
"""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


UpperCamelCase_					:						Optional[Any]				= logging.get_logger(__name__)





def 							A_     (__a				,   __a				):

					'''simple docstring'''





					try:
										with open(a_				,   "rb"				) as flax_state_f:
															A_				 =						from_bytes(a_				,   flax_state_f.read()				)
					except UnpicklingError as e:
										try:
															with open(a_				) 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(a_				,   a_				)





def 							A_     (__a				,   __a				):

					'''simple docstring'''





					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
					A_				 =						flatten_dict(jax.tree_util.tree_map(lambda __a				: x.dtype == jnp.bfloataa				,   a_				)				).values()
					if any(a_				):
										# 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."				)
										A_				 =						jax.tree_util.tree_map(
										    lambda __a				: params.astype(np.floataa				) if params.dtype == jnp.bfloataa else params				,   a_				)

					A_				 =						''''''

					A_				 =						flatten_dict(a_				,   sep="."				)
					A_				 =						pt_model.state_dict()

					# keep track of unexpected & missing keys
					A_				 =						[]
					A_				 =						set(pt_model_dict.keys()				)

					for flax_key_tuple, flax_tensor in flax_state_dict.items():
										A_				 =						flax_key_tuple.split("."				)

										if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
															A_				 =						flax_key_tuple_array[:-1] + ['''weight''']
															A_				 =						jnp.transpose(a_				,   (3, 2, 0, 1)				)
										elif flax_key_tuple_array[-1] == "kernel":
															A_				 =						flax_key_tuple_array[:-1] + ['''weight''']
															A_				 =						flax_tensor.T
										elif flax_key_tuple_array[-1] == "scale":
															A_				 =						flax_key_tuple_array[:-1] + ['''weight''']

										if "time_embedding" not in flax_key_tuple_array:
															for i, flax_key_tuple_string in enumerate(a_				):
																				A_				 =						(
																				    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"				)
																				)

										A_				 =						'''.'''.join(a_				)

										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
																				A_				 =						np.asarray(a_				) if not isinstance(a_				,   np.ndarray				) else flax_tensor
																				A_				 =						torch.from_numpy(a_				)
																				# remove from missing keys
																				missing_keys.remove(a_				)
										else:
															# weight is not expected by PyTorch model
															unexpected_keys.append(a_				)

					pt_model.load_state_dict(a_				)

					# re-transform missing_keys to list
					A_				 =						list(a_				)

					if len(a_				) > 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(a_				) > 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"""





def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						current_set.copy()
					for row_index, row in enumerate(__a				):
										A_				 =						row[0]
										for column_index, column in enumerate(__a				):
															if magnitude == 0:
																				A_				 =						column
																				continue
															A_				 =						column / magnitude
    # Subtract to cancel term
					A_				 =						current_set[0]
					A_				 =						[first_row]
					A_				 =						current_set[1::]
					for row in current_set:
										A_				 =						[]
										# If first term is 0, it is already in form we want, so we preserve it
										if row[0] == 0:
															final_set.append(__a				)
															continue
										for column_index in range(len(__a				)				):
															temp_row.append(first_row[column_index] - row[column_index]				)
										final_set.append(__a				)
					# Create next recursion iteration set
					if len(final_set[0]				) != 3:
										A_				 =						final_set[0]
										A_				 =						[]
										A_				 =						[]
										for row in final_set[1::]:
															current_first_column.append(row[0]				)
															next_iteration.append(row[1::]				)
										A_				 =						simplify(__a				)
										for i in range(len(__a				)				):
															resultant[i].insert(0				,   current_first_column[i]				)
										resultant.insert(0				,   __a				)
										A_				 =						resultant
					return final_set
def 							A_     (__a				):

					'''simple docstring'''





					if len(__a				) == 0:
										raise IndexError("solve_simultaneous() requires n lists of length n+1"				)
					A_				 =						len(__a				) + 1
					if any(len(__a				) != _length for item in equations				):
										raise IndexError("solve_simultaneous() requires n lists of length n+1"				)
					for row in equations:
										if any(not isinstance(__a				,   (int, float)				) for column in row				):
															raise ValueError("solve_simultaneous() requires lists of integers"				)
					if len(__a				) == 1:
										return [equations[0][-1] / equations[0][0]]
					A_				 =						equations.copy()
					if any(0 in row for row in data_set				):
										A_				 =						data_set.copy()
										A_				 =						[]
										for row_index, row in enumerate(__a				):
															if 0 not in row:
																				A_				 =						data_set.pop(__a				)
																				break
										if not full_row:
															raise ValueError("solve_simultaneous() requires at least 1 full equation"				)
										data_set.insert(0				,   __a				)
					A_				 =						data_set.copy()
					A_				 =						simplify(__a				)
					A_				 =						simplified[::-1]
					A_				 =						[]
					for row in simplified:
										A_				 =						row[-1]
										if not solutions:
															if row[-2] == 0:
																				solutions.append(0				)
																				continue
															solutions.append(current_solution / row[-2]				)
															continue
										A_				 =						row.copy()[: len(__a				) - 1 :]
										while temp_row[0] == 0:
															temp_row.pop(0				)
										if len(__a				) == 0:
															solutions.append(0				)
															continue
										A_				 =						temp_row[1::]
										A_				 =						temp_row[::-1]
										for column_index, column in enumerate(__a				):
															current_solution -= column * solutions[column_index]
										solutions.append(__a				)
					A_				 =						[]
					for item in solutions:
										final.append(float(round(__a				,   5				)				)				)
					return final[::-1]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		UpperCamelCase_					:						List[Any]				= [
		    [2, 1, 1, 1, 1, 4],
		    [1, 2, 1, 1, 1, 5],
		    [1, 1, 2, 1, 1, 6],
		    [1, 1, 1, 2, 1, 7],
		    [1, 1, 1, 1, 2, 8],
		]
		print(solve_simultaneous(eq))
		print(solve_simultaneous([[4, 2]]))




