
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()
__a         =   logging.get_logger(__name__)




def 							a		(				snake_case__:   Union[str, Any]   ):


				'''simple docstring'''
				lowercase_	    =   MobileNetVaConfig(layer_norm_eps=0.0_0_1   )

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

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

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

				return config




def 							a		(				):


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




@torch.no_grad()
def 							a		(				snake_case__:   List[Any]		,  snake_case__:   Optional[int]		,  snake_case__:   Tuple		,  snake_case__:   Dict=False   ):


				'''simple docstring'''
				lowercase_	    =   get_mobilenet_va_config(snake_case__   )

				# Load 🤗 model
				lowercase_	    =   MobileNetVaForImageClassification(snake_case__   ).eval()

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

				# Check outputs on an image, prepared by MobileNetV1ImageProcessor
				lowercase_	    =   MobileNetVaImageProcessor(
				    crop_size={'''width''': config.image_size, '''height''': config.image_size}		,  size={'''shortest_edge''': config.image_size + 32}		,  )
				lowercase_	    =   image_processor(images=prepare_img()		,  return_tensors='''pt'''   )
				lowercase_	    =   model(**snake_case__   )
				lowercase_	    =   outputs.logits

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

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

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

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

				if push_to_hub:
								print('''Pushing to the hub...'''   )
								lowercase_	    =   '''google/''' + model_name
								image_processor.push_to_hub(snake_case__   )
								model.push_to_hub(snake_case__   )


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

						__a         =   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
__lowerCamelCase				=			False
__lowerCamelCase				=			False


def      UpperCamelCase   (       __lowerCamelCase	:					Namespace       ):
       return TrainCommand(__lowerCamelCase       )






class     UpperCAmelCase						(		A_					):



   @staticmethod
   def 							_SCREAMING_SNAKE_CASE  (snake_case__						:   ArgumentParser					)						->      int:


          '''simple docstring'''


          snake_case			:  Any     							= parser.add_parser("train"       ,		help="CLI tool to train a model on a task."					)

          train_parser.add_argument(
              "--train_data"       ,		type=snake_case__       ,		required=snake_case__       ,		help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."       ,		)
          train_parser.add_argument(
              "--column_label"       ,		type=snake_case__       ,		default=0       ,		help="Column of the dataset csv file with example labels."					)
          train_parser.add_argument(
              "--column_text"       ,		type=snake_case__       ,		default=1       ,		help="Column of the dataset csv file with example texts."					)
          train_parser.add_argument(
              "--column_id"       ,		type=snake_case__       ,		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=snake_case__       ,		default=""       ,		help="path to validation dataset."					)
          train_parser.add_argument(
              "--validation_split"       ,		type=snake_case__       ,		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=snake_case__       ,		default="./"       ,		help="path to saved the trained model."					)

          train_parser.add_argument(
              "--task"       ,		type=snake_case__       ,		default="text_classification"       ,		help="Task to train the model on."					)
          train_parser.add_argument(
              "--model"       ,		type=snake_case__       ,		default="bert-base-uncased"       ,		help="Model's name or path to stored model."					)
          train_parser.add_argument("--train_batch_size"       ,		type=snake_case__       ,		default=32       ,		help="Batch size for training."					)
          train_parser.add_argument("--valid_batch_size"       ,		type=snake_case__       ,		default=64       ,		help="Batch size for validation."					)
          train_parser.add_argument("--learning_rate"       ,		type=snake_case__       ,		default=3e-5       ,		help="Learning rate."					)
          train_parser.add_argument("--adam_epsilon"       ,		type=snake_case__       ,		default=1e-08       ,		help="Epsilon for Adam optimizer."					)
          train_parser.set_defaults(func=snake_case__					)



   def __init__(self						:   List[Any]       ,		snake_case__						:   Namespace					)						->      Tuple:


          '''simple docstring'''


          snake_case			:  Any     							= logging.get_logger("transformers-cli/training"					)

          snake_case			:  List[Any]     							= "tf" if is_tf_available() else "torch"

          os.makedirs(args.output       ,		exist_ok=snake_case__					)
          snake_case			:  Any     							= args.output

          snake_case			:  List[Any]     							= args.column_label
          snake_case			:  Tuple     							= args.column_text
          snake_case			:  str     							= args.column_id

          self.logger.info(f"""Loading {args.task} pipeline for {args.model}"""					)
          if args.task == "text_classification":
                 snake_case			:  Optional[int]     							= 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}"""					)
          snake_case			:  Tuple     							= 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       ,		)
          snake_case			:  Optional[Any]     							= None
          if args.validation_data:
                 self.logger.info(f"""Loading validation dataset from {args.validation_data}"""					)
                 snake_case			:  Dict     							= 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       ,		)

          snake_case			:  Union[str, Any]     							= args.validation_split
          snake_case			:  Optional[Any]     							= args.train_batch_size
          snake_case			:  List[str]     							= args.valid_batch_size
          snake_case			:  List[Any]     							= args.learning_rate
          snake_case			:  List[Any]     							= args.adam_epsilon



   def 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      List[str]:


          '''simple docstring'''


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



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      Optional[Any]:


          '''simple docstring'''


          raise NotImplementedError



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]					)						->      Tuple:


          '''simple docstring'''


          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					)





