"""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


__magic_name__    				=					[
    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)

__magic_name__    				=					logging.getLogger()





def   _lowerCamelCase ( )							->     Any:


   '''simple docstring'''


   a__     				=		argparse.ArgumentParser()
   parser.add_argument('-f'					)
   a__     				=		parser.parse_args()
   return args.f





def   _lowerCamelCase ( UpperCAmelCase__					)							->     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 ( )							->     Tuple:


   '''simple docstring'''


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


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




class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 @skip('Skipping until shape inference for to_tf_dataset PR is merged.'				)
 def        _lowerCAmelCase				(   self		:      List[str]				)				->			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(_snake_case					,   'argv'					,   _snake_case				):
       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(_snake_case				)
       self.assertGreaterEqual(result['eval_accuracy']					,   0.75				)


 def        _lowerCAmelCase				(   self		:      Any				)				->			Optional[Any]:




    '''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(_snake_case					,   'argv'					,   _snake_case				):
       run_clm.main()
       a__     				=		get_results(_snake_case				)
       self.assertLess(result['eval_perplexity']					,   100				)


 def        _lowerCAmelCase				(   self		:      Any				)				->			Optional[Any]:




    '''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(_snake_case					,   'argv'					,   _snake_case				):
       run_mlm.main()
       a__     				=		get_results(_snake_case				)
       self.assertLess(result['eval_perplexity']					,   42				)


 def        _lowerCAmelCase				(   self		:      Optional[Any]				)				->			Dict:




    '''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(_snake_case					,   'argv'					,   _snake_case				):
       run_ner.main()
       a__     				=		get_results(_snake_case				)
       self.assertGreaterEqual(result['accuracy']					,   0.75				)


 def        _lowerCAmelCase				(   self		:      Optional[int]				)				->			Optional[Any]:




    '''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(_snake_case					,   'argv'					,   _snake_case				):
       run_squad.main()
       a__     				=		get_results(_snake_case				)
       self.assertGreaterEqual(result['f1']					,   30				)
       self.assertGreaterEqual(result['exact']					,   30				)


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




    '''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(_snake_case					,   'argv'					,   _snake_case				):
       run_swag.main()
       a__     				=		get_results(_snake_case				)
       self.assertGreaterEqual(result['val_accuracy']					,   0.8				)


 @slow
 def        _lowerCAmelCase				(   self		:      int				)				->			Tuple:




    '''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(_snake_case					,   'argv'					,   _snake_case				):
       run_summarization.main()
       a__     				=		get_results(_snake_case				)
       self.assertGreaterEqual(result['rouge1']					,   10				)
       self.assertGreaterEqual(result['rouge2']					,   2				)
       self.assertGreaterEqual(result['rougeL']					,   7				)
       self.assertGreaterEqual(result['rougeLsum']					,   7				)


 @slow
 def        _lowerCAmelCase				(   self		:      List[Any]				)				->			int:




    '''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(_snake_case					,   'argv'					,   _snake_case				):
       run_translation.main()
       a__     				=		get_results(_snake_case				)
       self.assertGreaterEqual(result['bleu']					,   30				)



 def        _lowerCAmelCase				(   self		:      Tuple				)				->			Optional[int]:




    '''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(_snake_case					,   'argv'					,   _snake_case				):
       run_image_classification.main()
       a__     				=		get_results(_snake_case				)
       self.assertGreaterEqual(result['accuracy']					,   0.7				)



"""simple docstring"""



import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__					)							->     Optional[int]:


   '''simple docstring'''


   a__     				=		CanineConfig()
   a__     				=		CanineModel(UpperCAmelCase__					)
   model.eval()

   print(f'''Building PyTorch model from configuration: {config}'''					)

   # Load weights from tf checkpoint
   load_tf_weights_in_canine(UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__					)

   # Save pytorch-model (weights and configuration)
   print(f'''Save PyTorch model to {pytorch_dump_path}'''					)
   model.save_pretrained(UpperCAmelCase__					)

   # Save tokenizer files
   a__     				=		CanineTokenizer()
   print(f'''Save tokenizer files to {pytorch_dump_path}'''					)
   tokenizer.save_pretrained(UpperCAmelCase__					)


if __name__ == "__main__":
     __magic_name__    				=					argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--tf_checkpoint_path",
         default=None,
         type=str,
         required=True,
         help="Path to the TensorFlow checkpoint. Should end with model.ckpt",
     )
     parser.add_argument(
         "--pytorch_dump_path",
         default=None,
         type=str,
         required=True,
         help="Path to a folder where the PyTorch model will be placed.",
     )
     __magic_name__    				=					parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)


