
from __future__ import annotations

import unittest

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

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


if is_tf_available():
  import numpy as np
  import tensorflow as tf

  from transformers import (
      TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      FlaubertConfig,
      TFFlaubertForMultipleChoice,
      TFFlaubertForQuestionAnsweringSimple,
      TFFlaubertForSequenceClassification,
      TFFlaubertForTokenClassification,
      TFFlaubertModel,
      TFFlaubertWithLMHeadModel,
  )

class __snake_case		:
    def __init__(  self		,snake_case		,):



           '''simple docstring'''







           lowercase							:     Any       	=      parent
           lowercase							:     Tuple       	=      13
           lowercase							:     str       	=      7
           lowercase							:     Dict       	=      True
           lowercase							:     Dict       	=      True
           lowercase							:     str       	=      True
           lowercase							:     List[str]       	=      True
           lowercase							:     int       	=      True
           lowercase							:     Union[str, Any]       	=      False
           lowercase							:     Dict       	=      False
           lowercase							:     List[Any]       	=      False
           lowercase							:     List[Any]       	=      2
           lowercase							:     Optional[Any]       	=      99
           lowercase							:     int       	=      0
           lowercase							:     Tuple       	=      32
           lowercase							:     int       	=      2
           lowercase							:     Tuple       	=      4
           lowercase							:     List[Any]       	=      0.1
           lowercase							:     Tuple       	=      0.1
           lowercase							:     List[Any]       	=      512
           lowercase							:     int       	=      16
           lowercase							:     Dict       	=      2
           lowercase							:     int       	=      0.02
           lowercase							:     Union[str, Any]       	=      3
           lowercase							:     Any       	=      4
           lowercase							:     List[Any]       	=      """last"""
           lowercase							:     Tuple       	=      True
           lowercase							:     List[Any]       	=      None
           lowercase							:     Any       	=      0
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      ids_tensor([self.batch_size, self.seq_length]		,self.vocab_size      )
           lowercase							:     List[str]       	=      random_attention_mask([self.batch_size, self.seq_length]		,dtype=tf.floataa      )

           lowercase							:     Tuple       	=      None
           if self.use_input_lengths:
                  lowercase							:     List[str]       	=      (
                      ids_tensor([self.batch_size]		,vocab_size=2      ) + self.seq_length - 2
                  )  # small variation of seq_length

           lowercase							:     Tuple       	=      None
           if self.use_token_type_ids:
                  lowercase							:     Optional[int]       	=      ids_tensor([self.batch_size, self.seq_length]		,self.n_langs      )

           lowercase							:     List[str]       	=      None
           lowercase							:     List[str]       	=      None
           lowercase							:     Optional[Any]       	=      None
           if self.use_labels:
                  lowercase							:     List[str]       	=      ids_tensor([self.batch_size]		,self.type_sequence_label_size      )
                  lowercase							:     List[str]       	=      ids_tensor([self.batch_size, self.seq_length]		,self.num_labels      )
                  lowercase							:     str       	=      ids_tensor([self.batch_size]		,2		,dtype=tf.floataa      )
                  lowercase							:     Optional[Any]       	=      ids_tensor([self.batch_size]		,self.num_choices      )

           lowercase							:     str       	=      FlaubertConfig(
               vocab_size=self.vocab_size		,n_special=self.n_special		,emb_dim=self.hidden_size		,n_layers=self.num_hidden_layers		,n_heads=self.num_attention_heads		,dropout=self.hidden_dropout_prob		,attention_dropout=self.attention_probs_dropout_prob		,gelu_activation=self.gelu_activation		,sinusoidal_embeddings=self.sinusoidal_embeddings		,asm=self.asm		,causal=self.causal		,n_langs=self.n_langs		,max_position_embeddings=self.max_position_embeddings		,initializer_range=self.initializer_range		,summary_type=self.summary_type		,use_proj=self.use_proj		,bos_token_id=self.bos_token_id		,)

           return (
               config,
               input_ids,
               token_type_ids,
               input_lengths,
               sequence_labels,
               token_labels,
               is_impossible_labels,
               choice_labels,
               input_mask,
           )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:     Tuple       	=      TFFlaubertModel(config=snake_case      )
           lowercase							:     str       	=      {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
           lowercase							:     Optional[Any]       	=      model(snake_case      )

           lowercase							:     List[Any]       	=      [input_ids, input_mask]
           lowercase							:     int       	=      model(snake_case      )
           self.parent.assertEqual(result.last_hidden_state.shape		,(self.batch_size, self.seq_length, self.hidden_size)      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      TFFlaubertWithLMHeadModel(snake_case      )

           lowercase							:     Optional[Any]       	=      {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
           lowercase							:     int       	=      model(snake_case      )

           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.seq_length, self.vocab_size)      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:     Tuple       	=      TFFlaubertForQuestionAnsweringSimple(snake_case      )

           lowercase							:     Union[str, Any]       	=      {"""input_ids""": input_ids, """lengths""": input_lengths}

           lowercase							:     Tuple       	=      model(snake_case      )

           self.parent.assertEqual(result.start_logits.shape		,(self.batch_size, self.seq_length)      )
           self.parent.assertEqual(result.end_logits.shape		,(self.batch_size, self.seq_length)      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      TFFlaubertForSequenceClassification(snake_case      )

           lowercase							:     str       	=      {"""input_ids""": input_ids, """lengths""": input_lengths}

           lowercase							:     str       	=      model(snake_case      )

           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.type_sequence_label_size)      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:     Any       	=      self.num_labels
           lowercase							:     List[str]       	=      TFFlaubertForTokenClassification(config=snake_case      )
           lowercase							:     Dict       	=      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
           lowercase							:     int       	=      model(snake_case      )
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.seq_length, self.num_labels)      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:     Any       	=      self.num_choices
           lowercase							:     Dict       	=      TFFlaubertForMultipleChoice(config=snake_case      )
           lowercase							:     Any       	=      tf.tile(tf.expand_dims(snake_case		,1      )		,(1, self.num_choices, 1)      )
           lowercase							:     Optional[Any]       	=      tf.tile(tf.expand_dims(snake_case		,1      )		,(1, self.num_choices, 1)      )
           lowercase							:     Dict       	=      tf.tile(tf.expand_dims(snake_case		,1      )		,(1, self.num_choices, 1)      )
           lowercase							:     Union[str, Any]       	=      {
               """input_ids""": multiple_choice_inputs_ids,
               """attention_mask""": multiple_choice_input_mask,
               """token_type_ids""": multiple_choice_token_type_ids,
           }
           lowercase							:     int       	=      model(snake_case      )
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.num_choices)      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      self.prepare_config_and_inputs()
           (
               (
               lowercase
           )	,			(
               lowercase
           )	,			(
               lowercase
           )	,			(
               lowercase
           )	,			(
               lowercase
           )	,			(
               lowercase
           )	,			(
               lowercase
           )	,			(
               lowercase
           )	,			(
               lowercase
           )	,			
           )							:     int       	=      config_and_inputs
           lowercase							:     List[str]       	=      {
               """input_ids""": input_ids,
               """token_type_ids""": token_type_ids,
               """langs""": token_type_ids,
               """lengths""": input_lengths,
           }
           return config, inputs_dict






@require_tf
class __snake_case		(      lowerCAmelCase					,	lowerCAmelCase					,	unittest.TestCase     ):
    _a		:						Dict=				(
        (
            TFFlaubertModel,
            TFFlaubertWithLMHeadModel,
            TFFlaubertForSequenceClassification,
            TFFlaubertForQuestionAnsweringSimple,
            TFFlaubertForTokenClassification,
            TFFlaubertForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    _a		:						Optional[Any]=				(
        (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
    )  # TODO (PVP): Check other models whether language generation is also applicable
    _a		:						Any=				(
        {
            "feature-extraction": TFFlaubertModel,
            "fill-mask": TFFlaubertWithLMHeadModel,
            "question-answering": TFFlaubertForQuestionAnsweringSimple,
            "text-classification": TFFlaubertForSequenceClassification,
            "token-classification": TFFlaubertForTokenClassification,
            "zero-shot": TFFlaubertForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    _a		:						Tuple=				False
    _a		:						int=				False
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           if (
               pipeline_test_casse_name == "QAPipelineTests"
               and tokenizer_name is not None
               and not tokenizer_name.endswith("""Fast"""      )
           ):
                  # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
                  # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
                  # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
                  return True

           return False
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     str       	=      TFFlaubertModelTester(self      )
           lowercase							:     List[Any]       	=      ConfigTester(self		,config_class=snake_case		,emb_dim=37      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           self.config_tester.run_common_tests()
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_model(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_lm_head(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_qa(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_sequence_classif(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     str       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_for_token_classification(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_for_multiple_choice(*snake_case      )
    @slow
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowercase							:     Dict       	=      TFFlaubertModel.from_pretrained(snake_case      )
                  self.assertIsNotNone(snake_case      )






@require_tf
@require_sentencepiece
@require_tokenizers
class __snake_case		(      unittest.TestCase     ):
    @slow
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      TFFlaubertModel.from_pretrained("""jplu/tf-flaubert-small-cased"""      )

           lowercase							:     int       	=      tf.convert_to_tensor(
               [[0, 158, 735, 2592, 1424, 6727, 82, 1]]		,dtype=tf.intaa		,)  # "J'aime flaubert !"

           lowercase							:     Dict       	=      model(snake_case      )[0]
           lowercase							:     Union[str, Any]       	=      tf.TensorShape((1, 8, 512)      )
           self.assertEqual(output.shape		,snake_case      )
           # compare the actual values for a slice.
           lowercase							:     Tuple       	=      tf.convert_to_tensor(
               [
                   [
                       [-1.8_768_773, -1.566_555, 0.27_072_418],
                       [-1.6_920_038, -0.5_873_505, 1.9_329_599],
                       [-2.9_563_985, -1.6_993_835, 1.7_972_052],
                   ]
               ]		,dtype=tf.floataa		,)

           self.assertTrue(np.allclose(output[:, :3, :3].numpy()		,expected_slice.numpy()		,atol=1e-4      )      )





import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     List[str]									= ArgumentParser(
		    description=(
		        '''PyTorch TPU distributed training launch '''
		        '''helper utility that will spawn up '''
		        '''multiple distributed processes'''
		    ) )

		# Optional arguments for the launch helper
		parser.add_argument('''--num_cores'''     ,type=__UpperCamelCase     ,default=1     ,help='''Number of TPU cores to use (1 or 8).''' )

		# positional
		parser.add_argument(
		    '''training_script'''     ,type=__UpperCamelCase     ,help=(
		        '''The full path to the single TPU training '''
		        '''program/script to be launched in parallel, '''
		        '''followed by all the arguments for the '''
		        '''training script'''
		    )     ,)

		# rest from the training program
		parser.add_argument('''training_script_args'''     ,nargs=__UpperCamelCase )

		return parser.parse_args()


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     str									= parse_args()

		# Import training_script as a module.
		lowerCAmelCase_			:     str									= Path(args.training_script )
		sys.path.append(str(script_fpath.parent.resolve() ) )
		lowerCAmelCase_			:     Tuple									= script_fpath.stem
		lowerCAmelCase_			:     Union[str, Any]									= importlib.import_module(__UpperCamelCase )

		# Patch sys.argv
		lowerCAmelCase_			:     Optional[int]									= [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores )]
		xmp.spawn(mod._mp_fn     ,args=()     ,nprocs=args.num_cores )


if __name__ == "__main__":
						main()





