





'''simple docstring'''







import unittest

from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       MPNetForMaskedLM,
       MPNetForMultipleChoice,
       MPNetForQuestionAnswering,
       MPNetForSequenceClassification,
       MPNetForTokenClassification,
       MPNetModel,
   )
class 			_lowercase						:







     def __init__( self    ,   A__    ,   A__=13    ,   A__=7    ,   A__=True    ,   A__=True    ,   A__=False    ,   A__=True    ,   A__=99    ,   A__=64    ,   A__=5    ,   A__=4    ,   A__=64    ,   A__="gelu"    ,   A__=0.1    ,   A__=0.1    ,   A__=5_12    ,   A__=16    ,   A__=2    ,   A__=0.0_2    ,   A__=3    ,   A__=4    ,   A__=None    ,   ) ->	Any:
      snake_case				=			parent
      snake_case				=			batch_size
      snake_case				=			seq_length
      snake_case				=			is_training
      snake_case				=			use_input_mask
      snake_case				=			use_token_type_ids
      snake_case				=			use_labels
      snake_case				=			vocab_size
      snake_case				=			hidden_size
      snake_case				=			num_hidden_layers
      snake_case				=			num_attention_heads
      snake_case				=			intermediate_size
      snake_case				=			hidden_act
      snake_case				=			hidden_dropout_prob
      snake_case				=			attention_probs_dropout_prob
      snake_case				=			max_position_embeddings
      snake_case				=			type_vocab_size
      snake_case				=			type_sequence_label_size
      snake_case				=			initializer_range
      snake_case				=			num_labels
      snake_case				=			num_choices
      snake_case				=			scope







     def     UpperCamelCase			( self    ) ->	List[Any]:
      return MPNetConfig.from_pretrained('''microsoft/mpnet-base'''    )







     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      snake_case				=			ids_tensor([self.batch_size, self.seq_length]    ,   self.vocab_size    )

      snake_case				=			None
      if self.use_input_mask:
       snake_case				=			random_attention_mask([self.batch_size, self.seq_length]    )

      snake_case				=			None
      snake_case				=			None
      snake_case				=			None
      if self.use_labels:
       snake_case				=			ids_tensor([self.batch_size]    ,   self.type_sequence_label_size    )
       snake_case				=			ids_tensor([self.batch_size, self.seq_length]    ,   self.num_labels    )
       snake_case				=			ids_tensor([self.batch_size]    ,   self.num_choices    )

      snake_case				=			self.get_config()
      return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels







     def     UpperCamelCase			( self    ) ->	Dict:
      return MPNetConfig(
          vocab_size=self.vocab_size    ,   hidden_size=self.hidden_size    ,   num_hidden_layers=self.num_hidden_layers    ,   num_attention_heads=self.num_attention_heads    ,   intermediate_size=self.intermediate_size    ,   hidden_act=self.hidden_act    ,   hidden_dropout_prob=self.hidden_dropout_prob    ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,   max_position_embeddings=self.max_position_embeddings    ,   initializer_range=self.initializer_range    ,   )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ) ->	str:
      snake_case				=			MPNetModel(config=A__    )
      model.to(A__    )
      model.eval()
      snake_case				=			model(A__    ,   A__    )
      snake_case				=			model(A__    )
      self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.seq_length, self.hidden_size)    )
      self.parent.assertEqual(result.pooler_output.shape    ,   (self.batch_size, self.hidden_size)    )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ) ->	str:
      snake_case				=			MPNetForQuestionAnswering(config=A__    )
      model.to(A__    )
      model.eval()
      snake_case				=			model(
          A__    ,   attention_mask=A__    ,   start_positions=A__    ,   end_positions=A__    ,   )
      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     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ) ->	Dict:
      snake_case				=			self.num_labels
      snake_case				=			MPNetForSequenceClassification(A__    )
      model.to(A__    )
      model.eval()
      snake_case				=			model(A__    ,   attention_mask=A__    ,   labels=A__    )
      self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.num_labels)    )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ) ->	Dict:
      snake_case				=			self.num_choices
      snake_case				=			MPNetForMultipleChoice(config=A__    )
      model.to(A__    )
      model.eval()
      snake_case				=			input_ids.unsqueeze(1    ).expand(-1    ,   self.num_choices    ,   -1    ).contiguous()
      snake_case				=			input_mask.unsqueeze(1    ).expand(-1    ,   self.num_choices    ,   -1    ).contiguous()
      snake_case				=			model(
          A__    ,   attention_mask=A__    ,   labels=A__    ,   )
      self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.num_choices)    )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ) ->	List[Any]:
      snake_case				=			self.num_labels
      snake_case				=			MPNetForTokenClassification(config=A__    )
      model.to(A__    )
      model.eval()
      snake_case				=			model(A__    ,   attention_mask=A__    ,   labels=A__    )
      self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.seq_length, self.num_labels)    )







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      snake_case				=			self.prepare_config_and_inputs()
      ((snake_case)				,		(snake_case)				,		(snake_case)				,		(snake_case)				,		(snake_case)				,		(snake_case))				=			config_and_inputs
      snake_case				=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
      return config, inputs_dict




@require_torch
class 			_lowercase						(       __a				,		__a				,		unittest.TestCase					):
     _UpperCAmelCase										=					(
         (
             MPNetForMaskedLM,
             MPNetForMultipleChoice,
             MPNetForQuestionAnswering,
             MPNetForSequenceClassification,
             MPNetForTokenClassification,
             MPNetModel,
         )
         if is_torch_available()
         else ()
     )
     _UpperCAmelCase										=					(
         {
             '''feature-extraction''': MPNetModel,
             '''fill-mask''': MPNetForMaskedLM,
             '''question-answering''': MPNetForQuestionAnswering,
             '''text-classification''': MPNetForSequenceClassification,
             '''token-classification''': MPNetForTokenClassification,
             '''zero-shot''': MPNetForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     _UpperCAmelCase										=					False
     _UpperCAmelCase										=					True







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      snake_case				=			MPNetModelTester(self    )
      snake_case				=			ConfigTester(self    ,   config_class=A__    ,   hidden_size=37    )







     def     UpperCamelCase			( self    ) ->	Tuple:
      self.config_tester.run_common_tests()







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_model(*A__    )







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_sequence_classification(*A__    )







     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_multiple_choice(*A__    )







     def     UpperCamelCase			( self    ) ->	str:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_token_classification(*A__    )







     def     UpperCamelCase			( self    ) ->	int:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_question_answering(*A__    )




@require_torch
class 			_lowercase						(       unittest.TestCase					):







     @slow
     def     UpperCamelCase			( self    ) ->	str:
      snake_case				=			MPNetModel.from_pretrained('''microsoft/mpnet-base'''    )
      snake_case				=			torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]    )
      snake_case				=			model(A__    )[0]
      snake_case				=			torch.Size((1, 11, 7_68)    )
      self.assertEqual(output.shape    ,   A__    )
      snake_case				=			torch.tensor(
          [[[-0.0_5_5_0, 0.1_9_4_3, -0.0_7_4_0], [-0.0_5_6_2, 0.2_2_1_1, -0.0_5_7_9], [-0.0_4_3_7, 0.3_3_3_7, -0.0_6_4_1]]]    )
      # compare the actual values for a slice.
      self.assertTrue(torch.allclose(output[:, :3, :3]    ,   A__    ,   atol=1e-4    )    )








'''simple docstring'''







import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


_lowercase       						=			[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-classification',
        'language-modeling',
        'summarization',
        'token-classification',
        'question-answering',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
   import run_clm_flax
   import run_flax_glue
   import run_flax_ner
   import run_mlm_flax
   import run_qa
   import run_summarization_flax
   import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

_lowercase       						=			logging.getLogger()

def    __UpperCamelCase	(	) ->Tuple:
 snake_case				=			argparse.ArgumentParser()
 parser.add_argument('''-f'''     )
 snake_case				=			parser.parse_args()
 return args.f

def    __UpperCamelCase	(	a    : Dict					,   a    : Tuple="eval"     ) ->List[Any]:
 snake_case				=			os.path.join(a					,   f"""{split}_results.json"""     )
 if os.path.exists(a     ):
  with open(a					,   '''r'''     ) as f:
   return json.load(a     )
 raise ValueError(f"""can't find {path}"""     )


_lowercase       						=			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class 			_lowercase						(       __a					):







     def     UpperCamelCase			( self    ) ->	List[str]:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

      with patch.object(A__    ,   '''argv'''    ,   A__    ):
       run_flax_glue.main()
       snake_case				=			get_results(A__    )
       self.assertGreaterEqual(result['''eval_accuracy''']    ,   0.7_5    )







     @slow
     def     UpperCamelCase			( self    ) ->	List[Any]:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""
            run_clm_flax.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

      with patch.object(A__    ,   '''argv'''    ,   A__    ):
       run_clm_flax.main()
       snake_case				=			get_results(A__    )
       self.assertLess(result['''eval_perplexity''']    ,   1_00    )







     @slow
     def     UpperCamelCase			( self    ) ->	int:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        """.split()

      with patch.object(A__    ,   '''argv'''    ,   A__    ):
       run_summarization_flax.main()
       snake_case				=			get_results(A__    ,   split='''test'''    )
       self.assertGreaterEqual(result['''test_rouge1''']    ,   10    )
       self.assertGreaterEqual(result['''test_rouge2''']    ,   2    )
       self.assertGreaterEqual(result['''test_rougeL''']    ,   7    )
       self.assertGreaterEqual(result['''test_rougeLsum''']    ,   7    )







     @slow
     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        """.split()

      with patch.object(A__    ,   '''argv'''    ,   A__    ):
       run_mlm_flax.main()
       snake_case				=			get_results(A__    )
       self.assertLess(result['''eval_perplexity''']    ,   42    )







     @slow
     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

      with patch.object(A__    ,   '''argv'''    ,   A__    ):
       run_ta_mlm_flax.main()
       snake_case				=			get_results(A__    )
       self.assertGreaterEqual(result['''eval_accuracy''']    ,   0.4_2    )







     @slow
     def     UpperCamelCase			( self    ) ->	int:
      # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
      snake_case				=			7 if get_gpu_count() > 1 else 2

      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""
            run_flax_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --logging_steps 2 --eval_steps 2
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

      with patch.object(A__    ,   '''argv'''    ,   A__    ):
       run_flax_ner.main()
       snake_case				=			get_results(A__    )
       self.assertGreaterEqual(result['''eval_accuracy''']    ,   0.7_5    )
       self.assertGreaterEqual(result['''eval_f1''']    ,   0.3    )







     @slow
     def     UpperCamelCase			( self    ) ->	Any:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

      with patch.object(A__    ,   '''argv'''    ,   A__    ):
       run_qa.main()
       snake_case				=			get_results(A__    )
       self.assertGreaterEqual(result['''eval_f1''']    ,   30    )
       self.assertGreaterEqual(result['''eval_exact''']    ,   30    )

