


from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


_lowercase       :      str	=	logging.get_logger(__name__)

_lowercase       :      Union[str, Any]	=	{
    "google/umt5-small": "https://huggingface.co/google/umt5-small/resolve/main/config.json",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= 'umt5'
				lowerCAmelCase 		= ['past_key_values']

				def __init__(						self				,   a__=25_0112				,   a__=512				,   a__=64				,   a__=1024				,   a__=8				,   a__=None				,   a__=6				,   a__=32				,   a__=128				,   a__=0.1				,   a__=1e-6				,   a__=1.0				,   a__="gated-gelu"				,   a__=True				,   a__=True				,   a__="T5Tokenizer"				,   a__=True				,   a__=0				,   a__=1				,   a__=0				,   **a__				,   )	->				List[Any]:
					super().__init__(
					    is_encoder_decoder=a__				,   tokenizer_class=a__				,   tie_word_embeddings=a__				,   pad_token_id=a__				,   eos_token_id=a__				,   decoder_start_token_id=a__				,   **a__				,   )
					A								=				vocab_size
					A								=				d_model
					A								=				d_kv
					A								=				d_ff
					A								=				num_layers
					A								=				(
					    num_decoder_layers if num_decoder_layers is not None else self.num_layers
					)  # default = symmetry
					A								=				num_heads
					A								=				relative_attention_num_buckets
					A								=				relative_attention_max_distance
					A								=				dropout_rate
					A								=				layer_norm_epsilon
					A								=				initializer_factor
					A								=				feed_forward_proj
					A								=				use_cache

					A								=				self.feed_forward_proj.split("""-"""   )
					A								=				act_info[-1]
					A								=				act_info[0] == """gated"""

					if len(a__   ) > 1 and act_info[0] != "gated" or len(a__   ) > 2:
						raise ValueError(
						    f'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
						    """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
						    """'gated-gelu' or 'relu'"""   )

					if feed_forward_proj == "gated-gelu":
						A								=				"""gelu_new"""

				@property
				def      _UpperCAmelCase					(						self   )	->				Any:
					return self.d_model

				@property
				def      _UpperCAmelCase					(						self   )	->				List[Any]:
					return self.num_heads

				@property
				def      _UpperCAmelCase					(						self   )	->				Tuple:
					return self.num_layers






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				@property
				# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
				def      _UpperCAmelCase					(						self   )	->				Mapping[str, Mapping[int, str]]:
					A								=				{
					    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
					    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
					}
					if self.use_past:
						A								=				"""past_encoder_sequence + sequence"""
						A								=				{0: """batch"""}
						A								=				{0: """batch""", 1: """past_decoder_sequence + sequence"""}
					else:
						A								=				{0: """batch""", 1: """decoder_sequence"""}
						A								=				{0: """batch""", 1: """decoder_sequence"""}

					if self.use_past:
						self.fill_with_past_key_values_(a__				,   direction="""inputs"""   )

					return common_inputs

				@property
				# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
				def      _UpperCAmelCase					(						self   )	->				int:
					return 13

				@property
				def      _UpperCAmelCase					(						self   )	->				float:
					return 5e-4







import os





def   _lowerCAmelCase				(   UpperCamelCase__:     str = "matrix.txt"						) ->   int:





	"""simple docstring"""







	with open(os.path.join(os.path.dirname(UpperCamelCase__						)							,					UpperCamelCase__						)						) as in_file:
		A								=				in_file.read()

	A								=				[[int(UpperCamelCase__						) for cell in row.split(""","""						)] for row in data.strip().splitlines()]
	A								=				[[0 for cell in row] for row in grid]
	A								=				len(grid[0]						)

	A								=				[[0 for i in range(UpperCamelCase__						)] for j in range(UpperCamelCase__						)]
	A								=				grid[0][0]
	for i in range(1							,					UpperCamelCase__						):
		A								=				grid[0][i] + dp[0][i - 1]
	for i in range(1							,					UpperCamelCase__						):
		A								=				grid[i][0] + dp[i - 1][0]

	for i in range(1							,					UpperCamelCase__						):
		for j in range(1							,					UpperCamelCase__						):
			A								=				grid[i][j] + min(dp[i - 1][j]							,					dp[i][j - 1]						)

	return dp[-1][-1]


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



