
import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


__snake_case								=  logging.get_logger(__name__)






def 							_lowercase							(     UpperCamelCase_		)		->		Optional[int]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= r'\w+[.]\d+'
					SCREAMING_SNAKE_CASE__													= re.findall(UpperCamelCase_	,	UpperCamelCase_		)
					for pat in pats:
										SCREAMING_SNAKE_CASE__													= key.replace(UpperCamelCase_	,	'_'.join(pat.split('.'		)		)		)
					return key






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		List[str]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= pt_tuple_key[:-1] + ('scale',)
					if (
					    any('norm' in str_ for str_ in pt_tuple_key		)
					    and (pt_tuple_key[-1] == "bias")
					    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
					    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
					):
										SCREAMING_SNAKE_CASE__													= pt_tuple_key[:-1] + ('scale',)
										return renamed_pt_tuple_key, pt_tensor
					elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
										SCREAMING_SNAKE_CASE__													= pt_tuple_key[:-1] + ('scale',)
										return renamed_pt_tuple_key, pt_tensor

					# embedding
					if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
										SCREAMING_SNAKE_CASE__													= pt_tuple_key[:-1] + ('embedding',)
										return renamed_pt_tuple_key, pt_tensor

					# conv layer
					SCREAMING_SNAKE_CASE__													= pt_tuple_key[:-1] + ('kernel',)
					if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
										SCREAMING_SNAKE_CASE__													= pt_tensor.transpose(2	,	3	,	1	,	0		)
										return renamed_pt_tuple_key, pt_tensor

					# linear layer
					SCREAMING_SNAKE_CASE__													= pt_tuple_key[:-1] + ('kernel',)
					if pt_tuple_key[-1] == "weight":
										SCREAMING_SNAKE_CASE__													= pt_tensor.T
										return renamed_pt_tuple_key, pt_tensor

					# old PyTorch layer norm weight
					SCREAMING_SNAKE_CASE__													= pt_tuple_key[:-1] + ('weight',)
					if pt_tuple_key[-1] == "gamma":
										return renamed_pt_tuple_key, pt_tensor

					# old PyTorch layer norm bias
					SCREAMING_SNAKE_CASE__													= pt_tuple_key[:-1] + ('bias',)
					if pt_tuple_key[-1] == "beta":
										return renamed_pt_tuple_key, pt_tensor

					return pt_tuple_key, pt_tensor






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_=42		)		->		int:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= {k: v.numpy() for k, v in pt_state_dict.items()}

					# Step 2: Since the model is stateless, get random Flax params
					SCREAMING_SNAKE_CASE__													= flax_model.init_weights(PRNGKey(UpperCamelCase_		)		)

					SCREAMING_SNAKE_CASE__													= flatten_dict(UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= {}

					# Need to change some parameters name to match Flax names
					for pt_key, pt_tensor in pt_state_dict.items():
										SCREAMING_SNAKE_CASE__													= rename_key(UpperCamelCase_		)
										SCREAMING_SNAKE_CASE__													= tuple(renamed_pt_key.split('.'		)		)

										# Correctly rename weight parameters
										SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= rename_key_and_reshape_tensor(UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)

										if flax_key in random_flax_state_dict:
															if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																				raise ValueError(
																				    F'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
																				    F'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'		)

        # also add unexpected weight so that warning is thrown
										SCREAMING_SNAKE_CASE__													= jnp.asarray(UpperCamelCase_		)

					return unflatten_dict(UpperCamelCase_		)







def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		str:
					'''simple docstring'''


					if not isinstance(UpperCamelCase_	,	UpperCamelCase_		):
										raise ValueError('iterations must be defined as integers'		)
					if not isinstance(UpperCamelCase_	,	UpperCamelCase_		) or not number >= 1:
										raise ValueError(
										    'starting number must be\n                         and integer and be more than 0'		)
					if not iterations >= 1:
										raise ValueError('Iterations must be done more than 0 times to play FizzBuzz'		)

					SCREAMING_SNAKE_CASE__													= ''
					while number <= iterations:
										if number % 3 == 0:
															out += "Fizz"
										if number % 5 == 0:
															out += "Buzz"
										if 0 not in (number % 3, number % 5):
															out += str(UpperCamelCase_		)

										# print(out)
										number += 1
										out += " "
					return out


if __name__ == "__main__":
				import doctest

				doctest.testmod()





