



from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__lowerCamelCase         =			logging.get_logger(__name__)

__lowerCamelCase         =			{
    '''EleutherAI/gpt-j-6B''': '''https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json''',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""gptj"""
       _lowerCamelCase			    =				{
           """max_position_embeddings""": """n_positions""",
           """hidden_size""": """n_embd""",
           """num_attention_heads""": """n_head""",
           """num_hidden_layers""": """n_layer""",
       }



       def __init__(     self     ,lowercase=50400     ,lowercase=2048     ,lowercase=4096     ,lowercase=28     ,lowercase=16     ,lowercase=64     ,lowercase=None     ,lowercase="gelu_new"     ,lowercase=0.0     ,lowercase=0.0     ,lowercase=0.0     ,lowercase=1E-5     ,lowercase=0.02     ,lowercase=True     ,lowercase=50256     ,lowercase=50256     ,lowercase=False     ,**lowercase     ,):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[int]				      =		vocab_size
             UpperCAmelCase_  :	List[Any]				      =		n_positions
             UpperCAmelCase_  :	List[str]				      =		n_embd
             UpperCAmelCase_  :	Dict				      =		n_layer
             UpperCAmelCase_  :	int				      =		n_head
             UpperCAmelCase_  :	Union[str, Any]				      =		n_inner
             UpperCAmelCase_  :	List[Any]				      =		rotary_dim
             UpperCAmelCase_  :	Union[str, Any]				      =		activation_function
             UpperCAmelCase_  :	int				      =		resid_pdrop
             UpperCAmelCase_  :	Any				      =		embd_pdrop
             UpperCAmelCase_  :	Union[str, Any]				      =		attn_pdrop
             UpperCAmelCase_  :	List[Any]				      =		layer_norm_epsilon
             UpperCAmelCase_  :	str				      =		initializer_range
             UpperCAmelCase_  :	Union[str, Any]				      =		use_cache

             UpperCAmelCase_  :	str				      =		bos_token_id
             UpperCAmelCase_  :	Union[str, Any]				      =		eos_token_id

             super().__init__(
                 bos_token_id=lowercase     ,eos_token_id=lowercase     ,tie_word_embeddings=lowercase     ,**lowercase)


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       def __init__(     self     ,lowercase     ,lowercase = "default"     ,lowercase = None     ,lowercase = False     ,):

             """simple docstring"""





             super().__init__(lowercase     ,task=lowercase     ,patching_specs=lowercase     ,use_past=lowercase)
             if not getattr(self._config     ,"pad_token_id"     ,lowercase):
                   # TODO: how to do that better?
                   UpperCAmelCase_  :	List[str]				      =		0



       @property
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		OrderedDict({"input_ids": {0: "batch", 1: "sequence"}})
             if self.use_past:
                   self.fill_with_past_key_values_(lowercase     ,direction="inputs")
                   UpperCAmelCase_  :	int				      =		{0: "batch", 1: "past_sequence + sequence"}
             else:
                   UpperCAmelCase_  :	Dict				      =		{0: "batch", 1: "sequence"}

             return common_inputs



       @property
       def  A_   (     self):

             """simple docstring"""





             return self._config.n_layer



       @property
       def  A_   (     self):

             """simple docstring"""





             return self._config.n_head



       def  A_   (     self     ,lowercase     ,lowercase = -1     ,lowercase = -1     ,lowercase = False     ,lowercase = None     ,):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		super(lowercase     ,self).generate_dummy_inputs(
                 lowercase     ,batch_size=lowercase     ,seq_length=lowercase     ,is_pair=lowercase     ,framework=lowercase)

             # We need to order the input in the way they appears in the forward()
             UpperCAmelCase_  :	Dict				      =		OrderedDict({"input_ids": common_inputs["input_ids"]})

             # Need to add the past_keys
             if self.use_past:
                   if not is_torch_available():
                         raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
                   else:
                         import torch

                         UpperCAmelCase_      ,      UpperCAmelCase_  :	str				      =		common_inputs["input_ids"].shape
                         # Not using the same length for past_key_values
                         UpperCAmelCase_  :	int				      =		seqlen + 2
                         UpperCAmelCase_  :	str				      =		(
                             batch,
                             self.num_attention_heads,
                             past_key_values_length,
                             self._config.hidden_size // self.num_attention_heads,
                         )
                         UpperCAmelCase_  :	int				      =		[
                             (torch.zeros(lowercase), torch.zeros(lowercase)) for _ in range(self.num_layers)
                         ]

             UpperCAmelCase_  :	Union[str, Any]				      =		common_inputs["attention_mask"]
             if self.use_past:
                   UpperCAmelCase_  :	List[str]				      =		ordered_inputs["attention_mask"].dtype
                   UpperCAmelCase_  :	List[str]				      =		torch.cat(
                       [ordered_inputs["attention_mask"], torch.ones(lowercase     ,lowercase     ,dtype=lowercase)]     ,dim=1)

             return ordered_inputs



       @property
       def  A_   (     self):

             """simple docstring"""





             return 13





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    _snake_case (       __snake_case    )     ->     Any:






      '''simple docstring'''







      return TrainCommand(__snake_case    )





class   snake_case_ (lowercase__  ):




       """simple docstring"""






       @staticmethod
       def  A_   (     lowercase):

             """simple docstring"""





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

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

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



       def __init__(     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		logging.get_logger("transformers-cli/training")

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

             os.makedirs(args.output     ,exist_ok=lowercase)
             UpperCAmelCase_  :	Union[str, Any]				      =		args.output

             UpperCAmelCase_  :	Optional[int]				      =		args.column_label
             UpperCAmelCase_  :	Dict				      =		args.column_text
             UpperCAmelCase_  :	Optional[Any]				      =		args.column_id

             self.logger.info(F"""Loading {args.task} pipeline for {args.model}""")
             if args.task == "text_classification":
                   UpperCAmelCase_  :	List[Any]				      =		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}""")
             UpperCAmelCase_  :	str				      =		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     ,)
             UpperCAmelCase_  :	str				      =		None
             if args.validation_data:
                   self.logger.info(F"""Loading validation dataset from {args.validation_data}""")
                   UpperCAmelCase_  :	Union[str, Any]				      =		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     ,)

             UpperCAmelCase_  :	List[str]				      =		args.validation_split
             UpperCAmelCase_  :	Tuple				      =		args.train_batch_size
             UpperCAmelCase_  :	Optional[int]				      =		args.valid_batch_size
             UpperCAmelCase_  :	Dict				      =		args.learning_rate
             UpperCAmelCase_  :	List[str]				      =		args.adam_epsilon



       def  A_   (     self):

             """simple docstring"""





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



       def  A_   (     self):

             """simple docstring"""





             raise NotImplementedError



       def  A_   (     self):

             """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)
