






"""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 							lowerCAmelCase			(   )							->				Tuple:







  '''simple docstring'''







  _a					      =						argparse.ArgumentParser()
  parser.add_argument("-f"       )
  _a					      =						parser.parse_args()
  return args.f






def 							lowerCAmelCase			(   UpperCamelCase_:				int       )							->				Tuple:







  '''simple docstring'''







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






def 							lowerCAmelCase			(   )							->				Union[str, Any]:







  '''simple docstring'''







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


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




class  lowercase_     (_UpperCAmelCase  ):






      @skip("Skipping until shape inference for to_tf_dataset PR is merged."						)
      def       lowerCamelCase__     (     self						)						->Optional[Any]:







        '''simple docstring'''


        _a					      =						self.get_auto_remove_tmp_dir()
        _a					      =						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"						)
          _a					      =						get_results(a_						)
          self.assertGreaterEqual(result["eval_accuracy"]  ,  0.75						)






      def       lowerCamelCase__     (     self						)						->Optional[int]:







        '''simple docstring'''


        _a					      =						self.get_auto_remove_tmp_dir()
        _a					      =						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()
          _a					      =						get_results(a_						)
          self.assertLess(result["eval_perplexity"]  ,  1_0_0						)






      def       lowerCamelCase__     (     self						)						->List[str]:







        '''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
            --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()
          _a					      =						get_results(a_						)
          self.assertLess(result["eval_perplexity"]  ,  4_2						)






      def       lowerCamelCase__     (     self						)						->int:







        '''simple docstring'''


        _a					      =						7 if get_gpu_count() > 1 else 2

        _a					      =						self.get_auto_remove_tmp_dir()
        _a					      =						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()
          _a					      =						get_results(a_						)
          self.assertGreaterEqual(result["accuracy"]  ,  0.75						)






      def       lowerCamelCase__     (     self						)						->str:







        '''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
            --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()
          _a					      =						get_results(a_						)
          self.assertGreaterEqual(result["f1"]  ,  3_0						)
          self.assertGreaterEqual(result["exact"]  ,  3_0						)






      def       lowerCamelCase__     (     self						)						->Tuple:







        '''simple docstring'''


        _a					      =						self.get_auto_remove_tmp_dir()
        _a					      =						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()
          _a					      =						get_results(a_						)
          self.assertGreaterEqual(result["val_accuracy"]  ,  0.8						)






      @slow
      def       lowerCamelCase__     (     self						)						->Union[str, Any]:







        '''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
            --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()
          _a					      =						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       lowerCamelCase__     (     self						)						->List[str]:







        '''simple docstring'''


        _a					      =						self.get_auto_remove_tmp_dir()
        _a					      =						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()
          _a					      =						get_results(a_						)
          self.assertGreaterEqual(result["bleu"]  ,  3_0						)






      def       lowerCamelCase__     (     self						)						->Union[str, Any]:







        '''simple docstring'''


        _a					      =						self.get_auto_remove_tmp_dir()
        _a					      =						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()
          _a					      =						get_results(a_						)
          self.assertGreaterEqual(result["accuracy"]  ,  0.7						)










"""simple docstring"""




from typing import TYPE_CHECKING

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


UpperCamelCase											=     {"""configuration_pegasus""": ["""PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusConfig"""]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase											=     ["""PegasusTokenizer"""]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase											=     ["""PegasusTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase											=     [
         """PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """PegasusForCausalLM""",
         """PegasusForConditionalGeneration""",
         """PegasusModel""",
         """PegasusPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase											=     [
         """TFPegasusForConditionalGeneration""",
         """TFPegasusModel""",
         """TFPegasusPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase											=     [
         """FlaxPegasusForConditionalGeneration""",
         """FlaxPegasusModel""",
         """FlaxPegasusPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_pegasus import PegasusTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_pegasus_fast import PegasusTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_pegasus import (
              PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
              PegasusForCausalLM,
              PegasusForConditionalGeneration,
              PegasusModel,
              PegasusPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_pegasus import (
              FlaxPegasusForConditionalGeneration,
              FlaxPegasusModel,
              FlaxPegasusPreTrainedModel,
          )

else:
     import sys

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


