
from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
	from transformers import (
	    TFAutoModel,
	    TFAutoModelForCausalLM,
	    TFAutoModelForMaskedLM,
	    TFAutoModelForPreTraining,
	    TFAutoModelForQuestionAnswering,
	    TFAutoModelForSeqaSeqLM,
	    TFAutoModelForSequenceClassification,
	    TFAutoModelForTableQuestionAnswering,
	    TFAutoModelForTokenClassification,
	    TFAutoModelWithLMHead,
	    TFBertForMaskedLM,
	    TFBertForPreTraining,
	    TFBertForQuestionAnswering,
	    TFBertForSequenceClassification,
	    TFBertModel,
	    TFFunnelBaseModel,
	    TFFunnelModel,
	    TFGPTaLMHeadModel,
	    TFRobertaForMaskedLM,
	    TFTaForConditionalGeneration,
	    TFTapasForQuestionAnswering,
	)
	from transformers.models.auto.modeling_tf_auto import (
	    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
	    TF_MODEL_FOR_MASKED_LM_MAPPING,
	    TF_MODEL_FOR_PRETRAINING_MAPPING,
	    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
	    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
	    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
	    TF_MODEL_MAPPING,
	)
	from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
	from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
	from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
	from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST







class 			A_  (     __lowerCamelCase  ):




	'''simple docstring'''


	_UpperCamelCase	:	Dict									=			"""new-model"""


if is_tf_available():







	class 			A_  (     __lowerCamelCase  ):




		'''simple docstring'''


		_UpperCamelCase	:	Tuple									=			NewModelConfig







@require_tf
class 			A_  (     unittest.TestCase  ):




	'''simple docstring'''


	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       'bert-base-cased'
		lowercase	    =       AutoConfig.from_pretrained(snake_case			)
		self.assertIsNotNone(snake_case			)
		self.assertIsInstance(snake_case						,       snake_case			)

		lowercase	    =       TFAutoModel.from_pretrained(snake_case			)
		self.assertIsNotNone(snake_case			)
		self.assertIsInstance(snake_case						,       snake_case			)





	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       'bert-base-cased'
		lowercase	    =       AutoConfig.from_pretrained(snake_case			)
		self.assertIsNotNone(snake_case			)
		self.assertIsInstance(snake_case						,       snake_case			)

		lowercase	    =       TFAutoModelForPreTraining.from_pretrained(snake_case			)
		self.assertIsNotNone(snake_case			)
		self.assertIsInstance(snake_case						,       snake_case			)





	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			lowercase	    =       AutoConfig.from_pretrained(snake_case			)
			self.assertIsNotNone(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)

			lowercase	    =       TFAutoModelForCausalLM.from_pretrained(snake_case			)
			lowercase    ,   lowercase	    =       TFAutoModelForCausalLM.from_pretrained(snake_case						,       output_loading_info=snake_case			)
			self.assertIsNotNone(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)





	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			lowercase	    =       AutoConfig.from_pretrained(snake_case			)
			self.assertIsNotNone(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)

			lowercase	    =       TFAutoModelWithLMHead.from_pretrained(snake_case			)
			self.assertIsNotNone(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)





	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			lowercase	    =       AutoConfig.from_pretrained(snake_case			)
			self.assertIsNotNone(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)

			lowercase	    =       TFAutoModelForMaskedLM.from_pretrained(snake_case			)
			lowercase    ,   lowercase	    =       TFAutoModelForMaskedLM.from_pretrained(snake_case						,       output_loading_info=snake_case			)
			self.assertIsNotNone(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)





	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			lowercase	    =       AutoConfig.from_pretrained(snake_case			)
			self.assertIsNotNone(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)

			lowercase	    =       TFAutoModelForSeqaSeqLM.from_pretrained(snake_case			)
			lowercase    ,   lowercase	    =       TFAutoModelForSeqaSeqLM.from_pretrained(snake_case						,       output_loading_info=snake_case			)
			self.assertIsNotNone(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)





	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
		for model_name in ["bert-base-uncased"]:
			lowercase	    =       AutoConfig.from_pretrained(snake_case			)
			self.assertIsNotNone(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)

			lowercase	    =       TFAutoModelForSequenceClassification.from_pretrained(snake_case			)
			self.assertIsNotNone(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)





	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
		for model_name in ["bert-base-uncased"]:
			lowercase	    =       AutoConfig.from_pretrained(snake_case			)
			self.assertIsNotNone(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)

			lowercase	    =       TFAutoModelForQuestionAnswering.from_pretrained(snake_case			)
			self.assertIsNotNone(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)





	@slow
	@require_tensorflow_probability
	def       SCREAMING_SNAKE_CASE__		(			self			):
		for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
			lowercase	    =       AutoConfig.from_pretrained(snake_case			)
			self.assertIsNotNone(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)

			lowercase	    =       TFAutoModelForTableQuestionAnswering.from_pretrained(snake_case			)
			lowercase    ,   lowercase	    =       TFAutoModelForTableQuestionAnswering.from_pretrained(
			    snake_case						,       output_loading_info=snake_case			)
			self.assertIsNotNone(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       TFAutoModelWithLMHead.from_pretrained(snake_case			)
		self.assertIsInstance(snake_case						,       snake_case			)
		self.assertEqual(model.num_parameters()						,       1_4410			)
		self.assertEqual(model.num_parameters(only_trainable=snake_case			)						,       1_4410			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       TFAutoModelWithLMHead.from_pretrained(snake_case			)
		self.assertIsInstance(snake_case						,       snake_case			)
		self.assertEqual(model.num_parameters()						,       1_4410			)
		self.assertEqual(model.num_parameters(only_trainable=snake_case			)						,       1_4410			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
		lowercase	    =       TFAutoModel.from_pretrained('sgugger/funnel-random-tiny'			)
		self.assertIsInstance(snake_case						,       snake_case			)

		lowercase	    =       copy.deepcopy(model.config			)
		lowercase	    =       ['FunnelBaseModel']
		lowercase	    =       TFAutoModel.from_config(snake_case			)
		self.assertIsInstance(snake_case						,       snake_case			)

		with tempfile.TemporaryDirectory() as tmp_dir:
			model.save_pretrained(snake_case			)
			lowercase	    =       TFAutoModel.from_pretrained(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		try:
			AutoConfig.register('new-model'						,       snake_case			)

			lowercase	    =       [
			    TFAutoModel,
			    TFAutoModelForCausalLM,
			    TFAutoModelForMaskedLM,
			    TFAutoModelForPreTraining,
			    TFAutoModelForQuestionAnswering,
			    TFAutoModelForSequenceClassification,
			    TFAutoModelForTokenClassification,
			]

			for auto_class in auto_classes:
				with self.subTest(auto_class.__name__			):
					# Wrong config class will raise an error
					with self.assertRaises(snake_case			):
						auto_class.register(snake_case						,       snake_case			)
					auto_class.register(snake_case						,       snake_case			)
					# Trying to register something existing in the Transformers library will raise an error
					with self.assertRaises(snake_case			):
						auto_class.register(snake_case						,       snake_case			)

					# Now that the config is registered, it can be used as any other config with the auto-API
					lowercase	    =       BertModelTester(self			).get_config()
					lowercase	    =       NewModelConfig(**tiny_config.to_dict()			)
					lowercase	    =       auto_class.from_config(snake_case			)
					self.assertIsInstance(snake_case						,       snake_case			)

					with tempfile.TemporaryDirectory() as tmp_dir:
						model.save_pretrained(snake_case			)
						lowercase	    =       auto_class.from_pretrained(snake_case			)
						self.assertIsInstance(snake_case						,       snake_case			)

		finally:
			if "new-model" in CONFIG_MAPPING._extra_content:
				del CONFIG_MAPPING._extra_content["new-model"]
			for mapping in (
			    TF_MODEL_MAPPING,
			    TF_MODEL_FOR_PRETRAINING_MAPPING,
			    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
			    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
			    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
			    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
			    TF_MODEL_FOR_MASKED_LM_MAPPING,
			):
				if NewModelConfig in mapping._extra_content:
					del mapping._extra_content[NewModelConfig]





	def       SCREAMING_SNAKE_CASE__		(			self			):
		with self.assertRaisesRegex(
		    snake_case						,       'bert-base is not a local folder and is not a valid model identifier'			):
			lowercase	    =       TFAutoModel.from_pretrained('bert-base'			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		with self.assertRaisesRegex(
		    snake_case						,       r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'			):
			lowercase	    =       TFAutoModel.from_pretrained(snake_case						,       revision='aaaaaa'			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		with self.assertRaisesRegex(
		    snake_case						,       'hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin'						,       ):
			lowercase	    =       TFAutoModel.from_pretrained('hf-internal-testing/config-no-model'			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		with self.assertRaisesRegex(snake_case						,       'Use `from_pt=True` to load this model'			):
			lowercase	    =       TFAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		# Make sure we have cached the model.
		lowercase	    =       TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert'			)
		with RequestCounter() as counter:
			lowercase	    =       TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert'			)
			self.assertEqual(counter.get_request_count						,       0			)
			self.assertEqual(counter.head_request_count						,       1			)
			self.assertEqual(counter.other_request_count						,       0			)

		# With a sharded checkpoint
		lowercase	    =       TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded'			)
		with RequestCounter() as counter:
			lowercase	    =       TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded'			)
			self.assertEqual(counter.get_request_count						,       0			)
			self.assertEqual(counter.head_request_count						,       1			)
			self.assertEqual(counter.other_request_count						,       0			)








'''simple docstring'''




import os




def 						__lowerCamelCase			(	UpperCAmelCase_ = "input.txt"							)   ->int:

	with open(os.path.join(os.path.dirname(UpperCAmelCase_							)					,				UpperCAmelCase_							)							) as input_file:
		snake_case__	       =   [
		    [int(UpperCAmelCase_							) for element in line.split(','							)]
		    for line in input_file.readlines()
		]

	snake_case__	       =   len(UpperCAmelCase_							)
	snake_case__	       =   len(matrix[0]							)

	snake_case__	       =   [[-1 for _ in range(UpperCAmelCase_							)] for _ in range(UpperCAmelCase_							)]
	for i in range(UpperCAmelCase_							):
		snake_case__	       =   matrix[i][0]

	for j in range(1					,				UpperCAmelCase_							):
		for i in range(UpperCAmelCase_							):
			snake_case__	       =   minimal_path_sums[i][j - 1] + matrix[i][j]

		for i in range(1					,				UpperCAmelCase_							):
			snake_case__	       =   min(
			    minimal_path_sums[i][j]					,				minimal_path_sums[i - 1][j] + matrix[i][j]							)

		for i in range(rows - 2					,				-1					,				-1							):
			snake_case__	       =   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() = }""")



