


'''simple docstring'''
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path

import pytest

import transformers
from transformers import (
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AutoTokenizer,
    BertConfig,
    BertTokenizer,
    BertTokenizerFast,
    CTRLTokenizer,
    GPTaTokenizer,
    GPTaTokenizerFast,
    PreTrainedTokenizerFast,
    RobertaTokenizer,
    RobertaTokenizerFast,
    is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
    TOKENIZER_MAPPING,
    get_tokenizer_config,
    tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
    DUMMY_DIFF_TOKENIZER_IDENTIFIER,
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tokenizers,
    slow,
)


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
							from test_module.custom_tokenization_fast import CustomTokenizerFast






class 					__lowerCAmelCase   (   unittest.TestCase						):

						"""simple docstring"""






						def 				_UpperCAmelCase       (  self							:      str	):
													A_			    =		0






						@slow
						def 				_UpperCAmelCase       (  self							:      Tuple	):
													for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
																				A_			    =		AutoTokenizer.from_pretrained(lowerCAmelCase	)
																				self.assertIsNotNone(lowerCAmelCase	)
																				self.assertIsInstance(lowerCAmelCase ,  (BertTokenizer, BertTokenizerFast)	)
																				self.assertGreater(len(lowerCAmelCase	) ,  0	)

													for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
																				A_			    =		AutoTokenizer.from_pretrained(lowerCAmelCase	)
																				self.assertIsNotNone(lowerCAmelCase	)
																				self.assertIsInstance(lowerCAmelCase ,  (GPTaTokenizer, GPTaTokenizerFast)	)
																				self.assertGreater(len(lowerCAmelCase	) ,  0	)






						def 				_UpperCAmelCase       (  self							:      Any	):
													A_			    =		AutoTokenizer.from_pretrained(lowerCAmelCase	)
													self.assertIsInstance(lowerCAmelCase ,  (BertTokenizer, BertTokenizerFast)	)
													self.assertEqual(tokenizer.vocab_size ,  12	)






						def 				_UpperCAmelCase       (  self							:      Tuple	):
													A_			    =		AutoTokenizer.from_pretrained(lowerCAmelCase	)
													self.assertIsInstance(lowerCAmelCase ,  (RobertaTokenizer, RobertaTokenizerFast)	)
													self.assertEqual(tokenizer.vocab_size ,  20	)






						def 				_UpperCAmelCase       (  self							:      List[Any]	):
													A_			    =		AutoConfig.from_pretrained(lowerCAmelCase	)
													self.assertIsInstance(lowerCAmelCase ,  lowerCAmelCase	)
													# Check that tokenizer_type ≠ model_type
													A_			    =		AutoTokenizer.from_pretrained(lowerCAmelCase ,  config=lowerCAmelCase	)
													self.assertIsInstance(lowerCAmelCase ,  (BertTokenizer, BertTokenizerFast)	)
													self.assertEqual(tokenizer.vocab_size ,  12	)






						def 				_UpperCAmelCase       (  self							:      Optional[Any]	):
													with tempfile.TemporaryDirectory() as tmp_dir:
																				shutil.copy("./tests/fixtures/vocab.txt" ,  os.path.join(lowerCAmelCase ,  "vocab.txt"	)	)

																				A_			    =		AutoTokenizer.from_pretrained(lowerCAmelCase ,  tokenizer_type="bert" ,  use_fast=lowerCAmelCase	)
																				self.assertIsInstance(lowerCAmelCase ,  lowerCAmelCase	)

													with tempfile.TemporaryDirectory() as tmp_dir:
																				shutil.copy("./tests/fixtures/vocab.json" ,  os.path.join(lowerCAmelCase ,  "vocab.json"	)	)
																				shutil.copy("./tests/fixtures/merges.txt" ,  os.path.join(lowerCAmelCase ,  "merges.txt"	)	)

																				A_			    =		AutoTokenizer.from_pretrained(lowerCAmelCase ,  tokenizer_type="gpt2" ,  use_fast=lowerCAmelCase	)
																				self.assertIsInstance(lowerCAmelCase ,  lowerCAmelCase	)






						@require_tokenizers
						def 				_UpperCAmelCase       (  self							:      Optional[int]	):
													with tempfile.TemporaryDirectory() as tmp_dir:
																				shutil.copy("./tests/fixtures/vocab.txt" ,  os.path.join(lowerCAmelCase ,  "vocab.txt"	)	)

																				A_			    =		AutoTokenizer.from_pretrained(lowerCAmelCase ,  tokenizer_type="bert"	)
																				self.assertIsInstance(lowerCAmelCase ,  lowerCAmelCase	)

													with tempfile.TemporaryDirectory() as tmp_dir:
																				shutil.copy("./tests/fixtures/vocab.json" ,  os.path.join(lowerCAmelCase ,  "vocab.json"	)	)
																				shutil.copy("./tests/fixtures/merges.txt" ,  os.path.join(lowerCAmelCase ,  "merges.txt"	)	)

																				A_			    =		AutoTokenizer.from_pretrained(lowerCAmelCase ,  tokenizer_type="gpt2"	)
																				self.assertIsInstance(lowerCAmelCase ,  lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Optional[Any]	):
													with pytest.raises(lowerCAmelCase	):
																				AutoTokenizer.from_pretrained("./" ,  tokenizer_type="xxx"	)






						@require_tokenizers
						def 				_UpperCAmelCase       (  self							:      str	):
													for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
																				A_			    =		tokenizer_class.from_pretrained("wietsedv/bert-base-dutch-cased"	)
																				self.assertIsInstance(lowerCAmelCase ,  (BertTokenizer, BertTokenizerFast)	)

																				if isinstance(lowerCAmelCase ,  lowerCAmelCase	):
																											self.assertEqual(tokenizer.basic_tokenizer.do_lower_case ,  lowerCAmelCase	)
																				else:
																											self.assertEqual(tokenizer.do_lower_case ,  lowerCAmelCase	)

																				self.assertEqual(tokenizer.model_max_length ,  5_12	)






						@require_tokenizers
						def 				_UpperCAmelCase       (  self							:      Any	):
													for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
																				with self.assertRaisesRegex(
																				    lowerCAmelCase ,  "julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier" ,  ):
																											A_			    =		tokenizer_class.from_pretrained("julien-c/herlolip-not-exists"	)






						def 				_UpperCAmelCase       (  self							:      Any	):
													# tests: https://github.com/huggingface/transformers/pull/13251
													# 1. models with `-`, e.g. xlm-roberta -> xlm_roberta
													# 2. models that don't remap 1-1 from model-name to model file, e.g., openai-gpt -> openai
													A_			    =		TOKENIZER_MAPPING.values()
													A_			    =		[]

													for slow_tok, fast_tok in tokenizers:
																				if slow_tok is not None:
																											tokenizer_names.append(slow_tok.__name__	)

																				if fast_tok is not None:
																											tokenizer_names.append(fast_tok.__name__	)

													for tokenizer_name in tokenizer_names:
																				# must find the right class
																				tokenizer_class_from_name(lowerCAmelCase	)






						@require_tokenizers
						def 				_UpperCAmelCase       (  self							:      Any	):
													self.assertIsInstance(AutoTokenizer.from_pretrained("bert-base-cased" ,  use_fast=lowerCAmelCase	) ,  lowerCAmelCase	)
													self.assertIsInstance(AutoTokenizer.from_pretrained("bert-base-cased"	) ,  lowerCAmelCase	)






						@require_tokenizers
						def 				_UpperCAmelCase       (  self							:      List[str]	):
													A_			    =		AutoTokenizer.from_pretrained("distilbert-base-uncased" ,  do_lower_case=lowerCAmelCase	)
													A_			    =		"Hello, world. How are you?"
													A_			    =		tokenizer.tokenize(lowerCAmelCase	)
													self.assertEqual("[UNK]" ,  tokens[0]	)

													A_			    =		AutoTokenizer.from_pretrained("microsoft/mpnet-base" ,  do_lower_case=lowerCAmelCase	)
													A_			    =		tokenizer.tokenize(lowerCAmelCase	)
													self.assertEqual("[UNK]" ,  tokens[0]	)






						@require_tokenizers
						def 				_UpperCAmelCase       (  self							:      Union[str, Any]	):
													A_			    =		AutoTokenizer.from_pretrained("robot-test/dummy-tokenizer-fast-with-model-config"	)
													self.assertEqual(type(lowerCAmelCase	) ,  lowerCAmelCase	)
													self.assertEqual(tokenizer.model_max_length ,  5_12	)
													self.assertEqual(tokenizer.vocab_size ,  3_00_00	)
													self.assertEqual(tokenizer.unk_token ,  "[UNK]"	)
													self.assertEqual(tokenizer.padding_side ,  "right"	)
													self.assertEqual(tokenizer.truncation_side ,  "right"	)






						def 				_UpperCAmelCase       (  self							:      int	):
													A_			    =		AutoTokenizer.from_pretrained(lowerCAmelCase	)
													self.assertIsInstance(lowerCAmelCase ,  (BertTokenizer, BertTokenizerFast)	)
													with tempfile.TemporaryDirectory() as tmp_dir:
																				tokenizer.save_pretrained(lowerCAmelCase	)
																				A_			    =		AutoTokenizer.from_pretrained(lowerCAmelCase	)

													self.assertIsInstance(lowerCAmelCase ,  tokenizer.__class__	)
													self.assertEqual(tokenizera.vocab_size ,  12	)






						def 				_UpperCAmelCase       (  self							:      Optional[Any]	):
													A_			    =		AutoTokenizer.from_pretrained("ctrl"	)
													# There is no fast CTRL so this always gives us a slow tokenizer.
													self.assertIsInstance(lowerCAmelCase ,  lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      str	):
													# Check we can load the tokenizer config of an online model.
													A_			    =		get_tokenizer_config("bert-base-cased"	)
													A_			    =		config.pop("_commit_hash" ,  lowerCAmelCase	)
													# If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
													self.assertEqual(lowerCAmelCase ,  {"do_lower_case": False}	)

													# This model does not have a tokenizer_config so we get back an empty dict.
													A_			    =		get_tokenizer_config(lowerCAmelCase	)
													self.assertDictEqual(lowerCAmelCase ,  {}	)

													# A tokenizer saved with `save_pretrained` always creates a tokenizer config.
													A_			    =		AutoTokenizer.from_pretrained(lowerCAmelCase	)
													with tempfile.TemporaryDirectory() as tmp_dir:
																				tokenizer.save_pretrained(lowerCAmelCase	)
																				A_			    =		get_tokenizer_config(lowerCAmelCase	)

													# Check the class of the tokenizer was properly saved (note that it always saves the slow class).
													self.assertEqual(config["tokenizer_class"] ,  "BertTokenizer"	)






						def 				_UpperCAmelCase       (  self							:      List[str]	):
													try:
																				AutoConfig.register("custom" ,  lowerCAmelCase	)

																				AutoTokenizer.register(lowerCAmelCase ,  slow_tokenizer_class=lowerCAmelCase	)
																				# Trying to register something existing in the Transformers library will raise an error
																				with self.assertRaises(lowerCAmelCase	):
																											AutoTokenizer.register(lowerCAmelCase ,  slow_tokenizer_class=lowerCAmelCase	)

																				A_			    =		CustomTokenizer.from_pretrained(lowerCAmelCase	)
																				with tempfile.TemporaryDirectory() as tmp_dir:
																											tokenizer.save_pretrained(lowerCAmelCase	)

																											A_			    =		AutoTokenizer.from_pretrained(lowerCAmelCase	)
																											self.assertIsInstance(lowerCAmelCase ,  lowerCAmelCase	)

													finally:
																				if "custom" in CONFIG_MAPPING._extra_content:
																											del CONFIG_MAPPING._extra_content["custom"]
																				if CustomConfig in TOKENIZER_MAPPING._extra_content:
																											del TOKENIZER_MAPPING._extra_content[CustomConfig]






						@require_tokenizers
						def 				_UpperCAmelCase       (  self							:      Union[str, Any]	):
													try:
																				AutoConfig.register("custom" ,  lowerCAmelCase	)

																				# Can register in two steps
																				AutoTokenizer.register(lowerCAmelCase ,  slow_tokenizer_class=lowerCAmelCase	)
																				self.assertEqual(TOKENIZER_MAPPING[CustomConfig] ,  (CustomTokenizer, None)	)
																				AutoTokenizer.register(lowerCAmelCase ,  fast_tokenizer_class=lowerCAmelCase	)
																				self.assertEqual(TOKENIZER_MAPPING[CustomConfig] ,  (CustomTokenizer, CustomTokenizerFast)	)

																				del TOKENIZER_MAPPING._extra_content[CustomConfig]
																				# Can register in one step
																				AutoTokenizer.register(
																				    lowerCAmelCase ,  slow_tokenizer_class=lowerCAmelCase ,  fast_tokenizer_class=lowerCAmelCase	)
																				self.assertEqual(TOKENIZER_MAPPING[CustomConfig] ,  (CustomTokenizer, CustomTokenizerFast)	)

																				# Trying to register something existing in the Transformers library will raise an error
																				with self.assertRaises(lowerCAmelCase	):
																											AutoTokenizer.register(lowerCAmelCase ,  fast_tokenizer_class=lowerCAmelCase	)

																				# We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
																				# and that model does not have a tokenizer.json
																				with tempfile.TemporaryDirectory() as tmp_dir:
																											A_			    =		BertTokenizerFast.from_pretrained(lowerCAmelCase	)
																											bert_tokenizer.save_pretrained(lowerCAmelCase	)
																											A_			    =		CustomTokenizerFast.from_pretrained(lowerCAmelCase	)

																				with tempfile.TemporaryDirectory() as tmp_dir:
																											tokenizer.save_pretrained(lowerCAmelCase	)

																											A_			    =		AutoTokenizer.from_pretrained(lowerCAmelCase	)
																											self.assertIsInstance(lowerCAmelCase ,  lowerCAmelCase	)

																											A_			    =		AutoTokenizer.from_pretrained(lowerCAmelCase ,  use_fast=lowerCAmelCase	)
																											self.assertIsInstance(lowerCAmelCase ,  lowerCAmelCase	)

													finally:
																				if "custom" in CONFIG_MAPPING._extra_content:
																											del CONFIG_MAPPING._extra_content["custom"]
																				if CustomConfig in TOKENIZER_MAPPING._extra_content:
																											del TOKENIZER_MAPPING._extra_content[CustomConfig]






						def 				_UpperCAmelCase       (  self							:      int	):
													# If remote code is not set, we will time out when asking whether to load the model.
													with self.assertRaises(lowerCAmelCase	):
																				A_			    =		AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer"	)
													# If remote code is disabled, we can't load this config.
													with self.assertRaises(lowerCAmelCase	):
																				A_			    =		AutoTokenizer.from_pretrained(
																				    "hf-internal-testing/test_dynamic_tokenizer" ,  trust_remote_code=lowerCAmelCase	)

													A_			    =		AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer" ,  trust_remote_code=lowerCAmelCase	)
													self.assertTrue(tokenizer.special_attribute_present	)
													# Test tokenizer can be reloaded.
													with tempfile.TemporaryDirectory() as tmp_dir:
																				tokenizer.save_pretrained(lowerCAmelCase	)
																				A_			    =		AutoTokenizer.from_pretrained(lowerCAmelCase ,  trust_remote_code=lowerCAmelCase	)
													self.assertTrue(reloaded_tokenizer.special_attribute_present	)

													if is_tokenizers_available():
																				self.assertEqual(tokenizer.__class__.__name__ ,  "NewTokenizerFast"	)
																				self.assertEqual(reloaded_tokenizer.__class__.__name__ ,  "NewTokenizerFast"	)

																				# Test we can also load the slow version
																				A_			    =		AutoTokenizer.from_pretrained(
																				    "hf-internal-testing/test_dynamic_tokenizer" ,  trust_remote_code=lowerCAmelCase ,  use_fast=lowerCAmelCase	)
																				self.assertTrue(tokenizer.special_attribute_present	)
																				self.assertEqual(tokenizer.__class__.__name__ ,  "NewTokenizer"	)
																				# Test tokenizer can be reloaded.
																				with tempfile.TemporaryDirectory() as tmp_dir:
																											tokenizer.save_pretrained(lowerCAmelCase	)
																											A_			    =		AutoTokenizer.from_pretrained(lowerCAmelCase ,  trust_remote_code=lowerCAmelCase ,  use_fast=lowerCAmelCase	)
																				self.assertEqual(reloaded_tokenizer.__class__.__name__ ,  "NewTokenizer"	)
																				self.assertTrue(reloaded_tokenizer.special_attribute_present	)
													else:
																				self.assertEqual(tokenizer.__class__.__name__ ,  "NewTokenizer"	)
																				self.assertEqual(reloaded_tokenizer.__class__.__name__ ,  "NewTokenizer"	)






						@require_tokenizers
						def 				_UpperCAmelCase       (  self							:      Dict	):






													class 					__lowerCAmelCase   (   lowercase						):

																			"""simple docstring"""






																			_UpperCAmelCase		:       List[Any]   						=False






													class 					__lowerCAmelCase   (   lowercase						):

																			"""simple docstring"""






																			_UpperCAmelCase		:       Any   						=NewTokenizer
																			_UpperCAmelCase		:       str   						=False

													try:
																				AutoConfig.register("custom" ,  lowerCAmelCase	)
																				AutoTokenizer.register(lowerCAmelCase ,  slow_tokenizer_class=lowerCAmelCase	)
																				AutoTokenizer.register(lowerCAmelCase ,  fast_tokenizer_class=lowerCAmelCase	)
																				# If remote code is not set, the default is to use local
																				A_			    =		AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer"	)
																				self.assertEqual(tokenizer.__class__.__name__ ,  "NewTokenizerFast"	)
																				self.assertFalse(tokenizer.special_attribute_present	)
																				A_			    =		AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer" ,  use_fast=lowerCAmelCase	)
																				self.assertEqual(tokenizer.__class__.__name__ ,  "NewTokenizer"	)
																				self.assertFalse(tokenizer.special_attribute_present	)

																				# If remote code is disabled, we load the local one.
																				A_			    =		AutoTokenizer.from_pretrained(
																				    "hf-internal-testing/test_dynamic_tokenizer" ,  trust_remote_code=lowerCAmelCase	)
																				self.assertEqual(tokenizer.__class__.__name__ ,  "NewTokenizerFast"	)
																				self.assertFalse(tokenizer.special_attribute_present	)
																				A_			    =		AutoTokenizer.from_pretrained(
																				    "hf-internal-testing/test_dynamic_tokenizer" ,  trust_remote_code=lowerCAmelCase ,  use_fast=lowerCAmelCase	)
																				self.assertEqual(tokenizer.__class__.__name__ ,  "NewTokenizer"	)
																				self.assertFalse(tokenizer.special_attribute_present	)

																				# If remote is enabled, we load from the Hub
																				A_			    =		AutoTokenizer.from_pretrained(
																				    "hf-internal-testing/test_dynamic_tokenizer" ,  trust_remote_code=lowerCAmelCase	)
																				self.assertEqual(tokenizer.__class__.__name__ ,  "NewTokenizerFast"	)
																				self.assertTrue(tokenizer.special_attribute_present	)
																				A_			    =		AutoTokenizer.from_pretrained(
																				    "hf-internal-testing/test_dynamic_tokenizer" ,  trust_remote_code=lowerCAmelCase ,  use_fast=lowerCAmelCase	)
																				self.assertEqual(tokenizer.__class__.__name__ ,  "NewTokenizer"	)
																				self.assertTrue(tokenizer.special_attribute_present	)

													finally:
																				if "custom" in CONFIG_MAPPING._extra_content:
																											del CONFIG_MAPPING._extra_content["custom"]
																				if CustomConfig in TOKENIZER_MAPPING._extra_content:
																											del TOKENIZER_MAPPING._extra_content[CustomConfig]






						def 				_UpperCAmelCase       (  self							:      int	):
													A_			    =		AutoTokenizer.from_pretrained(
													    "hf-internal-testing/test_dynamic_tokenizer_legacy" ,  trust_remote_code=lowerCAmelCase	)
													self.assertTrue(tokenizer.special_attribute_present	)
													if is_tokenizers_available():
																				self.assertEqual(tokenizer.__class__.__name__ ,  "NewTokenizerFast"	)

																				# Test we can also load the slow version
																				A_			    =		AutoTokenizer.from_pretrained(
																				    "hf-internal-testing/test_dynamic_tokenizer_legacy" ,  trust_remote_code=lowerCAmelCase ,  use_fast=lowerCAmelCase	)
																				self.assertTrue(tokenizer.special_attribute_present	)
																				self.assertEqual(tokenizer.__class__.__name__ ,  "NewTokenizer"	)
													else:
																				self.assertEqual(tokenizer.__class__.__name__ ,  "NewTokenizer"	)






						def 				_UpperCAmelCase       (  self							:      List[str]	):
													with self.assertRaisesRegex(
													    lowerCAmelCase ,  "bert-base is not a local folder and is not a valid model identifier"	):
																				A_			    =		AutoTokenizer.from_pretrained("bert-base"	)






						def 				_UpperCAmelCase       (  self							:      Optional[Any]	):
													with self.assertRaisesRegex(
													    lowerCAmelCase ,  r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"	):
																				A_			    =		AutoTokenizer.from_pretrained(lowerCAmelCase ,  revision="aaaaaa"	)






						def 				_UpperCAmelCase       (  self							:      Tuple	):
													# Make sure we have cached the tokenizer.
													A_			    =		AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"	)
													with RequestCounter() as counter:
																				A_			    =		AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"	)
																				self.assertEqual(counter.get_request_count ,  0	)
																				self.assertEqual(counter.head_request_count ,  1	)
																				self.assertEqual(counter.other_request_count ,  0	)









'''simple docstring'''
import argparse
import json
import re
from pathlib import Path

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

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE					:				List[Any]									=		logging.get_logger(__name__)







def       a_				(	UpperCamelCase_ ):
							A_			    =		MobileNetVaConfig(layer_norm_eps=0.001 )

							if "_quant" in model_name:
														raise ValueError("Quantized models are not supported." )

							A_			    =		re.match(R"^mobilenet_v1_([^_]*)_([^_]*)$"						,  UpperCamelCase_ )
							if matches:
														A_			    =		float(matches[1] )
														A_			    =		int(matches[2] )

							# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
							# the usual 1000. The first class (index 0) is "background".
							A_			    =		1_0_0_1
							A_			    =		"imagenet-1k-id2label.json"
							A_			    =		"huggingface/label-files"
							A_			    =		json.load(open(hf_hub_download(UpperCamelCase_						,  UpperCamelCase_						,  repo_type="dataset" )						,  "r" ) )
							A_			    =		{int(UpperCamelCase_ ) + 1: v for k, v in idalabel.items()}
							A_			    =		"background"
							A_			    =		idalabel
							A_			    =		{v: k for k, v in idalabel.items()}

							return config







def       a_				(	):
							A_			    =		"http://images.cocodataset.org/val2017/000000039769.jpg"
							A_			    =		Image.open(requests.get(UpperCamelCase_						,  stream=UpperCamelCase_ ).raw )
							return im







@torch.no_grad()
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_=False ):
							A_			    =		get_mobilenet_va_config(UpperCamelCase_ )

							# Load 🤗 model
							A_			    =		MobileNetVaForImageClassification(UpperCamelCase_ ).eval()

							# Load weights from TensorFlow checkpoint
							load_tf_weights_in_mobilenet_va(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ )

							# Check outputs on an image, prepared by MobileNetV1ImageProcessor
							A_			    =		MobileNetVaImageProcessor(
							    crop_size={"width": config.image_size, "height": config.image_size}						,  size={"shortest_edge": config.image_size + 3_2}						,  )
							A_			    =		image_processor(images=prepare_img()						,  return_tensors="pt" )
							A_			    =		model(**UpperCamelCase_ )
							A_			    =		outputs.logits

							assert logits.shape == (1, 1_0_0_1)

							if model_name == "mobilenet_v1_1.0_224":
														A_			    =		torch.tensor([-4.1739, -1.1233, 3.1205] )
							elif model_name == "mobilenet_v1_0.75_192":
														A_			    =		torch.tensor([-3.9440, -2.3141, -0.3333] )
							else:
														A_			    =		None

							if expected_logits is not None:
														assert torch.allclose(logits[0, :3]						,  UpperCamelCase_						,  atol=1e-4 )

							Path(UpperCamelCase_ ).mkdir(exist_ok=UpperCamelCase_ )
							print(f"Saving model {model_name} to {pytorch_dump_folder_path}" )
							model.save_pretrained(UpperCamelCase_ )
							print(f"Saving image processor to {pytorch_dump_folder_path}" )
							image_processor.save_pretrained(UpperCamelCase_ )

							if push_to_hub:
														print("Pushing to the hub..." )
														A_			    =		"google/" + model_name
														image_processor.push_to_hub(UpperCamelCase_ )
														model.push_to_hub(UpperCamelCase_ )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE					:				int									=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--model_name''',
							    default='''mobilenet_v1_1.0_224''',
							    type=str,
							    help='''Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.''',
							)
							parser.add_argument(
							    '''--checkpoint_path''', required=True, type=str, help='''Path to the original TensorFlow checkpoint (.ckpt file).'''
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
							)
							parser.add_argument(
							    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
							)

							__SCREAMING_SNAKE_CASE					:				Dict									=		parser.parse_args()
							convert_movilevit_checkpoint(
							    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)





