight_and_push(
          names_to_hidden_sizes[model_name]      ,    __lowerCAmelCase      ,    names_to_config[model_name]      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)
   else:
      for model_name, config in names_to_config.items():
         convert_weight_and_push(names_to_hidden_sizes[model_name]      ,    __lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)
   return config, expected_shape


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE							     =			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model_name",
     default=None,
     type=str,
     help="The name of the model you wish to convert, it must be one of the supported Levit* architecture,",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path",
     default="levit-dump-folder/",
     type=Path,
     required=False,
     help="Path to the output PyTorch model directory.",
 )
 parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
 parser.add_argument(
     "--no-push_to_hub",
     dest="push_to_hub",
     action="store_false",
     help="Do not push model and image processor to the hub",
 )

 _SCREAMING_SNAKE_CASE							     =			parser.parse_args()
 _SCREAMING_SNAKE_CASE							     =			args.pytorch_dump_folder_path
 pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
 convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)

