






"""simple docstring"""



from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_lowerCAmelCase	:	str				=      logging.get_logger(__name__)


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






     '''simple docstring'''



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

     UpperCAmelCase__						:    str							=							tf.shape(snake_case				)

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

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

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


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






     '''simple docstring'''



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


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[int]			,					snake_case       : Optional[Any]			,					snake_case       : Optional[int]			,					snake_case       : Union[str, Any]=1E-5			,					snake_case       : List[Any]=-1				)->		int:






     '''simple docstring'''



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

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

     # Compute layer normalization using the batch_normalization
     # function.
     UpperCAmelCase__						:    List[Any]							=							tf.nn.batch_normalization(
         snake_case			,					snake_case			,					snake_case			,					offset=snake_case			,					scale=snake_case			,					variance_epsilon=snake_case			,					)
     return outputs


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[int]			,					snake_case       : Any=0			,					snake_case       : int=-1				)->		Any:






     '''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

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


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : tf.Tensor				)->		tf.Tensor:






     '''simple docstring'''



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

     return encoder_extended_attention_mask


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : tf.Tensor			,					snake_case       : int			,					snake_case       : str = "input_ids"				)->		None:






     '''simple docstring'''



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


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : List[Any]			,					snake_case       : List[str]			,					snake_case       : Tuple				)->		List[str]:






     '''simple docstring'''



     UpperCAmelCase__						:    int							=							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.
     UpperCAmelCase__						:    int							=							[x for x in data if len(snake_case				) > 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}'				)

     UpperCAmelCase__						:    Union[str, Any]							=							np.asarray(snake_case				)

     UpperCAmelCase__						:    Union[str, Any]							=							1
     UpperCAmelCase__						:    Union[str, Any]							=							np.array_split(snake_case			,					snake_case				)

     # 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
          UpperCAmelCase__						:    Optional[Any]							=							np.array_split(snake_case			,					snake_case				)

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


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : str			,					snake_case       : List[Any]				)->		Dict:






     '''simple docstring'''



     if name in group.attrs:
          UpperCAmelCase__						:    Tuple							=							[n.decode("utf8"				) if hasattr(snake_case			,					"decode"				) else n for n in group.attrs[name]]
     else:
          UpperCAmelCase__						:    Tuple							=							[]
          UpperCAmelCase__						:    Union[str, Any]							=							0
          while "%s%d" % (name, chunk_id) in group.attrs:
               data.extend(
                   [n.decode("utf8"				) if hasattr(snake_case			,					"decode"				) else n for n in group.attrs["%s%d" % (name, chunk_id)]]				)
               chunk_id += 1
     return data



def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Dict				)->		Any:






     '''simple docstring'''



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

     return tf.nest.map_structure(_expand_single_ad_tensor			,					snake_case				)










"""simple docstring"""



import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
 import jax
 import jax.numpy as jnp
 from flax.jax_utils import replicate
 from flax.training.common_utils import shard




@slow
@require_flax
class 			lowerCAmelCase__			(    unittest.TestCase   ):
   def       __a							(		self      :	Optional[int]							):


        '''simple docstring'''
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()




   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/sd2-inpaint/init_image.png"							)
        UpperCAmelCase__						:    Any							=							load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"							)

        UpperCAmelCase__						:    str							=							"xvjiarui/stable-diffusion-2-inpainting"
        UpperCAmelCase__							,				UpperCAmelCase__						:    List[Any]							=							FlaxStableDiffusionInpaintPipeline.from_pretrained(snake_case__							,			safety_checker=snake_case__							)

        UpperCAmelCase__						:    List[str]							=							"Face of a yellow cat, high resolution, sitting on a park bench"

        UpperCAmelCase__						:    List[str]							=							jax.random.PRNGKey(0							)
        UpperCAmelCase__						:    Optional[int]							=							5_0

        UpperCAmelCase__						:    List[Any]							=							jax.device_count()
        UpperCAmelCase__						:    int							=							num_samples * [prompt]
        UpperCAmelCase__						:    str							=							num_samples * [init_image]
        UpperCAmelCase__						:    Optional[Any]							=							num_samples * [mask_image]
        UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							pipeline.prepare_inputs(snake_case__							,			snake_case__							,			snake_case__							)

        # shard inputs and rng
        UpperCAmelCase__						:    Dict							=							replicate(snake_case__							)
        UpperCAmelCase__						:    Dict							=							jax.random.split(snake_case__							,			jax.device_count()							)
        UpperCAmelCase__						:    List[str]							=							shard(snake_case__							)
        UpperCAmelCase__						:    str							=							shard(snake_case__							)
        UpperCAmelCase__						:    Optional[int]							=							shard(snake_case__							)

        UpperCAmelCase__						:    Dict							=							pipeline(
            snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			jit=snake_case__							)

        UpperCAmelCase__						:    List[str]							=							output.images.reshape(snake_case__							,			5_1_2							,			5_1_2							,			3							)

        UpperCAmelCase__						:    Tuple							=							images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        UpperCAmelCase__						:    str							=							jnp.asarray(jax.device_get(image_slice.flatten()							)							)
        UpperCAmelCase__						:    int							=							jnp.array(
            [0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084]							)
        print(f'output_slice: {output_slice}'							)
        assert jnp.abs(output_slice - expected_slice							).max() < 1e-2


