





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


_UpperCamelCase							=			logging.get_logger(__name__)







def    lowerCAmelCase__(						lowercase							:     Union[str, Any] ,  lowercase							:     Optional[Any]					)			->     Tuple:


  try:
    with open(lowercase ,  "rb"					) as flax_state_f:
      __snake_case :					Any          =							from_bytes(lowercase ,  flax_state_f.read()					)
  except UnpicklingError as e:
    try:
      with open(lowercase					) 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(lowercase ,  lowercase					)







def    lowerCAmelCase__(						lowercase							:     Any ,  lowercase							:     int					)			->     List[Any]:


  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
  __snake_case :					Any          =							flatten_dict(jax.tree_util.tree_map(lambda lowercase					: x.dtype == jnp.bfloataa ,  lowercase					)					).values()
  if any(lowercase					):
    # 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."					)
    __snake_case :					List[str]          =							jax.tree_util.tree_map(
        lambda lowercase					: params.astype(np.floataa					) if params.dtype == jnp.bfloataa else params ,  lowercase					)

  __snake_case :					List[str]          =							""

  __snake_case :					int          =							flatten_dict(lowercase ,  sep="."					)
  __snake_case :					str          =							pt_model.state_dict()

  # keep track of unexpected & missing keys
  __snake_case :					List[Any]          =							[]
  __snake_case :					int          =							set(pt_model_dict.keys()					)

  for flax_key_tuple, flax_tensor in flax_state_dict.items():
    __snake_case :					Any          =							flax_key_tuple.split("."					)

    if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
      __snake_case :					List[Any]          =							flax_key_tuple_array[:-1] + ["weight"]
      __snake_case :					Union[str, Any]          =							jnp.transpose(lowercase ,  (3, 2, 0, 1)					)
    elif flax_key_tuple_array[-1] == "kernel":
      __snake_case :					int          =							flax_key_tuple_array[:-1] + ["weight"]
      __snake_case :					List[Any]          =							flax_tensor.T
    elif flax_key_tuple_array[-1] == "scale":
      __snake_case :					Optional[int]          =							flax_key_tuple_array[:-1] + ["weight"]

    if "time_embedding" not in flax_key_tuple_array:
      for i, flax_key_tuple_string in enumerate(lowercase					):
        __snake_case :					Dict          =							(
            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"					)
        )

    __snake_case :					str          =							".".join(lowercase					)

    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
        __snake_case :					Tuple          =							np.asarray(lowercase					) if not isinstance(lowercase ,  np.ndarray					) else flax_tensor
        __snake_case :					List[Any]          =							torch.from_numpy(lowercase					)
        # remove from missing keys
        missing_keys.remove(lowercase					)
    else:
      # weight is not expected by PyTorch model
      unexpected_keys.append(lowercase					)

  pt_model.load_state_dict(lowercase					)

  # re-transform missing_keys to list
  __snake_case :					Optional[Any]          =							list(lowercase					)

  if len(lowercase					) > 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(lowercase					) > 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 requests
def 						snake_case_       (UpperCamelCase			:    str					,		UpperCamelCase			:    str ):





							'''simple docstring'''


							_a      	=							{'''Content-Type''': '''application/json'''}
							_a      	=							requests.post(UpperCamelCase					,		json={'''text''': message_body}					,		headers=UpperCamelCase )
							if response.status_code != 200:
														_a      	=							(
														    '''Request to slack returned an error '''
														    f'{response.status_code}, the response is:\n{response.text}'
														)
														raise ValueError(UpperCamelCase )


if __name__ == "__main__":
				# Set the slack url to the one provided by Slack when you create the webhook at
				# https://my.slack.com/services/new/incoming-webhook/
				send_slack_message('<YOUR MESSAGE BODY>', '<SLACK CHANNEL URL>')

