




'''simple docstring'''





from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch




@dataclass
class 					_snake_case						:
			lowerCAmelCase_	: str					=       42  # [batch_size x 3]
			lowerCAmelCase_	: str					=       42  # [batch_size x 3]
			lowerCAmelCase_	: Union[str, Any]					=       42  # [batch_size x 3]
			lowerCAmelCase_	: Union[str, Any]					=       42  # [batch_size x 3]
			lowerCAmelCase_	: str					=       42
			lowerCAmelCase_	: Tuple					=       42
			lowerCAmelCase_	: Union[str, Any]					=       42
			lowerCAmelCase_	: Tuple					=       42
			lowerCAmelCase_	: List[str]					=       42




			def   lowerCAmelCase__							(   self				)							->							Union[str, 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   lowerCAmelCase__							(   self				)							->							Optional[int]:

										'''simple docstring'''




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




			def   lowerCAmelCase__							(   self				)							->							str:

										'''simple docstring'''




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




			def   lowerCAmelCase__							(   self				)							->							torch.Tensor:

										'''simple docstring'''




										snake_case_         = torch.arange(self.height * self.width				)
										snake_case_         = torch.stack(
										    [
										        pixel_indices % self.width,
										        torch.div(a__							,				self.width							,				rounding_mode="trunc"				),
										    ]							,				axis=1							,				)
										return coords




			@property
			def   lowerCAmelCase__							(   self				)							->							Optional[int]:

										'''simple docstring'''




										snake_case_   , *snake_case_         = self.shape
										snake_case_         = int(np.prod(a__				)				)

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

										snake_case_         = rays.view(a__							,				inner_batch_size * self.height * self.width							,				2							,				3				)

										return rays




			def   lowerCAmelCase__							(   self							,				a__				)							->							torch.Tensor:

										'''simple docstring'''




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

										snake_case_         = coords.view(a__							,				-1							,				2				)

										snake_case_         = self.resolution()
										snake_case_         = self.fov()

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

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




			def   lowerCAmelCase__							(   self							,				a__							,				a__				)							->							"DifferentiableProjectiveCamera":

										'''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=a__							,				height=a__							,				x_fov=self.x_fov							,				y_fov=self.y_fov							,				)


def 	UpperCamelCase_(      snake_case				:			Optional[int]	):

							'''simple docstring'''






							snake_case_         = []
							snake_case_         = []
							snake_case_         = []
							snake_case_         = []
							for theta in np.linspace(0				,							2 * np.pi				,							num=2_0	):
														snake_case_         = np.array([np.sin(snake_case	), np.cos(snake_case	), -0.5]	)
														z /= np.sqrt(np.sum(z**2	)	)
														snake_case_         = -z * 4
														snake_case_         = np.array([np.cos(snake_case	), -np.sin(snake_case	), 0.0]	)
														snake_case_         = np.cross(snake_case				,							snake_case	)
														origins.append(snake_case	)
														xs.append(snake_case	)
														ys.append(snake_case	)
														zs.append(snake_case	)
							return DifferentiableProjectiveCamera(
							    origin=torch.from_numpy(np.stack(snake_case				,							axis=0	)	).float()				,							x=torch.from_numpy(np.stack(snake_case				,							axis=0	)	).float()				,							y=torch.from_numpy(np.stack(snake_case				,							axis=0	)	).float()				,							z=torch.from_numpy(np.stack(snake_case				,							axis=0	)	).float()				,							width=snake_case				,							height=snake_case				,							x_fov=0.7				,							y_fov=0.7				,							shape=(1, len(snake_case	))				,							)










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
SCREAMING_SNAKE_CASE__     :     Union[str, Any]		    =       False
SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       False

def lowercase    (  SCREAMING_SNAKE_CASE )			->							List[Any]:


			'''simple docstring'''







			return TrainCommand(SCREAMING_SNAKE_CASE )



class       a_       (     SCREAMING_SNAKE_CASE__     ):





	@staticmethod
	def 						A_(							SCREAMING_SNAKE_CASE      )  ->					Optional[int]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						parser.add_parser('train'      ,     help='CLI tool to train a model on a task.'      )

				train_parser.add_argument(
				    '--train_data'      ,     type=SCREAMING_SNAKE_CASE      ,     required=SCREAMING_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=SCREAMING_SNAKE_CASE      ,     default=0      ,     help='Column of the dataset csv file with example labels.'      )
				train_parser.add_argument(
				    '--column_text'      ,     type=SCREAMING_SNAKE_CASE      ,     default=1      ,     help='Column of the dataset csv file with example texts.'      )
				train_parser.add_argument(
				    '--column_id'      ,     type=SCREAMING_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=SCREAMING_SNAKE_CASE      ,     default=''      ,     help='path to validation dataset.'      )
				train_parser.add_argument(
				    '--validation_split'      ,     type=SCREAMING_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=SCREAMING_SNAKE_CASE      ,     default='./'      ,     help='path to saved the trained model.'      )

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





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					List[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						logging.get_logger('transformers-cli/training'      )

				SCREAMING_SNAKE_CASE_								=						'tf' if is_tf_available() else 'torch'

				os.makedirs(args.output      ,     exist_ok=SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						args.output

				SCREAMING_SNAKE_CASE_								=						args.column_label
				SCREAMING_SNAKE_CASE_								=						args.column_text
				SCREAMING_SNAKE_CASE_								=						args.column_id

				self.logger.info(f'Loading {args.task} pipeline for {args.model}'      )
				if args.task == "text_classification":
							SCREAMING_SNAKE_CASE_								=						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}'      )
				SCREAMING_SNAKE_CASE_								=						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      ,     )
				SCREAMING_SNAKE_CASE_								=						None
				if args.validation_data:
							self.logger.info(f'Loading validation dataset from {args.validation_data}'      )
							SCREAMING_SNAKE_CASE_								=						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      ,     )

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





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



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





	def 						A_(							self      )  ->					Optional[int]:




				"""simple docstring"""



				raise NotImplementedError





	def 						A_(							self      )  ->					Dict:




				"""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      )
