

import argparse
from collections import defaultdict



def   __UpperCAmelCase					(      __a :     Union[str, Any]						,__a :     Tuple						,__a :     Tuple						,__a :     Dict						,__a :     Tuple      )     ->	List[Any]:

					"""simple docstring"""

					_a      :       List[str]   =						F"""{file}_{class_name}_{test_name}"""
					done_test[_id] += 1

					with open(__a						,'''r'''      ) as f:
										_a      :       Dict   =						f.readlines()

					_a      :       str   =						F"""class {class_name}("""
					_a      :       Tuple   =						F"""{4 * ' '}def {test_name}("""
					_a      :       List[Any]   =						F"""{8 * ' '}{correct_line.split()[0]}"""
					_a      :       Tuple   =						F"""{16 * ' '}{correct_line.split()[0]}"""
					_a      :       Tuple   =						False
					_a      :       str   =						False
					_a      :       Any   =						False
					_a      :       Dict   =						False
					_a      :       Tuple   =						0
					_a      :       List[str]   =						0

					_a      :       List[Any]   =						[]
					for line in lines:
										if line.startswith(__a      ):
															_a      :       Tuple   =						True
										elif in_class and line.startswith(__a      ):
															_a      :       List[str]   =						True
										elif in_class and in_func and (line.startswith(__a      ) or line.startswith(__a      )):
															_a      :       Tuple   =						len(line.split(correct_line.split()[0]      )[0]      )
															count += 1

															if count == done_test[_id]:
																				_a      :       Dict   =						True

										if in_class and in_func and in_line:
															if ")" not in line:
																				continue
															else:
																				_a      :       Optional[Any]   =						True

										if in_class and in_func and in_line and insert_line:
															new_lines.append(F"""{spaces * ' '}{correct_line}"""      )
															_a      :       Optional[Any]   =						False
										else:
															new_lines.append(__a      )

					with open(__a						,'''w'''      ) as f:
										for line in new_lines:
															f.write(__a      )



def   __UpperCAmelCase					(      __a :     Dict						,__a :     Tuple=None      )     ->	Union[str, Any]:

					"""simple docstring"""

					if fail is not None:
										with open(__a						,'''r'''      ) as f:
															_a      :       Optional[int]   =						{l.strip() for l in f.readlines()}
					else:
										_a      :       List[Any]   =						None

					with open(__a						,'''r'''      ) as f:
										_a      :       List[Any]   =						f.readlines()

					_a      :       List[Any]   =						defaultdict(__a      )
					for line in correct_lines:
										_a ,   _a ,   _a ,   _a      :       Dict   =						line.split(''';'''      )
										if test_failures is None or "::".join([file, class_name, test_name]      ) in test_failures:
															overwrite_file(__a						,__a						,__a						,__a						,__a      )


if __name__ == "__main__":
			a__												=						argparse.ArgumentParser()
			parser.add_argument('''--correct_filename''', help='''filename of tests with expected result''')
			parser.add_argument('''--fail_filename''', help='''filename of test failures''', type=str, default=None)
			a__												=						parser.parse_args()

			main(args.correct_filename, args.fail_filename)







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


lowerCAmelCase__    :int       =				logging.get_logger(__name__)

def 			lowerCAmelCase__					(     a__:						Dict			)    ->						List[str]:


							'''simple docstring'''





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

def 			lowerCAmelCase__					(     a__:						Optional[int]     ,    a__:						Dict     ,    a__:						List[Any]			)    ->						str:


							'''simple docstring'''





							_UpperCAmelCase											=     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)
							):
														_UpperCAmelCase											=     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:
														_UpperCAmelCase											=     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:
														_UpperCAmelCase											=     pt_tuple_key[:-1] + ('embedding',)
														return renamed_pt_tuple_key, pt_tensor

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

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

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

							# old PyTorch layer norm bias
							_UpperCAmelCase											=     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 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Optional[Any]     ,    a__:						int=4_2			)    ->						List[str]:


							'''simple docstring'''





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

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

							_UpperCAmelCase											=     flatten_dict(a__			)
							_UpperCAmelCase											=     {}

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

														# Correctly rename weight parameters
														_UpperCAmelCase						,			_UpperCAmelCase											=     rename_key_and_reshape_tensor(a__     ,    a__     ,    a__			)

														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
														_UpperCAmelCase											=     jnp.asarray(a__			)

							return unflatten_dict(a__			)
