
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()
lowerCAmelCase__							=				logging.get_logger(__name__)



def        _lowerCamelCase ( __a    ):
		SCREAMING_SNAKE_CASE_						 = MobileNetVaConfig(layer_norm_eps=0.0_0_1    )

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

		SCREAMING_SNAKE_CASE_						 = re.match(r'''^mobilenet_v1_([^_]*)_([^_]*)$''',							_UpperCamelCase    )
		if matches:
				SCREAMING_SNAKE_CASE_						 = float(matches[1]    )
				SCREAMING_SNAKE_CASE_						 = int(matches[2]    )

		# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
		# the usual 1000. The first class (index 0) is "background".
		SCREAMING_SNAKE_CASE_						 = 1_001
		SCREAMING_SNAKE_CASE_						 = '''imagenet-1k-id2label.json'''
		SCREAMING_SNAKE_CASE_						 = '''huggingface/label-files'''
		SCREAMING_SNAKE_CASE_						 = json.load(open(hf_hub_download(_UpperCamelCase,							_UpperCamelCase,							repo_type='''dataset'''    ),							'''r'''    )    )
		SCREAMING_SNAKE_CASE_						 = {int(_UpperCamelCase    ) + 1: v for k, v in idalabel.items()}
		SCREAMING_SNAKE_CASE_						 = '''background'''
		SCREAMING_SNAKE_CASE_						 = idalabel
		SCREAMING_SNAKE_CASE_						 = {v: k for k, v in idalabel.items()}

		return config



def        _lowerCamelCase ( ):
		SCREAMING_SNAKE_CASE_						 = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		SCREAMING_SNAKE_CASE_						 = Image.open(requests.get(_UpperCamelCase,							stream=_UpperCamelCase    ).raw    )
		return im



@torch.no_grad()
def        _lowerCamelCase ( __a,							__a,							__a,							__a=False    ):
		SCREAMING_SNAKE_CASE_						 = get_mobilenet_va_config(_UpperCamelCase    )

		# Load 🤗 model
		SCREAMING_SNAKE_CASE_						 = 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
		SCREAMING_SNAKE_CASE_						 = MobileNetVaImageProcessor(
		    crop_size={'''width''': config.image_size, '''height''': config.image_size},							size={'''shortest_edge''': config.image_size + 32},							)
		SCREAMING_SNAKE_CASE_						 = image_processor(images=prepare_img(),							return_tensors='''pt'''    )
		SCREAMING_SNAKE_CASE_						 = model(**_UpperCamelCase    )
		SCREAMING_SNAKE_CASE_						 = outputs.logits

		assert logits.shape == (1, 1_001)

		if model_name == "mobilenet_v1_1.0_224":
				SCREAMING_SNAKE_CASE_						 = torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]    )
		elif model_name == "mobilenet_v1_0.75_192":
				SCREAMING_SNAKE_CASE_						 = torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]    )
		else:
				SCREAMING_SNAKE_CASE_						 = 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...'''    )
				SCREAMING_SNAKE_CASE_						 = '''google/''' + model_name
				image_processor.push_to_hub(_UpperCamelCase    )
				model.push_to_hub(_UpperCamelCase    )


if __name__ == "__main__":
						lowerCAmelCase__							=				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.'
						)

						lowerCAmelCase__							=				parser.parse_args()
						convert_movilevit_checkpoint(
						    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
						)

"""simple docstring"""




import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowerCAmelCase__							=				logging.getLogger(__name__)



def        _lowerCamelCase ( __a,							__a    ):
		return (preds == labels).mean()





@dataclass
class  snake_case     :
							UpperCAmelCase__						     =      field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}   )
							UpperCAmelCase__						     =      field(
							    default=__lowercase       ,			metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}   )
							UpperCAmelCase__						     =      field(
							    default=__lowercase       ,			metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}   )
							UpperCAmelCase__						     =      field(
							    default=__lowercase       ,			metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}       ,			)







@dataclass
class  snake_case     :
							UpperCAmelCase__						     =      field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys()   )}   )
							UpperCAmelCase__						     =      field(metadata={'''help''': '''Should contain the data files for the task.'''}   )
							UpperCAmelCase__						     =      field(
							    default=128       ,			metadata={
							        '''help''': (
							            '''The maximum total input sequence length after tokenization. Sequences longer '''
							            '''than this will be truncated, sequences shorter will be padded.'''
							        )
							    }       ,			)
							UpperCAmelCase__						     =      field(
							    default=__lowercase       ,			metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}   )







def        _lowerCamelCase ( ):
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.

		SCREAMING_SNAKE_CASE_						 = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
		SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = parser.parse_args_into_dataclasses()

		if (
		    os.path.exists(training_args.output_dir    )
		    and os.listdir(training_args.output_dir    )
		    and training_args.do_train
		    and not training_args.overwrite_output_dir
		):
				raise ValueError(
				    F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
				    ''' --overwrite_output_dir to overcome.'''    )

		# Setup logging
		logging.basicConfig(
		    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',							datefmt='''%m/%d/%Y %H:%M:%S''',							level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,							)
		logger.warning(
		    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''',							training_args.local_rank,							training_args.device,							training_args.n_gpu,							bool(training_args.local_rank != -1    ),							training_args.fpaa,							)
		# Set the verbosity to info of the Transformers logger (on main process only):
		if is_main_process(training_args.local_rank    ):
				transformers.utils.logging.set_verbosity_info()
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()
		logger.info('''Training/evaluation parameters %s''',							__a    )

		# Set seed
		set_seed(training_args.seed    )

		try:
				SCREAMING_SNAKE_CASE_						 = processors[data_args.task_name]()
				SCREAMING_SNAKE_CASE_						 = processor.get_labels()
				SCREAMING_SNAKE_CASE_						 = len(__a    )
		except KeyError:
				raise ValueError('''Task not found: %s''' % (data_args.task_name)    )

		# Load pretrained model and tokenizer
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.

		SCREAMING_SNAKE_CASE_						 = AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path,							num_labels=__a,							finetuning_task=data_args.task_name,							cache_dir=model_args.cache_dir,							)
		SCREAMING_SNAKE_CASE_						 = AutoTokenizer.from_pretrained(
		    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,							cache_dir=model_args.cache_dir,							)
		SCREAMING_SNAKE_CASE_						 = AutoModelForMultipleChoice.from_pretrained(
		    model_args.model_name_or_path,							from_tf=bool('''.ckpt''' in model_args.model_name_or_path    ),							config=__a,							cache_dir=model_args.cache_dir,							)

		# Get datasets
		SCREAMING_SNAKE_CASE_						 = (
		    MultipleChoiceDataset(
		        data_dir=data_args.data_dir,							tokenizer=__a,							task=data_args.task_name,							max_seq_length=data_args.max_seq_length,							overwrite_cache=data_args.overwrite_cache,							mode=Split.train,							)
		    if training_args.do_train
		    else None
		)
		SCREAMING_SNAKE_CASE_						 = (
		    MultipleChoiceDataset(
		        data_dir=data_args.data_dir,							tokenizer=__a,							task=data_args.task_name,							max_seq_length=data_args.max_seq_length,							overwrite_cache=data_args.overwrite_cache,							mode=Split.dev,							)
		    if training_args.do_eval
		    else None
		)

		def compute_metrics(__a    ) -> Dict:
				SCREAMING_SNAKE_CASE_						 = np.argmax(p.predictions,							axis=1    )
				return {"acc": simple_accuracy(__a,							p.label_ids    )}

		# Data collator
		SCREAMING_SNAKE_CASE_						 = DataCollatorWithPadding(__a,							pad_to_multiple_of=8    ) if training_args.fpaa else None

		# Initialize our Trainer
		SCREAMING_SNAKE_CASE_						 = Trainer(
		    model=__a,							args=__a,							train_dataset=__a,							eval_dataset=__a,							compute_metrics=__a,							data_collator=__a,							)

		# Training
		if training_args.do_train:
				trainer.train(
				    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path    ) else None    )
				trainer.save_model()
				# For convenience, we also re-save the tokenizer to the same directory,
				# so that you can share your model easily on huggingface.co/models =)
				if trainer.is_world_master():
						tokenizer.save_pretrained(training_args.output_dir    )

    # Evaluation
		SCREAMING_SNAKE_CASE_						 = {}
		if training_args.do_eval:
				logger.info('''*** Evaluate ***'''    )

				SCREAMING_SNAKE_CASE_						 = trainer.evaluate()

				SCREAMING_SNAKE_CASE_						 = os.path.join(training_args.output_dir,							'''eval_results.txt'''    )
				if trainer.is_world_master():
						with open(__a,							'''w'''    ) as writer:
								logger.info('''***** Eval results *****'''    )
								for key, value in result.items():
										logger.info('''  %s = %s''',							__a,							__a    )
										writer.write('''%s = %s\n''' % (key, value)    )

								results.update(__a    )

		return results



def        _lowerCamelCase ( __a    ):
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
						main()