


import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
   import os

   # The slow tests are often failing with OOM error on GPU
   # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
   # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
   UpperCAmelCase_      : Optional[int]  					=       'platform'
   import jax
   import jax.numpy as jnp
   import numpy as np

   from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class  SCREAMING_SNAKE_CASE__  :
      snake_case__				:     Any        =						PegasusConfig
      snake_case__				:     Any        =						{}
      snake_case__				:     str        =						'''gelu'''
      def __init__(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=1_3     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=7     ,							SCREAMING_SNAKE_CASE__				:       Dict=True     ,							SCREAMING_SNAKE_CASE__				:       Tuple=False     ,							SCREAMING_SNAKE_CASE__				:       Tuple=9_9     ,							SCREAMING_SNAKE_CASE__				:       Any=3_2     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=5     ,							SCREAMING_SNAKE_CASE__				:       Tuple=4     ,							SCREAMING_SNAKE_CASE__				:       str=3_7     ,							SCREAMING_SNAKE_CASE__				:       Dict=0.1     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=0.1     ,							SCREAMING_SNAKE_CASE__				:       str=2_0     ,							SCREAMING_SNAKE_CASE__				:       Tuple=2     ,							SCREAMING_SNAKE_CASE__				:       List[str]=1     ,							SCREAMING_SNAKE_CASE__				:       Any=0     ,							)		->			Optional[Any]:
             a_					:		List[Any]							=  parent
             a_					:		Optional[Any]							=  batch_size
             a_					:		Dict							=  seq_length
             a_					:		Optional[int]							=  is_training
             a_					:		List[Any]							=  use_labels
             a_					:		List[str]							=  vocab_size
             a_					:		str							=  hidden_size
             a_					:		Tuple							=  num_hidden_layers
             a_					:		List[str]							=  num_attention_heads
             a_					:		Tuple							=  intermediate_size

             a_					:		List[str]							=  hidden_dropout_prob
             a_					:		List[Any]							=  attention_probs_dropout_prob
             a_					:		List[Any]							=  max_position_embeddings
             a_					:		List[str]							=  eos_token_id
             a_					:		Optional[Any]							=  pad_token_id
             a_					:		Union[str, Any]							=  bos_token_id
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			Dict:
             a_					:		str							=  ids_tensor([self.batch_size, self.seq_length - 1]     ,							self.vocab_size     ).clip(3     ,							self.vocab_size     )
             a_					:		List[Any]							=  np.expand_dims(np.array([self.eos_token_id] * self.batch_size     )     ,							1     )
             a_					:		List[str]							=  np.concatenate([input_ids, eos_tensor]     ,							axis=1     )

             a_					:		Union[str, Any]							=  ids_tensor([self.batch_size, self.seq_length]     ,							self.vocab_size     )

             a_					:		Optional[Any]							=  self.config_cls(
                 vocab_size=self.vocab_size     ,							d_model=self.hidden_size     ,							encoder_layers=self.num_hidden_layers     ,							decoder_layers=self.num_hidden_layers     ,							encoder_attention_heads=self.num_attention_heads     ,							decoder_attention_heads=self.num_attention_heads     ,							encoder_ffn_dim=self.intermediate_size     ,							decoder_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_ids=[2]     ,							bos_token_id=self.bos_token_id     ,							pad_token_id=self.pad_token_id     ,							decoder_start_token_id=self.pad_token_id     ,							**self.config_updates     ,							)
             a_					:		Optional[Any]							=  prepare_pegasus_inputs_dict(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
             return config, inputs_dict
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Dict     )		->			Optional[Any]:
             a_					:		Any							=  2_0
             a_					:		int							=  model_class_name(SCREAMING_SNAKE_CASE__     )

             a_					:		Union[str, Any]							=  model.encode(inputs_dict['input_ids']     )

             a_							,		a_					:		List[Any]							=  (
                 inputs_dict['decoder_input_ids'],
                 inputs_dict['decoder_attention_mask'],
             )

             a_					:		List[Any]							=  model.init_cache(decoder_input_ids.shape[0]     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,							dtype='i4'     )

             a_					:		List[Any]							=  jnp.broadcast_to(
                 jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]     ,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,							)
             a_					:		Union[str, Any]							=  model.decode(
                 decoder_input_ids[:, :-1]     ,							SCREAMING_SNAKE_CASE__     ,							decoder_attention_mask=SCREAMING_SNAKE_CASE__     ,							past_key_values=SCREAMING_SNAKE_CASE__     ,							decoder_position_ids=SCREAMING_SNAKE_CASE__     ,							)

             a_					:		int							=  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,							dtype='i4'     )
             a_					:		Union[str, Any]							=  model.decode(
                 decoder_input_ids[:, -1:]     ,							SCREAMING_SNAKE_CASE__     ,							decoder_attention_mask=SCREAMING_SNAKE_CASE__     ,							past_key_values=outputs_cache.past_key_values     ,							decoder_position_ids=SCREAMING_SNAKE_CASE__     ,							)

             a_					:		Dict							=  model.decode(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

             a_					:		Optional[int]							=  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
             self.parent.assertTrue(diff < 1E-3     ,							msg=F"""Max diff is {diff}"""     )

      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       List[str]     )		->			Optional[Any]:
             a_					:		Tuple							=  2_0
             a_					:		Optional[int]							=  model_class_name(SCREAMING_SNAKE_CASE__     )

             a_					:		int							=  model.encode(inputs_dict['input_ids']     )

             a_							,		a_					:		Dict							=  (
                 inputs_dict['decoder_input_ids'],
                 inputs_dict['decoder_attention_mask'],
             )

             a_					:		Optional[int]							=  jnp.concatenate(
                 [
                     decoder_attention_mask,
                     jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])     ),
                 ]     ,							axis=-1     ,							)

             a_					:		str							=  model.init_cache(decoder_input_ids.shape[0]     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
             a_					:		int							=  jnp.broadcast_to(
                 jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]     ,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,							)

             a_					:		List[str]							=  model.decode(
                 decoder_input_ids[:, :-1]     ,							SCREAMING_SNAKE_CASE__     ,							decoder_attention_mask=SCREAMING_SNAKE_CASE__     ,							past_key_values=SCREAMING_SNAKE_CASE__     ,							decoder_position_ids=SCREAMING_SNAKE_CASE__     ,							)
             a_					:		Optional[int]							=  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,							dtype='i4'     )
             a_					:		Any							=  model.decode(
                 decoder_input_ids[:, -1:]     ,							SCREAMING_SNAKE_CASE__     ,							past_key_values=outputs_cache.past_key_values     ,							decoder_attention_mask=SCREAMING_SNAKE_CASE__     ,							decoder_position_ids=SCREAMING_SNAKE_CASE__     ,							)

             a_					:		Optional[int]							=  model.decode(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							decoder_attention_mask=SCREAMING_SNAKE_CASE__     )

             a_					:		List[Any]							=  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
             self.parent.assertTrue(diff < 1E-3     ,							msg=F"""Max diff is {diff}"""     )







def        SCREAMING_SNAKE_CASE_						(			__A :			Dict  ,    __A :			int  ,    __A :			Union[str, Any]  ,    __A :			Optional[int]=None  ,    __A :			str=None  ,    )	->		List[Any]:

       """simple docstring"""

       if attention_mask is None:
              a_					:		List[str]							=  np.not_equal(__A  ,    config.pad_token_id				).astype(np.inta				)
       if decoder_attention_mask is None:
              a_					:		str							=  np.concatenate(
                  [
                      np.ones(decoder_input_ids[:, :1].shape  ,    dtype=np.inta				),
                      np.not_equal(decoder_input_ids[:, 1:]  ,    config.pad_token_id				).astype(np.inta				),
                  ]  ,    axis=-1  ,    )
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": decoder_attention_mask,
       }
@require_flax
class  SCREAMING_SNAKE_CASE__  (				lowercase__						,    unittest.TestCase			):
      snake_case__				:     List[Any]        =						(
          (
              FlaxPegasusForConditionalGeneration,
              FlaxPegasusModel,
          )
          if is_flax_available()
          else ()
      )
      snake_case__				:     Optional[Any]        =						(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
      snake_case__				:     Optional[Any]        =						True
      snake_case__				:     List[str]        =						False
      snake_case__				:     str        =						False
      snake_case__				:     Any        =						False
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Any:
             a_					:		Any							=  FlaxPegasusModelTester(self     )
             a_					:		Tuple							=  ConfigTester(self     ,							config_class=SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			Tuple:
             self.config_tester.run_common_tests()
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			Dict:
             a_							,		a_					:		str							=  self.model_tester.prepare_config_and_inputs_for_common()
             for model_class in self.all_model_classes:
                    self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			str:
             a_							,		a_					:		Union[str, Any]							=  self.model_tester.prepare_config_and_inputs_for_common()
             for model_class in self.all_model_classes:
                    self.model_tester.check_use_cache_forward_with_attn_mask(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			List[Any]:
             a_							,		a_					:		List[str]							=  self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    with self.subTest(model_class.__name__     ):
                           a_					:		Dict							=  self._prepare_for_class(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
                           a_					:		Union[str, Any]							=  model_class(SCREAMING_SNAKE_CASE__     )

                           @jax.jit
                           def encode_jitted(SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Tuple=None     ,							**SCREAMING_SNAKE_CASE__				:       Any     ):
                                  return model.encode(input_ids=SCREAMING_SNAKE_CASE__     ,							attention_mask=SCREAMING_SNAKE_CASE__     )

                           with self.subTest('JIT Enabled'     ):
                                  a_					:		Any							=  encode_jitted(**SCREAMING_SNAKE_CASE__     ).to_tuple()

                           with self.subTest('JIT Disabled'     ):
                                  with jax.disable_jit():
                                         a_					:		Optional[int]							=  encode_jitted(**SCREAMING_SNAKE_CASE__     ).to_tuple()

                           self.assertEqual(len(SCREAMING_SNAKE_CASE__     )     ,							len(SCREAMING_SNAKE_CASE__     )     )
                           for jitted_output, output in zip(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ):
                                  self.assertEqual(jitted_output.shape     ,							output.shape     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			Dict:
             a_							,		a_					:		Optional[Any]							=  self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    with self.subTest(model_class.__name__     ):
                           a_					:		List[str]							=  model_class(SCREAMING_SNAKE_CASE__     )
                           a_					:		int							=  model.encode(inputs_dict['input_ids']     ,							inputs_dict['attention_mask']     )

                           a_					:		str							=  {
                               'decoder_input_ids': inputs_dict['decoder_input_ids'],
                               'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
                               'encoder_outputs': encoder_outputs,
                           }

                           @jax.jit
                           def decode_jitted(SCREAMING_SNAKE_CASE__				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       List[str]     ):
                                  return model.decode(
                                      decoder_input_ids=SCREAMING_SNAKE_CASE__     ,							decoder_attention_mask=SCREAMING_SNAKE_CASE__     ,							encoder_outputs=SCREAMING_SNAKE_CASE__     ,							)

                           with self.subTest('JIT Enabled'     ):
                                  a_					:		List[str]							=  decode_jitted(**SCREAMING_SNAKE_CASE__     ).to_tuple()

                           with self.subTest('JIT Disabled'     ):
                                  with jax.disable_jit():
                                         a_					:		Dict							=  decode_jitted(**SCREAMING_SNAKE_CASE__     ).to_tuple()

                           self.assertEqual(len(SCREAMING_SNAKE_CASE__     )     ,							len(SCREAMING_SNAKE_CASE__     )     )
                           for jitted_output, output in zip(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ):
                                  self.assertEqual(jitted_output.shape     ,							output.shape     )
      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			str:
             for model_class_name in self.all_model_classes:
                    a_					:		Dict							=  model_class_name.from_pretrained('google/pegasus-large'     ,							from_pt=SCREAMING_SNAKE_CASE__     )
                    a_					:		Optional[Any]							=  np.ones((1, 1)     )
                    a_					:		Union[str, Any]							=  model(SCREAMING_SNAKE_CASE__     )
                    self.assertIsNotNone(SCREAMING_SNAKE_CASE__     )

      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Tuple:
             a_					:		List[Any]							=  FlaxPegasusForConditionalGeneration.from_pretrained('google/pegasus-xsum'     )
             a_					:		List[Any]							=  PegasusTokenizer.from_pretrained('google/pegasus-xsum'     )

             a_					:		Optional[Any]							=  [
                 ' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.',
                 ' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ',
             ]

             a_					:		Union[str, Any]							=  [
                 'California\'s largest electricity provider has turned off power to hundreds of thousands of customers.',
                 'Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.',
             ]

             a_					:		Any							=  tokenizer(SCREAMING_SNAKE_CASE__     ,							return_tensors='np'     ,							truncation=SCREAMING_SNAKE_CASE__     ,							max_length=5_1_2     ,							padding=SCREAMING_SNAKE_CASE__     )
             a_					:		List[str]							=  model.generate(**SCREAMING_SNAKE_CASE__     ,							num_beams=2     ).sequences
             a_					:		Tuple							=  tokenizer.batch_decode(SCREAMING_SNAKE_CASE__     ,							skip_special_tokens=SCREAMING_SNAKE_CASE__     )
             assert tgt_text == decoded






"""simple docstring"""







import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


lowerCAmelCase__      				=   logging.getLogger(__name__)


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				Tuple   ="token-classification"

       def __init__(						self		,     snake_case__						):






         """simple docstring"""





         if type(snake_case__						) == dict:
           lowerCAmelCase     :     List[str]								=							Namespace(**snake_case__						)
         lowerCAmelCase     :     Optional[Any]								=							import_module("tasks"						)
         try:
           lowerCAmelCase     :     Dict								=							getattr(snake_case__		,     hparams.task_type						)
           lowerCAmelCase     :     TokenClassificationTask								=							token_classification_task_clazz()
         except AttributeError:
           raise ValueError(
               f"""Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
               f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""						)
         lowerCAmelCase     :     Dict								=							self.token_classification_task.get_labels(hparams.labels						)
         lowerCAmelCase     :     str								=							CrossEntropyLoss().ignore_index
         super().__init__(snake_case__		,     len(self.labels						)		,     self.mode						)

       def        lowercase__							(						self		,     **snake_case__						):






         """simple docstring"""





         return self.model(**snake_case__						)

       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
         if self.config.model_type != "distilbert":
           lowerCAmelCase     :     Optional[int]								=							(
               batch[2] if self.config.model_type in ["bert", "xlnet"] else None
           )  # XLM and RoBERTa don"t use token_type_ids

         lowerCAmelCase     :     str								=							self(**snake_case__						)
         lowerCAmelCase     :     Tuple								=							outputs[0]
         # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
         return {"loss": loss}

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							self.hparams
         for mode in ["train", "dev", "test"]:
           lowerCAmelCase     :     Optional[int]								=							self._feature_file(snake_case__						)
           if os.path.exists(snake_case__						) and not args.overwrite_cache:
             logger.info("Loading features from cached file %s"		,     snake_case__						)
             lowerCAmelCase     :     Dict								=							torch.load(snake_case__						)
           else:
             logger.info("Creating features from dataset file at %s"		,     args.data_dir						)
             lowerCAmelCase     :     Optional[int]								=							self.token_classification_task.read_examples_from_file(args.data_dir		,     snake_case__						)
             lowerCAmelCase     :     Optional[int]								=							self.token_classification_task.convert_examples_to_features(
                 snake_case__		,     self.labels		,     args.max_seq_length		,     self.tokenizer		,     cls_token_at_end=bool(self.config.model_type in ["xlnet"]						)		,     cls_token=self.tokenizer.cls_token		,     cls_token_segment_id=2 if self.config.model_type in ["xlnet"] else 0		,     sep_token=self.tokenizer.sep_token		,     sep_token_extra=snake_case__		,     pad_on_left=bool(self.config.model_type in ["xlnet"]						)		,     pad_token=self.tokenizer.pad_token_id		,     pad_token_segment_id=self.tokenizer.pad_token_type_id		,     pad_token_label_id=self.pad_token_label_id		,     )
             logger.info("Saving features into cached file %s"		,     snake_case__						)
             torch.save(snake_case__		,     snake_case__						)

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__ = False						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							self._feature_file(snake_case__						)
         logger.info("Loading features from cached file %s"		,     snake_case__						)
         lowerCAmelCase     :     Optional[Any]								=							torch.load(snake_case__						)
         lowerCAmelCase     :     Optional[int]								=							torch.tensor([f.input_ids for f in features]		,     dtype=torch.long						)
         lowerCAmelCase     :     int								=							torch.tensor([f.attention_mask for f in features]		,     dtype=torch.long						)
         if features[0].token_type_ids is not None:
           lowerCAmelCase     :     Tuple								=							torch.tensor([f.token_type_ids for f in features]		,     dtype=torch.long						)
         else:
           lowerCAmelCase     :     Optional[Any]								=							torch.tensor([0 for f in features]		,     dtype=torch.long						)
           # HACK(we will not use this anymore soon)
         lowerCAmelCase     :     Optional[int]								=							torch.tensor([f.label_ids for f in features]		,     dtype=torch.long						)
         return DataLoader(
             TensorDataset(snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						)		,     batch_size=snake_case__						)

       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         """Compute validation""" ""
         lowerCAmelCase     :     str								=							{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
         if self.config.model_type != "distilbert":
           lowerCAmelCase     :     List[Any]								=							(
               batch[2] if self.config.model_type in ["bert", "xlnet"] else None
           )  # XLM and RoBERTa don"t use token_type_ids
         lowerCAmelCase     :     Any								=							self(**snake_case__						)
         lowerCAmelCase						,  lowerCAmelCase     :     List[str]								=							outputs[:2]
         lowerCAmelCase     :     Dict								=							logits.detach().cpu().numpy()
         lowerCAmelCase     :     Optional[Any]								=							inputs["labels"].detach().cpu().numpy()
         return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							torch.stack([x["val_loss"] for x in outputs]						).mean()
         lowerCAmelCase     :     Optional[Any]								=							np.concatenate([x["pred"] for x in outputs]		,     axis=0						)
         lowerCAmelCase     :     List[Any]								=							np.argmax(snake_case__		,     axis=2						)
         lowerCAmelCase     :     Optional[int]								=							np.concatenate([x["target"] for x in outputs]		,     axis=0						)

         lowerCAmelCase     :     str								=							dict(enumerate(self.labels						)						)
         lowerCAmelCase     :     Optional[int]								=							[[] for _ in range(out_label_ids.shape[0]						)]
         lowerCAmelCase     :     Dict								=							[[] for _ in range(out_label_ids.shape[0]						)]

         for i in range(out_label_ids.shape[0]						):
           for j in range(out_label_ids.shape[1]						):
             if out_label_ids[i, j] != self.pad_token_label_id:
               out_label_list[i].append(label_map[out_label_ids[i][j]]						)
               preds_list[i].append(label_map[preds[i][j]]						)

         lowerCAmelCase     :     Dict								=							{
             "val_loss": val_loss_mean,
             "accuracy_score": accuracy_score(snake_case__		,     snake_case__						),
             "precision": precision_score(snake_case__		,     snake_case__						),
             "recall": recall_score(snake_case__		,     snake_case__						),
             "f1": fa_score(snake_case__		,     snake_case__						),
         }

         lowerCAmelCase     :     int								=							dict(results.items()						)
         lowerCAmelCase     :     Tuple								=							results
         return ret, preds_list, out_label_list

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase     :     Dict								=							self._eval_end(snake_case__						)
         lowerCAmelCase     :     int								=							ret["log"]
         return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase     :     int								=							self._eval_end(snake_case__						)

         # Converting to the dict required by pl
         # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
         # pytorch_lightning/trainer/logging.py#L139
         lowerCAmelCase     :     str								=							ret["log"]
         # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
         return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







       @staticmethod
       def        lowercase__							(						snake_case__		,     snake_case__						):






         """simple docstring"""





         BaseTransformer.add_model_specific_args(snake_case__		,     snake_case__						)
         parser.add_argument(
             "--task_type"		,     default="NER"		,     type=snake_case__		,     help="Task type to fine tune in training (e.g. NER, POS, etc)"						)
         parser.add_argument(
             "--max_seq_length"		,     default=128		,     type=snake_case__		,     help=(
                 "The maximum total input sequence length after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )		,     )

         parser.add_argument(
             "--labels"		,     default=""		,     type=snake_case__		,     help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."		,     )
         parser.add_argument(
             "--gpus"		,     default=0		,     type=snake_case__		,     help="The number of GPUs allocated for this, it is by default 0 meaning none"		,     )

         parser.add_argument(
             "--overwrite_cache"		,     action="store_true"		,     help="Overwrite the cached training and evaluation sets"						)

         return parser


if __name__ == "__main__":
     lowerCAmelCase__      				=   argparse.ArgumentParser()
     add_generic_args(parser, os.getcwd())
     lowerCAmelCase__      				=   NERTransformer.add_model_specific_args(parser, os.getcwd())
     lowerCAmelCase__      				=   parser.parse_args()
     lowerCAmelCase__      				=   NERTransformer(args)
     lowerCAmelCase__      				=   generic_train(model, args)

     if args.do_predict:
          # See https://github.com/huggingface/transformers/issues/3159
          # pl use this default format to create a checkpoint:
          # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
          # /pytorch_lightning/callbacks/model_checkpoint.py#L322
          lowerCAmelCase__      				=   sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
          lowerCAmelCase__      				=   model.load_from_checkpoint(checkpoints[-1])
          trainer.test(model)





