
"""simple docstring"""


import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class  __lowerCamelCase  ( lowerCAmelCase						,	unittest.TestCase			):
				a__:      Union[str, Any]          =    RobertaTokenizer
				a__:      Any          =    RobertaTokenizerFast
				a__:      int          =    True
				a__:      List[str]          =    {'cls_token': '<s>'}




				def        UpperCAmelCase__						(				self   ):
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							lowerCamelCase_					=  [
							    '''l''',
							    '''o''',
							    '''w''',
							    '''e''',
							    '''r''',
							    '''s''',
							    '''t''',
							    '''i''',
							    '''d''',
							    '''n''',
							    '''\u0120''',
							    '''\u0120l''',
							    '''\u0120n''',
							    '''\u0120lo''',
							    '''\u0120low''',
							    '''er''',
							    '''\u0120lowest''',
							    '''\u0120newer''',
							    '''\u0120wider''',
							    '''<unk>''',
							]
							lowerCamelCase_					=  dict(zip(UpperCAmelCase							,						range(len(UpperCAmelCase   )   )   )   )
							lowerCamelCase_					=  ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
							lowerCamelCase_					=  {'''unk_token''': '''<unk>'''}

							lowerCamelCase_					=  os.path.join(self.tmpdirname							,						VOCAB_FILES_NAMES['''vocab_file''']   )
							lowerCamelCase_					=  os.path.join(self.tmpdirname							,						VOCAB_FILES_NAMES['''merges_file''']   )
							with open(self.vocab_file							,						'''w'''							,						encoding='''utf-8'''   ) as fp:
										fp.write(json.dumps(UpperCAmelCase   ) + '''\n'''   )
							with open(self.merges_file							,						'''w'''							,						encoding='''utf-8'''   ) as fp:
										fp.write('''\n'''.join(UpperCAmelCase   )   )




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							kwargs.update(self.special_tokens_map   )
							return self.tokenizer_class.from_pretrained(self.tmpdirname							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							kwargs.update(self.special_tokens_map   )
							return RobertaTokenizerFast.from_pretrained(self.tmpdirname							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  '''lower newer'''
							lowerCamelCase_					=  '''lower newer'''
							return input_text, output_text




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.tokenizer_class(self.vocab_file							,						self.merges_file							,						**self.special_tokens_map   )
							lowerCamelCase_					=  '''lower newer'''
							lowerCamelCase_					=  ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
							lowerCamelCase_					=  tokenizer.tokenize(UpperCAmelCase   )  # , add_prefix_space=True)
							self.assertListEqual(UpperCAmelCase							,						UpperCAmelCase   )

							lowerCamelCase_					=  tokens + [tokenizer.unk_token]
							lowerCamelCase_					=  [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase   )							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_tokenizer()

							self.assertListEqual(tokenizer.encode('''Hello world!'''							,						add_special_tokens=UpperCAmelCase   )							,						[0, 3_1414, 232, 328, 2]   )
							self.assertListEqual(
							    tokenizer.encode('''Hello world! cécé herlolip 418'''							,						add_special_tokens=UpperCAmelCase   )							,						[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]							,						)




				@slow
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.tokenizer_class.from_pretrained('''roberta-base'''   )

							lowerCamelCase_					=  tokenizer.encode('''sequence builders'''							,						add_special_tokens=UpperCAmelCase   )
							lowerCamelCase_					=  tokenizer.encode('''multi-sequence build'''							,						add_special_tokens=UpperCAmelCase   )

							lowerCamelCase_					=  tokenizer.encode(
							    '''sequence builders'''							,						add_special_tokens=UpperCAmelCase							,						add_prefix_space=UpperCAmelCase   )
							lowerCamelCase_					=  tokenizer.encode(
							    '''sequence builders'''							,						'''multi-sequence build'''							,						add_special_tokens=UpperCAmelCase							,						add_prefix_space=UpperCAmelCase   )

							lowerCamelCase_					=  tokenizer.build_inputs_with_special_tokens(UpperCAmelCase   )
							lowerCamelCase_					=  tokenizer.build_inputs_with_special_tokens(UpperCAmelCase							,						UpperCAmelCase   )

							assert encoded_sentence == encoded_text_from_decode
							assert encoded_pair == encoded_pair_from_decode




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_tokenizer()

							lowerCamelCase_					=  '''Encode this sequence.'''
							lowerCamelCase_					=  tokenizer.byte_encoder[''' '''.encode('''utf-8'''   )[0]]

							# Testing encoder arguments
							lowerCamelCase_					=  tokenizer.encode(UpperCAmelCase							,						add_special_tokens=UpperCAmelCase							,						add_prefix_space=UpperCAmelCase   )
							lowerCamelCase_					=  tokenizer.convert_ids_to_tokens(encoded[0]   )[0]
							self.assertNotEqual(UpperCAmelCase							,						UpperCAmelCase   )

							lowerCamelCase_					=  tokenizer.encode(UpperCAmelCase							,						add_special_tokens=UpperCAmelCase							,						add_prefix_space=UpperCAmelCase   )
							lowerCamelCase_					=  tokenizer.convert_ids_to_tokens(encoded[0]   )[0]
							self.assertEqual(UpperCAmelCase							,						UpperCAmelCase   )

							tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}   )
							lowerCamelCase_					=  tokenizer.encode(UpperCAmelCase							,						add_special_tokens=UpperCAmelCase   )
							lowerCamelCase_					=  tokenizer.convert_ids_to_tokens(encoded[1]   )[0]
							self.assertNotEqual(UpperCAmelCase							,						UpperCAmelCase   )

							# Testing spaces after special tokens
							lowerCamelCase_					=  '''<mask>'''
							tokenizer.add_special_tokens(
							    {'''mask_token''': AddedToken(UpperCAmelCase							,						lstrip=UpperCAmelCase							,						rstrip=UpperCAmelCase   )}   )  # mask token has a left space
							lowerCamelCase_					=  tokenizer.convert_tokens_to_ids(UpperCAmelCase   )

							lowerCamelCase_					=  '''Encode <mask> sequence'''
							lowerCamelCase_					=  '''Encode <mask>sequence'''

							lowerCamelCase_					=  tokenizer.encode(UpperCAmelCase   )
							lowerCamelCase_					=  encoded.index(UpperCAmelCase   )
							lowerCamelCase_					=  tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]   )[0]
							self.assertEqual(UpperCAmelCase							,						UpperCAmelCase   )

							lowerCamelCase_					=  tokenizer.encode(UpperCAmelCase   )
							lowerCamelCase_					=  encoded.index(UpperCAmelCase   )
							lowerCamelCase_					=  tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]   )[0]
							self.assertNotEqual(UpperCAmelCase							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							pass




				def        UpperCAmelCase__						(				self   ):
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"   ):
													lowerCamelCase_					=  self.rust_tokenizer_class.from_pretrained(UpperCAmelCase							,						**UpperCAmelCase   )
													lowerCamelCase_					=  self.tokenizer_class.from_pretrained(UpperCAmelCase							,						**UpperCAmelCase   )
													lowerCamelCase_					=  '''A, <mask> AllenNLP sentence.'''
													lowerCamelCase_					=  tokenizer_r.encode_plus(UpperCAmelCase							,						add_special_tokens=UpperCAmelCase							,						return_token_type_ids=UpperCAmelCase   )
													lowerCamelCase_					=  tokenizer_p.encode_plus(UpperCAmelCase							,						add_special_tokens=UpperCAmelCase							,						return_token_type_ids=UpperCAmelCase   )

													# token_type_ids should put 0 everywhere
													self.assertEqual(sum(tokens_r['''token_type_ids''']   )							,						sum(tokens_p['''token_type_ids''']   )   )

													# attention_mask should put 1 everywhere, so sum over length should be 1
													self.assertEqual(
													    sum(tokens_r['''attention_mask''']   ) / len(tokens_r['''attention_mask''']   )							,						sum(tokens_p['''attention_mask''']   ) / len(tokens_p['''attention_mask''']   )							,						)

													lowerCamelCase_					=  tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']   )
													lowerCamelCase_					=  tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']   )

													# Rust correctly handles the space before the mask while python doesnt
													self.assertSequenceEqual(tokens_p['''input_ids''']							,						[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]   )
													self.assertSequenceEqual(tokens_r['''input_ids''']							,						[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]   )

													self.assertSequenceEqual(
													    UpperCAmelCase							,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']   )
													self.assertSequenceEqual(
													    UpperCAmelCase							,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']   )




				def        UpperCAmelCase__						(				self   ):
							for trim_offsets, add_prefix_space in itertools.product([True, False]							,						repeat=2   ):
										lowerCamelCase_					=  self.rust_tokenizer_class.from_pretrained(
										    self.tmpdirname							,						use_fast=UpperCAmelCase							,						add_prefix_space=UpperCAmelCase							,						trim_offsets=UpperCAmelCase   )

										lowerCamelCase_					=  json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()   )
										lowerCamelCase_					=  json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()   )

										self.assertEqual(pre_tokenizer_state['''add_prefix_space''']							,						UpperCAmelCase   )

										self.assertEqual(post_processor_state['''add_prefix_space''']							,						UpperCAmelCase   )
										self.assertEqual(post_processor_state['''trim_offsets''']							,						UpperCAmelCase   )





				def        UpperCAmelCase__						(				self   ):
							# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
							# `trim_offsets`
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"   ):
													lowerCamelCase_					=  '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
													lowerCamelCase_					=  f"{text_of_1_token} {text_of_1_token}"

													lowerCamelCase_					=  self.rust_tokenizer_class.from_pretrained(
													    UpperCAmelCase							,						use_fast=UpperCAmelCase							,						add_prefix_space=UpperCAmelCase							,						trim_offsets=UpperCAmelCase   )
													lowerCamelCase_					=  tokenizer_r(UpperCAmelCase							,						return_offsets_mapping=UpperCAmelCase							,						add_special_tokens=UpperCAmelCase   )
													self.assertEqual(encoding.offset_mapping[0]							,						(0, len(UpperCAmelCase   ))   )
													self.assertEqual(
													    encoding.offset_mapping[1]							,						(len(UpperCAmelCase   ) + 1, len(UpperCAmelCase   ) + 1 + len(UpperCAmelCase   ))							,						)

													lowerCamelCase_					=  self.rust_tokenizer_class.from_pretrained(
													    UpperCAmelCase							,						use_fast=UpperCAmelCase							,						add_prefix_space=UpperCAmelCase							,						trim_offsets=UpperCAmelCase   )
													lowerCamelCase_					=  tokenizer_r(UpperCAmelCase							,						return_offsets_mapping=UpperCAmelCase							,						add_special_tokens=UpperCAmelCase   )
													self.assertEqual(encoding.offset_mapping[0]							,						(0, len(UpperCAmelCase   ))   )
													self.assertEqual(
													    encoding.offset_mapping[1]							,						(len(UpperCAmelCase   ) + 1, len(UpperCAmelCase   ) + 1 + len(UpperCAmelCase   ))							,						)

													lowerCamelCase_					=  self.rust_tokenizer_class.from_pretrained(
													    UpperCAmelCase							,						use_fast=UpperCAmelCase							,						add_prefix_space=UpperCAmelCase							,						trim_offsets=UpperCAmelCase   )
													lowerCamelCase_					=  tokenizer_r(UpperCAmelCase							,						return_offsets_mapping=UpperCAmelCase							,						add_special_tokens=UpperCAmelCase   )
													self.assertEqual(encoding.offset_mapping[0]							,						(0, len(UpperCAmelCase   ))   )
													self.assertEqual(
													    encoding.offset_mapping[1]							,						(len(UpperCAmelCase   ), len(UpperCAmelCase   ) + 1 + len(UpperCAmelCase   ))							,						)

													lowerCamelCase_					=  self.rust_tokenizer_class.from_pretrained(
													    UpperCAmelCase							,						use_fast=UpperCAmelCase							,						add_prefix_space=UpperCAmelCase							,						trim_offsets=UpperCAmelCase   )
													lowerCamelCase_					=  tokenizer_r(UpperCAmelCase							,						return_offsets_mapping=UpperCAmelCase							,						add_special_tokens=UpperCAmelCase   )
													self.assertEqual(encoding.offset_mapping[0]							,						(0, len(UpperCAmelCase   ))   )
													self.assertEqual(
													    encoding.offset_mapping[1]							,						(len(UpperCAmelCase   ), len(UpperCAmelCase   ) + 1 + len(UpperCAmelCase   ))							,						)

													lowerCamelCase_					=  f" {text}"

													# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
													#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
													# )
													# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
													# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
													# self.assertEqual(
													#     encoding.offset_mapping[1],
													#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
													# )

													lowerCamelCase_					=  self.rust_tokenizer_class.from_pretrained(
													    UpperCAmelCase							,						use_fast=UpperCAmelCase							,						add_prefix_space=UpperCAmelCase							,						trim_offsets=UpperCAmelCase   )
													lowerCamelCase_					=  tokenizer_r(UpperCAmelCase							,						return_offsets_mapping=UpperCAmelCase							,						add_special_tokens=UpperCAmelCase   )
													self.assertEqual(encoding.offset_mapping[0]							,						(1, 1 + len(UpperCAmelCase   ))   )
													self.assertEqual(
													    encoding.offset_mapping[1]							,						(1 + len(UpperCAmelCase   ) + 1, 1 + len(UpperCAmelCase   ) + 1 + len(UpperCAmelCase   ))							,						)

													lowerCamelCase_					=  self.rust_tokenizer_class.from_pretrained(
													    UpperCAmelCase							,						use_fast=UpperCAmelCase							,						add_prefix_space=UpperCAmelCase							,						trim_offsets=UpperCAmelCase   )
													lowerCamelCase_					=  tokenizer_r(UpperCAmelCase							,						return_offsets_mapping=UpperCAmelCase							,						add_special_tokens=UpperCAmelCase   )
													self.assertEqual(encoding.offset_mapping[0]							,						(0, 1 + len(UpperCAmelCase   ))   )
													self.assertEqual(
													    encoding.offset_mapping[1]							,						(1 + len(UpperCAmelCase   ), 1 + len(UpperCAmelCase   ) + 1 + len(UpperCAmelCase   ))							,						)

													lowerCamelCase_					=  self.rust_tokenizer_class.from_pretrained(
													    UpperCAmelCase							,						use_fast=UpperCAmelCase							,						add_prefix_space=UpperCAmelCase							,						trim_offsets=UpperCAmelCase   )
													lowerCamelCase_					=  tokenizer_r(UpperCAmelCase							,						return_offsets_mapping=UpperCAmelCase							,						add_special_tokens=UpperCAmelCase   )
													self.assertEqual(encoding.offset_mapping[0]							,						(0, 1 + len(UpperCAmelCase   ))   )
													self.assertEqual(
													    encoding.offset_mapping[1]							,						(1 + len(UpperCAmelCase   ), 1 + len(UpperCAmelCase   ) + 1 + len(UpperCAmelCase   ))							,						)





"""simple docstring"""


import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  [2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
			lowerCamelCase_					=  True if '''large''' in model_name or '''huge''' in model_name else False
			lowerCamelCase_					=  True if '''large''' in model_name or '''huge''' in model_name else False
			lowerCamelCase_					=  True if '''large''' in model_name or '''huge''' in model_name else False

			if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
						if "fl3" in model_name:
									lowerCamelCase_					=  [3, 3, 3, 3]
									lowerCamelCase_					=  [5, 5, 5, 5]
						elif "fl4" in model_name:
									lowerCamelCase_					=  [4, 4, 4, 4]
									lowerCamelCase_					=  [3, 3, 3, 3]

			if "tiny" in model_name or "small" in model_name or "base" in model_name:
						lowerCamelCase_					=  [3, 3, 3, 3]
						if "lrf" in model_name:
									lowerCamelCase_					=  [3, 3, 3, 3]
						else:
									lowerCamelCase_					=  [2, 2, 2, 2]

			if "tiny" in model_name:
						lowerCamelCase_					=  96
			elif "small" in model_name:
						lowerCamelCase_					=  96
			elif "base" in model_name:
						lowerCamelCase_					=  128
			elif "large" in model_name:
						lowerCamelCase_					=  192
			elif "xlarge" in model_name:
						lowerCamelCase_					=  256
			elif "huge" in model_name:
						lowerCamelCase_					=  352

			# set label information
			lowerCamelCase_					=  '''huggingface/label-files'''
			if "large" in model_name or "huge" in model_name:
						lowerCamelCase_					=  '''imagenet-22k-id2label.json'''
			else:
						lowerCamelCase_					=  '''imagenet-1k-id2label.json'''

			lowerCamelCase_					=  json.load(open(hf_hub_download(lowerCAmelCase__	,lowerCAmelCase__	,repo_type='''dataset'''						)	,'''r'''						)						)
			lowerCamelCase_					=  {int(lowerCAmelCase__						): v for k, v in idalabel.items()}
			lowerCamelCase_					=  {v: k for k, v in idalabel.items()}

			lowerCamelCase_					=  FocalNetConfig(
			    embed_dim=lowerCAmelCase__	,depths=lowerCAmelCase__	,focal_levels=lowerCAmelCase__	,focal_windows=lowerCAmelCase__	,use_conv_embed=lowerCAmelCase__	,idalabel=lowerCAmelCase__	,labelaid=lowerCAmelCase__	,use_post_layernorm=lowerCAmelCase__	,use_layerscale=lowerCAmelCase__	,)

			return config






def  lowercase						(	lowerCAmelCase__						):
			if "patch_embed.proj" in name:
						lowerCamelCase_					=  name.replace('''patch_embed.proj'''	,'''embeddings.patch_embeddings.projection'''						)
			if "patch_embed.norm" in name:
						lowerCamelCase_					=  name.replace('''patch_embed.norm'''	,'''embeddings.norm'''						)
			if "layers" in name:
						lowerCamelCase_					=  '''encoder.''' + name
			if "encoder.layers" in name:
						lowerCamelCase_					=  name.replace('''encoder.layers'''	,'''encoder.stages'''						)
			if "downsample.proj" in name:
						lowerCamelCase_					=  name.replace('''downsample.proj'''	,'''downsample.projection'''						)
			if "blocks" in name:
						lowerCamelCase_					=  name.replace('''blocks'''	,'''layers'''						)
			if "modulation.f.weight" in name or "modulation.f.bias" in name:
						lowerCamelCase_					=  name.replace('''modulation.f'''	,'''modulation.projection_in'''						)
			if "modulation.h.weight" in name or "modulation.h.bias" in name:
						lowerCamelCase_					=  name.replace('''modulation.h'''	,'''modulation.projection_context'''						)
			if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
						lowerCamelCase_					=  name.replace('''modulation.proj'''	,'''modulation.projection_out'''						)

			if name == "norm.weight":
						lowerCamelCase_					=  '''layernorm.weight'''
			if name == "norm.bias":
						lowerCamelCase_					=  '''layernorm.bias'''

			if "head" in name:
						lowerCamelCase_					=  name.replace('''head'''	,'''classifier'''						)
			else:
						lowerCamelCase_					=  '''focalnet.''' + name

			return name






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__=False						):
			# fmt: off
			lowerCamelCase_					=  {
			    '''focalnet-tiny''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth''',
			    '''focalnet-tiny-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth''',
			    '''focalnet-small''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth''',
			    '''focalnet-small-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth''',
			    '''focalnet-base''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth''',
			    '''focalnet-base-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth''',
			    '''focalnet-large-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth''',
			    '''focalnet-large-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth''',
			    '''focalnet-xlarge-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth''',
			    '''focalnet-xlarge-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth''',
			}
			# fmt: on

			lowerCamelCase_					=  model_name_to_url[model_name]
			print('''Checkpoint URL: '''	,lowerCAmelCase__						)
			lowerCamelCase_					=  torch.hub.load_state_dict_from_url(lowerCAmelCase__	,map_location='''cpu'''						)['''model''']

			# rename keys
			for key in state_dict.copy().keys():
						lowerCamelCase_					=  state_dict.pop(lowerCAmelCase__						)
						lowerCamelCase_					=  val

			lowerCamelCase_					=  get_focalnet_config(lowerCAmelCase__						)
			lowerCamelCase_					=  FocalNetForImageClassification(lowerCAmelCase__						)
			model.eval()

			# load state dict
			model.load_state_dict(lowerCAmelCase__						)

			# verify conversion
			lowerCamelCase_					=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''

			lowerCamelCase_					=  BitImageProcessor(
			    do_resize=lowerCAmelCase__	,size={'''shortest_edge''': 256}	,resample=PILImageResampling.BILINEAR	,do_center_crop=lowerCAmelCase__	,crop_size=224	,do_normalize=lowerCAmelCase__	,image_mean=lowerCAmelCase__	,image_std=lowerCAmelCase__	,)
			lowerCamelCase_					=  Image.open(requests.get(lowerCAmelCase__	,stream=lowerCAmelCase__						).raw						)
			lowerCamelCase_					=  processor(images=lowerCAmelCase__	,return_tensors='''pt'''						)

			lowerCamelCase_					=  transforms.Compose(
			    [
			        transforms.Resize(256						),
			        transforms.CenterCrop(224						),
			        transforms.ToTensor(),
			        transforms.Normalize(mean=[0.485, 0.456, 0.406]	,std=[0.229, 0.224, 0.225]						),
			    ]						)

			lowerCamelCase_					=  image_transforms(lowerCAmelCase__						).unsqueeze(0						)

			# verify pixel_values
			assert torch.allclose(inputs.pixel_values	,lowerCAmelCase__	,atol=1E-4						)

			lowerCamelCase_					=  model(**lowerCAmelCase__						)

			lowerCamelCase_					=  outputs.logits.argmax(-1						).item()
			print('''Predicted class:'''	,model.config.idalabel[predicted_class_idx]						)

			print('''First values of logits:'''	,outputs.logits[0, :3]						)

			if model_name == "focalnet-tiny":
						lowerCamelCase_					=  torch.tensor([0.2_166, -0.4_368, 0.2_191]						)
			elif model_name == "focalnet-tiny-lrf":
						lowerCamelCase_					=  torch.tensor([1.1_669, 0.0_125, -0.1_695]						)
			elif model_name == "focalnet-small":
						lowerCamelCase_					=  torch.tensor([0.4_917, -0.0_430, 0.1_341]						)
			elif model_name == "focalnet-small-lrf":
						lowerCamelCase_					=  torch.tensor([-0.2_588, -0.5_342, -0.2_331]						)
			elif model_name == "focalnet-base":
						lowerCamelCase_					=  torch.tensor([-0.1_655, -0.4_090, -0.1_730]						)
			elif model_name == "focalnet-base-lrf":
						lowerCamelCase_					=  torch.tensor([0.5_306, -0.0_483, -0.3_928]						)
			assert torch.allclose(outputs.logits[0, :3]	,lowerCAmelCase__	,atol=1E-4						)
			print('''Looks ok!'''						)

			if pytorch_dump_folder_path is not None:
						print(f"Saving model and processor of {model_name} to {pytorch_dump_folder_path}"						)
						model.save_pretrained(lowerCAmelCase__						)
						processor.save_pretrained(lowerCAmelCase__						)

			if push_to_hub:
						print(f"Pushing model and processor of {model_name} to the hub..."						)
						model.push_to_hub(f"{model_name}"						)
						processor.push_to_hub(f"{model_name}"						)


if __name__ == "__main__":
						A_    							=					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--model_name""",
						    default="""focalnet-tiny""",
						    type=str,
						    help="""Name of the FocalNet model you'd like to convert.""",
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
						)
						parser.add_argument(
						    """--push_to_hub""",
						    action="""store_true""",
						    help="""Whether to push the model and processor to the hub.""",
						)

						A_    							=					parser.parse_args()
						convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



