

'''simple docstring'''







from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


__lowerCamelCase										=			logging.get_logger(__name__)
def 					UpperCAmelCase__			(					UpperCAmelCase__ )	->	Optional[Any]:
       if isinstance(__UpperCamelCase,				np.ndarray ):
              return list(tensor.shape )

       A_		    =					tf.shape(__UpperCamelCase )

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

       A_		    =					tensor.shape.as_list()

       return [dynamic[i] if s is None else s for i, s in enumerate(__UpperCamelCase )]
def 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__ = None,				UpperCAmelCase__ = None )	->	Optional[Any]:
       return tf.nn.softmax(logits=logits + 1e-9,				axis=__UpperCamelCase,				name=__UpperCamelCase )
def 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__,				UpperCAmelCase__,				UpperCAmelCase__=1e-5,				UpperCAmelCase__=-1 )	->	List[Any]:
       # 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(__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
       A_    ,	A_		    =					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
              A_		    =					[1] * inputs.shape.rank
              A_		    =					shape_list(__UpperCamelCase )[axis]
              A_		    =					tf.reshape(__UpperCamelCase,				__UpperCamelCase )
              A_		    =					tf.reshape(__UpperCamelCase,				__UpperCamelCase )

       # Compute layer normalization using the batch_normalization
       # function.
       A_		    =					tf.nn.batch_normalization(
           __UpperCamelCase,				__UpperCamelCase,				__UpperCamelCase,				offset=__UpperCamelCase,				scale=__UpperCamelCase,				variance_epsilon=__UpperCamelCase,				)
       return outputs
def 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__=0,				UpperCAmelCase__=-1 )	->	Dict:
       # 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

       A_		    =					tf.shape(__UpperCamelCase )
       A_		    =					tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
       A_		    =					tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]],				axis=0 )
       return tf.reshape(__UpperCamelCase,				__UpperCamelCase )
def 					UpperCAmelCase__			(					UpperCAmelCase__ )	->	Union[str, Any]:
       if not isinstance(__UpperCamelCase,				tf.Tensor ):
              A_		    =					tf.convert_to_tensor(__UpperCamelCase )  # Catches stray NumPy inputs
       if encoder_attention_mask.shape.rank == 3:
              A_		    =					encoder_attention_mask[:, None, :, :]
       if encoder_attention_mask.shape.rank == 2:
              A_		    =					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))
       A_		    =					(
           tf.cast(1,				encoder_attention_mask.dtype ) - encoder_extended_attention_mask
       ) * encoder_extended_attention_mask.dtype.min

       return encoder_extended_attention_mask
def 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__,				UpperCAmelCase__ = "input_ids" )	->	Union[str, Any]:
       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 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__,				UpperCAmelCase__ )	->	Optional[Any]:
       A_		    =					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.
       A_		    =					[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}''' )

       A_		    =					np.asarray(__UpperCamelCase )

       A_		    =					1
       A_		    =					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
              A_		    =					np.array_split(__UpperCamelCase,				__UpperCamelCase )

       if num_chunks > 1:
              for chunk_id, chunk_data in enumerate(__UpperCamelCase ):
                     A_		    =					chunk_data
       else:
              A_		    =					data
def 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__ )	->	Union[str, Any]:
       if name in group.attrs:
              A_		    =					[n.decode("""utf8""" ) if hasattr(__UpperCamelCase,				"""decode""" ) else n for n in group.attrs[name]]
       else:
              A_		    =					[]
              A_		    =					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 					UpperCAmelCase__			(					UpperCAmelCase__ )	->	Optional[int]:

       def _expand_single_ad_tensor(UpperCAmelCase__ ):
              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 )

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  42
  lowerCamelCase__   =  42


if is_transformers_available() and is_torch_available():
      from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline


