
'''simple docstring'''
import os

try:
  from .build_directory_md import good_file_paths
except ImportError:
  from build_directory_md import good_file_paths  # type: ignore

a_											=						list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

a_											=						[file for file in filepaths if file != file.lower()]
if upper_files:
  print(f"""{len(upper_files)} files contain uppercase characters:""")
  print("\n".join(upper_files) + "\n")

a_											=						[file for file in filepaths if " " in file]
if space_files:
  print(f"""{len(space_files)} files contain space characters:""")
  print("\n".join(space_files) + "\n")

a_											=						[file for file in filepaths if "-" in file]
if hyphen_files:
  print(f"""{len(hyphen_files)} files contain hyphen characters:""")
  print("\n".join(hyphen_files) + "\n")

a_											=						[file for file in filepaths if os.sep not in file]
if nodir_files:
  print(f"""{len(nodir_files)} files are not in a directory:""")
  print("\n".join(nodir_files) + "\n")

a_											=						len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
  import sys

  sys.exit(bad_files)


'''simple docstring'''
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


a_											=						logging.get_logger(__name__)

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						r"\w+[.]\d+"
    snake_case_ :    Tuple     			=						re.findall(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    for pat in pats:
        snake_case_ :    Union[str, Any]     			=						key.replace(__SCREAMING_SNAKE_CASE,		"_".join(pat.split("."       )       )       )
    return key

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Tuple     			=						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)
    ):
        snake_case_ :    int     			=						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:
        snake_case_ :    Dict     			=						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:
        snake_case_ :    List[Any]     			=						pt_tuple_key[:-1] + ("embedding",)
        return renamed_pt_tuple_key, pt_tensor

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

    # linear layer
    snake_case_ :    str     			=						pt_tuple_key[:-1] + ("kernel",)
    if pt_tuple_key[-1] == "weight":
        snake_case_ :    Any     			=						pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    snake_case_ :    Union[str, Any]     			=						pt_tuple_key[:-1] + ("weight",)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    snake_case_ :    Union[str, Any]     			=						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 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=4_2       ):
    """simple docstring"""






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

    # Step 2: Since the model is stateless, get random Flax params
    snake_case_ :    Optional[int]     			=						flax_model.init_weights(PRNGKey(__SCREAMING_SNAKE_CASE       )       )

    snake_case_ :    Union[str, Any]     			=						flatten_dict(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    str     			=						{}

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        snake_case_ :    List[str]     			=						rename_key(__SCREAMING_SNAKE_CASE       )
        snake_case_ :    Tuple     			=						tuple(renamed_pt_key.split("."       )       )

        # Correctly rename weight parameters
        snake_case_			, snake_case_ :    Dict     			=						rename_key_and_reshape_tensor(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

        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
        snake_case_ :    Any     			=						jnp.asarray(__SCREAMING_SNAKE_CASE       )

    return unflatten_dict(__SCREAMING_SNAKE_CASE       )




