






import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
a__       =     [
    """kernels/rwkv/wkv_cuda.cu""",
    """kernels/rwkv/wkv_op.cpp""",
    """kernels/deformable_detr/ms_deform_attn.h""",
    """kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh""",
    """models/graphormer/algos_graphormer.pyx""",
]

def _UpperCAmelCase   (	a						:	Optional[Any] ):
							# Test all the extensions added in the setup
							for file in FILES_TO_FIND:
														if not (transformers_path / file).exists():
																					return False
							return True


if __name__ == "__main__":
	a__       =     argparse.ArgumentParser()
	parser.add_argument("""--check_lib""", action="""store_true""", help="""Whether to check the build or the actual package.""")
	a__       =     parser.parse_args()
	if args.check_lib:
		a__       =     importlib.import_module("""transformers""")
		a__       =     Path(transformers_module.__file__).parent
	else:
		a__       =     Path.cwd() / """build/lib/transformers"""
	if not test_custom_files_are_present(transformers_path):
		raise ValueError("""The built release does not contain the custom files. Fix this before going further!""")








from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


a__       =     logging.get_logger(__name__)

def _UpperCAmelCase   (	a						:	Union[tf.Tensor, np.ndarray] ):
							if isinstance(a  ,					np.ndarray ):
														return list(tensor.shape )

							snake_case__	  =       tf.shape(a )

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

							snake_case__	  =       tensor.shape.as_list()

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

def _UpperCAmelCase   (	a						:	tf.Tensor  ,					a						:	Optional[int] = None  ,					a						:	Optional[str] = None ):
							return tf.nn.softmax(logits=logits + 1e-9  ,					axis=a  ,					name=a )

def _UpperCAmelCase   (	a						:	Optional[int]  ,					a						:	Union[str, Any]  ,					a						:	Dict  ,					a						:	int=1e-5  ,					a						:	Dict=-1 ):
							# This is a very simplified functional layernorm, designed to duplicate
							# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
							# models in Transformers.

							if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(a  ,					a ):
														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
							snake_case__  , snake_case__	  =       tf.nn.moments(a  ,					axes=[axis]  ,					keepdims=a )

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

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

def _UpperCAmelCase   (	a						:	Optional[int]  ,					a						:	Dict=0  ,					a						:	List[Any]=-1 ):
							# Replicates the behavior of torch.flatten in TF

							# If end_dim or start_dim is negative, count them from the end
							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

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

def _UpperCAmelCase   (	a						:	tf.Tensor ):
							if not isinstance(a  ,					tf.Tensor ):
														snake_case__	  =       tf.convert_to_tensor(a )  # Catches stray NumPy inputs
							if encoder_attention_mask.shape.rank == 3:
														snake_case__	  =       encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.shape.rank == 2:
														snake_case__	  =       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))
							snake_case__	  =       (
							    tf.cast(1  ,					encoder_attention_mask.dtype ) - encoder_extended_attention_mask
							) * encoder_extended_attention_mask.dtype.min

							return encoder_extended_attention_mask

def _UpperCAmelCase   (	a						:	tf.Tensor  ,					a						:	int  ,					a						:	str = "input_ids" ):
							tf.debugging.assert_less(
							    a  ,					tf.cast(a  ,					dtype=tensor.dtype )  ,					message=(
							        F'''The maximum value of {tensor_name} ({tf.math.reduce_max(a )}) must be smaller than the embedding '''
							        F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
							    )  ,					)

def _UpperCAmelCase   (	a						:	str  ,					a						:	Tuple  ,					a						:	Optional[int] ):
							snake_case__	  =       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.
							snake_case__	  =       [x for x in data if len(a ) > 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}''' )

							snake_case__	  =       np.asarray(a )

							snake_case__	  =       1
							snake_case__	  =       np.array_split(a  ,					a )

							# 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
														snake_case__	  =       np.array_split(a  ,					a )

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

def _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	Optional[int] ):
							if name in group.attrs:
														snake_case__	  =       [n.decode("""utf8""" ) if hasattr(a  ,					"""decode""" ) else n for n in group.attrs[name]]
							else:
														snake_case__	  =       []
														snake_case__	  =       0
														while "%s%d" % (name, chunk_id) in group.attrs:
																					data.extend(
																					    [n.decode("""utf8""" ) if hasattr(a  ,					"""decode""" ) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]] )
																					chunk_id += 1
							return data



def _UpperCAmelCase   (	a						:	Optional[int] ):

							def _expand_single_ad_tensor(a						:	Any ):
														if isinstance(a  ,					tf.Tensor ) and t.shape.rank == 1:
																					return tf.expand_dims(a  ,					axis=-1 )
														return t

							return tf.nest.map_structure(_expand_single_ad_tensor  ,					a )
