
'''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


__lowercase							:				int					=       [
    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)

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




def lowercase_		(				):
					'''simple docstring'''

					lowerCamelCase_   : Any       =						argparse.ArgumentParser()
					parser.add_argument('''-f'''			)
					lowerCamelCase_   : int       =						parser.parse_args()
					return args.f




def lowercase_		(				_lowercase			):
					'''simple docstring'''

					lowerCamelCase_   : Union[str, Any]       =						{}
					lowerCamelCase_   : int       =						os.path.join(_lowercase     ,    '''all_results.json'''			)
					if os.path.exists(_lowercase			):
										with open(_lowercase     ,    '''r'''			) as f:
															lowerCamelCase_   : List[Any]       =						json.load(_lowercase			)
					else:
										raise ValueError(F"""can't find {path}"""			)
					return results




def lowercase_		(				):
					'''simple docstring'''

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


__lowercase							:				List[Any]					=       logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class 		__lowercase			(					_lowercase      ):

			@skip('''Skipping until shape inference for to_tf_dataset PR is merged.''' )
			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Any       =						self.get_auto_remove_tmp_dir()
								lowerCamelCase_   : Optional[Any]       =						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(A     ,				'''argv'''     ,				A ):
													run_text_classification.main()
													# Reset the mixed precision policy so we don't break other tests
													tf.keras.mixed_precision.set_global_policy('''float32''' )
													lowerCamelCase_   : Dict       =						get_results(A )
													self.assertGreaterEqual(result['''eval_accuracy''']     ,				0.75 )

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Union[str, Any]       =						self.get_auto_remove_tmp_dir()
								lowerCamelCase_   : Any       =						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(A     ,				'''argv'''     ,				A ):
													run_clm.main()
													lowerCamelCase_   : int       =						get_results(A )
													self.assertLess(result['''eval_perplexity''']     ,				1_0_0 )

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Tuple       =						self.get_auto_remove_tmp_dir()
								lowerCamelCase_   : 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
            --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(A     ,				'''argv'''     ,				A ):
													run_mlm.main()
													lowerCamelCase_   : Tuple       =						get_results(A )
													self.assertLess(result['''eval_perplexity''']     ,				4_2 )

			def     UpperCAmelCase__	(self ):
								# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
								lowerCamelCase_   : Tuple       =						7 if get_gpu_count() > 1 else 2

								lowerCamelCase_   : List[Any]       =						self.get_auto_remove_tmp_dir()
								lowerCamelCase_   : Optional[Any]       =						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(A     ,				'''argv'''     ,				A ):
													run_ner.main()
													lowerCamelCase_   : Union[str, Any]       =						get_results(A )
													self.assertGreaterEqual(result['''accuracy''']     ,				0.75 )

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Tuple       =						self.get_auto_remove_tmp_dir()
								lowerCamelCase_   : Any       =						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(A     ,				'''argv'''     ,				A ):
													run_squad.main()
													lowerCamelCase_   : List[str]       =						get_results(A )
													self.assertGreaterEqual(result['''f1''']     ,				3_0 )
													self.assertGreaterEqual(result['''exact''']     ,				3_0 )

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : List[str]       =						self.get_auto_remove_tmp_dir()
								lowerCamelCase_   : Dict       =						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(A     ,				'''argv'''     ,				A ):
													run_swag.main()
													lowerCamelCase_   : List[Any]       =						get_results(A )
													self.assertGreaterEqual(result['''val_accuracy''']     ,				0.8 )

			@slow
			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Optional[Any]       =						self.get_auto_remove_tmp_dir()
								lowerCamelCase_   : 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
            --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(A     ,				'''argv'''     ,				A ):
													run_summarization.main()
													lowerCamelCase_   : Dict       =						get_results(A )
													self.assertGreaterEqual(result['''rouge1''']     ,				1_0 )
													self.assertGreaterEqual(result['''rouge2''']     ,				2 )
													self.assertGreaterEqual(result['''rougeL''']     ,				7 )
													self.assertGreaterEqual(result['''rougeLsum''']     ,				7 )

			@slow
			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : int       =						self.get_auto_remove_tmp_dir()
								lowerCamelCase_   : str       =						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(A     ,				'''argv'''     ,				A ):
													run_translation.main()
													lowerCamelCase_   : List[str]       =						get_results(A )
													self.assertGreaterEqual(result['''bleu''']     ,				3_0 )






			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : List[str]       =						self.get_auto_remove_tmp_dir()
								lowerCamelCase_   : int       =						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(A     ,				'''argv'''     ,				A ):
													run_image_classification.main()
													lowerCamelCase_   : Union[str, Any]       =						get_results(A )
													self.assertGreaterEqual(result['''accuracy''']     ,				0.7 )




'''simple docstring'''


from typing import TYPE_CHECKING

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


__lowercase							:				Any					=       {
    '''configuration_camembert''': ['''CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CamembertConfig''', '''CamembertOnnxConfig'''],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				Any					=       ['''CamembertTokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				str					=       ['''CamembertTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				List[Any]					=       [
					    '''CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''CamembertForCausalLM''',
					    '''CamembertForMaskedLM''',
					    '''CamembertForMultipleChoice''',
					    '''CamembertForQuestionAnswering''',
					    '''CamembertForSequenceClassification''',
					    '''CamembertForTokenClassification''',
					    '''CamembertModel''',
					    '''CamembertPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				Any					=       [
					    '''TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFCamembertForCausalLM''',
					    '''TFCamembertForMaskedLM''',
					    '''TFCamembertForMultipleChoice''',
					    '''TFCamembertForQuestionAnswering''',
					    '''TFCamembertForSequenceClassification''',
					    '''TFCamembertForTokenClassification''',
					    '''TFCamembertModel''',
					    '''TFCamembertPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_camembert import CamembertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_camembert_fast import CamembertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_camembert import (
										    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CamembertForCausalLM,
										    CamembertForMaskedLM,
										    CamembertForMultipleChoice,
										    CamembertForQuestionAnswering,
										    CamembertForSequenceClassification,
										    CamembertForTokenClassification,
										    CamembertModel,
										    CamembertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_camembert import (
										    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCamembertForCausalLM,
										    TFCamembertForMaskedLM,
										    TFCamembertForMultipleChoice,
										    TFCamembertForQuestionAnswering,
										    TFCamembertForSequenceClassification,
										    TFCamembertForTokenClassification,
										    TFCamembertModel,
										    TFCamembertPreTrainedModel,
										)

else:
					import sys

					__lowercase							:				Union[str, Any]					=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


