




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__				:      Union[str, 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)

lowercase__				:      List[Any]	   = logging.getLogger()

def  SCREAMING_SNAKE_CASE							(       )      ->					List[Any]:
     a     =				argparse.ArgumentParser()
     parser.add_argument("-f")
     a     =				parser.parse_args()
     return args.f

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase="eval")      ->					Union[str, Any]:
     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}''')


lowercase__				:      Optional[int]	   = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class       a__	( UpperCamelCase__				):







     def       lowerCAmelCase_    (     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(A  ,		"argv"  ,		A			):
               run_flax_glue.main()
               a     =				get_results(A			)
               self.assertGreaterEqual(result["eval_accuracy"]  ,		0.7_5			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''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(A  ,		"argv"  ,		A			):
               run_clm_flax.main()
               a     =				get_results(A			)
               self.assertLess(result["eval_perplexity"]  ,		100			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''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(A  ,		"argv"  ,		A			):
               run_summarization_flax.main()
               a     =				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       lowerCAmelCase_    (     self			)  ->   List[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(A  ,		"argv"  ,		A			):
               run_mlm_flax.main()
               a     =				get_results(A			)
               self.assertLess(result["eval_perplexity"]  ,		42			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   Union[str, 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(A  ,		"argv"  ,		A			):
               run_ta_mlm_flax.main()
               a     =				get_results(A			)
               self.assertGreaterEqual(result["eval_accuracy"]  ,		0.4_2			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''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(A  ,		"argv"  ,		A			):
               run_flax_ner.main()
               a     =				get_results(A			)
               self.assertGreaterEqual(result["eval_accuracy"]  ,		0.7_5			)
               self.assertGreaterEqual(result["eval_f1"]  ,		0.3			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   Optional[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(A  ,		"argv"  ,		A			):
               run_qa.main()
               a     =				get_results(A			)
               self.assertGreaterEqual(result["eval_f1"]  ,		30			)
               self.assertGreaterEqual(result["eval_exact"]  ,		30			)







lowercase__				:      str	   = "\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowercase__				:      Any	   = [{"type": "code", "content": INSTALL_CONTENT}]
lowercase__				:      Any	   = {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}

