


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

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()






def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				str       ,       __magic_name__	:				str       ,       __magic_name__	:				PreTrainedTokenizer       ,       __magic_name__	:				int       ,       __magic_name__	:				Optional[int] = None       ,       )  ->    Union[str, Any]:
		"""simple docstring"""




		lowercase__	     =						{}

		if train_file is not None:
				lowercase__	     =						[train_file]
		if eval_file is not None:
				lowercase__	     =						[eval_file]
		if test_file is not None:
				lowercase__	     =						[test_file]

		lowercase__	     =						datasets.load_dataset("""csv"""       ,       data_files=__magic_name__	)
		lowercase__	     =						list(ds[list(files.keys()	)[0]].features.keys()	)
		lowercase__	     =						features_name.pop(__magic_name__	)
		lowercase__	     =						list(set(ds[list(files.keys()	)[0]][label_name]	)	)
		lowercase__	     =						{label: i for i, label in enumerate(__magic_name__	)}
		lowercase__	     =						tokenizer.model_input_names
		lowercase__	     =						{}

		if len(__magic_name__	) == 1:
				for k in files.keys():
						lowercase__	     =						ds[k].map(
						    lambda __magic_name__	: tokenizer.batch_encode_plus(
						        example[features_name[0]]       ,       truncation=__magic_name__       ,       max_length=__magic_name__       ,       padding="""max_length"""	)       ,       batched=__magic_name__       ,       )
		elif len(__magic_name__	) == 2:
				for k in files.keys():
						lowercase__	     =						ds[k].map(
						    lambda __magic_name__	: tokenizer.batch_encode_plus(
						        (example[features_name[0]], example[features_name[1]])       ,       truncation=__magic_name__       ,       max_length=__magic_name__       ,       padding="""max_length"""       ,       )       ,       batched=__magic_name__       ,       )

		def gen_train():
				for ex in transformed_ds[datasets.Split.TRAIN]:
						lowercase__	     =						{k: v for k, v in ex.items() if k in input_names}
						lowercase__	     =						labelaid[ex[label_name]]
						yield (d, label)

		def gen_val():
				for ex in transformed_ds[datasets.Split.VALIDATION]:
						lowercase__	     =						{k: v for k, v in ex.items() if k in input_names}
						lowercase__	     =						labelaid[ex[label_name]]
						yield (d, label)

		def gen_test():
				for ex in transformed_ds[datasets.Split.TEST]:
						lowercase__	     =						{k: v for k, v in ex.items() if k in input_names}
						lowercase__	     =						labelaid[ex[label_name]]
						yield (d, label)

		lowercase__	     =						(
		    tf.data.Dataset.from_generator(
		        __magic_name__       ,       ({k: tf.intaa for k in input_names}, tf.intaa)       ,       ({k: tf.TensorShape([None]	) for k in input_names}, tf.TensorShape([]	))       ,       )
		    if datasets.Split.TRAIN in transformed_ds
		    else None
		)

		if train_ds is not None:
				lowercase__	     =						train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]	)	)	)

		lowercase__	     =						(
		    tf.data.Dataset.from_generator(
		        __magic_name__       ,       ({k: tf.intaa for k in input_names}, tf.intaa)       ,       ({k: tf.TensorShape([None]	) for k in input_names}, tf.TensorShape([]	))       ,       )
		    if datasets.Split.VALIDATION in transformed_ds
		    else None
		)

		if val_ds is not None:
				lowercase__	     =						val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]	)	)	)

		lowercase__	     =						(
		    tf.data.Dataset.from_generator(
		        __magic_name__       ,       ({k: tf.intaa for k in input_names}, tf.intaa)       ,       ({k: tf.TensorShape([None]	) for k in input_names}, tf.TensorShape([]	))       ,       )
		    if datasets.Split.TEST in transformed_ds
		    else None
		)

		if test_ds is not None:
				lowercase__	     =						test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]	)	)	)

		return train_ds, val_ds, test_ds, labelaid


A			:						Optional[Any]        =  logging.getLogger(__name__)



@dataclass
class 				A       :
					'''simple docstring'''






					A__     =					field(metadata={'''help''': '''Which column contains the label'''}				)
					A__     =					field(default=UpperCAmelCase__					,    metadata={'''help''': '''The path of the training file'''}				)
					A__     =					field(default=UpperCAmelCase__					,    metadata={'''help''': '''The path of the development file'''}				)
					A__     =					field(default=UpperCAmelCase__					,    metadata={'''help''': '''The path of the test file'''}				)
					A__     =					field(
					    default=1_28					,    metadata={
					        '''help''': (
					            '''The maximum total input sequence length after tokenization. Sequences longer '''
					            '''than this will be truncated, sequences shorter will be padded.'''
					        )
					    }					,    )
					A__     =					field(
					    default=UpperCAmelCase__					,    metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}				)






@dataclass
class 				A       :
					'''simple docstring'''






					A__     =					field(
					    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}				)
					A__     =					field(
					    default=UpperCAmelCase__					,    metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}				)
					A__     =					field(
					    default=UpperCAmelCase__					,    metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}				)
					A__     =					field(default=UpperCAmelCase__					,    metadata={'''help''': '''Set this flag to use fast tokenization.'''}				)
					# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
					# or just modify its tokenizer_config.json.
					A__     =					field(
					    default=UpperCAmelCase__					,    metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}					,    )






def 					UpperCamelCase   (      )  ->    Optional[Any]:
		"""simple docstring"""




		lowercase__	     =						HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
		lowercase__     ,      lowercase__     ,      lowercase__	     =						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       ,       )
		logger.info(
		    f'''n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1	)}, '''
		    f'''16-bits training: {training_args.fpaa}'''	)
		logger.info(f'''Training/evaluation parameters {training_args}'''	)

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

		lowercase__	     =						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       ,       )

		lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__	     =						get_tfds(
		    train_file=data_args.train_file       ,       eval_file=data_args.dev_file       ,       test_file=data_args.test_file       ,       tokenizer=__magic_name__       ,       label_column_id=data_args.label_column_id       ,       max_seq_length=data_args.max_seq_length       ,       )

		lowercase__	     =						AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,       num_labels=len(__magic_name__	)       ,       labelaid=__magic_name__       ,       idalabel={id: label for label, id in labelaid.items()}       ,       finetuning_task="""text-classification"""       ,       cache_dir=model_args.cache_dir       ,       )

		with training_args.strategy.scope():
				lowercase__	     =						TFAutoModelForSequenceClassification.from_pretrained(
				    model_args.model_name_or_path       ,       from_pt=bool(""".bin""" in model_args.model_name_or_path	)       ,       config=__magic_name__       ,       cache_dir=model_args.cache_dir       ,       )

		def compute_metrics(__magic_name__	:				EvalPrediction	) -> Dict:
				lowercase__	     =						np.argmax(p.predictions       ,       axis=1	)

				return {"acc": (preds == p.label_ids).mean()}

		# Initialize our Trainer
		lowercase__	     =						TFTrainer(
		    model=__magic_name__       ,       args=__magic_name__       ,       train_dataset=__magic_name__       ,       eval_dataset=__magic_name__       ,       compute_metrics=__magic_name__       ,       )

		# Training
		if training_args.do_train:
				trainer.train()
				trainer.save_model()
				tokenizer.save_pretrained(training_args.output_dir	)

		# Evaluation
		lowercase__	     =						{}
		if training_args.do_eval:
				logger.info("""*** Evaluate ***"""	)
				lowercase__	     =						trainer.evaluate()
				lowercase__	     =						os.path.join(training_args.output_dir       ,       """eval_results.txt"""	)

				with open(__magic_name__       ,       """w"""	) as writer:
						logger.info("""***** Eval results *****"""	)

						for key, value in result.items():
								logger.info(f'''  {key} = {value}'''	)
								writer.write(f'''{key} = {value}\n'''	)

						results.update(__magic_name__	)

		return results


if __name__ == "__main__":
	main()







from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch



@dataclass
class    lowercase_		:
						lowerCAmelCase__			      =42  # [batch_size x 3]
						lowerCAmelCase__			      =42  # [batch_size x 3]
						lowerCAmelCase__			      =42  # [batch_size x 3]
						lowerCAmelCase__			      =42  # [batch_size x 3]
						lowerCAmelCase__			      =42
						lowerCAmelCase__			      =42
						lowerCAmelCase__			      =42
						lowerCAmelCase__			      =42
						lowerCAmelCase__			      =42



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
								assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
								assert len(self.x.shape  ) == len(self.y.shape  ) == len(self.z.shape  ) == len(self.origin.shape  ) == 2



						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								return torch.from_numpy(np.array([self.width, self.height]					,							dtype=np.floataa  )  )



						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								return torch.from_numpy(np.array([self.x_fov, self.y_fov]					,							dtype=np.floataa  )  )



						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							torch.arange(self.height * self.width  )
								SCREAMING_SNAKE_CASE_					      =							torch.stack(
								    [
								        pixel_indices % self.width,
								        torch.div(snake_case__					,							self.width					,							rounding_mode='trunc'  ),
								    ]					,							axis=1					,							)
								return coords



						@property
						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_      ,				*SCREAMING_SNAKE_CASE_					      =							self.shape
								SCREAMING_SNAKE_CASE_					      =							int(np.prod(snake_case__  )  )

								SCREAMING_SNAKE_CASE_					      =							self.get_image_coords()
								SCREAMING_SNAKE_CASE_					      =							torch.broadcast_to(coords.unsqueeze(0  )					,							[batch_size * inner_batch_size, *coords.shape]  )
								SCREAMING_SNAKE_CASE_					      =							self.get_camera_rays(snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							rays.view(snake_case__					,							inner_batch_size * self.height * self.width					,							2					,							3  )

								return rays



						def 	__a					(			self	:   Optional[Any]					,							snake_case__	:   torch.Tensor  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_      ,				*SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							coords.shape
								assert n_coords == 2
								assert batch_size == self.origin.shape[0]

								SCREAMING_SNAKE_CASE_					      =							coords.view(snake_case__					,							-1					,							2  )

								SCREAMING_SNAKE_CASE_					      =							self.resolution()
								SCREAMING_SNAKE_CASE_					      =							self.fov()

								SCREAMING_SNAKE_CASE_					      =							(flat.float() / (res - 1)) * 2 - 1
								SCREAMING_SNAKE_CASE_					      =							fracs * torch.tan(fov / 2  )

								SCREAMING_SNAKE_CASE_					      =							fracs.view(snake_case__					,							-1					,							2  )
								SCREAMING_SNAKE_CASE_					      =							(
								    self.z.view(snake_case__					,							1					,							3  )
								    + self.x.view(snake_case__					,							1					,							3  ) * fracs[:, :, :1]
								    + self.y.view(snake_case__					,							1					,							3  ) * fracs[:, :, 1:]
								)
								SCREAMING_SNAKE_CASE_					      =							directions / directions.norm(dim=-1					,							keepdim=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							torch.stack(
								    [
								        torch.broadcast_to(self.origin.view(snake_case__					,							1					,							3  )					,							[batch_size, directions.shape[1], 3]  ),
								        directions,
								    ]					,							dim=2					,							)
								return rays.view(snake_case__					,							*snake_case__					,							2					,							3  )






						def 	__a					(			self	:   Optional[int]					,							snake_case__	:   int					,							snake_case__	:   int  ):

								"""simple docstring"""

								assert width * self.height == height * self.width, "The aspect ratio should not change."
								return DifferentiableProjectiveCamera(
								    origin=self.origin					,							x=self.x					,							y=self.y					,							z=self.z					,							width=snake_case__					,							height=snake_case__					,							x_fov=self.x_fov					,							y_fov=self.y_fov					,							)


def 					_a							(					lowerCAmelCase )->	DifferentiableProjectiveCamera:
		SCREAMING_SNAKE_CASE_					      =							[]
		SCREAMING_SNAKE_CASE_					      =							[]
		SCREAMING_SNAKE_CASE_					      =							[]
		SCREAMING_SNAKE_CASE_					      =							[]
		for theta in np.linspace(0    ,						2 * np.pi    ,						num=20 ):
				SCREAMING_SNAKE_CASE_					      =							np.array([np.sin(lowerCAmelCase ), np.cos(lowerCAmelCase ), -0.5] )
				z /= np.sqrt(np.sum(z**2 ) )
				SCREAMING_SNAKE_CASE_					      =							-z * 4
				SCREAMING_SNAKE_CASE_					      =							np.array([np.cos(lowerCAmelCase ), -np.sin(lowerCAmelCase ), 0.0] )
				SCREAMING_SNAKE_CASE_					      =							np.cross(lowerCAmelCase    ,						lowerCAmelCase )
				origins.append(lowerCAmelCase )
				xs.append(lowerCAmelCase )
				ys.append(lowerCAmelCase )
				zs.append(lowerCAmelCase )
		return DifferentiableProjectiveCamera(
		    origin=torch.from_numpy(np.stack(lowerCAmelCase    ,						axis=0 ) ).float()    ,						x=torch.from_numpy(np.stack(lowerCAmelCase    ,						axis=0 ) ).float()    ,						y=torch.from_numpy(np.stack(lowerCAmelCase    ,						axis=0 ) ).float()    ,						z=torch.from_numpy(np.stack(lowerCAmelCase    ,						axis=0 ) ).float()    ,						width=lowerCAmelCase    ,						height=lowerCAmelCase    ,						x_fov=0.7    ,						y_fov=0.7    ,						shape=(1, len(lowerCAmelCase ))    ,						)