

"""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    _a						(unittest.TestCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       Any  							=	MODEL_FOR_MASKED_LM_MAPPING
					UpperCAmelCase__:       Any  							=	TF_MODEL_FOR_MASKED_LM_MAPPING






					def   __A      (			self      ):
						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   __A      (			self      ):
						A__							:   Any					       =		pipeline(task="""fill-mask"""   ,	model="""sshleifer/tiny-distilroberta-base"""   ,	top_k=2   ,	framework="""tf"""      )
						A__							:   Dict					       =		unmasker("""My name is <mask>"""      )
						self.assertEqual(
						    nested_simplify(A__   ,	decimals=6      )   ,	[
						        {"""sequence""": """My name is grouped""", """score""": 2.1e-05, """token""": 3_8015, """token_str""": """ grouped"""},
						        {"""sequence""": """My name is accuser""", """score""": 2.1e-05, """token""": 2_5506, """token_str""": """ accuser"""},
						    ]   ,	)

						A__							:   Dict					       =		unmasker("""The largest city in France is <mask>"""      )
						self.assertEqual(
						    nested_simplify(A__   ,	decimals=6      )   ,	[
						        {
						            """sequence""": """The largest city in France is grouped""",
						            """score""": 2.1e-05,
						            """token""": 3_8015,
						            """token_str""": """ grouped""",
						        },
						        {
						            """sequence""": """The largest city in France is accuser""",
						            """score""": 2.1e-05,
						            """token""": 2_5506,
						            """token_str""": """ accuser""",
						        },
						    ]   ,	)

						A__							:   List[Any]					       =		unmasker("""My name is <mask>"""   ,	targets=[""" Patrick""", """ Clara""", """ Teven"""]   ,	top_k=3      )
						self.assertEqual(
						    nested_simplify(A__   ,	decimals=6      )   ,	[
						        {"""sequence""": """My name is Clara""", """score""": 2e-05, """token""": 1_3606, """token_str""": """ Clara"""},
						        {"""sequence""": """My name is Patrick""", """score""": 2e-05, """token""": 3499, """token_str""": """ Patrick"""},
						        {"""sequence""": """My name is Te""", """score""": 1.9e-05, """token""": 2941, """token_str""": """ Te"""},
						    ]   ,	)






					@require_torch
					def   __A      (			self      ):
						A__							:   Tuple					       =		pipeline(task="""fill-mask"""   ,	model="""sshleifer/tiny-distilroberta-base"""   ,	top_k=2   ,	framework="""pt"""      )

						A__							:   List[Any]					       =		unmasker("""My name is <mask>"""      )
						self.assertEqual(
						    nested_simplify(A__   ,	decimals=6      )   ,	[
						        {"""sequence""": """My name is Maul""", """score""": 2.2e-05, """token""": 3_5676, """token_str""": """ Maul"""},
						        {"""sequence""": """My name isELS""", """score""": 2.2e-05, """token""": 1_6416, """token_str""": """ELS"""},
						    ]   ,	)

						A__							:   Tuple					       =		unmasker("""The largest city in France is <mask>"""      )
						self.assertEqual(
						    nested_simplify(A__   ,	decimals=6      )   ,	[
						        {
						            """sequence""": """The largest city in France is Maul""",
						            """score""": 2.2e-05,
						            """token""": 3_5676,
						            """token_str""": """ Maul""",
						        },
						        {"""sequence""": """The largest city in France isELS""", """score""": 2.2e-05, """token""": 1_6416, """token_str""": """ELS"""},
						    ]   ,	)

						A__							:   Optional[Any]					       =		unmasker("""My name is <mask>"""   ,	targets=[""" Patrick""", """ Clara""", """ Teven"""]   ,	top_k=3      )
						self.assertEqual(
						    nested_simplify(A__   ,	decimals=6      )   ,	[
						        {"""sequence""": """My name is Patrick""", """score""": 2.1e-05, """token""": 3499, """token_str""": """ Patrick"""},
						        {"""sequence""": """My name is Te""", """score""": 2e-05, """token""": 2941, """token_str""": """ Te"""},
						        {"""sequence""": """My name is Clara""", """score""": 2e-05, """token""": 1_3606, """token_str""": """ Clara"""},
						    ]   ,	)

						A__							:   List[Any]					       =		unmasker("""My name is <mask> <mask>"""   ,	top_k=2      )

						self.assertEqual(
						    nested_simplify(A__   ,	decimals=6      )   ,	[
						        [
						            {
						                """score""": 2.2e-05,
						                """token""": 3_5676,
						                """token_str""": """ Maul""",
						                """sequence""": """<s>My name is Maul<mask></s>""",
						            },
						            {"""score""": 2.2e-05, """token""": 1_6416, """token_str""": """ELS""", """sequence""": """<s>My name isELS<mask></s>"""},
						        ],
						        [
						            {
						                """score""": 2.2e-05,
						                """token""": 3_5676,
						                """token_str""": """ Maul""",
						                """sequence""": """<s>My name is<mask> Maul</s>""",
						            },
						            {"""score""": 2.2e-05, """token""": 1_6416, """token_str""": """ELS""", """sequence""": """<s>My name is<mask>ELS</s>"""},
						        ],
						    ]   ,	)






					@require_torch_gpu
					def   __A      (			self      ):
						A__							:   Tuple					       =		pipeline("""fill-mask"""   ,	model="""hf-internal-testing/tiny-random-distilbert"""   ,	device=0   ,	framework="""pt"""      )

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

						A__							:   Optional[Any]					       =		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(A__   ,	A__      )






					@slow
					@require_torch
					def   __A      (			self      ):
						A__							:   Tuple					       =		pipeline(task="""fill-mask"""   ,	model="""distilroberta-base"""   ,	top_k=2   ,	framework="""pt"""      )
						self.run_large_test(A__      )






					@slow
					@require_tf
					def   __A      (			self      ):
						A__							:   Union[str, Any]					       =		pipeline(task="""fill-mask"""   ,	model="""distilroberta-base"""   ,	top_k=2   ,	framework="""tf"""      )
						self.run_large_test(A__      )






					def   __A      (			self   ,	A__      ):
						A__							:   List[Any]					       =		unmasker("""My name is <mask>"""      )
						self.assertEqual(
						    nested_simplify(A__      )   ,	[
						        {"""sequence""": """My name is John""", """score""": 0.0_0_8, """token""": 610, """token_str""": """ John"""},
						        {"""sequence""": """My name is Chris""", """score""": 0.0_0_7, """token""": 1573, """token_str""": """ Chris"""},
						    ]   ,	)
						A__							:   Any					       =		unmasker("""The largest city in France is <mask>"""      )
						self.assertEqual(
						    nested_simplify(A__      )   ,	[
						        {
						            """sequence""": """The largest city in France is Paris""",
						            """score""": 0.2_5_1,
						            """token""": 2201,
						            """token_str""": """ Paris""",
						        },
						        {
						            """sequence""": """The largest city in France is Lyon""",
						            """score""": 0.2_1_4,
						            """token""": 1_2790,
						            """token_str""": """ Lyon""",
						        },
						    ]   ,	)

						A__							:   List[str]					       =		unmasker("""My name is <mask>"""   ,	targets=[""" Patrick""", """ Clara""", """ Teven"""]   ,	top_k=3      )
						self.assertEqual(
						    nested_simplify(A__      )   ,	[
						        {"""sequence""": """My name is Patrick""", """score""": 0.0_0_5, """token""": 3499, """token_str""": """ Patrick"""},
						        {"""sequence""": """My name is Clara""", """score""": 0.0_0_0, """token""": 1_3606, """token_str""": """ Clara"""},
						        {"""sequence""": """My name is Te""", """score""": 0.0_0_0, """token""": 2941, """token_str""": """ Te"""},
						    ]   ,	)






					@require_torch
					def   __A      (			self      ):
						A__							:   Dict					       =		pipeline(task="""fill-mask"""   ,	model="""sshleifer/tiny-distilroberta-base"""   ,	framework="""pt"""      )
						A__							:   Tuple					       =		None
						A__							:   List[str]					       =		None
						self.run_pipeline_test(A__   ,	[]      )






					@require_tf
					def   __A      (			self      ):
						A__							:   Optional[Any]					       =		pipeline(task="""fill-mask"""   ,	model="""sshleifer/tiny-distilroberta-base"""   ,	framework="""tf"""      )
						A__							:   List[str]					       =		None
						A__							:   Optional[Any]					       =		None
						self.run_pipeline_test(A__   ,	[]      )






					def   __A      (			self   ,	A__   ,	A__   ,	A__      ):
						if tokenizer is None or tokenizer.mask_token_id is None:
							self.skipTest("""The provided tokenizer has no mask token, (probably reformer or wav2vec2)"""      )

						A__							:   int					       =		FillMaskPipeline(model=A__   ,	tokenizer=A__      )
						A__							:   Dict					       =		[
						    F"""This is another {tokenizer.mask_token} test""",
						]
						return fill_masker, examples






					def   __A      (			self   ,	A__   ,	A__      ):
						A__							:   List[str]					       =		fill_masker.tokenizer
						A__							:   Optional[Any]					       =		fill_masker.model

						A__							:   Tuple					       =		fill_masker(
						    F"""This is a {tokenizer.mask_token}"""   ,	)
						self.assertEqual(
						    A__   ,	[
						        {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						        {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						        {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						        {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						        {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						    ]   ,	)

						A__							:   int					       =		fill_masker([F"""This is a {tokenizer.mask_token}"""]      )
						self.assertEqual(
						    A__   ,	[
						        {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						        {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						        {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						        {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						        {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						    ]   ,	)

						A__							:   Optional[Any]					       =		fill_masker([F"""This is a {tokenizer.mask_token}""", F"""Another {tokenizer.mask_token} great test."""]      )
						self.assertEqual(
						    A__   ,	[
						        [
						            {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						            {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						            {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						            {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						            {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						        ],
						        [
						            {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						            {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						            {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						            {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						            {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						        ],
						    ]   ,	)

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

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






					def   __A      (			self   ,	A__   ,	A__      ):
						A__							:   Any					       =		tokenizer.get_vocab()
						A__							:   List[Any]					       =		sorted(vocab.keys()      )[:2]
						# Pipeline argument
						A__							:   Any					       =		FillMaskPipeline(model=A__   ,	tokenizer=A__   ,	targets=A__      )
						A__							:   int					       =		fill_masker(F"""This is a {tokenizer.mask_token}"""      )
						self.assertEqual(
						    A__   ,	[
						        {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						        {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						    ]   ,	)
						A__							:   Tuple					       =		{vocab[el] for el in targets}
						self.assertEqual({el["""token"""] for el in outputs}   ,	A__      )
						A__							:   Optional[Any]					       =		[tokenizer.decode([x]      ) for x in target_ids]
						self.assertEqual({el["""token_str"""] for el in outputs}   ,	set(A__      )      )

						# Call argument
						A__							:   List[Any]					       =		FillMaskPipeline(model=A__   ,	tokenizer=A__      )
						A__							:   Union[str, Any]					       =		fill_masker(F"""This is a {tokenizer.mask_token}"""   ,	targets=A__      )
						self.assertEqual(
						    A__   ,	[
						        {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						        {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						    ]   ,	)
						A__							:   Tuple					       =		{vocab[el] for el in targets}
						self.assertEqual({el["""token"""] for el in outputs}   ,	A__      )
						A__							:   Optional[int]					       =		[tokenizer.decode([x]      ) for x in target_ids]
						self.assertEqual({el["""token_str"""] for el in outputs}   ,	set(A__      )      )

						# Score equivalence
						A__							:   Optional[Any]					       =		fill_masker(F"""This is a {tokenizer.mask_token}"""   ,	targets=A__      )
						A__							:   List[str]					       =		[top_mask["""token_str"""] for top_mask in outputs]
						A__							:   str					       =		[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(A__      ) == set(A__      ):
							A__							:   Optional[Any]					       =		fill_masker(F"""This is a {tokenizer.mask_token}"""   ,	targets=A__      )
							A__							:   Tuple					       =		[top_mask["""score"""] for top_mask in unmasked_targets]
							self.assertEqual(nested_simplify(A__      )   ,	nested_simplify(A__      )      )

						# Raises with invalid
						with self.assertRaises(A__      ):
							A__							:   List[Any]					       =		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(A__      ):
								A__							:   Union[str, Any]					       =		fill_masker(F"""This is a {tokenizer.mask_token}"""   ,	targets=[""""""]      )
							with self.assertRaises(A__      ):
								A__							:   Tuple					       =		fill_masker(F"""This is a {tokenizer.mask_token}"""   ,	targets=""""""      )






					def   __A      (			self   ,	A__   ,	A__      ):
						A__							:   Any					       =		FillMaskPipeline(model=A__   ,	tokenizer=A__   ,	top_k=2      )
						A__							:   Dict					       =		fill_masker(F"""This is a {tokenizer.mask_token}"""      )
						self.assertEqual(
						    A__   ,	[
						        {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						        {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						    ]   ,	)

						A__							:   Dict					       =		FillMaskPipeline(model=A__   ,	tokenizer=A__      )
						A__							:   str					       =		fill_masker(F"""This is a {tokenizer.mask_token}"""   ,	top_k=2      )
						self.assertEqual(
						    A__   ,	[
						        {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						        {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						    ]   ,	)
						self.assertEqual(nested_simplify(A__      )   ,	nested_simplify(A__      )      )






					def   __A      (			self   ,	A__   ,	A__      ):
						A__							:   Optional[Any]					       =		tokenizer.get_vocab()
						A__							:   Tuple					       =		FillMaskPipeline(model=A__   ,	tokenizer=A__      )

						# top_k=2, ntargets=3
						A__							:   Optional[Any]					       =		sorted(vocab.keys()      )[:3]
						A__							:   int					       =		fill_masker(F"""This is a {tokenizer.mask_token}"""   ,	top_k=2   ,	targets=A__      )

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






					def   __A      (			self   ,	A__   ,	A__      ):
						A__							:   Optional[int]					       =		FillMaskPipeline(model=A__   ,	tokenizer=A__      )
						A__							:   Union[str, Any]					       =		tokenizer.get_vocab()
						# String duplicates + id duplicates
						A__							:   Any					       =		sorted(vocab.keys()      )[:3]
						A__							:   List[str]					       =		[targets[0], targets[1], targets[0], targets[2], targets[1]]
						A__							:   Optional[int]					       =		fill_masker(F"""My name is {tokenizer.mask_token}"""   ,	targets=A__   ,	top_k=10      )

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






					def   __A      (			self   ,	A__   ,	A__      ):
						A__							:   List[Any]					       =		FillMaskPipeline(model=A__   ,	tokenizer=A__      )

						A__							:   List[Any]					       =		fill_masker(
						    F"""This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}"""   ,	top_k=2      )
						self.assertEqual(
						    A__   ,	[
						        [
						            {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						            {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						        ],
						        [
						            {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						            {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						        ],
						        [
						            {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						            {"""sequence""": ANY(A__      ), """score""": ANY(A__      ), """token""": ANY(A__      ), """token_str""": ANY(A__      )},
						        ],
						    ]   ,	)






import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling

def 						UpperCamelCase (lowercase_:			str   ,				lowercase_:			Optional[int] )					->				str:
	A__							:   Union[str, Any]					       =		old_name

	if "patch_embed" in old_name:
		A__       ,       A__       ,       A__							:   Any					       =		old_name.split(""".""" )

		if layer == "0":
			A__							:   List[Any]					       =		old_name.replace("""0"""   ,				"""convolution1""" )
		elif layer == "1":
			A__							:   Optional[int]					       =		old_name.replace("""1"""   ,				"""batchnorm_before""" )
		elif layer == "3":
			A__							:   int					       =		old_name.replace("""3"""   ,				"""convolution2""" )
		else:
			A__							:   Dict					       =		old_name.replace("""4"""   ,				"""batchnorm_after""" )

	if "network" in old_name and re.search(r"""\d\.\d"""   ,				lowercase_ ):
		A__							:   str					       =		r"""\b\d{2}\b"""
		if bool(re.search(lowercase_   ,				lowercase_ ) ):
			A__							:   Optional[Any]					       =		re.search(r"""\d\.\d\d."""   ,				lowercase_ ).group()
		else:
			A__							:   int					       =		re.search(r"""\d\.\d."""   ,				lowercase_ ).group()
		if int(match[0] ) < 6:
			A__							:   Optional[Any]					       =		old_name.replace(lowercase_   ,				"""""" )
			A__							:   Tuple					       =		trimmed_name.replace("""network"""   ,				match[0] + """.meta4D_layers.blocks.""" + match[2:-1] )
			A__							:   int					       =		"""intermediate_stages.""" + trimmed_name
		else:
			A__							:   Dict					       =		old_name.replace(lowercase_   ,				"""""" )
			if int(match[2] ) < num_meta4D_last_stage:
				A__							:   Optional[int]					       =		trimmed_name.replace("""network"""   ,				"""meta4D_layers.blocks.""" + match[2] )
			else:
				A__							:   Optional[Any]					       =		str(int(match[2] ) - num_meta4D_last_stage )
				A__							:   Dict					       =		trimmed_name.replace("""network"""   ,				"""meta3D_layers.blocks.""" + layer_index )
				if "norm1" in old_name:
					A__							:   str					       =		trimmed_name.replace("""norm1"""   ,				"""layernorm1""" )
				elif "norm2" in old_name:
					A__							:   Optional[int]					       =		trimmed_name.replace("""norm2"""   ,				"""layernorm2""" )
				elif "fc1" in old_name:
					A__							:   List[Any]					       =		trimmed_name.replace("""fc1"""   ,				"""linear_in""" )
				elif "fc2" in old_name:
					A__							:   Optional[Any]					       =		trimmed_name.replace("""fc2"""   ,				"""linear_out""" )

			A__							:   str					       =		"""last_stage.""" + trimmed_name

	elif "network" in old_name and re.search(r""".\d."""   ,				lowercase_ ):
		A__							:   List[str]					       =		old_name.replace("""network"""   ,				"""intermediate_stages""" )

	if "fc" in new_name:
		A__							:   Optional[int]					       =		new_name.replace("""fc"""   ,				"""convolution""" )
	elif ("norm1" in new_name) and ("layernorm1" not in new_name):
		A__							:   Optional[int]					       =		new_name.replace("""norm1"""   ,				"""batchnorm_before""" )
	elif ("norm2" in new_name) and ("layernorm2" not in new_name):
		A__							:   int					       =		new_name.replace("""norm2"""   ,				"""batchnorm_after""" )
	if "proj" in new_name:
		A__							:   Tuple					       =		new_name.replace("""proj"""   ,				"""projection""" )
	if "dist_head" in new_name:
		A__							:   Optional[int]					       =		new_name.replace("""dist_head"""   ,				"""distillation_classifier""" )
	elif "head" in new_name:
		A__							:   Optional[Any]					       =		new_name.replace("""head"""   ,				"""classifier""" )
	elif "patch_embed" in new_name:
		A__							:   Optional[Any]					       =		"""efficientformer.""" + new_name
	elif new_name == "norm.weight" or new_name == "norm.bias":
		A__							:   Union[str, Any]					       =		new_name.replace("""norm"""   ,				"""layernorm""" )
		A__							:   Union[str, Any]					       =		"""efficientformer.""" + new_name
	else:
		A__							:   int					       =		"""efficientformer.encoder.""" + new_name

	return new_name

def 						UpperCamelCase (lowercase_:			Optional[Any]   ,				lowercase_:			Union[str, Any] )					->				Tuple:
	for key in checkpoint.copy().keys():
		A__							:   List[Any]					       =		checkpoint.pop(lowercase_ )
		A__							:   Dict					       =		val

	return checkpoint

def 						UpperCamelCase ()					->				Optional[int]:
	A__							:   List[str]					       =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	A__							:   List[str]					       =		Image.open(requests.get(lowercase_   ,				stream=lowercase_ ).raw )

	return image

def 						UpperCamelCase (lowercase_:			Path   ,				lowercase_:			Path   ,				lowercase_:			Path   ,				lowercase_:			bool )					->				Tuple:
	A__							:   Any					       =		torch.load(lowercase_   ,				map_location="""cpu""" )["""model"""]
	A__							:   List[Any]					       =		EfficientFormerConfig.from_json_file(lowercase_ )
	A__							:   Any					       =		EfficientFormerForImageClassificationWithTeacher(lowercase_ )
	A__							:   List[str]					       =		"""_""".join(checkpoint_path.split("""/""" )[-1].split(""".""" )[0].split("""_""" )[:-1] )

	A__							:   Union[str, Any]					       =		config.depths[-1] - config.num_metaad_blocks + 1
	A__							:   Any					       =		convert_torch_checkpoint(lowercase_   ,				lowercase_ )

	model.load_state_dict(lowercase_ )
	model.eval()

	A__							:   Tuple					       =		{
	    """bilinear""": PILImageResampling.BILINEAR,
	    """bicubic""": PILImageResampling.BICUBIC,
	    """nearest""": PILImageResampling.NEAREST,
	}

	# prepare image
	A__							:   Optional[int]					       =		prepare_img()
	A__							:   Optional[Any]					       =		256
	A__							:   str					       =		224
	A__							:   List[str]					       =		EfficientFormerImageProcessor(
	    size={"""shortest_edge""": image_size}   ,				crop_size={"""height""": crop_size, """width""": crop_size}   ,				resample=pillow_resamplings["""bicubic"""]   ,				)
	A__							:   Tuple					       =		processor(images=lowercase_   ,				return_tensors="""pt""" ).pixel_values

	# original processing pipeline
	A__							:   List[Any]					       =		Compose(
	    [
	        Resize(lowercase_   ,				interpolation=pillow_resamplings["""bicubic"""] ),
	        CenterCrop(lowercase_ ),
	        ToTensor(),
	        Normalize(lowercase_   ,				lowercase_ ),
	    ] )
	A__							:   Any					       =		image_transforms(lowercase_ ).unsqueeze(0 )

	assert torch.allclose(lowercase_   ,				lowercase_ )

	A__							:   Optional[int]					       =		model(lowercase_ )
	A__							:   List[str]					       =		outputs.logits

	A__							:   Tuple					       =		(1, 1000)

	if "l1" in model_name:
		A__							:   List[str]					       =		torch.Tensor(
		    [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328] )
		assert torch.allclose(logits[0, :10]   ,				lowercase_   ,				atol=1E-3 )
		assert logits.shape == expected_shape
	elif "l3" in model_name:
		A__							:   Any					       =		torch.Tensor(
		    [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127] )
		assert torch.allclose(logits[0, :10]   ,				lowercase_   ,				atol=1E-3 )
		assert logits.shape == expected_shape
	elif "l7" in model_name:
		A__							:   Union[str, Any]					       =		torch.Tensor(
		    [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878] )
		assert logits.shape == expected_shape
	else:
		raise ValueError(
		    f"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7""" )

	# Save Checkpoints
	Path(lowercase_ ).mkdir(exist_ok=lowercase_ )
	model.save_pretrained(lowercase_ )
	print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""" )
	processor.save_pretrained(lowercase_ )
	print(f"""Processor successfuly saved at {pytorch_dump_path}""" )

	if push_to_hub:
		print("""Pushing model to the hub...""" )

		model.push_to_hub(
		    repo_id=f"""Bearnardd/{pytorch_dump_path}"""   ,				commit_message="""Add model"""   ,				use_temp_dir=lowercase_   ,				)
		processor.push_to_hub(
		    repo_id=f"""Bearnardd/{pytorch_dump_path}"""   ,				commit_message="""Add image processor"""   ,				use_temp_dir=lowercase_   ,				)


if __name__ == "__main__":
	A_	:			List[str]  	=      argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--pytorch_model_path',
	    default=None,
	    type=str,
	    required=True,
	    help='Path to EfficientFormer pytorch checkpoint.',
	)
	parser.add_argument(
	    '--config_file',
	    default=None,
	    type=str,
	    required=True,
	    help='The json file for EfficientFormer model config.',
	)
	parser.add_argument(
	    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)

	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',
	)
	parser.set_defaults(push_to_hub=True)

	A_	:			List[Any]  	=      parser.parse_args()
	convert_efficientformer_checkpoint(
	    checkpoint_path=args.pytorch_model_path,
	    efficientformer_config_file=args.config_file,
	    pytorch_dump_path=args.pytorch_dump_path,
	    push_to_hub=args.push_to_hub,
	)



