






'''simple docstring'''

import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


UpperCAmelCase				   =  [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        '''text-generation''',
        '''text-classification''',
        '''token-classification''',
        '''language-modeling''',
        '''multiple-choice''',
        '''question-answering''',
        '''summarization''',
        '''translation''',
        '''image-classification''',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
       import run_clm
       import run_image_classification
       import run_mlm
       import run_ner
       import run_qa as run_squad
       import run_summarization
       import run_swag
       import run_text_classification
       import run_translation


logging.basicConfig(level=logging.DEBUG)

UpperCAmelCase				   =  logging.getLogger()

def 				__UpperCamelCase							(	):






     '''simple docstring'''





     __lowercase					=argparse.ArgumentParser()
     parser.add_argument('-f' )
     __lowercase					=parser.parse_args()
     return args.f

def 				__UpperCamelCase							(	lowercase__						:  int ):






     '''simple docstring'''





     __lowercase					={}
     __lowercase					=os.path.join(lowercase__,					'all_results.json' )
     if os.path.exists(lowercase__ ):
          with open(lowercase__,					'r' ) as f:
               __lowercase					=json.load(lowercase__ )
     else:
          raise ValueError(F'''can\'t find {path}''' )
     return results

def 				__UpperCamelCase							(	):






     '''simple docstring'''





     return bool(tf.config.list_physical_devices('GPU' ) )


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







class   lowerCAmelCase     (      A   ):







       @skip('Skipping until shape inference for to_tf_dataset PR is merged.'      )
       def 			snake_case   (							self			: Union[str, Any]      ):







            """simple docstring"""







            __lowercase					=self.get_auto_remove_tmp_dir()
            __lowercase					=f'''
            run_text_classification.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

            if is_cuda_available():
                 testargs.append('--fp16'      )

            with patch.object(__lowercase						,      'argv'						,      __lowercase      ):
                 run_text_classification.main()
                 # Reset the mixed precision policy so we don't break other tests
                 tf.keras.mixed_precision.set_global_policy('float32'      )
                 __lowercase					=get_results(__lowercase      )
                 self.assertGreaterEqual(result['eval_accuracy']						,      0.7_5      )







       def 			snake_case   (							self			: Optional[int]      ):







            """simple docstring"""







            __lowercase					=self.get_auto_remove_tmp_dir()
            __lowercase					=f'''
            run_clm.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 2
            --per_device_eval_batch_size 1
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            '''.split()

            if len(tf.config.list_physical_devices('GPU'      )      ) > 1:
                 # Skipping because there are not enough batches to train the model + would need a drop_last to work.
                 return

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







       def 			snake_case   (							self			: Dict      ):







            """simple docstring"""







            __lowercase					=self.get_auto_remove_tmp_dir()
            __lowercase					=f'''
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --max_seq_length 64
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --prediction_loss_only
            --num_train_epochs=1
            --learning_rate=1e-4
        '''.split()

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







       def 			snake_case   (							self			: List[Any]      ):







            """simple docstring"""







            __lowercase					=7 if get_gpu_count() > 1 else 2

            __lowercase					=self.get_auto_remove_tmp_dir()
            __lowercase					=f'''
            run_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
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        '''.split()

            with patch.object(__lowercase						,      'argv'						,      __lowercase      ):
                 run_ner.main()
                 __lowercase					=get_results(__lowercase      )
                 self.assertGreaterEqual(result['accuracy']						,      0.7_5      )







       def 			snake_case   (							self			: str      ):







            """simple docstring"""







            __lowercase					=self.get_auto_remove_tmp_dir()
            __lowercase					=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
            --max_steps=10
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

            with patch.object(__lowercase						,      'argv'						,      __lowercase      ):
                 run_squad.main()
                 __lowercase					=get_results(__lowercase      )
                 self.assertGreaterEqual(result['f1']						,      30      )
                 self.assertGreaterEqual(result['exact']						,      30      )







       def 			snake_case   (							self			: List[str]      ):







            """simple docstring"""







            __lowercase					=self.get_auto_remove_tmp_dir()
            __lowercase					=f'''
            run_swag.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=20
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

            with patch.object(__lowercase						,      'argv'						,      __lowercase      ):
                 run_swag.main()
                 __lowercase					=get_results(__lowercase      )
                 self.assertGreaterEqual(result['val_accuracy']						,      0.8      )







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







            """simple docstring"""







            __lowercase					=self.get_auto_remove_tmp_dir()
            __lowercase					=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
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=50
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

            with patch.object(__lowercase						,      'argv'						,      __lowercase      ):
                 run_summarization.main()
                 __lowercase					=get_results(__lowercase      )
                 self.assertGreaterEqual(result['rouge1']						,      10      )
                 self.assertGreaterEqual(result['rouge2']						,      2      )
                 self.assertGreaterEqual(result['rougeL']						,      7      )
                 self.assertGreaterEqual(result['rougeLsum']						,      7      )







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







            """simple docstring"""







            __lowercase					=self.get_auto_remove_tmp_dir()
            __lowercase					=f'''
            run_translation.py
            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=3e-3
            --num_train_epochs 12
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
        '''.split()

            with patch.object(__lowercase						,      'argv'						,      __lowercase      ):
                 run_translation.main()
                 __lowercase					=get_results(__lowercase      )
                 self.assertGreaterEqual(result['bleu']						,      30      )







       def 			snake_case   (							self			: Any      ):







            """simple docstring"""







            __lowercase					=self.get_auto_remove_tmp_dir()
            __lowercase					=f'''
            run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --model_name_or_path microsoft/resnet-18
            --do_train
            --do_eval
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --dataloader_num_workers 16
            --num_train_epochs 2
            --train_val_split 0.1
            --seed 42
            --ignore_mismatched_sizes True
            '''.split()

            with patch.object(__lowercase						,      'argv'						,      __lowercase      ):
                 run_image_classification.main()
                 __lowercase					=get_results(__lowercase      )
                 self.assertGreaterEqual(result['accuracy']						,      0.7      )





'''simple docstring'''

from __future__ import annotations

def 				__UpperCamelCase							(	lowercase__						:  list[int | float],					lowercase__						:  int,					lowercase__						:  int ):






     '''simple docstring'''





     if len(lowercase__ ) == 0:
          raise ValueError('find_min() arg is an empty sequence' )
     if (
         left >= len(lowercase__ )
         or left < -len(lowercase__ )
         or right >= len(lowercase__ )
         or right < -len(lowercase__ )
     ):
          raise IndexError('list index out of range' )
     if left == right:
          return nums[left]
     __lowercase					=(left + right) >> 1  # the middle
     __lowercase					=find_min(lowercase__,					lowercase__,					lowercase__ )  # find min in range[left, mid]
     __lowercase					=find_min(lowercase__,					mid + 1,					lowercase__ )  # find min in range[mid + 1, right]

     return left_min if left_min <= right_min else right_min


if __name__ == "__main__":
       import doctest

       doctest.testmod(verbose=True)


