






'''simple docstring'''
from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel





def  _a(  UpperCamelCase__				:						Optional[int],	UpperCamelCase__				:						Dict,	UpperCamelCase__				:						str=None,	UpperCamelCase__				:						List[Any]=None							):




      '''simple docstring'''






      if attention_mask is None:
            SCREAMING_SNAKE_CASE__     :				List[Any]									=tf.cast(tf.math.not_equal(UpperCamelCase__,	config.pad_token_id							),	tf.inta							)
      return {"input_ids": input_ids, "attention_mask": attention_mask}






@require_tf
class      __SCREAMING_SNAKE_CASE      :
 snake_case_  							=							OPTConfig
 snake_case_  							=							{}
 snake_case_  							=							"""gelu"""




 def __init__(      self	:							Optional[Any]					,      __lowercase	:							str					,      __lowercase	:							Tuple=13					,      __lowercase	:							Optional[Any]=7					,      __lowercase	:							List[str]=True					,      __lowercase	:							Any=False					,      __lowercase	:							Union[str, Any]=99					,      __lowercase	:							str=16					,      __lowercase	:							Union[str, Any]=2					,      __lowercase	:							Tuple=4					,      __lowercase	:							Dict=4					,      __lowercase	:							Dict="gelu"					,      __lowercase	:							Tuple=0.1					,      __lowercase	:							Tuple=0.1					,      __lowercase	:							Any=20					,      __lowercase	:							Any=2					,      __lowercase	:							List[Any]=1					,      __lowercase	:							Any=0					,      __lowercase	:							Any=16					,      __lowercase	:							Dict=16					,      )	->						Optional[int]:
       SCREAMING_SNAKE_CASE__     :				str									=parent
       SCREAMING_SNAKE_CASE__     :				str									=batch_size
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=seq_length
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=is_training
       SCREAMING_SNAKE_CASE__     :				str									=use_labels
       SCREAMING_SNAKE_CASE__     :				Any									=vocab_size
       SCREAMING_SNAKE_CASE__     :				str									=hidden_size
       SCREAMING_SNAKE_CASE__     :				str									=num_hidden_layers
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=num_attention_heads
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=intermediate_size
       SCREAMING_SNAKE_CASE__     :				int									=hidden_act
       SCREAMING_SNAKE_CASE__     :				Dict									=hidden_dropout_prob
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__     :				Any									=max_position_embeddings
       SCREAMING_SNAKE_CASE__     :				Tuple									=eos_token_id
       SCREAMING_SNAKE_CASE__     :				Tuple									=pad_token_id
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=bos_token_id
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=embed_dim
       SCREAMING_SNAKE_CASE__     :				Dict									=word_embed_proj_dim
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=False




 def      __magic_name__	(      self	:							Optional[int]      )	->						Union[str, Any]:
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=ids_tensor([self.batch_size, self.seq_length - 1]					,      self.vocab_size      )
       SCREAMING_SNAKE_CASE__     :				int									=tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      )					,      1      )
       SCREAMING_SNAKE_CASE__     :				Any									=tf.concat([input_ids, eos_tensor]					,      axis=1      )

       SCREAMING_SNAKE_CASE__     :				List[Any]									=self.config_cls(
           vocab_size=self.vocab_size					,      hidden_size=self.hidden_size					,      num_hidden_layers=self.num_hidden_layers					,      num_attention_heads=self.num_attention_heads					,      ffn_dim=self.intermediate_size					,      dropout=self.hidden_dropout_prob					,      attention_dropout=self.attention_probs_dropout_prob					,      max_position_embeddings=self.max_position_embeddings					,      eos_token_id=self.eos_token_id					,      bos_token_id=self.bos_token_id					,      pad_token_id=self.pad_token_id					,      embed_dim=self.embed_dim					,      word_embed_proj_dim=self.word_embed_proj_dim					,      is_encoder_decoder=__lowercase					,      **self.config_updates					,      )
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=prepare_opt_inputs_dict(__lowercase					,      __lowercase      )
       return config, inputs_dict




 def      __magic_name__	(      self	:							str					,      __lowercase	:							Optional[int]					,      __lowercase	:							Any      )	->						Dict:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=TFOPTModel(config=__lowercase      )
       SCREAMING_SNAKE_CASE__     :				int									=inputs_dict['''input_ids''']

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=input_ids[:1, :]
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=inputs_dict['''attention_mask'''][:1, :]
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=1

       # first forward pass
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=model(__lowercase					,      attention_mask=__lowercase					,      use_cache=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				str									=outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       SCREAMING_SNAKE_CASE__     :				Any									=ids_tensor((self.batch_size, 3)					,      config.vocab_size      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									=tf.cast(ids_tensor((self.batch_size, 3)					,      2      )					,      tf.inta      )

       # append to next input_ids and
       SCREAMING_SNAKE_CASE__     :				List[str]									=tf.concat([input_ids, next_tokens]					,      axis=-1      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=tf.concat([attention_mask, next_attn_mask]					,      axis=-1      )

       SCREAMING_SNAKE_CASE__     :				str									=model(__lowercase					,      attention_mask=__lowercase      )[0]
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=model(__lowercase					,      attention_mask=__lowercase					,      past_key_values=__lowercase      )[0]

       self.parent.assertEqual(next_tokens.shape[1]					,      output_from_past.shape[1]      )

       # select random slice
       SCREAMING_SNAKE_CASE__     :				List[Any]									=int(ids_tensor((1,)					,      output_from_past.shape[-1]      )      )
       SCREAMING_SNAKE_CASE__     :				str									=output_from_no_past[:, -3:, random_slice_idx]
       SCREAMING_SNAKE_CASE__     :				int									=output_from_past[:, :, random_slice_idx]

       # test that outputs are equal for slice
       tf.debugging.assert_near(__lowercase					,      __lowercase					,      rtol=1e-3      )






@require_tf
class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase	,				lowerCamelCase	,				unittest.TestCase				):
 snake_case_  							=							(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
 snake_case_  							=							(TFOPTForCausalLM,) if is_tf_available() else ()
 snake_case_  							=							(
     {"""feature-extraction""": TFOPTModel, """text-generation""": TFOPTForCausalLM} if is_tf_available() else {}
 )
 snake_case_  							=							False
 snake_case_  							=							False
 snake_case_  							=							False
 snake_case_  							=							10




 def      __magic_name__	(      self	:							Optional[int]      )	->						Optional[Any]:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=TFOPTModelTester(self      )
       SCREAMING_SNAKE_CASE__     :				Any									=ConfigTester(self					,      config_class=__lowercase      )




 def      __magic_name__	(      self	:							str      )	->						Tuple:
       self.config_tester.run_common_tests()




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						List[Any]:
       SCREAMING_SNAKE_CASE__     :				str									=self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*__lowercase      )




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Optional[int]:
       SCREAMING_SNAKE_CASE__     :				Tuple									=self.model_tester.prepare_config_and_inputs_for_common()

       def _get_word_embedding_weight(__lowercase	:							List[str]					,      __lowercase	:							Union[str, Any]      ):
             if hasattr(__lowercase					,      '''weight'''      ):
                   return embedding_layer.weight
             else:
                   # Here we build the word embeddings weights if not exists.
                   # And then we retry to get the attribute once built.
                   model.build()
                   if hasattr(__lowercase					,      '''weight'''      ):
                         return embedding_layer.weight
                   else:
                         return None

       for model_class in self.all_model_classes:
             for size in [config.vocab_size - 10, config.vocab_size + 10]:
                   # build the embeddings
                   SCREAMING_SNAKE_CASE__     :				str									=model_class(config=__lowercase      )
                   SCREAMING_SNAKE_CASE__     :				Tuple									=_get_word_embedding_weight(__lowercase					,      model.get_input_embeddings()      )
                   SCREAMING_SNAKE_CASE__     :				int									=_get_word_embedding_weight(__lowercase					,      model.get_output_embeddings()      )

                   # reshape the embeddings
                   model.resize_token_embeddings(__lowercase      )
                   SCREAMING_SNAKE_CASE__     :				Optional[int]									=_get_word_embedding_weight(__lowercase					,      model.get_input_embeddings()      )
                   SCREAMING_SNAKE_CASE__     :				int									=_get_word_embedding_weight(__lowercase					,      model.get_output_embeddings()      )

                   # check that the resized embeddings size matches the desired size.
                   SCREAMING_SNAKE_CASE__     :				List[Any]									=size if size is not None else config.vocab_size

                   self.assertEqual(new_input_embeddings.shape[0]					,      __lowercase      )

                   # check that weights remain the same after resizing
                   SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=True
                   for pa, pa in zip(old_input_embeddings.value()					,      new_input_embeddings.value()      ):
                         if tf.math.reduce_sum(tf.math.abs(pa - pa      )      ) > 0:
                               SCREAMING_SNAKE_CASE__     :				List[str]									=False
                   self.assertTrue(__lowercase      )

                   if old_output_embeddings is not None and new_output_embeddings is not None:
                         self.assertEqual(new_output_embeddings.shape[0]					,      __lowercase      )

                         SCREAMING_SNAKE_CASE__     :				Any									=True
                         for pa, pa in zip(old_output_embeddings.value()					,      new_output_embeddings.value()      ):
                               if tf.math.reduce_sum(tf.math.abs(pa - pa      )      ) > 0:
                                     SCREAMING_SNAKE_CASE__     :				Any									=False
                         self.assertTrue(__lowercase      )





def  _a(  UpperCamelCase__				:						Dict							):




      '''simple docstring'''






      return tf.constant(UpperCamelCase__,	dtype=tf.intaa							)






@require_tf
class      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):
 snake_case_  							=							99




 def      __magic_name__	(      self	:							Dict      )	->						Optional[Any]:
       SCREAMING_SNAKE_CASE__     :				str									=tf.ones((4, 1)					,      dtype=tf.intaa      ) * 2
       SCREAMING_SNAKE_CASE__     :				Any									=tf.concat([ids_tensor((4, 6)					,      self.vocab_size - 3      ) + 3, eos_column_vector]					,      axis=1      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=input_ids.shape[0]
       SCREAMING_SNAKE_CASE__     :				Dict									=OPTConfig(
           vocab_size=self.vocab_size					,      hidden_size=24					,      num_hidden_layers=2					,      num_attention_heads=2					,      ffn_dim=32					,      max_position_embeddings=48					,      eos_token_id=2					,      pad_token_id=1					,      bos_token_id=0					,      )
       return config, input_ids, batch_size






@require_sentencepiece
@require_tf
class      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):




 @slow
 def      __magic_name__	(      self	:							Optional[Any]      )	->						int:
       SCREAMING_SNAKE_CASE__     :				Tuple									=TFOPTModel.from_pretrained('''facebook/opt-350m'''      )
       SCREAMING_SNAKE_CASE__     :				Any									=_long_tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=tf.not_equal(__lowercase					,      model.config.pad_token_id      )
       with tf.GradientTape():
             SCREAMING_SNAKE_CASE__     :				List[str]									=model(input_ids=__lowercase					,      attention_mask=__lowercase      ).last_hidden_state
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=(1, 11, 5_12)
       self.assertEqual(output.shape					,      __lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									=tf.constant(
           [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]]      )
       self.assertTrue(np.allclose(output[:, :3, :3]					,      __lowercase					,      atol=4e-3      )      )

       SCREAMING_SNAKE_CASE__     :				str									=tf.function(__lowercase					,      jit_compile=__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									=xla_generate(__lowercase					,      __lowercase      )[0]
       self.assertTrue(np.allclose(output[:, :3, :3]					,      __lowercase					,      atol=4e-2      )      )






@require_tf
@slow
class      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						List[str]:
       super().setUp()
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									='''facebook/opt-350m'''




 def      __magic_name__	(      self	:							Dict      )	->						List[Any]:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=TFOPTForCausalLM.from_pretrained(self.path_model      )
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=GPTaTokenizer.from_pretrained(self.path_model      )

       SCREAMING_SNAKE_CASE__     :				Dict									=[
           '''Today is a beautiful day and I want to''',
           '''In the city of''',
           '''Paris is the capital of France and''',
           '''Computers and mobile phones have taken''',
       ]
       # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
       SCREAMING_SNAKE_CASE__     :				List[Any]									=tokenizer(__lowercase					,      return_tensors='''tf'''					,      padding=__lowercase					,      add_special_tokens=__lowercase      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									=tf.math.reduce_mean(model(inputs.input_ids					,      attention_mask=inputs.attention_mask      )[0]					,      axis=-1      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=tf.constant(
           [
               [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
               [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
               [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
               [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
           ]      )
       self.assertTrue(np.allclose(__lowercase					,      __lowercase					,      atol=1e-4      )      )

       SCREAMING_SNAKE_CASE__     :				Tuple									=tf.function(__lowercase					,      jit_compile=__lowercase      )
       SCREAMING_SNAKE_CASE__     :				int									=tf.math.reduce_mean(xla_generate(inputs.input_ids					,      attention_mask=inputs.attention_mask      )[0]					,      axis=-1      )
       self.assertTrue(np.allclose(__lowercase					,      __lowercase					,      atol=1e-4      )      )






@require_tf
@slow
class      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):




 @property
 def      __magic_name__	(      self	:							List[str]      )	->						Optional[int]:
       return [
           "Today is a beautiful day and I want",
           "In the city of",
           "Paris is the capital of France and",
           "Computers and mobile phones have taken",
       ]




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Optional[int]:
       SCREAMING_SNAKE_CASE__     :				int									='''facebook/opt-125m'''

       SCREAMING_SNAKE_CASE__     :				Any									=[
           '''Today is a beautiful day and I want to''',
           '''In the city of New York, the city''',
           '''Paris is the capital of France and the capital''',
           '''Computers and mobile phones have taken over the''',
       ]

       SCREAMING_SNAKE_CASE__     :				List[str]									=[]
       SCREAMING_SNAKE_CASE__     :				Tuple									=GPTaTokenizer.from_pretrained(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=TFOPTForCausalLM.from_pretrained(__lowercase      )

       for prompt in self.prompts:
             SCREAMING_SNAKE_CASE__     :				Dict									=tokenizer(__lowercase					,      return_tensors='''tf'''      ).input_ids

             SCREAMING_SNAKE_CASE__     :				Dict									=model.generate(__lowercase					,      max_length=10      )

             SCREAMING_SNAKE_CASE__     :				Optional[int]									=tokenizer.batch_decode(__lowercase					,      skip_special_tokens=__lowercase      )
             predicted_outputs += generated_string

       self.assertListEqual(__lowercase					,      __lowercase      )




 def      __magic_name__	(      self	:							Dict      )	->						Optional[Any]:
       SCREAMING_SNAKE_CASE__     :				Any									='''facebook/opt-350m'''

       SCREAMING_SNAKE_CASE__     :				List[Any]									=GPTaTokenizer.from_pretrained(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=TFOPTForCausalLM.from_pretrained(__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Any									='''left'''

       # use different length sentences to test batching
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=[
           '''Hello, my dog is a little''',
           '''Today, I''',
       ]

       SCREAMING_SNAKE_CASE__     :				int									=tokenizer(__lowercase					,      return_tensors='''tf'''					,      padding=__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=inputs['''input_ids''']

       SCREAMING_SNAKE_CASE__     :				Dict									=model.generate(input_ids=__lowercase					,      attention_mask=inputs['''attention_mask''']      )

       SCREAMING_SNAKE_CASE__     :				Any									=tokenizer(sentences[0]					,      return_tensors='''tf'''      ).input_ids
       SCREAMING_SNAKE_CASE__     :				List[str]									=model.generate(input_ids=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				int									=inputs_non_padded.shape[-1] - tf.math.reduce_sum(
           tf.cast(inputs['''attention_mask'''][-1]					,      tf.intaa      )      )
       SCREAMING_SNAKE_CASE__     :				int									=tokenizer(sentences[1]					,      return_tensors='''tf'''      ).input_ids
       SCREAMING_SNAKE_CASE__     :				Any									=model.generate(input_ids=__lowercase					,      max_length=model.config.max_length - num_paddings      )

       SCREAMING_SNAKE_CASE__     :				Any									=tokenizer.batch_decode(__lowercase					,      skip_special_tokens=__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Dict									=tokenizer.decode(output_non_padded[0]					,      skip_special_tokens=__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=tokenizer.decode(output_padded[0]					,      skip_special_tokens=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				List[str]									=[
           '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
           '''Today, I was in the middle of a conversation with a friend about the''',
       ]
       self.assertListEqual(__lowercase					,      __lowercase      )
       self.assertListEqual(__lowercase					,      [non_padded_sentence, padded_sentence]      )




 def      __magic_name__	(      self	:							Tuple      )	->						Optional[int]:
       SCREAMING_SNAKE_CASE__     :				List[str]									='''facebook/opt-350m'''

       SCREAMING_SNAKE_CASE__     :				List[str]									=[
           '''Today is a beautiful day and I want to''',
           '''In the city of San Francisco, the city''',
           '''Paris is the capital of France and the capital''',
           '''Computers and mobile phones have taken over the''',
       ]

       SCREAMING_SNAKE_CASE__     :				int									=[]
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=GPTaTokenizer.from_pretrained(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Dict									=TFOPTForCausalLM.from_pretrained(__lowercase      )

       for prompt in self.prompts:
             SCREAMING_SNAKE_CASE__     :				str									=tokenizer(__lowercase					,      return_tensors='''tf'''      ).input_ids

             SCREAMING_SNAKE_CASE__     :				int									=model.generate(__lowercase					,      max_length=10      )

             SCREAMING_SNAKE_CASE__     :				int									=tokenizer.batch_decode(__lowercase					,      skip_special_tokens=__lowercase      )
             predicted_outputs += generated_string

       self.assertListEqual(__lowercase					,      __lowercase      )













'''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
a_					= False
a_					= False





def  _a(  UpperCamelCase__				:						Namespace							):




      '''simple docstring'''






      return TrainCommand(UpperCamelCase__							)







class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):




 @staticmethod
 def      __magic_name__	(      __lowercase	:							ArgumentParser      )	->						Any:
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=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	:							Tuple					,      __lowercase	:							Namespace      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				Tuple									=logging.get_logger('''transformers-cli/training'''      )

       SCREAMING_SNAKE_CASE__     :				int									='''tf''' if is_tf_available() else '''torch'''

       os.makedirs(args.output					,      exist_ok=__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									=args.output

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

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

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




 def      __magic_name__	(      self	:							Any      )	->						str:
       if self.framework == "tf":
             return self.run_tf()
       return self.run_torch()




 def      __magic_name__	(      self	:							Optional[int]      )	->						Tuple:
       raise NotImplementedError




 def      __magic_name__	(      self	:							Dict      )	->						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      )