

'''simple docstring'''


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 	_a       (				__lowerCAmelCase		:      Any   ,							__lowerCAmelCase		:      Any     ):

		"""simple docstring"""

		try:
				with open(__lowerCAmelCase   ,							'''rb'''     ) as flax_state_f:
						snake_case__						:       Any       						=  from_bytes(__lowerCAmelCase   ,							flax_state_f.read()     )
		except UnpicklingError as e:
				try:
						with open(__lowerCAmelCase     ) 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(__lowerCAmelCase   ,							__lowerCAmelCase     )





def 	_a       (				__lowerCAmelCase		:      Union[str, Any]   ,							__lowerCAmelCase		:      List[Any]     ):

		"""simple docstring"""

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

		snake_case__						:       int       						=  ''''''

		snake_case__						:       Tuple       						=  flatten_dict(__lowerCAmelCase   ,							sep='''.'''     )
		snake_case__						:       int       						=  pt_model.state_dict()

		# keep track of unexpected & missing keys
		snake_case__						:       Dict       						=  []
		snake_case__						:       Optional[Any]       						=  set(pt_model_dict.keys()     )

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

				if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
						snake_case__						:       Optional[Any]       						=  flax_key_tuple_array[:-1] + ['''weight''']
						snake_case__						:       Any       						=  jnp.transpose(__lowerCAmelCase   ,							(3, 2, 0, 1)     )
				elif flax_key_tuple_array[-1] == "kernel":
						snake_case__						:       str       						=  flax_key_tuple_array[:-1] + ['''weight''']
						snake_case__						:       List[Any]       						=  flax_tensor.T
				elif flax_key_tuple_array[-1] == "scale":
						snake_case__						:       Dict       						=  flax_key_tuple_array[:-1] + ['''weight''']

				if "time_embedding" not in flax_key_tuple_array:
						for i, flax_key_tuple_string in enumerate(__lowerCAmelCase     ):
								snake_case__						:       Optional[int]       						=  (
								    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__						:       int       						=  '''.'''.join(__lowerCAmelCase     )

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

		pt_model.load_state_dict(__lowerCAmelCase     )

		# re-transform missing_keys to list
		snake_case__						:       int       						=  list(__lowerCAmelCase     )

		if len(__lowerCAmelCase     ) > 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(__lowerCAmelCase     ) > 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

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()




def 	_a       (				__lowerCAmelCase		:      Union[str, Any]   ,							__lowerCAmelCase		:      Any   ,							__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      List[Any]     ):

		"""simple docstring"""

		snake_case__						:       Any       						=  BigBirdConfig.from_json_file(__lowerCAmelCase     )
		print(F"""Building PyTorch model from configuration: {config}"""     )

		if is_trivia_qa:
				snake_case__						:       str       						=  BigBirdForQuestionAnswering(__lowerCAmelCase     )
		else:
				snake_case__						:       Tuple       						=  BigBirdForPreTraining(__lowerCAmelCase     )

		# Load weights from tf checkpoint
		load_tf_weights_in_big_bird(__lowerCAmelCase   ,							__lowerCAmelCase   ,							is_trivia_qa=__lowerCAmelCase     )

		# Save pytorch-model
		print(F"""Save PyTorch model to {pytorch_dump_path}"""     )
		model.save_pretrained(__lowerCAmelCase     )


if __name__ == "__main__":
					lowerCAmelCase__						:       Union[str, Any]										=						argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
					)
					parser.add_argument(
					    """--big_bird_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained BERT model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					parser.add_argument(
					    """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
					)
					lowerCAmelCase__						:       Optional[int]										=						parser.parse_args()
					convert_tf_checkpoint_to_pytorch(
					    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
					)



