





'''simple docstring'''







import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class 			_lowercase						(       unittest.TestCase					):







     def     UpperCamelCase			( self    ) ->	Tuple:
      snake_case				=			10







     def     UpperCamelCase			( self    ) ->	int:
      snake_case				=			[1, 2, 3, 4]
      snake_case				=			[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
      self.assertEqual(truncate_or_pad(A__    ,   self.block_size    ,   0    )    ,   A__    )







     def     UpperCamelCase			( self    ) ->	List[str]:
      snake_case				=			[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
      snake_case				=			[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
      self.assertEqual(truncate_or_pad(A__    ,   self.block_size    ,   0    )    ,   A__    )







     def     UpperCamelCase			( self    ) ->	List[Any]:
      snake_case				=			[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
      snake_case				=			[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
      self.assertEqual(truncate_or_pad(A__    ,   self.block_size    ,   0    )    ,   A__    )







     def     UpperCamelCase			( self    ) ->	Optional[int]:
      snake_case				=			'''It was the year of Our Lord one thousand seven hundred and
        seventy-five.\n\nSpiritual revelations were conceded to England at that
        favoured period, as at this.'''
      snake_case				,		snake_case				=			process_story(A__    )
      self.assertEqual(A__    ,   []    )







     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      snake_case				=			''''''
      snake_case				,		snake_case				=			process_story(A__    )
      self.assertEqual(A__    ,   []    )
      self.assertEqual(A__    ,   []    )







     def     UpperCamelCase			( self    ) ->	Any:
      snake_case				=			(
          '''It was the year of Our Lord one thousand seven hundred and '''
          '''seventy-five\n\nSpiritual revelations were conceded to England '''
          '''at that favoured period, as at this.\n@highlight\n\nIt was the best of times'''
      )
      snake_case				,		snake_case				=			process_story(A__    )

      snake_case				=			[
          '''It was the year of Our Lord one thousand seven hundred and seventy-five.''',
          '''Spiritual revelations were conceded to England at that favoured period, as at this.''',
      ]
      self.assertEqual(A__    ,   A__    )

      snake_case				=			['''It was the best of times.''']
      self.assertEqual(A__    ,   A__    )







     def     UpperCamelCase			( self    ) ->	str:
      snake_case				=			torch.tensor([1, 2, 3, 4]    )
      snake_case				=			torch.tensor([1, 1, 1, 1]    )
      np.testing.assert_array_equal(build_mask(A__    ,   0    ).numpy()    ,   expected.numpy()    )







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      snake_case				=			torch.tensor([1, 2, 3, 4, 23, 23, 23]    )
      snake_case				=			torch.tensor([1, 1, 1, 1, 0, 0, 0]    )
      np.testing.assert_array_equal(build_mask(A__    ,   23    ).numpy()    ,   expected.numpy()    )







     def     UpperCamelCase			( self    ) ->	Optional[int]:
      snake_case				=			torch.tensor([8, 2, 3, 4, 1, 1, 1]    )
      snake_case				=			torch.tensor([1, 1, 1, 1, 0, 0, 0]    )
      np.testing.assert_array_equal(build_mask(A__    ,   1    ).numpy()    ,   expected.numpy()    )







     def     UpperCamelCase			( self    ) ->	Tuple:
      snake_case				=			1_01
      snake_case				=			torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 1_01, 5, 6], [1, 1_01, 3, 4, 1_01, 6]]    )
      snake_case				=			torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]    )

      snake_case				=			compute_token_type_ids(A__    ,   A__    )
      np.testing.assert_array_equal(A__    ,   A__    )








'''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    )

