






"""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_     :					Optional[int]							=     logging.getLogger()






def 			lowerCamelCase_    (		):
 lowerCamelCase__	:     Tuple								=		argparse.ArgumentParser()
 parser.add_argument('-f'							)
 lowerCamelCase__	:     Union[str, Any]								=		parser.parse_args()
 return args.f






def 			lowerCamelCase_    (		_lowerCamelCase							):
 lowerCamelCase__	:     Tuple								=		{}
 lowerCamelCase__	:     Any								=		os.path.join(_lowerCamelCase     ,  'all_results.json'							)
 if os.path.exists(_lowerCamelCase							):
  with open(_lowerCamelCase     ,  'r'							) as f:
   lowerCamelCase__	:     List[Any]								=		json.load(_lowerCamelCase							)
 else:
  raise ValueError(f'''can\'t find {path}'''							)
 return results






def 			lowerCamelCase_    (		):
 return bool(tf.config.list_physical_devices('GPU'							)							)


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





class 						a_ (      snake_case_					):







      '''simple docstring'''

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

       '''simple docstring'''



       lowerCamelCase__	:     int								=		self.get_auto_remove_tmp_dir()
       lowerCamelCase__	:     int								=		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'   )
        lowerCamelCase__	:     Optional[Any]								=		get_results(lowerCamelCase_   )
        self.assertGreaterEqual(result['eval_accuracy'],	0.75   )




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Union[str, Any]								=		self.get_auto_remove_tmp_dir()
       lowerCamelCase__	:     List[Any]								=		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()
        lowerCamelCase__	:     Optional[Any]								=		get_results(lowerCamelCase_   )
        self.assertLess(result['eval_perplexity'],	1_0_0   )




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Optional[Any]								=		self.get_auto_remove_tmp_dir()
       lowerCamelCase__	:     Optional[Any]								=		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()
        lowerCamelCase__	:     int								=		get_results(lowerCamelCase_   )
        self.assertLess(result['eval_perplexity'],	4_2   )




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Any								=		7 if get_gpu_count() > 1 else 2

       lowerCamelCase__	:     int								=		self.get_auto_remove_tmp_dir()
       lowerCamelCase__	:     Tuple								=		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()
        lowerCamelCase__	:     Optional[Any]								=		get_results(lowerCamelCase_   )
        self.assertGreaterEqual(result['accuracy'],	0.75   )




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     str								=		self.get_auto_remove_tmp_dir()
       lowerCamelCase__	:     Union[str, Any]								=		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()
        lowerCamelCase__	:     Any								=		get_results(lowerCamelCase_   )
        self.assertGreaterEqual(result['f1'],	3_0   )
        self.assertGreaterEqual(result['exact'],	3_0   )




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Dict								=		self.get_auto_remove_tmp_dir()
       lowerCamelCase__	:     Union[str, Any]								=		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()
        lowerCamelCase__	:     List[str]								=		get_results(lowerCamelCase_   )
        self.assertGreaterEqual(result['val_accuracy'],	0.8   )




      @slow
      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     int								=		self.get_auto_remove_tmp_dir()
       lowerCamelCase__	:     Optional[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()
        lowerCamelCase__	:     Dict								=		get_results(lowerCamelCase_   )
        self.assertGreaterEqual(result['rouge1'],	1_0   )
        self.assertGreaterEqual(result['rouge2'],	2   )
        self.assertGreaterEqual(result['rougeL'],	7   )
        self.assertGreaterEqual(result['rougeLsum'],	7   )




      @slow
      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     List[str]								=		self.get_auto_remove_tmp_dir()
       lowerCamelCase__	:     Union[str, Any]								=		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()
        lowerCamelCase__	:     int								=		get_results(lowerCamelCase_   )
        self.assertGreaterEqual(result['bleu'],	3_0   )





      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Any								=		self.get_auto_remove_tmp_dir()
       lowerCamelCase__	:     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()
        lowerCamelCase__	:     Dict								=		get_results(lowerCamelCase_   )
        self.assertGreaterEqual(result['accuracy'],	0.7   )









"""simple docstring"""
def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase     ,  ):
 lowerCamelCase__	:     Any								=		list(_lowerCamelCase							)
 lowerCamelCase__	:     List[Any]								=		list(_lowerCamelCase							)
 lowerCamelCase__	:     int								=		len(_lowerCamelCase							)
 lowerCamelCase__	:     Optional[int]								=		len(_lowerCamelCase							)

 lowerCamelCase__	:     Tuple								=		[
     [0 for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
 ]
 lowerCamelCase__	:     Dict								=		[
     ['0' for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
 ]

 for i in range(1     ,  len_source_seq + 1							):
  lowerCamelCase__	:     Optional[int]								=		i * delete_cost
  lowerCamelCase__	:     List[str]								=		f'''D{source_seq[i - 1]:c}'''

 for i in range(1     ,  len_destination_seq + 1							):
  lowerCamelCase__	:     int								=		i * insert_cost
  lowerCamelCase__	:     str								=		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]:
    lowerCamelCase__	:     Any								=		costs[i - 1][j - 1] + copy_cost
    lowerCamelCase__	:     str								=		f'''C{source_seq[i - 1]:c}'''
   else:
    lowerCamelCase__	:     Tuple								=		costs[i - 1][j - 1] + replace_cost
    lowerCamelCase__	:     List[str]								=		f'''R{source_seq[i - 1]:c}''' + str(destination_seq[j - 1]							)

   if costs[i - 1][j] + delete_cost < costs[i][j]:
    lowerCamelCase__	:     Any								=		costs[i - 1][j] + delete_cost
    lowerCamelCase__	:     Union[str, Any]								=		f'''D{source_seq[i - 1]:c}'''

   if costs[i][j - 1] + insert_cost < costs[i][j]:
    lowerCamelCase__	:     Optional[Any]								=		costs[i][j - 1] + insert_cost
    lowerCamelCase__	:     Union[str, Any]								=		f'''I{destination_seq[j - 1]:c}'''

 return costs, ops






def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase							):
 if i == 0 and j == 0:
  return []
 else:
  if ops[i][j][0] == "C" or ops[i][j][0] == "R":
   lowerCamelCase__	:     List[str]								=		assemble_transformation(_lowerCamelCase     ,  i - 1     ,  j - 1							)
   seq.append(ops[i][j]							)
   return seq
  elif ops[i][j][0] == "D":
   lowerCamelCase__	:     Union[str, Any]								=		assemble_transformation(_lowerCamelCase     ,  i - 1     ,  _lowerCamelCase							)
   seq.append(ops[i][j]							)
   return seq
  else:
   lowerCamelCase__	:     Tuple								=		assemble_transformation(_lowerCamelCase     ,  _lowerCamelCase     ,  j - 1							)
   seq.append(ops[i][j]							)
   return seq


if __name__ == "__main__":
      A_,			A_     :					List[str]							=     compute_transform_tables("Python", "Algorithms", -1, 1, 2, 2)

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

      A_     :					int							=     list("Python")
      A_     :					Optional[Any]							=     0
      A_     :					Tuple							=     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":
                        A_     :					str							=     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))

