
'''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


lowerCAmelCase					:    List[str]           = logging.get_logger(__name__)







def 		A_(     A						:		Union[str, Any]		,     A						:		List[str]):
							try:
														with open(A		,     'rb') as flax_state_f:
																					UpperCamelCase   			=     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						:		Tuple		,     A						:		List[str]):

							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
							UpperCamelCase   			=     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.')
														UpperCamelCase   			=     jax.tree_util.tree_map(
														    lambda A: params.astype(np.floataa) if params.dtype == jnp.bfloataa else params		,     A)

							UpperCamelCase   			=     ''

							UpperCamelCase   			=     flatten_dict(A		,     sep='.')
							UpperCamelCase   			=     pt_model.state_dict()

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

							for flax_key_tuple, flax_tensor in flax_state_dict.items():
														UpperCamelCase   			=     flax_key_tuple.split('.')

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

														if "time_embedding" not in flax_key_tuple_array:
																					for i, flax_key_tuple_string in enumerate(A):
																												UpperCamelCase   			=     (
																												    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')
																												)

														UpperCamelCase   			=     '.'.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
																												UpperCamelCase   			=     np.asarray(A) if not isinstance(A		,     np.ndarray) else flax_tensor
																												UpperCamelCase   			=     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
							UpperCamelCase   			=     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'''
import numpy as np







def 		A_(     A						:		str		,     A						:		Optional[Any]		,     A						:		Tuple		,     A						:		Optional[int]		,     A						:		str):
							UpperCamelCase   			=     int(np.ceil((x_end - xa) / h))
							UpperCamelCase   			=     np.zeros((n + 1,))
							UpperCamelCase   			=     ya
							UpperCamelCase   			=     xa

							for k in range(A):
														UpperCamelCase   			=     f(A		,     y[k])
														UpperCamelCase   			=     f(x + 0.5 * h		,     y[k] + 0.5 * h * ka)
														UpperCamelCase   			=     f(x + 0.5 * h		,     y[k] + 0.5 * h * ka)
														UpperCamelCase   			=     f(x + h		,     y[k] + h * ka)
														UpperCamelCase   			=     y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
														x += h

							return y


if __name__ == "__main__":
						import doctest

						doctest.testmod()




