




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


lowerCAmelCase_					    = [
    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)

lowerCAmelCase_					    = logging.getLogger()



def      __lowerCAmelCase			(					)     ->	Union[str, Any]:
  lowerCAmelCase__				:  Optional[int]		       =     argparse.ArgumentParser()
  parser.add_argument('''-f'''      )
  lowerCAmelCase__				:  str		       =     parser.parse_args()
  return args.f



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase="eval"      )     ->	Tuple:
  lowerCAmelCase__				:  Any		       =     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}"""      )


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






class  _lowerCAmelCase     (  _lowercase	):

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[Any]		       =     self.get_auto_remove_tmp_dir()
        lowerCAmelCase__				:  List[str]		       =     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()
          lowerCAmelCase__				:  Dict		       =     get_results(__UpperCAmelCase		)
          self.assertGreaterEqual(result['''eval_accuracy''']      ,				0.75		)

      @slow
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  str		       =     self.get_auto_remove_tmp_dir()
        lowerCAmelCase__				:  Any		       =     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()
          lowerCAmelCase__				:  Optional[int]		       =     get_results(__UpperCAmelCase		)
          self.assertLess(result['''eval_perplexity''']      ,				100		)

      @slow
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  str		       =     self.get_auto_remove_tmp_dir()
        lowerCAmelCase__				:  Tuple		       =     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()
          lowerCAmelCase__				:  Dict		       =     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 	__magic_name__(   self		):
        lowerCAmelCase__				:  Any		       =     self.get_auto_remove_tmp_dir()
        lowerCAmelCase__				:  Dict		       =     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()
          lowerCAmelCase__				:  Tuple		       =     get_results(__UpperCAmelCase		)
          self.assertLess(result['''eval_perplexity''']      ,				42		)

      @slow
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  int		       =     self.get_auto_remove_tmp_dir()
        lowerCAmelCase__				:  Union[str, Any]		       =     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()
          lowerCAmelCase__				:  Any		       =     get_results(__UpperCAmelCase		)
          self.assertGreaterEqual(result['''eval_accuracy''']      ,				0.42		)

      @slow
      def 	__magic_name__(   self		):
        # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
        lowerCAmelCase__				:  List[Any]		       =     7 if get_gpu_count() > 1 else 2

        lowerCAmelCase__				:  Union[str, Any]		       =     self.get_auto_remove_tmp_dir()
        lowerCAmelCase__				:  Any		       =     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()
          lowerCAmelCase__				:  List[str]		       =     get_results(__UpperCAmelCase		)
          self.assertGreaterEqual(result['''eval_accuracy''']      ,				0.75		)
          self.assertGreaterEqual(result['''eval_f1''']      ,				0.3		)







      @slow
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Tuple		       =     self.get_auto_remove_tmp_dir()
        lowerCAmelCase__				:  Dict		       =     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()
          lowerCAmelCase__				:  Any		       =     get_results(__UpperCAmelCase		)
          self.assertGreaterEqual(result['''eval_f1''']      ,				30		)
          self.assertGreaterEqual(result['''eval_exact''']      ,				30		)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_					    = {
    """configuration_longformer""": [
        """LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LongformerConfig""",
        """LongformerOnnxConfig""",
    ],
    """tokenization_longformer""": ["""LongformerTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase_					    = ["""LongformerTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase_					    = [
         """LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """LongformerForMaskedLM""",
         """LongformerForMultipleChoice""",
         """LongformerForQuestionAnswering""",
         """LongformerForSequenceClassification""",
         """LongformerForTokenClassification""",
         """LongformerModel""",
         """LongformerPreTrainedModel""",
         """LongformerSelfAttention""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase_					    = [
         """TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFLongformerForMaskedLM""",
         """TFLongformerForMultipleChoice""",
         """TFLongformerForQuestionAnswering""",
         """TFLongformerForSequenceClassification""",
         """TFLongformerForTokenClassification""",
         """TFLongformerModel""",
         """TFLongformerPreTrainedModel""",
         """TFLongformerSelfAttention""",
     ]


if TYPE_CHECKING:
     from .configuration_longformer import (
         LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         LongformerConfig,
         LongformerOnnxConfig,
     )
     from .tokenization_longformer import LongformerTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_longformer_fast import LongformerTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_longformer import (
              LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              LongformerForMaskedLM,
              LongformerForMultipleChoice,
              LongformerForQuestionAnswering,
              LongformerForSequenceClassification,
              LongformerForTokenClassification,
              LongformerModel,
              LongformerPreTrainedModel,
              LongformerSelfAttention,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_longformer import (
              TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFLongformerForMaskedLM,
              TFLongformerForMultipleChoice,
              TFLongformerForQuestionAnswering,
              TFLongformerForSequenceClassification,
              TFLongformerForTokenClassification,
              TFLongformerModel,
              TFLongformerPreTrainedModel,
              TFLongformerSelfAttention,
          )

else:
     import sys

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

