


import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CTRLForSequenceClassification,
				    CTRLLMHeadModel,
				    CTRLModel,
				)







class     _lowercase				:


							"""simple docstring"""




							def __init__(     self							:    List[str]      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    Any=14      ,   __lowerCamelCase							:    Any=7      ,   __lowerCamelCase							:    List[str]=True      ,   __lowerCamelCase							:    List[str]=True      ,   __lowerCamelCase							:    Union[str, Any]=True      ,   __lowerCamelCase							:    int=True      ,   __lowerCamelCase							:    Any=True      ,   __lowerCamelCase							:    Union[str, Any]=99      ,   __lowerCamelCase							:    Union[str, Any]=32      ,   __lowerCamelCase							:    int=5      ,   __lowerCamelCase							:    Optional[Any]=4      ,   __lowerCamelCase							:    List[str]=37      ,   __lowerCamelCase							:    Optional[int]="gelu"      ,   __lowerCamelCase							:    str=0.1      ,   __lowerCamelCase							:    Any=0.1      ,   __lowerCamelCase							:    str=512      ,   __lowerCamelCase							:    int=16      ,   __lowerCamelCase							:    List[Any]=2      ,   __lowerCamelCase							:    int=0.0_2      ,   __lowerCamelCase							:    int=3      ,   __lowerCamelCase							:    Dict=4      ,   __lowerCamelCase							:    Optional[int]=None      ,   ):


													'''simple docstring'''



													lowerCamelCase__	:       List[Any]					=						parent
													lowerCamelCase__	:       Dict					=						batch_size
													lowerCamelCase__	:       Union[str, Any]					=						seq_length
													lowerCamelCase__	:       List[str]					=						is_training
													lowerCamelCase__	:       int					=						use_token_type_ids
													lowerCamelCase__	:       Dict					=						use_input_mask
													lowerCamelCase__	:       Any					=						use_labels
													lowerCamelCase__	:       List[str]					=						use_mc_token_ids
													lowerCamelCase__	:       str					=						vocab_size
													lowerCamelCase__	:       int					=						hidden_size
													lowerCamelCase__	:       Union[str, Any]					=						num_hidden_layers
													lowerCamelCase__	:       Dict					=						num_attention_heads
													lowerCamelCase__	:       Union[str, Any]					=						intermediate_size
													lowerCamelCase__	:       Optional[Any]					=						hidden_act
													lowerCamelCase__	:       List[str]					=						hidden_dropout_prob
													lowerCamelCase__	:       Tuple					=						attention_probs_dropout_prob
													lowerCamelCase__	:       Tuple					=						max_position_embeddings
													lowerCamelCase__	:       Any					=						type_vocab_size
													lowerCamelCase__	:       List[str]					=						type_sequence_label_size
													lowerCamelCase__	:       int					=						initializer_range
													lowerCamelCase__	:       str					=						num_labels
													lowerCamelCase__	:       str					=						num_choices
													lowerCamelCase__	:       Tuple					=						scope
													lowerCamelCase__	:       str					=						self.vocab_size - 1





							def 		lowerCAmelCase  (     self							:    Any ):


													'''simple docstring'''



													lowerCamelCase__	:       Optional[Any]					=						ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size )

													lowerCamelCase__	:       Dict					=						None
													if self.use_input_mask:
																			lowerCamelCase__	:       str					=						random_attention_mask([self.batch_size, self.seq_length] )

													lowerCamelCase__	:       int					=						None
													if self.use_token_type_ids:
																			lowerCamelCase__	:       Dict					=						ids_tensor([self.batch_size, self.seq_length]      ,   self.type_vocab_size )

													lowerCamelCase__	:       Tuple					=						None
													if self.use_mc_token_ids:
																			lowerCamelCase__	:       int					=						ids_tensor([self.batch_size, self.num_choices]      ,   self.seq_length )

													lowerCamelCase__	:       Optional[Any]					=						None
													lowerCamelCase__	:       List[Any]					=						None
													lowerCamelCase__	:       int					=						None
													if self.use_labels:
																			lowerCamelCase__	:       Optional[int]					=						ids_tensor([self.batch_size]      ,   self.type_sequence_label_size )
																			lowerCamelCase__	:       str					=						ids_tensor([self.batch_size, self.seq_length]      ,   self.num_labels )
																			lowerCamelCase__	:       List[Any]					=						ids_tensor([self.batch_size]      ,   self.num_choices )

													lowerCamelCase__	:       Tuple					=						self.get_config()

													lowerCamelCase__	:       Dict					=						ids_tensor([self.num_hidden_layers, self.num_attention_heads]      ,   2 )

													return (
													    config,
													    input_ids,
													    input_mask,
													    head_mask,
													    token_type_ids,
													    mc_token_ids,
													    sequence_labels,
													    token_labels,
													    choice_labels,
													)





							def 		lowerCAmelCase  (     self							:    Optional[Any] ):


													'''simple docstring'''



													return CTRLConfig(
													    vocab_size=self.vocab_size      ,   n_embd=self.hidden_size      ,   n_layer=self.num_hidden_layers      ,   n_head=self.num_attention_heads      ,   n_positions=self.max_position_embeddings      ,   pad_token_id=self.pad_token_id      ,   )





							def 		lowerCAmelCase  (     self							:    int      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    Tuple      ,   __lowerCamelCase							:    Any      ,   *__lowerCamelCase							:    Tuple ):


													'''simple docstring'''



													lowerCamelCase__	:       Dict					=						CTRLModel(config=__lowerCamelCase )
													model.to(__lowerCamelCase )
													model.eval()

													model(__lowerCamelCase      ,   token_type_ids=__lowerCamelCase      ,   head_mask=__lowerCamelCase )
													model(__lowerCamelCase      ,   token_type_ids=__lowerCamelCase )
													lowerCamelCase__	:       Union[str, Any]					=						model(__lowerCamelCase )
													self.parent.assertEqual(result.last_hidden_state.shape      ,   (self.batch_size, self.seq_length, self.hidden_size) )
													self.parent.assertEqual(len(result.past_key_values )      ,   config.n_layer )





							def 		lowerCAmelCase  (     self							:    Any      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    Tuple      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    Optional[int]      ,   *__lowerCamelCase							:    str ):


													'''simple docstring'''



													lowerCamelCase__	:       int					=						CTRLLMHeadModel(__lowerCamelCase )
													model.to(__lowerCamelCase )
													model.eval()

													lowerCamelCase__	:       Any					=						model(__lowerCamelCase      ,   token_type_ids=__lowerCamelCase      ,   labels=__lowerCamelCase )
													self.parent.assertEqual(result.loss.shape      ,   () )
													self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.seq_length, self.vocab_size) )





							def 		lowerCAmelCase  (     self							:    str ):


													'''simple docstring'''



													lowerCamelCase__	:       Optional[Any]					=						self.prepare_config_and_inputs()

													(
													    lowerCamelCase__     
													)	:       Tuple					=						config_and_inputs

													lowerCamelCase__	:       Optional[Any]					=						{"input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask}

													return config, inputs_dict





							def 		lowerCAmelCase  (     self							:    int      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    Tuple      ,   *__lowerCamelCase							:    Dict ):


													'''simple docstring'''



													lowerCamelCase__	:       List[Any]					=						self.num_labels
													lowerCamelCase__	:       List[Any]					=						CTRLForSequenceClassification(__lowerCamelCase )
													model.to(__lowerCamelCase )
													model.eval()
													lowerCamelCase__	:       Tuple					=						ids_tensor([self.batch_size]      ,   self.type_sequence_label_size )
													lowerCamelCase__	:       Dict					=						model(__lowerCamelCase      ,   token_type_ids=__lowerCamelCase      ,   labels=__lowerCamelCase )
													self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.num_labels) )







@require_torch
class     _lowercase				(							lowercase__							,						lowercase__							,						lowercase__							,						unittest.TestCase):


							"""simple docstring"""




							A__      		=    (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
							A__      		=    (CTRLLMHeadModel,) if is_torch_available() else ()
							A__      		=    (
							    {
							        "feature-extraction": CTRLModel,
							        "text-classification": CTRLForSequenceClassification,
							        "text-generation": CTRLLMHeadModel,
							        "zero-shot": CTRLForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							A__      		=    True
							A__      		=    False
							A__      		=    False





							def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    Union[str, Any] ):


													'''simple docstring'''



													if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
																			# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
																			# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
																			# config could not be created.
																			return True

													return False





							def 		lowerCAmelCase  (     self							:    Any ):


													'''simple docstring'''



													lowerCamelCase__	:       Union[str, Any]					=						CTRLModelTester(self )
													lowerCamelCase__	:       str					=						ConfigTester(self      ,   config_class=__lowerCamelCase      ,   n_embd=37 )





							def 		lowerCAmelCase  (     self							:    Any ):


													'''simple docstring'''



													super().tearDown()
													# clean-up as much as possible GPU memory occupied by PyTorch
													gc.collect()
													torch.cuda.empty_cache()





							def 		lowerCAmelCase  (     self							:    Optional[Any] ):


													'''simple docstring'''



													self.config_tester.run_common_tests()





							def 		lowerCAmelCase  (     self							:    Dict ):


													'''simple docstring'''



													lowerCamelCase__	:       Tuple					=						self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_ctrl_model(*__lowerCamelCase )





							def 		lowerCAmelCase  (     self							:    Dict ):


													'''simple docstring'''



													lowerCamelCase__	:       Dict					=						self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_lm_head_model(*__lowerCamelCase )





							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
							def 		lowerCAmelCase  (     self							:    Optional[Any] ):


													'''simple docstring'''



													pass





							@slow
							def 		lowerCAmelCase  (     self							:    Optional[int] ):


													'''simple docstring'''



													for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			lowerCamelCase__	:       str					=						CTRLModel.from_pretrained(__lowerCamelCase )
																			self.assertIsNotNone(__lowerCamelCase )





							@unittest.skip("The model doesn't support left padding" )  # and it's not used enough to be worth fixing :)
							def 		lowerCAmelCase  (     self							:    Optional[Any] ):


													'''simple docstring'''



													pass







@require_torch
class     _lowercase				(							unittest.TestCase):


							"""simple docstring"""




							def 		lowerCAmelCase  (     self							:    Optional[int] ):


													'''simple docstring'''



													super().tearDown()
													# clean-up as much as possible GPU memory occupied by PyTorch
													gc.collect()
													torch.cuda.empty_cache()





							@slow
							def 		lowerCAmelCase  (     self							:    Tuple ):


													'''simple docstring'''



													lowerCamelCase__	:       int					=						CTRLLMHeadModel.from_pretrained("ctrl" )
													model.to(__lowerCamelCase )
													lowerCamelCase__	:       Optional[int]					=						torch.tensor(
													    [[11859, 0, 1611, 8]]      ,   dtype=torch.long      ,   device=__lowerCamelCase )  # Legal the president is
													lowerCamelCase__	:       Optional[Any]					=						[
													    11859,
													    0,
													    1611,
													    8,
													    5,
													    150,
													    26449,
													    2,
													    19,
													    348,
													    469,
													    3,
													    2595,
													    48,
													    20740,
													    246533,
													    246533,
													    19,
													    30,
													    5,
													]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

													lowerCamelCase__	:       List[Any]					=						model.generate(__lowerCamelCase      ,   do_sample=__lowerCamelCase )
													self.assertListEqual(output_ids[0].tolist()      ,   __lowerCamelCase )






import os

def    lowercase_	(	_A    :					str = "input.txt"		):

      """simple docstring"""




      with open(os.path.join(os.path.dirname(_A		)					,      _A		)		) as input_file:
            lowerCamelCase__	:       List[Any]					=						[
                [int(_A		) for element in line.split(","		)]
                for line in input_file.readlines()
            ]

      lowerCamelCase__	:       Optional[Any]					=						len(_A		)
      lowerCamelCase__	:       Union[str, Any]					=						len(matrix[0]		)

      lowerCamelCase__	:       Union[str, Any]					=						[[-1 for _ in range(_A		)] for _ in range(_A		)]
      for i in range(_A		):
            lowerCamelCase__	:       Optional[Any]					=						matrix[i][0]

      for j in range(1					,      _A		):
            for i in range(_A		):
                  lowerCamelCase__	:       int					=						minimal_path_sums[i][j - 1] + matrix[i][j]

            for i in range(1					,      _A		):
                  lowerCamelCase__	:       Tuple					=						min(
                      minimal_path_sums[i][j]					,      minimal_path_sums[i - 1][j] + matrix[i][j]		)

            for i in range(rows - 2					,      -1					,      -1		):
                  lowerCamelCase__	:       str					=						min(
                      minimal_path_sums[i][j]					,      minimal_path_sums[i + 1][j] + matrix[i][j]		)

      return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums		)


if __name__ == "__main__":
    print(f'{solution() = }')


