from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


SCREAMING_SNAKE_CASE__		:					int						=					logging.get_logger(__name__)







def     _a				(      lowercase__  :			int ):







					'''simple docstring'''


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

					SCREAMING_SNAKE_CASE__				:				Optional[int]			=		tf.shape(lowercase__ )

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

					SCREAMING_SNAKE_CASE__				:				List[Any]			=		tensor.shape.as_list()

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







def     _a				(      lowercase__  :			str     ,  lowercase__  :			int = None     ,  lowercase__  :			str = None ):







					'''simple docstring'''


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







def     _a				(      lowercase__  :			List[str]     ,  lowercase__  :			Any     ,  lowercase__  :			List[str]     ,  lowercase__  :			List[Any]=1E-5     ,  lowercase__  :			Dict=-1 ):







					'''simple docstring'''


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

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

					# Compute layer normalization using the batch_normalization
					# function.
					SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		tf.nn.batch_normalization(
					    lowercase__     ,  lowercase__     ,  lowercase__     ,  offset=lowercase__     ,  scale=lowercase__     ,  variance_epsilon=lowercase__     ,  )
					return outputs







def     _a				(      lowercase__  :			Dict     ,  lowercase__  :			int=0     ,  lowercase__  :			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

					SCREAMING_SNAKE_CASE__				:				Dict			=		tf.shape(lowercase__ )
					SCREAMING_SNAKE_CASE__				:				List[Any]			=		tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
					SCREAMING_SNAKE_CASE__				:				Optional[int]			=		tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]     ,  axis=0 )
					return tf.reshape(lowercase__     ,  lowercase__ )







def     _a				(      lowercase__  :			Dict ):







					'''simple docstring'''


					if not isinstance(lowercase__     ,  tf.Tensor ):
										SCREAMING_SNAKE_CASE__				:				Tuple			=		tf.convert_to_tensor(lowercase__ )  # Catches stray NumPy inputs
					if encoder_attention_mask.shape.rank == 3:
										SCREAMING_SNAKE_CASE__				:				Dict			=		encoder_attention_mask[:, None, :, :]
					if encoder_attention_mask.shape.rank == 2:
										SCREAMING_SNAKE_CASE__				:				Optional[int]			=		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))
					SCREAMING_SNAKE_CASE__				:				int			=		(
					    tf.cast(1     ,  encoder_attention_mask.dtype ) - encoder_extended_attention_mask
					) * encoder_extended_attention_mask.dtype.min

					return encoder_extended_attention_mask







def     _a				(      lowercase__  :			Union[str, Any]     ,  lowercase__  :			Any     ,  lowercase__  :			List[str] = "input_ids" ):







					'''simple docstring'''


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







def     _a				(      lowercase__  :			Optional[Any]     ,  lowercase__  :			Dict     ,  lowercase__  :			Tuple ):







					'''simple docstring'''


					SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		6_45_12
					# 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.
					SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[x for x in data if len(lowercase__ ) > 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}''' )

					SCREAMING_SNAKE_CASE__				:				Dict			=		np.asarray(lowercase__ )

					SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		1
					SCREAMING_SNAKE_CASE__				:				str			=		np.array_split(lowercase__     ,  lowercase__ )

					# 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
										SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		np.array_split(lowercase__     ,  lowercase__ )

					if num_chunks > 1:
										for chunk_id, chunk_data in enumerate(lowercase__ ):
															SCREAMING_SNAKE_CASE__				:				List[Any]			=		chunk_data
					else:
										SCREAMING_SNAKE_CASE__				:				List[str]			=		data







def     _a				(      lowercase__  :			List[str]     ,  lowercase__  :			Tuple ):







					'''simple docstring'''


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







def     _a				(      lowercase__  :			Any ):







					'''simple docstring'''


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

					return tf.nest.map_structure(_expand_single_ad_tensor     ,  lowercase__ )




from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
      import accelerate







def     _a				(      lowercase__  :			List[str] ):







     '''simple docstring'''


     if not is_accelerate_available():
          return method
     SCREAMING_SNAKE_CASE__				:				str			=		version.parse(accelerate.__version__ ).base_version
     if version.parse(lowercase__ ) < version.parse('0.17.0' ):
          return method

     def wrapper(self  :			Optional[int]     ,  *lowercase__  :			int     ,  **lowercase__  :			Tuple ):
          if hasattr(self     ,  '_hf_hook' ) and hasattr(self._hf_hook     ,  'pre_forward' ):
               self._hf_hook.pre_forward(self )
          return method(self     ,  *lowercase__     ,  **lowercase__ )

     return wrapper



