




from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


__lowerCamelCase   :						Tuple				=			logging.get_logger(__name__)







def 		lowercase__   (		__A:	Union[tf.Tensor, np.ndarray]				):





					'''simple docstring'''






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

					__magic_name__							:	Optional[Any]    							=							tf.shape(__A				)

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

					__magic_name__							:	Union[str, Any]    							=							tensor.shape.as_list()

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







def 		lowercase__   (		__A:	tf.Tensor							,__A:	Optional[int] = None							,__A:	Optional[str] = None				):





					'''simple docstring'''






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







def 		lowercase__   (		__A:	Dict							,__A:	Optional[int]							,__A:	Dict							,__A:	Union[str, Any]=1e-5							,__A:	Dict=-1				):





					'''simple docstring'''






					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
					__magic_name__							,    __magic_name__							:	Any    							=							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
										__magic_name__							:	Union[str, Any]    							=							[1] * inputs.shape.rank
										__magic_name__							:	Tuple    							=							shape_list(__A				)[axis]
										__magic_name__							:	str    							=							tf.reshape(__A							,__A				)
										__magic_name__							:	Dict    							=							tf.reshape(__A							,__A				)

					# Compute layer normalization using the batch_normalization
					# function.
					__magic_name__							:	Tuple    							=							tf.nn.batch_normalization(
					    __A							,__A							,__A							,offset=__A							,scale=__A							,variance_epsilon=__A							,)
					return outputs







def 		lowercase__   (		__A:	Any							,__A:	List[Any]=0							,__A:	Union[str, Any]=-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

					__magic_name__							:	str    							=							tf.shape(__A				)
					__magic_name__							:	str    							=							tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]				)
					__magic_name__							:	Optional[int]    							=							tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]							,axis=0				)
					return tf.reshape(__A							,__A				)







def 		lowercase__   (		__A:	tf.Tensor				):





					'''simple docstring'''






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

					return encoder_extended_attention_mask







def 		lowercase__   (		__A:	tf.Tensor							,__A:	int							,__A:	str = "input_ids"				):





					'''simple docstring'''






					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 		lowercase__   (		__A:	List[Any]							,__A:	Optional[Any]							,__A:	List[Any]				):





					'''simple docstring'''






					__magic_name__							:	Optional[int]    							=							6_4_5_1_2
					# 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.
					__magic_name__							:	Union[str, Any]    							=							[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}'''				)

					__magic_name__							:	Optional[Any]    							=							np.asarray(__A				)

					__magic_name__							:	Dict    							=							1
					__magic_name__							:	Optional[Any]    							=							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
										__magic_name__							:	Dict    							=							np.array_split(__A							,__A				)

					if num_chunks > 1:
										for chunk_id, chunk_data in enumerate(__A				):
															__magic_name__							:	Optional[int]    							=							chunk_data
					else:
										__magic_name__							:	List[Any]    							=							data







def 		lowercase__   (		__A:	Tuple							,__A:	Dict				):





					'''simple docstring'''






					if name in group.attrs:
										__magic_name__							:	Union[str, Any]    							=							[n.decode('''utf8'''				) if hasattr(__A							,'''decode'''				) else n for n in group.attrs[name]]
					else:
										__magic_name__							:	Any    							=							[]
										__magic_name__							:	str    							=							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 		lowercase__   (		__A:	Union[str, Any]				):





					'''simple docstring'''






					def _expand_single_ad_tensor(__A:	Optional[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				)









# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    '''stable diffusion controlnet''',
    '''0.22.0''',
    '''Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.''',
    standard_warn=False,
    stacklevel=3,
)



