


'''simple docstring'''



import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class 		_snake_case					(     a__       ,   a__	):




					@register_to_config
					def __init__(      self							,							*,
					    _lowerCamelCase = 4							,							_lowerCamelCase = 768							,							_lowerCamelCase							,							_lowerCamelCase							,							):
								super().__init__()

								UpperCAmelCase__  :					List[str]        =     nn.Parameter(torch.zeros(_lowerCamelCase))

								# parameters for additional clip time embeddings
								UpperCAmelCase__  :					List[Any]        =     nn.Linear(_lowerCamelCase							,							_lowerCamelCase)
								UpperCAmelCase__  :					Union[str, Any]        =     nn.Linear(_lowerCamelCase							,							_lowerCamelCase)

								# parameters for encoder hidden states
								UpperCAmelCase__  :					Any        =     clip_extra_context_tokens
								UpperCAmelCase__  :					Tuple        =     nn.Linear(
								    _lowerCamelCase							,							self.clip_extra_context_tokens * cross_attention_dim)
								UpperCAmelCase__  :					Optional[int]        =     nn.Linear(_lowerCamelCase							,							_lowerCamelCase)
								UpperCAmelCase__  :					Optional[int]        =     nn.LayerNorm(_lowerCamelCase)




					def 				snake_case__						(      self							,							*, _lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								if do_classifier_free_guidance:
											# Add the classifier free guidance embeddings to the image embeddings
											UpperCAmelCase__  :					Union[str, Any]        =     image_embeddings.shape[0]
											UpperCAmelCase__  :					Any        =     self.learned_classifier_free_guidance_embeddings.unsqueeze(0)
											UpperCAmelCase__  :					Union[str, Any]        =     classifier_free_guidance_embeddings.expand(
											    _lowerCamelCase							,							-1)
											UpperCAmelCase__  :					List[str]        =     torch.cat([classifier_free_guidance_embeddings, image_embeddings]							,							dim=0)

								# The image embeddings batch size and the text embeddings batch size are equal
								assert image_embeddings.shape[0] == prompt_embeds.shape[0]

								UpperCAmelCase__  :					Dict        =     prompt_embeds.shape[0]

								# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
								# adding CLIP embeddings to the existing timestep embedding, ...
								UpperCAmelCase__  :					str        =     self.embedding_proj(_lowerCamelCase)
								UpperCAmelCase__  :					str        =     self.clip_image_embeddings_project_to_time_embeddings(_lowerCamelCase)
								UpperCAmelCase__  :					List[str]        =     time_projected_image_embeddings + time_projected_prompt_embeds

								# ... and by projecting CLIP embeddings into four
								# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
								UpperCAmelCase__  :					List[str]        =     self.clip_extra_context_tokens_proj(_lowerCamelCase)
								UpperCAmelCase__  :					str        =     clip_extra_context_tokens.reshape(_lowerCamelCase							,							-1							,							self.clip_extra_context_tokens)
								UpperCAmelCase__  :					Any        =     clip_extra_context_tokens.permute(0							,							2							,							1)

								UpperCAmelCase__  :					List[str]        =     self.encoder_hidden_states_proj(_lowerCamelCase)
								UpperCAmelCase__  :					str        =     self.text_encoder_hidden_states_norm(_lowerCamelCase)
								UpperCAmelCase__  :					Tuple        =     torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]							,							dim=1)

								return text_encoder_hidden_states, additive_clip_time_embeddings



'''simple docstring'''



import os







def     _UpperCamelCase      (		UpperCamelCase__ = "input.txt"					):

			with open(os.path.join(os.path.dirname(UpperCamelCase__					) ,      UpperCamelCase__					)					) as input_file:
						UpperCAmelCase__  :					Tuple        =     [
						    [int(UpperCamelCase__					) for element in line.split(""","""					)]
						    for line in input_file.readlines()
						]

			UpperCAmelCase__  :					Optional[Any]        =     len(UpperCamelCase__					)
			UpperCAmelCase__  :					Any        =     len(matrix[0]					)

			UpperCAmelCase__  :					Optional[int]        =     [[-1 for _ in range(UpperCamelCase__					)] for _ in range(UpperCamelCase__					)]
			for i in range(UpperCamelCase__					):
						UpperCAmelCase__  :					Any        =     matrix[i][0]

			for j in range(1 ,      UpperCamelCase__					):
						for i in range(UpperCamelCase__					):
									UpperCAmelCase__  :					Any        =     minimal_path_sums[i][j - 1] + matrix[i][j]

						for i in range(1 ,      UpperCamelCase__					):
									UpperCAmelCase__  :					Dict        =     min(
									    minimal_path_sums[i][j] ,      minimal_path_sums[i - 1][j] + matrix[i][j]					)

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