

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__	:       Union[str, Any]			      =      [
    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__	:       Union[str, Any]			      =      logging.getLogger()






def    __lowercase (       )       ->  List[str]:
      SCREAMING_SNAKE_CASE	: int					       =				argparse.ArgumentParser()
      parser.add_argument("""-f"""     )
      SCREAMING_SNAKE_CASE	: Dict					       =				parser.parse_args()
      return args.f






def    __lowercase (       _A     )       ->  str:
      SCREAMING_SNAKE_CASE	: Tuple					       =				{}
      SCREAMING_SNAKE_CASE	: List[Any]					       =				os.path.join(_A  ,      """all_results.json"""     )
      if os.path.exists(_A     ):
            with open(_A  ,      """r"""     ) as f:
                  SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				json.load(_A     )
      else:
            raise ValueError(F"can't find {path}"     )
      return results






def    __lowercase (       )       ->  Any:
      return bool(tf.config.list_physical_devices("""GPU"""     )     )


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







class     a__    (				UpperCAmelCase    ):







  """simple docstring"""




  @skip("""Skipping until shape inference for to_tf_dataset PR is merged."""						)
  def 	_lowercase (     self      :	Optional[int]						)   ->Dict:
        """simple docstring"""







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

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

        with patch.object(UpperCAmelCase__			,			"""argv"""			,			UpperCAmelCase__						):
              run_text_classification.main()
              # Reset the mixed precision policy so we don't break other tests
              tf.keras.mixed_precision.set_global_policy("""float32"""						)
              SCREAMING_SNAKE_CASE	: int					       =				get_results(UpperCAmelCase__						)
              self.assertGreaterEqual(result["""eval_accuracy"""]			,			0.75						)


  def 	_lowercase (     self      :	Optional[Any]						)   ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Optional[int]					       =				self.get_auto_remove_tmp_dir()
        SCREAMING_SNAKE_CASE	: int					       =				f"\n            run_clm.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".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(UpperCAmelCase__			,			"""argv"""			,			UpperCAmelCase__						):
              run_clm.main()
              SCREAMING_SNAKE_CASE	: Tuple					       =				get_results(UpperCAmelCase__						)
              self.assertLess(result["""eval_perplexity"""]			,			1_0_0						)


  def 	_lowercase (     self      :	Union[str, Any]						)   ->Any:
        """simple docstring"""







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

        with patch.object(UpperCAmelCase__			,			"""argv"""			,			UpperCAmelCase__						):
              run_mlm.main()
              SCREAMING_SNAKE_CASE	: Dict					       =				get_results(UpperCAmelCase__						)
              self.assertLess(result["""eval_perplexity"""]			,			4_2						)


  def 	_lowercase (     self      :	Optional[Any]						)   ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Tuple					       =				7 if get_gpu_count() > 1 else 2

        SCREAMING_SNAKE_CASE	: str					       =				self.get_auto_remove_tmp_dir()
        SCREAMING_SNAKE_CASE	: Tuple					       =				f"\n            run_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        ".split()

        with patch.object(UpperCAmelCase__			,			"""argv"""			,			UpperCAmelCase__						):
              run_ner.main()
              SCREAMING_SNAKE_CASE	: int					       =				get_results(UpperCAmelCase__						)
              self.assertGreaterEqual(result["""accuracy"""]			,			0.75						)


  def 	_lowercase (     self      :	Tuple						)   ->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Dict					       =				self.get_auto_remove_tmp_dir()
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				f"\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=10\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

        with patch.object(UpperCAmelCase__			,			"""argv"""			,			UpperCAmelCase__						):
              run_squad.main()
              SCREAMING_SNAKE_CASE	: int					       =				get_results(UpperCAmelCase__						)
              self.assertGreaterEqual(result["""f1"""]			,			3_0						)
              self.assertGreaterEqual(result["""exact"""]			,			3_0						)


  def 	_lowercase (     self      :	Any						)   ->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Any					       =				self.get_auto_remove_tmp_dir()
        SCREAMING_SNAKE_CASE	: List[Any]					       =				f"\n            run_swag.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=20\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

        with patch.object(UpperCAmelCase__			,			"""argv"""			,			UpperCAmelCase__						):
              run_swag.main()
              SCREAMING_SNAKE_CASE	: str					       =				get_results(UpperCAmelCase__						)
              self.assertGreaterEqual(result["""val_accuracy"""]			,			0.8						)


  @slow
  def 	_lowercase (     self      :	Any						)   ->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: str					       =				self.get_auto_remove_tmp_dir()
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				f"\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=50\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

        with patch.object(UpperCAmelCase__			,			"""argv"""			,			UpperCAmelCase__						):
              run_summarization.main()
              SCREAMING_SNAKE_CASE	: Any					       =				get_results(UpperCAmelCase__						)
              self.assertGreaterEqual(result["""rouge1"""]			,			1_0						)
              self.assertGreaterEqual(result["""rouge2"""]			,			2						)
              self.assertGreaterEqual(result["""rougeL"""]			,			7						)
              self.assertGreaterEqual(result["""rougeLsum"""]			,			7						)


  @slow
  def 	_lowercase (     self      :	List[str]						)   ->Optional[Any]:
        """simple docstring"""







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

        with patch.object(UpperCAmelCase__			,			"""argv"""			,			UpperCAmelCase__						):
              run_translation.main()
              SCREAMING_SNAKE_CASE	: Dict					       =				get_results(UpperCAmelCase__						)
              self.assertGreaterEqual(result["""bleu"""]			,			3_0						)




  def 	_lowercase (     self      :	Optional[int]						)   ->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self.get_auto_remove_tmp_dir()
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				f"\n            run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --model_name_or_path microsoft/resnet-18\n            --do_train\n            --do_eval\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --dataloader_num_workers 16\n            --num_train_epochs 2\n            --train_val_split 0.1\n            --seed 42\n            --ignore_mismatched_sizes True\n            ".split()

        with patch.object(UpperCAmelCase__			,			"""argv"""			,			UpperCAmelCase__						):
              run_image_classification.main()
              SCREAMING_SNAKE_CASE	: List[Any]					       =				get_results(UpperCAmelCase__						)
              self.assertGreaterEqual(result["""accuracy"""]			,			0.7						)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCAmelCase__	:       int			      =      {
    """configuration_speech_to_text_2""": ["""SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2Text2Config"""],
    """processing_speech_to_text_2""": ["""Speech2Text2Processor"""],
    """tokenization_speech_to_text_2""": ["""Speech2Text2Tokenizer"""],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__	:       int			      =      [
      """SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Speech2Text2ForCausalLM""",
      """Speech2Text2PreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
  from .processing_speech_to_text_a import SpeechaTextaProcessor
  from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_speech_to_text_a import (
        SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
        SpeechaTextaForCausalLM,
        SpeechaTextaPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase__	:       int			      =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




