



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_		:     Union[str, Any]        =     logging.get_logger(__name__)


def 	UpperCAmelCase__      (      UpperCAmelCase__ :int					):






 '''simple docstring'''

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


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Union[str, Any] ,					UpperCAmelCase__ :Union[str, Any] ,					UpperCAmelCase__ :Tuple					):






 '''simple docstring'''

 a						=		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)
 ):
  a						=		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:
  a						=		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:
  a						=		pt_tuple_key[:-1] + ("embedding",)
  return renamed_pt_tuple_key, pt_tensor

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

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

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

 # old PyTorch layer norm bias
 a						=		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__      (      UpperCAmelCase__ :int ,					UpperCAmelCase__ :Union[str, Any] ,					UpperCAmelCase__ :Tuple=42					):






 '''simple docstring'''

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

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

 a						=		flatten_dict(__lowerCAmelCase					)
 a						=		{}

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

  # Correctly rename weight parameters
  a		,  a						=		rename_key_and_reshape_tensor(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					)

  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
  a						=		jnp.asarray(__lowerCAmelCase					)

 return unflatten_dict(__lowerCAmelCase					)






import re
from pathlib import Path
from unittest import TestCase

import pytest



@pytest.mark.integration
class 		_lowercase		(       UpperCAmelCase__	):

   def 		A							(      self			:       Optional[int]							,   __lowerCAmelCase			:       str							)       ->				Union[str, Any]:


    """simple docstring"""






    with open(__lowerCAmelCase							,   encoding="utf-8"							) as input_file:
     a						=		re.compile(R"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)"							)
     a						=		input_file.read()
     a						=		regexp.search(__lowerCAmelCase							)

    return match

   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       str							)       ->				Dict:


    """simple docstring"""






    with open(__lowerCAmelCase							,   encoding="utf-8"							) as input_file:
     a						=		re.compile(R"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()"							,   re.DOTALL							)
     a						=		input_file.read()
     # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
     a						=		regexp.finditer(__lowerCAmelCase							)

    a						=		[match for match in matches if match is not None and match.group(1							) is not None]
    return matches[0] if matches else None

   def 		A							(      self			:       List[str]							)       ->				List[Any]:


    """simple docstring"""






    a						=		Path("./datasets"							)
    a						=		list(dataset_paths.absolute().glob("**/*.py"							)							)

    for dataset in dataset_files:
     if self._no_encoding_on_file_open(str(__lowerCAmelCase							)							):
      raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}"""							)




   def 		A							(      self			:       Tuple							)       ->				Union[str, Any]:


    """simple docstring"""






    a						=		Path("./datasets"							)
    a						=		list(dataset_paths.absolute().glob("**/*.py"							)							)

    for dataset in dataset_files:
     if self._no_print_statements(str(__lowerCAmelCase							)							):
      raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead."""							)

