






"""simple docstring"""



from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


__A						:			Any    							=						logging.get_logger(__name__)



def 		lowercase       (							__snake_case							:					Any						,   __snake_case							:					Union[str, Any]      ):
		try:
				with open(UpperCAmelCase_						,   '''rb'''      ) as flax_state_f:
						lowercase_					:					List[str]							=  from_bytes(UpperCAmelCase_						,   flax_state_f.read()      )
		except UnpicklingError as e:
				try:
						with open(UpperCAmelCase_      ) as f:
								if f.read().startswith('''version'''      ):
										raise OSError(
										    '''You seem to have cloned a repository without having git-lfs installed. Please'''
										    ''' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'''
										    ''' folder you cloned.'''      )
								else:
										raise ValueError from e
				except (UnicodeDecodeError, ValueError):
						raise EnvironmentError(F'''Unable to convert {model_file} to Flax deserializable object. '''      )

		return load_flax_weights_in_pytorch_model(UpperCAmelCase_						,   UpperCAmelCase_      )






def 		lowercase       (							__snake_case							:					Optional[Any]						,   __snake_case							:					Union[str, Any]      ):

		try:
				import torch  # noqa: F401
		except ImportError:
				logger.error(
				    '''Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'''
				    ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
				    ''' instructions.'''      )
				raise

		# check if we have bf16 weights
		lowercase_					:					Optional[Any]							=  flatten_dict(jax.tree_util.tree_map(lambda __snake_case      : x.dtype == jnp.bfloataa						,   UpperCAmelCase_      )      ).values()
		if any(UpperCAmelCase_      ):
				# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

				# and bf16 is not fully supported in PT yet.
				logger.warning(
				    '''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
				    '''before loading those in PyTorch model.'''      )
				lowercase_					:					List[Any]							=  jax.tree_util.tree_map(
				    lambda __snake_case      : params.astype(np.floataa      ) if params.dtype == jnp.bfloataa else params						,   UpperCAmelCase_      )

		lowercase_					:					Optional[Any]							=  ''''''

		lowercase_					:					Optional[int]							=  flatten_dict(UpperCAmelCase_						,   sep='''.'''      )
		lowercase_					:					str							=  pt_model.state_dict()

		# keep track of unexpected & missing keys
		lowercase_					:					List[Any]							=  []
		lowercase_					:					Any							=  set(pt_model_dict.keys()      )

		for flax_key_tuple, flax_tensor in flax_state_dict.items():
				lowercase_					:					List[Any]							=  flax_key_tuple.split('''.'''      )

				if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
						lowercase_					:					Optional[int]							=  flax_key_tuple_array[:-1] + ['''weight''']
						lowercase_					:					List[str]							=  jnp.transpose(UpperCAmelCase_						,   (3, 2, 0, 1)      )
				elif flax_key_tuple_array[-1] == "kernel":
						lowercase_					:					str							=  flax_key_tuple_array[:-1] + ['''weight''']
						lowercase_					:					List[str]							=  flax_tensor.T
				elif flax_key_tuple_array[-1] == "scale":
						lowercase_					:					Tuple							=  flax_key_tuple_array[:-1] + ['''weight''']

				if "time_embedding" not in flax_key_tuple_array:
						for i, flax_key_tuple_string in enumerate(UpperCAmelCase_      ):
								lowercase_					:					Optional[Any]							=  (
								    flax_key_tuple_string.replace('''_0'''						,   '''.0'''      )
								    .replace('''_1'''						,   '''.1'''      )
								    .replace('''_2'''						,   '''.2'''      )
								    .replace('''_3'''						,   '''.3'''      )
								    .replace('''_4'''						,   '''.4'''      )
								    .replace('''_5'''						,   '''.5'''      )
								    .replace('''_6'''						,   '''.6'''      )
								    .replace('''_7'''						,   '''.7'''      )
								    .replace('''_8'''						,   '''.8'''      )
								    .replace('''_9'''						,   '''.9'''      )
								)

				lowercase_					:					Union[str, Any]							=  '''.'''.join(UpperCAmelCase_      )

				if flax_key in pt_model_dict:
						if flax_tensor.shape != pt_model_dict[flax_key].shape:
								raise ValueError(
								    F'''Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '''
								    F'''to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'''      )
						else:
								# add weight to pytorch dict
								lowercase_					:					List[Any]							=  np.asarray(UpperCAmelCase_      ) if not isinstance(UpperCAmelCase_						,   np.ndarray      ) else flax_tensor
								lowercase_					:					int							=  torch.from_numpy(UpperCAmelCase_      )
								# remove from missing keys
								missing_keys.remove(UpperCAmelCase_      )
				else:
						# weight is not expected by PyTorch model
						unexpected_keys.append(UpperCAmelCase_      )

		pt_model.load_state_dict(UpperCAmelCase_      )

		# re-transform missing_keys to list
		lowercase_					:					str							=  list(UpperCAmelCase_      )

		if len(UpperCAmelCase_      ) > 0:
				logger.warning(
				    '''Some weights of the Flax model were not used when initializing the PyTorch model'''
				    F''' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'''
				    F''' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'''
				    ''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
				    F''' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'''
				    ''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
				    ''' FlaxBertForSequenceClassification model).'''      )
		if len(UpperCAmelCase_      ) > 0:
				logger.warning(
				    F'''Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'''
				    F''' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'''
				    ''' use it for predictions and inference.'''      )

		return pt_model










'''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() = }""")



