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     lowercase_       (    ):
    lowercase__				:     Tuple					      =       argparse.ArgumentParser()
    parser.add_argument("-f")
    lowercase__				:     Optional[int]					      =       parser.parse_args()
    return args.f

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

def     lowercase_       (    ):
    return bool(tf.config.list_physical_devices("GPU"))


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



class 						snake_case_ (	__A ):






   @skip("Skipping until shape inference for to_tf_dataset PR is merged."						)
   def    __UpperCamelCase  (   self       :  Tuple						)      ->		Optional[int]:
       lowercase__				:     Tuple					      =       self.get_auto_remove_tmp_dir()
       lowercase__				:     List[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(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__				:     List[Any]					      =       get_results(lowercase_						)
           self.assertGreaterEqual(result["eval_accuracy"]			,		0.75						)






   def    __UpperCamelCase  (   self       :  int						)      ->		Union[str, Any]:
       lowercase__				:     List[Any]					      =       self.get_auto_remove_tmp_dir()
       lowercase__				:     str					      =       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__				:     Union[str, Any]					      =       get_results(lowercase_						)
           self.assertLess(result["eval_perplexity"]			,		1_00						)






   def    __UpperCamelCase  (   self       :  Dict						)      ->		Union[str, Any]:
       lowercase__				:     List[str]					      =       self.get_auto_remove_tmp_dir()
       lowercase__				:     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
            --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__				:     List[str]					      =       get_results(lowercase_						)
           self.assertLess(result["eval_perplexity"]			,		42						)






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

       lowercase__				:     Any					      =       self.get_auto_remove_tmp_dir()
       lowercase__				:     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(lowercase_			,		"argv"			,		lowercase_						):
           run_ner.main()
           lowercase__				:     List[str]					      =       get_results(lowercase_						)
           self.assertGreaterEqual(result["accuracy"]			,		0.75						)






   def    __UpperCamelCase  (   self       :  Dict						)      ->		Tuple:
       lowercase__				:     List[str]					      =       self.get_auto_remove_tmp_dir()
       lowercase__				:     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(lowercase_			,		"argv"			,		lowercase_						):
           run_squad.main()
           lowercase__				:     List[str]					      =       get_results(lowercase_						)
           self.assertGreaterEqual(result["f1"]			,		30						)
           self.assertGreaterEqual(result["exact"]			,		30						)






   def    __UpperCamelCase  (   self       :  Any						)      ->		Optional[Any]:
       lowercase__				:     Optional[Any]					      =       self.get_auto_remove_tmp_dir()
       lowercase__				:     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(lowercase_			,		"argv"			,		lowercase_						):
           run_swag.main()
           lowercase__				:     Optional[int]					      =       get_results(lowercase_						)
           self.assertGreaterEqual(result["val_accuracy"]			,		0.8						)






   @slow
   def    __UpperCamelCase  (   self       :  int						)      ->		Any:
       lowercase__				:     Any					      =       self.get_auto_remove_tmp_dir()
       lowercase__				:     int					      =       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__				:     Dict					      =       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    __UpperCamelCase  (   self       :  Optional[Any]						)      ->		Dict:
       lowercase__				:     List[Any]					      =       self.get_auto_remove_tmp_dir()
       lowercase__				:     Optional[Any]					      =       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__				:     Any					      =       get_results(lowercase_						)
           self.assertGreaterEqual(result["bleu"]			,		30						)







   def    __UpperCamelCase  (   self       :  List[str]						)      ->		List[Any]:
       lowercase__				:     Union[str, Any]					      =       self.get_auto_remove_tmp_dir()
       lowercase__				:     Tuple					      =       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__				:     List[str]					      =       get_results(lowercase_						)
           self.assertGreaterEqual(result["accuracy"]			,		0.7						)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase													=		{
    '''configuration_mobilenet_v1''': [
        '''MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileNetV1Config''',
        '''MobileNetV1OnnxConfig''',
    ],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase													=		['''MobileNetV1FeatureExtractor''']
      UpperCamelCase													=		['''MobileNetV1ImageProcessor''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase													=		[
          '''MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''MobileNetV1ForImageClassification''',
          '''MobileNetV1Model''',
          '''MobileNetV1PreTrainedModel''',
          '''load_tf_weights_in_mobilenet_v1''',
      ]


if TYPE_CHECKING:
      from .configuration_mobilenet_va import (
          MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
          MobileNetVaConfig,
          MobileNetVaOnnxConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
            from .image_processing_mobilenet_va import MobileNetVaImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilenet_va import (
                MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileNetVaForImageClassification,
                MobileNetVaModel,
                MobileNetVaPreTrainedModel,
                load_tf_weights_in_mobilenet_va,
            )


else:
      import sys

      UpperCamelCase													=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



