

"""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


SCREAMING_SNAKE_CASE_					:	Any							  =						[
    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)

SCREAMING_SNAKE_CASE_					:	Any							  =						logging.getLogger()





def  _snake_case     (    ):
     A__										=  argparse.ArgumentParser()
     parser.add_argument("""-f"""			)
     A__										=  parser.parse_args()
     return args.f





def  _snake_case     (    UpperCAmelCase_  :					str					,   UpperCAmelCase_  :					Optional[Any]="eval"			):
     A__										=  os.path.join(UpperCAmelCase_					,   F"""{split}_results.json"""			)
     if os.path.exists(UpperCAmelCase_			):
          with open(UpperCAmelCase_					,   """r"""			) as f:
               return json.load(UpperCAmelCase_			)
     raise ValueError(F"""can't find {path}"""			)


SCREAMING_SNAKE_CASE_					:	List[str]							  =						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    def 				UpperCamelCase	(     self: List[str]       ):






         """simple docstring"""


         A__										=  self.get_auto_remove_tmp_dir()
         A__										=  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(UpperCamelCase    ,						"""argv"""    ,						UpperCamelCase       ):
              run_flax_glue.main()
              A__										=  get_results(UpperCamelCase       )
              self.assertGreaterEqual(result["""eval_accuracy"""]    ,						0.75       )

    @slow
    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


         A__										=  self.get_auto_remove_tmp_dir()
         A__										=  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(UpperCamelCase    ,						"""argv"""    ,						UpperCamelCase       ):
              run_clm_flax.main()
              A__										=  get_results(UpperCamelCase       )
              self.assertLess(result["""eval_perplexity"""]    ,						1_00       )

    @slow
    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


         A__										=  self.get_auto_remove_tmp_dir()
         A__										=  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(UpperCamelCase    ,						"""argv"""    ,						UpperCamelCase       ):
              run_summarization_flax.main()
              A__										=  get_results(UpperCamelCase    ,						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]       ):






         """simple docstring"""


         A__										=  self.get_auto_remove_tmp_dir()
         A__										=  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(UpperCamelCase    ,						"""argv"""    ,						UpperCamelCase       ):
              run_mlm_flax.main()
              A__										=  get_results(UpperCamelCase       )
              self.assertLess(result["""eval_perplexity"""]    ,						42       )

    @slow
    def 				UpperCamelCase	(     self: List[Any]       ):






         """simple docstring"""


         A__										=  self.get_auto_remove_tmp_dir()
         A__										=  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(UpperCamelCase    ,						"""argv"""    ,						UpperCamelCase       ):
              run_ta_mlm_flax.main()
              A__										=  get_results(UpperCamelCase       )
              self.assertGreaterEqual(result["""eval_accuracy"""]    ,						0.42       )

    @slow
    def 				UpperCamelCase	(     self: List[str]       ):






         """simple docstring"""


         A__										=  7 if get_gpu_count() > 1 else 2

         A__										=  self.get_auto_remove_tmp_dir()
         A__										=  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(UpperCamelCase    ,						"""argv"""    ,						UpperCamelCase       ):
              run_flax_ner.main()
              A__										=  get_results(UpperCamelCase       )
              self.assertGreaterEqual(result["""eval_accuracy"""]    ,						0.75       )
              self.assertGreaterEqual(result["""eval_f1"""]    ,						0.3       )





    @slow
    def 				UpperCamelCase	(     self: List[Any]       ):






         """simple docstring"""


         A__										=  self.get_auto_remove_tmp_dir()
         A__										=  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(UpperCamelCase    ,						"""argv"""    ,						UpperCamelCase       ):
              run_qa.main()
              A__										=  get_results(UpperCamelCase       )
              self.assertGreaterEqual(result["""eval_f1"""]    ,						30       )
              self.assertGreaterEqual(result["""eval_exact"""]    ,						30       )









"""simple docstring"""



SCREAMING_SNAKE_CASE_					:	str							  =						'0.21.0'

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
 from .utils import rich






