





'''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


a__			:					List[str]       						=						[
    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)

a__			:					Optional[Any]       						=						logging.getLogger()
def       __snake_case		( )							->     List[Any]:

       """simple docstring"""


       UpperCAmelCase  =						argparse.ArgumentParser()
       parser.add_argument('''-f''' )
       UpperCAmelCase  =						parser.parse_args()
       return args.f
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			Union[str, Any]			,	SCREAMING_SNAKE_CASE_ :			Union[str, Any]="eval" )							->     int:

       """simple docstring"""


       UpperCAmelCase  =						os.path.join(SCREAMING_SNAKE_CASE_			,	f"{split}_results.json" )
       if os.path.exists(SCREAMING_SNAKE_CASE_ ):
              with open(SCREAMING_SNAKE_CASE_			,	'''r''' ) as f:
                     return json.load(SCREAMING_SNAKE_CASE_ )
       raise ValueError(f"can't find {path}" )


a__			:					Union[str, Any]       						=						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class     lowerCAmelCase__ (				UpperCAmelCase_    ):

       '''simple docstring'''

       def 			__snake_case      ( self					:				str						):
              UpperCAmelCase  =						self.get_auto_remove_tmp_dir()
              UpperCAmelCase  =						f"\n            run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --eval_steps=2\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

              with patch.object(a__       ,     '''argv'''       ,     a__						):
                     run_flax_glue.main()
                     UpperCAmelCase  =						get_results(a__						)
                     self.assertGreaterEqual(result['''eval_accuracy''']       ,     0.75						)

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

              with patch.object(a__       ,     '''argv'''       ,     a__						):
                     run_clm_flax.main()
                     UpperCAmelCase  =						get_results(a__						)
                     self.assertLess(result['''eval_perplexity''']       ,     100						)

       @slow
       def 			__snake_case      ( self					:				Optional[Any]						):
              UpperCAmelCase  =						self.get_auto_remove_tmp_dir()
              UpperCAmelCase  =						f"\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --test_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --do_predict\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --predict_with_generate\n        ".split()

              with patch.object(a__       ,     '''argv'''       ,     a__						):
                     run_summarization_flax.main()
                     UpperCAmelCase  =						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 			__snake_case      ( self					:				List[Any]						):
              UpperCAmelCase  =						self.get_auto_remove_tmp_dir()
              UpperCAmelCase  =						f"\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --logging_steps 2 --eval_steps 2\n            --do_train\n            --do_eval\n            --num_train_epochs=1\n        ".split()

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

       @slow
       def 			__snake_case      ( self					:				str						):
              UpperCAmelCase  =						self.get_auto_remove_tmp_dir()
              UpperCAmelCase  =						f"\n            run_t5_mlm_flax.py\n            --model_name_or_path t5-small\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".split()

              with patch.object(a__       ,     '''argv'''       ,     a__						):
                     run_ta_mlm_flax.main()
                     UpperCAmelCase  =						get_results(a__						)
                     self.assertGreaterEqual(result['''eval_accuracy''']       ,     0.42						)

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

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

              with patch.object(a__       ,     '''argv'''       ,     a__						):
                     run_flax_ner.main()
                     UpperCAmelCase  =						get_results(a__						)
                     self.assertGreaterEqual(result['''eval_accuracy''']       ,     0.75						)
                     self.assertGreaterEqual(result['''eval_f1''']       ,     0.3						)



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

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









'''simple docstring'''

from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a   	=    {
    "configuration_informer": [
        "INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "InformerConfig",
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a   	=    [
        "INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
        "InformerForPrediction",
        "InformerModel",
        "InformerPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_informer import (
            INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            InformerForPrediction,
            InformerModel,
            InformerPreTrainedModel,
        )

else:
    import sys

    a   	=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





