'''simple docstring'''

from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
							from PIL import Image

							from ..image_utils import load_image

if is_tf_available():
							from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
							import torch

							from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

lowercase_     		= logging.get_logger(__name__)
@add_end_docstrings(__lowercase)
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		def __init__(   self		,						*__A		,						**__A				) ->     Optional[int]:
								super().__init__(*__A		,						**__A				)
								requires_backends(self		,						'vision'				)
								self.check_model_type(
								    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == 'tf' else MODEL_FOR_VISION_2_SEQ_MAPPING				)






		def 		UpperCamelCase__       (   self		,						__A=None		,						__A=None		,						__A=None				) ->     Tuple:
								_lowerCAmelCase										={}
								_lowerCAmelCase										={}

								if prompt is not None:
														_lowerCAmelCase										=prompt

								if generate_kwargs is not None:
														_lowerCAmelCase										=generate_kwargs
								if max_new_tokens is not None:
														if "generate_kwargs" not in forward_kwargs:
																				_lowerCAmelCase										={}
														if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
																				raise ValueError(
																				    '\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'
																				    ' please use only one'				)
														_lowerCAmelCase										=max_new_tokens
								return preprocess_params, forward_kwargs, {}






		def __call__(   self		,						__A		,						**__A				) ->     str:
								return super().__call__(__A		,						**__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None				) ->     Union[str, Any]:
								_lowerCAmelCase										=load_image(__A				)

								if prompt is not None:
														if not isinstance(__A		,						__A				):
																				raise ValueError(
																				    F'''Received an invalid text input, got - {type(__A				)} - but expected a single string. '''
																				    'Note also that one single text can be provided for conditional image to text generation.'				)

														_lowerCAmelCase										=self.model.config.model_type

														if model_type == "git":
																				_lowerCAmelCase										=self.image_processor(images=__A		,						return_tensors=self.framework				)
																				_lowerCAmelCase										=self.tokenizer(text=__A		,						add_special_tokens=__A				).input_ids
																				_lowerCAmelCase										=[self.tokenizer.cls_token_id] + input_ids
																				_lowerCAmelCase										=torch.tensor(__A				).unsqueeze(0				)
																				model_inputs.update({'input_ids': input_ids}				)

														elif model_type == "pix2struct":
																				_lowerCAmelCase										=self.image_processor(images=__A		,						header_text=__A		,						return_tensors=self.framework				)

														elif model_type != "vision-encoder-decoder":
																				# vision-encoder-decoder does not support conditional generation
																				_lowerCAmelCase										=self.image_processor(images=__A		,						return_tensors=self.framework				)
																				_lowerCAmelCase										=self.tokenizer(__A		,						return_tensors=self.framework				)
																				model_inputs.update(__A				)

														else:
																				raise ValueError(F'''Model type {model_type} does not support conditional text generation'''				)

								else:
														_lowerCAmelCase										=self.image_processor(images=__A		,						return_tensors=self.framework				)

								if self.model.config.model_type == "git" and prompt is None:
														_lowerCAmelCase										=None

								return model_inputs






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None				) ->     List[Any]:
								# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
								# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
								if (
								    "input_ids" in model_inputs
								    and isinstance(model_inputs['input_ids']		,						__A				)
								    and all(x is None for x in model_inputs['input_ids']				)
								):
														_lowerCAmelCase										=None

								if generate_kwargs is None:
														_lowerCAmelCase										={}
								# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
								#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
								#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
								#  in the `_prepare_model_inputs` method.
								_lowerCAmelCase										=model_inputs.pop(self.model.main_input_name				)
								_lowerCAmelCase										=self.model.generate(__A		,						**__A		,						**__A				)
								return model_outputs






		def 		UpperCamelCase__       (   self		,						__A				) ->     List[str]:
								_lowerCAmelCase										=[]
								for output_ids in model_outputs:
														_lowerCAmelCase										={
														    'generated_text': self.tokenizer.decode(
														        __A		,						skip_special_tokens=__A		,						)
														}
														records.append(__A				)
								return records






'''simple docstring'''

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
lowercase_     		= False
lowercase_     		= False



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						return TrainCommand(a__      )


class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		@staticmethod
		def 		UpperCamelCase__       (   __A				) ->     Tuple:
								_lowerCAmelCase										=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-08		,						help='Epsilon for Adam optimizer.'				)
								train_parser.set_defaults(func=__A				)






		def __init__(   self		,						__A				) ->     List[str]:
								_lowerCAmelCase										=logging.get_logger('transformers-cli/training'				)

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

								os.makedirs(args.output		,						exist_ok=__A				)
								_lowerCAmelCase										=args.output

								_lowerCAmelCase										=args.column_label
								_lowerCAmelCase										=args.column_text
								_lowerCAmelCase										=args.column_id

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

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






		def 		UpperCamelCase__       (   self				) ->     List[str]:
								if self.framework == "tf":
														return self.run_tf()
								return self.run_torch()






		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								raise NotImplementedError






		def 		UpperCamelCase__       (   self				) ->     List[Any]:
								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				)





