




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


lowerCamelCase__					:  List[str]      							=				logging.get_logger(__name__)

def    UpperCAmelCase_				( __UpperCAmelCase		:      Optional[Any]     ,     __UpperCAmelCase		:      Union[str, Any]				)   ->					Union[str, Any]:
      try:
            with open(__UpperCAmelCase     ,     'rb'				) as flax_state_f:
                  SCREAMING_SNAKE_CASE_      		=     from_bytes(__UpperCAmelCase     ,     flax_state_f.read()				)
      except UnpicklingError as e:
            try:
                  with open(__UpperCAmelCase				) 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(__UpperCAmelCase     ,     __UpperCAmelCase				)





def    UpperCAmelCase_				( __UpperCAmelCase		:      str     ,     __UpperCAmelCase		:      Optional[int]				)   ->					Dict:

      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
      SCREAMING_SNAKE_CASE_      		=     flatten_dict(jax.tree_util.tree_map(lambda __UpperCAmelCase				: x.dtype == jnp.bfloataa     ,     __UpperCAmelCase				)				).values()
      if any(__UpperCAmelCase				):
            # 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.'				)
            SCREAMING_SNAKE_CASE_      		=     jax.tree_util.tree_map(
                lambda __UpperCAmelCase				: params.astype(np.floataa				) if params.dtype == jnp.bfloataa else params     ,     __UpperCAmelCase				)

      SCREAMING_SNAKE_CASE_      		=     ''

      SCREAMING_SNAKE_CASE_      		=     flatten_dict(__UpperCAmelCase     ,     sep='.'				)
      SCREAMING_SNAKE_CASE_      		=     pt_model.state_dict()

      # keep track of unexpected & missing keys
      SCREAMING_SNAKE_CASE_      		=     []
      SCREAMING_SNAKE_CASE_      		=     set(pt_model_dict.keys()				)

      for flax_key_tuple, flax_tensor in flax_state_dict.items():
            SCREAMING_SNAKE_CASE_      		=     flax_key_tuple.split('.'				)

            if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
                  SCREAMING_SNAKE_CASE_      		=     flax_key_tuple_array[:-1] + ['weight']
                  SCREAMING_SNAKE_CASE_      		=     jnp.transpose(__UpperCAmelCase     ,     (3, 2, 0, 1)				)
            elif flax_key_tuple_array[-1] == "kernel":
                  SCREAMING_SNAKE_CASE_      		=     flax_key_tuple_array[:-1] + ['weight']
                  SCREAMING_SNAKE_CASE_      		=     flax_tensor.T
            elif flax_key_tuple_array[-1] == "scale":
                  SCREAMING_SNAKE_CASE_      		=     flax_key_tuple_array[:-1] + ['weight']

            if "time_embedding" not in flax_key_tuple_array:
                  for i, flax_key_tuple_string in enumerate(__UpperCAmelCase				):
                        SCREAMING_SNAKE_CASE_      		=     (
                            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'				)
                        )

            SCREAMING_SNAKE_CASE_      		=     '.'.join(__UpperCAmelCase				)

            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
                        SCREAMING_SNAKE_CASE_      		=     np.asarray(__UpperCAmelCase				) if not isinstance(__UpperCAmelCase     ,     np.ndarray				) else flax_tensor
                        SCREAMING_SNAKE_CASE_      		=     torch.from_numpy(__UpperCAmelCase				)
                        # remove from missing keys
                        missing_keys.remove(__UpperCAmelCase				)
            else:
                  # weight is not expected by PyTorch model
                  unexpected_keys.append(__UpperCAmelCase				)

      pt_model.load_state_dict(__UpperCAmelCase				)

      # re-transform missing_keys to list
      SCREAMING_SNAKE_CASE_      		=     list(__UpperCAmelCase				)

      if len(__UpperCAmelCase				) > 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(__UpperCAmelCase				) > 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 argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


A		=						logging.get_logger(__name__)

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							List[Any] ):
						"""simple docstring"""






						snake_case   :			Optional[Any]						    = set()
						snake_case   :			Optional[int]						    = []

						def parse_line(lowerCamelCase_:							Union[str, Any] ):
												for line in fp:
																		if isinstance(lowerCamelCase_     ,		lowerCamelCase_ ):
																								snake_case   :			Optional[Any]						    = line.decode("UTF-8" )
																		if "warnings summary (final)" in line:
																								continue
																		# This means we are outside the body of a warning
																		elif not line.startswith(" " ):
																								# process a single warning and move it to `selected_warnings`.
																								if len(lowerCamelCase_ ) > 0:
																														snake_case   :			str						    = "\n".join(lowerCamelCase_ )
																														# Only keep the warnings specified in `targets`
																														if any(f''': {x}: ''' in warning for x in targets ):
																																				selected_warnings.add(lowerCamelCase_ )
																														buffer.clear()
																								continue
																		else:
																								snake_case   :			Dict						    = line.strip()
																								buffer.append(lowerCamelCase_ )

						if from_gh:
												for filename in os.listdir(lowerCamelCase_ ):
																		snake_case   :			Tuple						    = os.path.join(lowerCamelCase_     ,		lowerCamelCase_ )
																		if not os.path.isdir(lowerCamelCase_ ):
																								# read the file
																								if filename != "warnings.txt":
																														continue
																								with open(lowerCamelCase_ ) as fp:
																														parse_line(lowerCamelCase_ )
						else:
												try:
																		with zipfile.ZipFile(lowerCamelCase_ ) as z:
																								for filename in z.namelist():
																														if not os.path.isdir(lowerCamelCase_ ):
																																				# read the file
																																				if filename != "warnings.txt":
																																										continue
																																				with z.open(lowerCamelCase_ ) as fp:
																																										parse_line(lowerCamelCase_ )
												except Exception:
																		logger.warning(
																		    f'''{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.''' )

						return selected_warnings

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Union[str, Any]     ,		lowerCamelCase_:							Any ):
						"""simple docstring"""






						snake_case   :			Dict						    = set()

						snake_case   :			str						    = [os.path.join(lowerCamelCase_     ,		lowerCamelCase_ ) for p in os.listdir(lowerCamelCase_ ) if (p.endswith(".zip" ) or from_gh)]
						for p in paths:
												selected_warnings.update(extract_warnings_from_single_artifact(lowerCamelCase_     ,		lowerCamelCase_ ) )

						return selected_warnings


if __name__ == "__main__":

			def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Dict ):
									"""simple docstring"""






									return values.split("," )

			A		=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
			parser.add_argument(
			    '--output_dir',
			    type=str,
			    required=True,
			    help='Where to store the downloaded artifacts and other result files.',
			)
			parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.')
			# optional parameters
			parser.add_argument(
			    '--targets',
			    default='DeprecationWarning,UserWarning,FutureWarning',
			    type=list_str,
			    help='Comma-separated list of target warning(s) which we want to extract.',
			)
			parser.add_argument(
			    '--from_gh',
			    action='store_true',
			    help='If running from a GitHub action workflow and collecting warnings from its artifacts.',
			)

			A		=						parser.parse_args()

			A		=						args.from_gh
			if from_gh:
						# The artifacts have to be downloaded using `actions/download-artifact@v3`
						pass
			else:
						os.makedirs(args.output_dir, exist_ok=True)

						# get download links
						A		=						get_artifacts_links(args.workflow_run_id, token=args.token)
						with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp:
									json.dump(artifacts, fp, ensure_ascii=False, indent=4)

						# download artifacts
						for idx, (name, url) in enumerate(artifacts.items()):
									print(name)
									print(url)
									print('=' * 8_0)
									download_artifact(name, url, args.output_dir, args.token)
									# Be gentle to GitHub
									time.sleep(1)

    # extract warnings from artifacts
			A		=						extract_warnings(args.output_dir, args.targets)
			A		=						sorted(selected_warnings)
			with open(os.path.join(args.output_dir, 'selected_warnings.json'), 'w', encoding='UTF-8') as fp:
						json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)



