



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()
UpperCAmelCase							 =					logging.get_logger(__name__)

def 			A_      (	__a     :				Dict		):

				"""simple docstring"""
				a__				      =		MobileNetVaConfig(layer_norm_eps=0.0_0_1		)

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

				a__				      =		re.match(r"""^mobilenet_v1_([^_]*)_([^_]*)$""" ,		__A		)
				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_001
				a__				      =		"""imagenet-1k-id2label.json"""
				a__				      =		"""huggingface/label-files"""
				a__				      =		json.load(open(hf_hub_download(__A ,		__A ,		repo_type="""dataset"""		) ,		"""r"""		)		)
				a__				      =		{int(__A		) + 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_      (	):

				"""simple docstring"""
				a__				      =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
				a__				      =		Image.open(requests.get(__A ,		stream=__A		).raw		)
				return im

@torch.no_grad()
def 			A_      (	__a     :				Any ,		__a     :				Optional[Any] ,		__a     :				Union[str, Any] ,		__a     :				Tuple=False		):

				"""simple docstring"""
				a__				      =		get_mobilenet_va_config(__A		)

				# Load 🤗 model
				a__				      =		MobileNetVaForImageClassification(__A		).eval()

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

				# 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 + 32} ,		)
				a__				      =		image_processor(images=prepare_img() ,		return_tensors="""pt"""		)
				a__				      =		model(**__A		)
				a__				      =		outputs.logits

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

				if model_name == "mobilenet_v1_1.0_224":
								a__				      =		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":
								a__				      =		torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]		)
				else:
								a__				      =		None

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

				Path(__A		).mkdir(exist_ok=__A		)
				print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''		)
				model.save_pretrained(__A		)
				print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
				image_processor.save_pretrained(__A		)

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


if __name__ == "__main__":
				UpperCAmelCase							 =					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."""
				)

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






import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
	raise RuntimeError("""At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training""")

# TF training parameters
UpperCAmelCase							 =					False
UpperCAmelCase							 =					False

def 			A_      (	__a     :				Namespace		):

				"""simple docstring"""
				return TrainCommand(__a		)




class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							@staticmethod
							def _a      (							a_		):
											a__				      =		parser.add_parser("""train"""  ,  help="""CLI tool to train a model on a task."""		)

											train_parser.add_argument(
											    """--train_data"""  ,  type=a_  ,  required=a_  ,  help="""path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."""  ,  )
											train_parser.add_argument(
											    """--column_label"""  ,  type=a_  ,  default=0  ,  help="""Column of the dataset csv file with example labels."""		)
											train_parser.add_argument(
											    """--column_text"""  ,  type=a_  ,  default=1  ,  help="""Column of the dataset csv file with example texts."""		)
											train_parser.add_argument(
											    """--column_id"""  ,  type=a_  ,  default=2  ,  help="""Column of the dataset csv file with example ids."""		)
											train_parser.add_argument(
											    """--skip_first_row"""  ,  action="""store_true"""  ,  help="""Skip the first row of the csv file (headers)."""		)

											train_parser.add_argument("""--validation_data"""  ,  type=a_  ,  default=""""""  ,  help="""path to validation dataset."""		)
											train_parser.add_argument(
											    """--validation_split"""  ,  type=a_  ,  default=0.1  ,  help="""if validation dataset is not provided, fraction of train dataset to use as validation dataset."""  ,  )

											train_parser.add_argument("""--output"""  ,  type=a_  ,  default="""./"""  ,  help="""path to saved the trained model."""		)

											train_parser.add_argument(
											    """--task"""  ,  type=a_  ,  default="""text_classification"""  ,  help="""Task to train the model on."""		)
											train_parser.add_argument(
											    """--model"""  ,  type=a_  ,  default="""bert-base-uncased"""  ,  help="""Model's name or path to stored model."""		)
											train_parser.add_argument("""--train_batch_size"""  ,  type=a_  ,  default=32  ,  help="""Batch size for training."""		)
											train_parser.add_argument("""--valid_batch_size"""  ,  type=a_  ,  default=64  ,  help="""Batch size for validation."""		)
											train_parser.add_argument("""--learning_rate"""  ,  type=a_  ,  default=3E-5  ,  help="""Learning rate."""		)
											train_parser.add_argument("""--adam_epsilon"""  ,  type=a_  ,  default=1E-0_8  ,  help="""Epsilon for Adam optimizer."""		)
											train_parser.set_defaults(func=a_		)


							def __init__(							self  ,  a_		):
											a__				      =		logging.get_logger("""transformers-cli/training"""		)

											a__				      =		"""tf""" if is_tf_available() else """torch"""

											os.makedirs(args.output  ,  exist_ok=a_		)
											a__				      =		args.output

											a__				      =		args.column_label
											a__				      =		args.column_text
											a__				      =		args.column_id

											self.logger.info(F'''Loading {args.task} pipeline for {args.model}'''		)
											if args.task == "text_classification":
															a__				      =		TextClassificationPipeline.from_pretrained(args.model		)
											elif args.task == "token_classification":
															raise NotImplementedError
											elif args.task == "question_answering":
															raise NotImplementedError

											self.logger.info(F'''Loading dataset from {args.train_data}'''		)
											a__				      =		Processor.create_from_csv(
											    args.train_data  ,  column_label=args.column_label  ,  column_text=args.column_text  ,  column_id=args.column_id  ,  skip_first_row=args.skip_first_row  ,  )
											a__				      =		None
											if args.validation_data:
															self.logger.info(F'''Loading validation dataset from {args.validation_data}'''		)
															a__				      =		Processor.create_from_csv(
															    args.validation_data  ,  column_label=args.column_label  ,  column_text=args.column_text  ,  column_id=args.column_id  ,  skip_first_row=args.skip_first_row  ,  )

											a__				      =		args.validation_split
											a__				      =		args.train_batch_size
											a__				      =		args.valid_batch_size
											a__				      =		args.learning_rate
											a__				      =		args.adam_epsilon


							def _a      (							self		):
											if self.framework == "tf":
															return self.run_tf()
											return self.run_torch()


							def _a      (							self		):
											raise NotImplementedError







							def _a      (							self		):
											self.pipeline.fit(
											    self.train_dataset  ,  validation_data=self.valid_dataset  ,  validation_split=self.validation_split  ,  learning_rate=self.learning_rate  ,  adam_epsilon=self.adam_epsilon  ,  train_batch_size=self.train_batch_size  ,  valid_batch_size=self.valid_batch_size  ,  )

											# Save trained pipeline
											self.pipeline.save_pretrained(self.output		)

