



'''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


lowerCAmelCase						:		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)

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



def 	__lowerCAmelCase       (				):






	'''simple docstring'''




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



def 	__lowerCAmelCase       (				lowerCamelCase :    List[Any]       ):






	'''simple docstring'''




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



def 	__lowerCAmelCase       (				):






	'''simple docstring'''




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


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






class        UpperCAmelCase__						(		_lowerCamelCase			):



					@skip("Skipping until shape inference for to_tf_dataset PR is merged."  )
					def 							UpperCAmelCase_			(     self  )  ->      Any:
						__lowerCAmelCase       				=	self.get_auto_remove_tmp_dir()
						__lowerCAmelCase       				=	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"  )
							__lowerCAmelCase       				=	get_results(UpperCamelCase  )
							self.assertGreaterEqual(result["eval_accuracy"]      ,					0.75  )



					def 							UpperCAmelCase_			(     self  )  ->      List[str]:
						__lowerCAmelCase       				=	self.get_auto_remove_tmp_dir()
						__lowerCAmelCase       				=	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()
							__lowerCAmelCase       				=	get_results(UpperCamelCase  )
							self.assertLess(result["eval_perplexity"]      ,					100  )



					def 							UpperCAmelCase_			(     self  )  ->      Any:
						__lowerCAmelCase       				=	self.get_auto_remove_tmp_dir()
						__lowerCAmelCase       				=	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()
							__lowerCAmelCase       				=	get_results(UpperCamelCase  )
							self.assertLess(result["eval_perplexity"]      ,					42  )



					def 							UpperCAmelCase_			(     self  )  ->      Optional[int]:
						__lowerCAmelCase       				=	7 if get_gpu_count() > 1 else 2

						__lowerCAmelCase       				=	self.get_auto_remove_tmp_dir()
						__lowerCAmelCase       				=	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()
							__lowerCAmelCase       				=	get_results(UpperCamelCase  )
							self.assertGreaterEqual(result["accuracy"]      ,					0.75  )



					def 							UpperCAmelCase_			(     self  )  ->      Dict:
						__lowerCAmelCase       				=	self.get_auto_remove_tmp_dir()
						__lowerCAmelCase       				=	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()
							__lowerCAmelCase       				=	get_results(UpperCamelCase  )
							self.assertGreaterEqual(result["f1"]      ,					30  )
							self.assertGreaterEqual(result["exact"]      ,					30  )



					def 							UpperCAmelCase_			(     self  )  ->      Optional[Any]:
						__lowerCAmelCase       				=	self.get_auto_remove_tmp_dir()
						__lowerCAmelCase       				=	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()
							__lowerCAmelCase       				=	get_results(UpperCamelCase  )
							self.assertGreaterEqual(result["val_accuracy"]      ,					0.8  )



					@slow
					def 							UpperCAmelCase_			(     self  )  ->      Dict:
						__lowerCAmelCase       				=	self.get_auto_remove_tmp_dir()
						__lowerCAmelCase       				=	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()
							__lowerCAmelCase       				=	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  )  ->      int:
						__lowerCAmelCase       				=	self.get_auto_remove_tmp_dir()
						__lowerCAmelCase       				=	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()
							__lowerCAmelCase       				=	get_results(UpperCamelCase  )
							self.assertGreaterEqual(result["bleu"]      ,					30  )



					def 							UpperCAmelCase_			(     self  )  ->      List[Any]:
						__lowerCAmelCase       				=	self.get_auto_remove_tmp_dir()
						__lowerCAmelCase       				=	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()
							__lowerCAmelCase       				=	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))






