




'''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


a		:	str       =      [
    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)

a		:	List[str]       =      logging.getLogger()





def UpperCAmelCase    (       ):
    _a				:							List[str]							       =      argparse.ArgumentParser()
    parser.add_argument("""-f"""						)
    _a				:							List[str]							       =      parser.parse_args()
    return args.f





def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							Dict							       =      {}
    _a				:							List[str]							       =      os.path.join(UpperCAmelCase_   ,	"""all_results.json"""						)
    if os.path.exists(UpperCAmelCase_						):
        with open(UpperCAmelCase_   ,	"""r"""						) as f:
            _a				:							Any							       =      json.load(UpperCAmelCase_						)
    else:
        raise ValueError(F'''can\'t find {path}'''						)
    return results





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


a		:	str       =      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class      UpperCamelCase       (	snake_case__       ):



  @skip("""Skipping until shape inference for to_tf_dataset PR is merged.""" )
  def     a_							(			self ):







      '''simple docstring'''


      _a				:							int							       =      self.get_auto_remove_tmp_dir()
      _a				:							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(__lowerCAmelCase		,     """argv"""		,     __lowerCAmelCase ):
          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				:							Any							       =      get_results(__lowerCAmelCase )
          self.assertGreaterEqual(result["""eval_accuracy"""]		,     0.75 )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      self.get_auto_remove_tmp_dir()
      _a				:							int							       =      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(__lowerCAmelCase		,     """argv"""		,     __lowerCAmelCase ):
          run_clm.main()
          _a				:							str							       =      get_results(__lowerCAmelCase )
          self.assertLess(result["""eval_perplexity"""]		,     100 )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      self.get_auto_remove_tmp_dir()
      _a				:							Optional[int]							       =      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(__lowerCAmelCase		,     """argv"""		,     __lowerCAmelCase ):
          run_mlm.main()
          _a				:							Optional[int]							       =      get_results(__lowerCAmelCase )
          self.assertLess(result["""eval_perplexity"""]		,     42 )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Dict							       =      7 if get_gpu_count() > 1 else 2

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



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Tuple							       =      self.get_auto_remove_tmp_dir()
      _a				:							Tuple							       =      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(__lowerCAmelCase		,     """argv"""		,     __lowerCAmelCase ):
          run_squad.main()
          _a				:							List[str]							       =      get_results(__lowerCAmelCase )
          self.assertGreaterEqual(result["""f1"""]		,     30 )
          self.assertGreaterEqual(result["""exact"""]		,     30 )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      self.get_auto_remove_tmp_dir()
      _a				:							str							       =      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(__lowerCAmelCase		,     """argv"""		,     __lowerCAmelCase ):
          run_swag.main()
          _a				:							int							       =      get_results(__lowerCAmelCase )
          self.assertGreaterEqual(result["""val_accuracy"""]		,     0.8 )



  @slow
  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      self.get_auto_remove_tmp_dir()
      _a				:							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(__lowerCAmelCase		,     """argv"""		,     __lowerCAmelCase ):
          run_summarization.main()
          _a				:							Tuple							       =      get_results(__lowerCAmelCase )
          self.assertGreaterEqual(result["""rouge1"""]		,     10 )
          self.assertGreaterEqual(result["""rouge2"""]		,     2 )
          self.assertGreaterEqual(result["""rougeL"""]		,     7 )
          self.assertGreaterEqual(result["""rougeLsum"""]		,     7 )



  @slow
  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      self.get_auto_remove_tmp_dir()
      _a				:							List[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(__lowerCAmelCase		,     """argv"""		,     __lowerCAmelCase ):
          run_translation.main()
          _a				:							List[Any]							       =      get_results(__lowerCAmelCase )
          self.assertGreaterEqual(result["""bleu"""]		,     30 )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							List[Any]							       =      self.get_auto_remove_tmp_dir()
      _a				:							Optional[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(__lowerCAmelCase		,     """argv"""		,     __lowerCAmelCase ):
          run_image_classification.main()
          _a				:							Any							       =      get_results(__lowerCAmelCase )
          self.assertGreaterEqual(result["""accuracy"""]		,     0.7 )







import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase							)		->		Dict:







						"""simple docstring"""

						A__						:				str         =			BertConfig.from_pretrained(
						    "bert-large-cased"				,  vocab_size=__lowercase				,  max_position_embeddings=5_1_2				,  is_decoder=__lowercase				,  add_cross_attention=__lowercase				,  )
						A__						:				List[str]         =			bert_config.to_dict()
						del bert_config_dict["type_vocab_size"]
						A__						:				Union[str, Any]         =			BertGenerationConfig(**__lowercase							)
						if is_encoder:
												A__						:				Optional[int]         =			BertGenerationEncoder(__lowercase							)
						else:
												A__						:				List[Any]         =			BertGenerationDecoder(__lowercase							)
						print(F'Building PyTorch model from configuration: {config}'							)

						# Load weights from tf checkpoint
						load_tf_weights_in_bert_generation(
						    __lowercase				,  __lowercase				,  model_class="bert"				,  is_encoder_named_decoder=__lowercase				,  is_encoder=__lowercase				,  )

						# Save pytorch-model
						print(F'Save PyTorch model and config to {pytorch_dump_path}'							)
						model.save_pretrained(__lowercase							)


if __name__ == "__main__":
		snake_case     :    List[Any]						=    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		parser.add_argument(
		    '--is_encoder_named_decoder',
		    action='store_true',
		    help='If decoder has to be renamed to encoder in PyTorch model.',
		)
		parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
		parser.add_argument('--vocab_size', default=5_0_3_5_8, type=int, help='Vocab size of model')
		snake_case     :    List[Any]						=    parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.tf_hub_path,
		    args.pytorch_dump_path,
		    args.is_encoder_named_decoder,
		    args.vocab_size,
		    is_encoder=args.is_encoder,
		)