





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       :     int   =     [
    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       :     str   =     logging.getLogger()



def 		A_				(     )						-> List[str]:
 UpperCamelCase	:					List[str]       	=   argparse.ArgumentParser()
 parser.add_argument("-f"     )
 UpperCamelCase	:					Union[str, Any]       	=   parser.parse_args()
 return args.f



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase="eval"     )						-> Optional[int]:
 UpperCamelCase	:					Optional[Any]       	=   os.path.join(_lowerCAmelCase							,   F"""{split}_results.json"""     )
 if os.path.exists(_lowerCAmelCase     ):
  with open(_lowerCAmelCase							,   "r"     ) as f:
   return json.load(_lowerCAmelCase     )
 raise ValueError(F"""can't find {path}"""     )


__lowerCamelCase       :     Any   =     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)



class 					A__   (       __snake_case			):





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					str       	=   self.get_auto_remove_tmp_dir()
       UpperCamelCase	:					Optional[Any]       	=   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()
        UpperCamelCase	:					int       	=   get_results(A_   )
        self.assertGreaterEqual(result["eval_accuracy"]       ,							0.75   )





      @slow
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   self.get_auto_remove_tmp_dir()
       UpperCamelCase	:					Optional[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(A_       ,							"argv"       ,							A_   ):
        run_clm_flax.main()
        UpperCamelCase	:					Optional[Any]       	=   get_results(A_   )
        self.assertLess(result["eval_perplexity"]       ,							100   )





      @slow
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   self.get_auto_remove_tmp_dir()
       UpperCamelCase	:					Optional[Any]       	=   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()
        UpperCamelCase	:					Optional[Any]       	=   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   ):
       '''simple docstring'''




       UpperCamelCase	:					str       	=   self.get_auto_remove_tmp_dir()
       UpperCamelCase	:					Tuple       	=   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()
        UpperCamelCase	:					Dict       	=   get_results(A_   )
        self.assertLess(result["eval_perplexity"]       ,							42   )





      @slow
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




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





      @slow
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Union[str, Any]       	=   7 if get_gpu_count() > 1 else 2

       UpperCamelCase	:					int       	=   self.get_auto_remove_tmp_dir()
       UpperCamelCase	:					Dict       	=   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()
        UpperCamelCase	:					Tuple       	=   get_results(A_   )
        self.assertGreaterEqual(result["eval_accuracy"]       ,							0.75   )
        self.assertGreaterEqual(result["eval_f1"]       ,							0.3   )





      @slow
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					str       	=   self.get_auto_remove_tmp_dir()
       UpperCamelCase	:					str       	=   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()
        UpperCamelCase	:					Optional[int]       	=   get_results(A_   )
        self.assertGreaterEqual(result["eval_f1"]       ,							30   )
        self.assertGreaterEqual(result["eval_exact"]       ,							30   )







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCamelCase       :     List[Any]   =     logging.get_logger(__name__)

__lowerCamelCase       :     str   =     {
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}



class 					A__   (       __snake_case			):
      _UpperCAmelCase	:Union[str, Any]						    =   'roberta'





      def __init__( self       ,							A_=5_0265       ,							A_=768       ,							A_=12       ,							A_=12       ,							A_=3072       ,							A_="gelu"       ,							A_=0.1       ,							A_=0.1       ,							A_=512       ,							A_=2       ,							A_=0.02       ,							A_=1e-12       ,							A_=1       ,							A_=0       ,							A_=2       ,							A_="absolute"       ,							A_=True       ,							A_=None       ,							**A_       ,							):
       '''simple docstring'''




       super().__init__(pad_token_id=A_       ,							bos_token_id=A_       ,							eos_token_id=A_       ,							**A_   )

       UpperCamelCase	:					Optional[int]       	=   vocab_size
       UpperCamelCase	:					Dict       	=   hidden_size
       UpperCamelCase	:					str       	=   num_hidden_layers
       UpperCamelCase	:					Any       	=   num_attention_heads
       UpperCamelCase	:					List[str]       	=   hidden_act
       UpperCamelCase	:					Optional[Any]       	=   intermediate_size
       UpperCamelCase	:					Tuple       	=   hidden_dropout_prob
       UpperCamelCase	:					Tuple       	=   attention_probs_dropout_prob
       UpperCamelCase	:					Tuple       	=   max_position_embeddings
       UpperCamelCase	:					Any       	=   type_vocab_size
       UpperCamelCase	:					int       	=   initializer_range
       UpperCamelCase	:					str       	=   layer_norm_eps
       UpperCamelCase	:					Dict       	=   position_embedding_type
       UpperCamelCase	:					Any       	=   use_cache
       UpperCamelCase	:					Union[str, Any]       	=   classifier_dropout



class 					A__   (       __snake_case			):





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       if self.task == "multiple-choice":
        UpperCamelCase	:					Optional[int]       	=   {0: "batch", 1: "choice", 2: "sequence"}
       else:
        UpperCamelCase	:					Optional[int]       	=   {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]   )
