
"""simple docstring"""



from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_snake_case        =    logging.get_logger(__name__)






def 						__snake_case	(     SCREAMING_SNAKE_CASE:       Union[tf.Tensor, np.ndarray]    ):






							"""simple docstring"""



							if isinstance(UpperCamelCase__  ,	np.ndarray    ):
														return list(tensor.shape    )

							_lowerCAmelCase							=      tf.shape(UpperCamelCase__    )

							if tensor.shape == tf.TensorShape(UpperCamelCase__    ):
														return dynamic

							_lowerCAmelCase							=      tensor.shape.as_list()

							return [dynamic[i] if s is None else s for i, s in enumerate(UpperCamelCase__    )]






def 						__snake_case	(     SCREAMING_SNAKE_CASE:       tf.Tensor  ,	SCREAMING_SNAKE_CASE:       Optional[int] = None  ,	SCREAMING_SNAKE_CASE:       Optional[str] = None    ):






							"""simple docstring"""



							return tf.nn.softmax(logits=logits + 1e-9  ,	axis=UpperCamelCase__  ,	name=UpperCamelCase__    )






def 						__snake_case	(     SCREAMING_SNAKE_CASE:       Optional[int]  ,	SCREAMING_SNAKE_CASE:       Dict  ,	SCREAMING_SNAKE_CASE:       Union[str, Any]  ,	SCREAMING_SNAKE_CASE:       Tuple=1e-5  ,	SCREAMING_SNAKE_CASE:       Optional[int]=-1    ):






							"""simple docstring"""



							if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(UpperCamelCase__  ,	UpperCamelCase__    ):
														raise NotImplementedError('Only 1D weight and bias tensors are supported for now, with only a single axis.'    )

							# Get mean and variance on the axis to be normalized
							_lowerCAmelCase						,		_lowerCAmelCase							=      tf.nn.moments(UpperCamelCase__  ,	axes=[axis]  ,	keepdims=UpperCamelCase__    )

							if axis != -1:
														# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
														# on every dimension except axis
														_lowerCAmelCase							=      [1] * inputs.shape.rank
														_lowerCAmelCase							=      shape_list(UpperCamelCase__    )[axis]
														_lowerCAmelCase							=      tf.reshape(UpperCamelCase__  ,	UpperCamelCase__    )
														_lowerCAmelCase							=      tf.reshape(UpperCamelCase__  ,	UpperCamelCase__    )

							# Compute layer normalization using the batch_normalization
							# function.
							_lowerCAmelCase							=      tf.nn.batch_normalization(
							    UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	offset=UpperCamelCase__  ,	scale=UpperCamelCase__  ,	variance_epsilon=UpperCamelCase__  ,	)
							return outputs






def 						__snake_case	(     SCREAMING_SNAKE_CASE:       List[str]  ,	SCREAMING_SNAKE_CASE:       List[str]=0  ,	SCREAMING_SNAKE_CASE:       Dict=-1    ):






							"""simple docstring"""



							if end_dim < 0:
														end_dim += input.shape.rank
							if start_dim < 0:
														start_dim += input.shape.rank

							if start_dim == end_dim:
														return input

							_lowerCAmelCase							=      tf.shape(UpperCamelCase__    )
							_lowerCAmelCase							=      tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]    )
							_lowerCAmelCase							=      tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]  ,	axis=0    )
							return tf.reshape(UpperCamelCase__  ,	UpperCamelCase__    )






def 						__snake_case	(     SCREAMING_SNAKE_CASE:       tf.Tensor    ):






							"""simple docstring"""



							if not isinstance(UpperCamelCase__  ,	tf.Tensor    ):
														_lowerCAmelCase							=      tf.convert_to_tensor(UpperCamelCase__    )  # Catches stray NumPy inputs
							if encoder_attention_mask.shape.rank == 3:
														_lowerCAmelCase							=      encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.shape.rank == 2:
														_lowerCAmelCase							=      encoder_attention_mask[:, None, None, :]
							# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
							# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
							# /transformer/transformer_layers.py#L270
							# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
							# encoder_extended_attention_mask.transpose(-1, -2))
							_lowerCAmelCase							=      (
							    tf.cast(1  ,	encoder_attention_mask.dtype    ) - encoder_extended_attention_mask
							) * encoder_extended_attention_mask.dtype.min

							return encoder_extended_attention_mask






def 						__snake_case	(     SCREAMING_SNAKE_CASE:       tf.Tensor  ,	SCREAMING_SNAKE_CASE:       int  ,	SCREAMING_SNAKE_CASE:       str = "input_ids"    ):






							"""simple docstring"""



							tf.debugging.assert_less(
							    UpperCamelCase__  ,	tf.cast(UpperCamelCase__  ,	dtype=tensor.dtype    )  ,	message=(
							        f"""The maximum value of {tensor_name} ({tf.math.reduce_max(UpperCamelCase__    )}) must be smaller than the embedding """
							        f"""layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."""
							    )  ,	)






def 						__snake_case	(     SCREAMING_SNAKE_CASE:       Union[str, Any]  ,	SCREAMING_SNAKE_CASE:       Union[str, Any]  ,	SCREAMING_SNAKE_CASE:       Optional[int]    ):






							"""simple docstring"""



							_lowerCAmelCase							=      6_4512
							# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
							# because in that case even chunking the array would not make the saving
							# possible.
							_lowerCAmelCase							=      [x for x in data if len(UpperCamelCase__    ) > HDF5_OBJECT_HEADER_LIMIT]

							# Expecting this to never be true.
							if bad_attributes:
														raise RuntimeError(
														    'The following attributes cannot be saved to HDF5 file because '
														    f"""they are larger than {HDF5_OBJECT_HEADER_LIMIT} """
														    f"""bytes: {bad_attributes}"""    )

							_lowerCAmelCase							=      np.asarray(UpperCamelCase__    )

							_lowerCAmelCase							=      1
							_lowerCAmelCase							=      np.array_split(UpperCamelCase__  ,	UpperCamelCase__    )

							# This will never loop forever thanks to the test above.
							while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data    ):
														num_chunks += 1
														_lowerCAmelCase							=      np.array_split(UpperCamelCase__  ,	UpperCamelCase__    )

							if num_chunks > 1:
														for chunk_id, chunk_data in enumerate(UpperCamelCase__    ):
																					_lowerCAmelCase							=      chunk_data
							else:
														_lowerCAmelCase							=      data






def 						__snake_case	(     SCREAMING_SNAKE_CASE:       Union[str, Any]  ,	SCREAMING_SNAKE_CASE:       Dict    ):






							"""simple docstring"""



							if name in group.attrs:
														_lowerCAmelCase							=      [n.decode('utf8'    ) if hasattr(UpperCamelCase__  ,	'decode'    ) else n for n in group.attrs[name]]
							else:
														_lowerCAmelCase							=      []
														_lowerCAmelCase							=      0
														while "%s%d" % (name, chunk_id) in group.attrs:
																					data.extend(
																					    [n.decode('utf8'    ) if hasattr(UpperCamelCase__  ,	'decode'    ) else n for n in group.attrs['%s%d' % (name, chunk_id)]]    )
																					chunk_id += 1
							return data






def 						__snake_case	(     SCREAMING_SNAKE_CASE:       List[Any]    ):






							"""simple docstring"""



							def _expand_single_ad_tensor(SCREAMING_SNAKE_CASE:       Optional[Any]    ):
														if isinstance(UpperCamelCase__  ,	tf.Tensor    ) and t.shape.rank == 1:
																					return tf.expand_dims(UpperCamelCase__  ,	axis=-1    )
														return t

							return tf.nest.map_structure(_expand_single_ad_tensor  ,	UpperCamelCase__    )













import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()


def       UpperCamelCase__(			UpperCamelCase__     :			Any					,	UpperCamelCase__     :			int					,	UpperCamelCase__     :			List[Any]    )->Optional[int]:
       # Initialise PyTorch model
       A__		   =		RemBertConfig.from_json_file(UpperCamelCase__    )
       print('''Building PyTorch model from configuration: {}'''.format(str(UpperCamelCase__    )    )    )
       A__		   =		RemBertModel(UpperCamelCase__    )

       # Load weights from tf checkpoint
       load_tf_weights_in_rembert(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__    )

       # Save pytorch-model
       print('''Save PyTorch model to {}'''.format(UpperCamelCase__    )    )
       torch.save(model.state_dict()					,	UpperCamelCase__    )


if __name__ == "__main__":
       a__: List[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(
           '--rembert_config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained RemBERT 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.'
       )
       a__: Union[str, Any]							   =						parser.parse_args()
       convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)





