

"""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


SCREAMING_SNAKE_CASE_       :						int					=    [
    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)

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





def 			_snake_case  (     ):
     A__				      =	argparse.ArgumentParser()
     parser.add_argument("""-f"""      )
     A__				      =	parser.parse_args()
     return args.f





def 			_snake_case  (     UpperCAmelCase_	:       List[Any]      ):
     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 			_snake_case  (     ):
     return bool(tf.config.list_physical_devices("""GPU"""      )      )


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



class        a    (							_lowerCamelCase					):



    """simple docstring"""



    @skip("""Skipping until shape inference for to_tf_dataset PR is merged."""							)
    def 					UpperCamelCase						(  self:			List[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(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"""							)
              A__				      =	get_results(UpperCamelCase							)
              self.assertGreaterEqual(result["""eval_accuracy"""]     ,    0.75							)

    def 					UpperCamelCase						(  self:			List[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(UpperCamelCase     ,    """argv"""     ,    UpperCamelCase							):
              run_clm.main()
              A__				      =	get_results(UpperCamelCase							)
              self.assertLess(result["""eval_perplexity"""]     ,    1_00							)

    def 					UpperCamelCase						(  self:			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(UpperCamelCase     ,    """argv"""     ,    UpperCamelCase							):
              run_mlm.main()
              A__				      =	get_results(UpperCamelCase							)
              self.assertLess(result["""eval_perplexity"""]     ,    42							)

    def 					UpperCamelCase						(  self:			Tuple							):






         """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(UpperCamelCase     ,    """argv"""     ,    UpperCamelCase							):
              run_ner.main()
              A__				      =	get_results(UpperCamelCase							)
              self.assertGreaterEqual(result["""accuracy"""]     ,    0.75							)

    def 					UpperCamelCase						(  self:			int							):






         """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(UpperCamelCase     ,    """argv"""     ,    UpperCamelCase							):
              run_squad.main()
              A__				      =	get_results(UpperCamelCase							)
              self.assertGreaterEqual(result["""f1"""]     ,    30							)
              self.assertGreaterEqual(result["""exact"""]     ,    30							)

    def 					UpperCamelCase						(  self:			Optional[Any]							):






         """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(UpperCamelCase     ,    """argv"""     ,    UpperCamelCase							):
              run_swag.main()
              A__				      =	get_results(UpperCamelCase							)
              self.assertGreaterEqual(result["""val_accuracy"""]     ,    0.8							)

    @slow
    def 					UpperCamelCase						(  self:			Dict							):






         """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(UpperCamelCase     ,    """argv"""     ,    UpperCamelCase							):
              run_summarization.main()
              A__				      =	get_results(UpperCamelCase							)
              self.assertGreaterEqual(result["""rouge1"""]     ,    10							)
              self.assertGreaterEqual(result["""rouge2"""]     ,    2							)
              self.assertGreaterEqual(result["""rougeL"""]     ,    7							)
              self.assertGreaterEqual(result["""rougeLsum"""]     ,    7							)

    @slow
    def 					UpperCamelCase						(  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(UpperCamelCase     ,    """argv"""     ,    UpperCamelCase							):
              run_translation.main()
              A__				      =	get_results(UpperCamelCase							)
              self.assertGreaterEqual(result["""bleu"""]     ,    30							)





    def 					UpperCamelCase						(  self:			Dict							):






         """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(UpperCamelCase     ,    """argv"""     ,    UpperCamelCase							):
              run_image_classification.main()
              A__				      =	get_results(UpperCamelCase							)
              self.assertGreaterEqual(result["""accuracy"""]     ,    0.7							)









"""simple docstring"""



def 			_snake_case  (     UpperCAmelCase_	:       str       , UpperCAmelCase_	:       str       , UpperCAmelCase_	:       int       , UpperCAmelCase_	:       int       , UpperCAmelCase_	:       int       , UpperCAmelCase_	:       int       , ):
     A__				      =	list(UpperCAmelCase_      )
     A__				      =	list(UpperCAmelCase_      )
     A__				      =	len(UpperCAmelCase_      )
     A__				      =	len(UpperCAmelCase_      )

     A__				      =	[
         [0 for _ in range(len_destination_seq + 1      )] for _ in range(len_source_seq + 1      )
     ]
     A__				      =	[
         ["""0""" for _ in range(len_destination_seq + 1      )] for _ in range(len_source_seq + 1      )
     ]

     for i in range(1       , len_source_seq + 1      ):
          A__				      =	i * delete_cost
          A__				      =	F"""D{source_seq[i - 1]:c}"""

     for i in range(1       , len_destination_seq + 1      ):
          A__				      =	i * insert_cost
          A__				      =	F"""I{destination_seq[i - 1]:c}"""

     for i in range(1       , len_source_seq + 1      ):
          for j in range(1       , len_destination_seq + 1      ):
               if source_seq[i - 1] == destination_seq[j - 1]:
                    A__				      =	costs[i - 1][j - 1] + copy_cost
                    A__				      =	F"""C{source_seq[i - 1]:c}"""
               else:
                    A__				      =	costs[i - 1][j - 1] + replace_cost
                    A__				      =	F"""R{source_seq[i - 1]:c}""" + str(destination_seq[j - 1]      )

               if costs[i - 1][j] + delete_cost < costs[i][j]:
                    A__				      =	costs[i - 1][j] + delete_cost
                    A__				      =	F"""D{source_seq[i - 1]:c}"""

               if costs[i][j - 1] + insert_cost < costs[i][j]:
                    A__				      =	costs[i][j - 1] + insert_cost
                    A__				      =	F"""I{destination_seq[j - 1]:c}"""

     return costs, ops





def 			_snake_case  (     UpperCAmelCase_	:       list[list[str]]       , UpperCAmelCase_	:       int       , UpperCAmelCase_	:       int      ):
     if i == 0 and j == 0:
          return []
     else:
          if ops[i][j][0] == "C" or ops[i][j][0] == "R":
               A__				      =	assemble_transformation(UpperCAmelCase_       , i - 1       , j - 1      )
               seq.append(ops[i][j]      )
               return seq
          elif ops[i][j][0] == "D":
               A__				      =	assemble_transformation(UpperCAmelCase_       , i - 1       , UpperCAmelCase_      )
               seq.append(ops[i][j]      )
               return seq
          else:
               A__				      =	assemble_transformation(UpperCAmelCase_       , UpperCAmelCase_       , j - 1      )
               seq.append(ops[i][j]      )
               return seq


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_	,SCREAMING_SNAKE_CASE_       :						List[Any]					=    compute_transform_tables('Python', 'Algorithms', -1, 1, 2, 2)

 SCREAMING_SNAKE_CASE_       :						Any					=    len(operations)
 SCREAMING_SNAKE_CASE_       :						Dict					=    len(operations[0])
 SCREAMING_SNAKE_CASE_       :						Any					=    assemble_transformation(operations, m - 1, n - 1)

 SCREAMING_SNAKE_CASE_       :						List[str]					=    list('Python')
 SCREAMING_SNAKE_CASE_       :						Dict					=    0
 SCREAMING_SNAKE_CASE_       :						Optional[int]					=    0

 with open('min_cost.txt', 'w') as file:
  for op in sequence:
   print(''.join(string))

   if op[0] == "C":
    file.write('%-16s' % 'Copy %c' % op[1])
    file.write('\t\t\t' + ''.join(string))
    file.write('\r\n')

    cost -= 1
   elif op[0] == "R":
    SCREAMING_SNAKE_CASE_       :						int					=    op[2]

    file.write('%-16s' % ('Replace %c' % op[1] + ' with ' + str(op[2])))
    file.write('\t\t' + ''.join(string))
    file.write('\r\n')

    cost += 1
   elif op[0] == "D":
    string.pop(i)

    file.write('%-16s' % 'Delete %c' % op[1])
    file.write('\t\t\t' + ''.join(string))
    file.write('\r\n')

    cost += 2
   else:
    string.insert(i, op[1])

    file.write('%-16s' % 'Insert %c' % op[1])
    file.write('\t\t\t' + ''.join(string))
    file.write('\r\n')

    cost += 2

   i += 1

  print(''.join(string))
  print('Cost: ', cost)

  file.write('\r\nMinimum cost: ' + str(cost))






