


'''simple docstring'''




from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_a   :   List[Any]												=   logging.get_logger(__name__)





def   _a		(lowercase__			:   Union[tf.Tensor, np.ndarray]						)			->			List[int]:






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

     __snake_case           =						tf.shape(lowercase__						)

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

     __snake_case           =						tensor.shape.as_list()

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





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






     """simple docstring"""
     return tf.nn.softmax(logits=logits + 1e-9							,   axis=lowercase__							,   name=lowercase__						)





def   _a		(lowercase__			:   Optional[Any]							,   lowercase__			:   Tuple							,   lowercase__			:   int							,   lowercase__			:   int=1e-5							,   lowercase__			:   int=-1						)			->			int:






     """simple docstring"""
     # 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(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
     __snake_case      ,       __snake_case           =						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
          __snake_case           =						[1] * inputs.shape.rank
          __snake_case           =						shape_list(lowercase__						)[axis]
          __snake_case           =						tf.reshape(lowercase__							,   lowercase__						)
          __snake_case           =						tf.reshape(lowercase__							,   lowercase__						)

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





def   _a		(lowercase__			:   str							,   lowercase__			:   Optional[int]=0							,   lowercase__			:   List[str]=-1						)			->			int:






     """simple docstring"""
     # 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(lowercase__						)
     __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(lowercase__							,   lowercase__						)





def   _a		(lowercase__			:   tf.Tensor						)			->			tf.Tensor:






     """simple docstring"""
     if not isinstance(lowercase__							,   tf.Tensor						):
          __snake_case           =						tf.convert_to_tensor(lowercase__						)  # 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   _a		(lowercase__			:   tf.Tensor							,   lowercase__			:   int							,   lowercase__			:   str = "input_ids"						)			->			None:






     """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__			:   int							,   lowercase__			:   Union[str, Any]							,   lowercase__			:   Union[str, Any]						)			->			Optional[int]:






     """simple docstring"""
     __snake_case           =						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.
     __snake_case           =						[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}'						)

     __snake_case           =						np.asarray(lowercase__						)

     __snake_case           =						1
     __snake_case           =						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
          __snake_case           =						np.array_split(lowercase__							,   lowercase__						)

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





def   _a		(lowercase__			:   Dict							,   lowercase__			:   Optional[int]						)			->			Optional[int]:






     """simple docstring"""
     if name in group.attrs:
          __snake_case           =						[n.decode('utf8'						) if hasattr(lowercase__							,   '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(lowercase__							,   'decode'						) else n for n in group.attrs['%s%d' % (name, chunk_id)]]						)
               chunk_id += 1
     return data





def   _a		(lowercase__			:   str						)			->			List[str]:






     """simple docstring"""

     def _expand_single_ad_tensor(lowercase__			:   str						):
          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__						)








'''simple docstring'''
from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
					from .multicontrolnet import MultiControlNetModel
					from .pipeline_controlnet import StableDiffusionControlNetPipeline
					from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
					from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
					from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline


