

import functools
import math

import flax.linen as nn
import jax
import jax.numpy as jnp


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Any	,				_SCREAMING_SNAKE_CASE   :						List[Any]	,				_SCREAMING_SNAKE_CASE   :						Any	,				_SCREAMING_SNAKE_CASE   :						Tuple	,				_SCREAMING_SNAKE_CASE   :						List[Any] = 4_096    ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  key.shape[-3:]
    SCREAMING_SNAKE_CASE_													=  value.shape[-1]
    SCREAMING_SNAKE_CASE_													=  min(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    )
    SCREAMING_SNAKE_CASE_													=  query / jnp.sqrt(_SCREAMING_SNAKE_CASE    )

    @functools.partial(jax.checkpoint	,				prevent_cse=_SCREAMING_SNAKE_CASE    )
    def summarize_chunk(_SCREAMING_SNAKE_CASE   :						Union[str, Any]	,				_SCREAMING_SNAKE_CASE   :						List[str]	,				_SCREAMING_SNAKE_CASE   :						str    ):
        SCREAMING_SNAKE_CASE_													=  jnp.einsum('...qhd,...khd->...qhk'	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				precision=_SCREAMING_SNAKE_CASE    )

        SCREAMING_SNAKE_CASE_													=  jnp.max(_SCREAMING_SNAKE_CASE	,				axis=-1	,				keepdims=_SCREAMING_SNAKE_CASE    )
        SCREAMING_SNAKE_CASE_													=  jax.lax.stop_gradient(_SCREAMING_SNAKE_CASE    )
        SCREAMING_SNAKE_CASE_													=  jnp.exp(attn_weights - max_score    )

        SCREAMING_SNAKE_CASE_													=  jnp.einsum('...vhf,...qhv->...qhf'	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				precision=_SCREAMING_SNAKE_CASE    )
        SCREAMING_SNAKE_CASE_													=  jnp.einsum('...qhk->...qh'	,				_SCREAMING_SNAKE_CASE    )

        return (exp_values, exp_weights.sum(axis=-1    ), max_score)

    def chunk_scanner(_SCREAMING_SNAKE_CASE   :						Any    ):
        # julienne key array
        SCREAMING_SNAKE_CASE_													=  jax.lax.dynamic_slice(
            operand=_SCREAMING_SNAKE_CASE	,				start_indices=[0] * (key.ndim - 3) + [chunk_idx, 0, 0]	,				slice_sizes=list(key.shape[:-3]    ) + [key_chunk_size, num_heads, k_features]	,				)

        # julienne value array
        SCREAMING_SNAKE_CASE_													=  jax.lax.dynamic_slice(
            operand=_SCREAMING_SNAKE_CASE	,				start_indices=[0] * (value.ndim - 3) + [chunk_idx, 0, 0]	,				slice_sizes=list(value.shape[:-3]    ) + [key_chunk_size, num_heads, v_features]	,				)

        return summarize_chunk(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    )

    SCREAMING_SNAKE_CASE_													=  jax.lax.map(f=_SCREAMING_SNAKE_CASE	,				xs=jnp.arange(0	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    )    )

    SCREAMING_SNAKE_CASE_													=  jnp.max(_SCREAMING_SNAKE_CASE	,				axis=0	,				keepdims=_SCREAMING_SNAKE_CASE    )
    SCREAMING_SNAKE_CASE_													=  jnp.exp(chunk_max - global_max    )

    chunk_values *= jnp.expand_dims(_SCREAMING_SNAKE_CASE	,				axis=-1    )
    chunk_weights *= max_diffs

    SCREAMING_SNAKE_CASE_													=  chunk_values.sum(axis=0    )
    SCREAMING_SNAKE_CASE_													=  jnp.expand_dims(_SCREAMING_SNAKE_CASE	,				-1    ).sum(axis=0    )

    return all_values / all_weights


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Dict	,				_SCREAMING_SNAKE_CASE   :						Optional[int]	,				_SCREAMING_SNAKE_CASE   :						Optional[int]	,				_SCREAMING_SNAKE_CASE   :						Any=jax.lax.Precision.HIGHEST	,				_SCREAMING_SNAKE_CASE   :						Optional[Any] = 1_024	,				_SCREAMING_SNAKE_CASE   :						Optional[int] = 4_096    ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  query.shape[-3:]

    def chunk_scanner(_SCREAMING_SNAKE_CASE   :						List[str]	,				_SCREAMING_SNAKE_CASE   :						Optional[Any]    ):
        # julienne query array
        SCREAMING_SNAKE_CASE_													=  jax.lax.dynamic_slice(
            operand=_SCREAMING_SNAKE_CASE	,				start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0]	,				slice_sizes=list(query.shape[:-3]    ) + [min(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    ), num_heads, q_features]	,				)

        return (
            chunk_idx + query_chunk_size,  # unused ignore it
            _query_chunk_attention(
                query=_SCREAMING_SNAKE_CASE	,				key=_SCREAMING_SNAKE_CASE	,				value=_SCREAMING_SNAKE_CASE	,				precision=_SCREAMING_SNAKE_CASE	,				key_chunk_size=_SCREAMING_SNAKE_CASE    ),
        )

    SCREAMING_SNAKE_CASE_													=  jax.lax.scan(
        f=_SCREAMING_SNAKE_CASE	,				init=0	,				xs=_SCREAMING_SNAKE_CASE	,				length=math.ceil(num_q / query_chunk_size    )    )

    return jnp.concatenate(_SCREAMING_SNAKE_CASE	,				axis=-3    )  # fuse the chunked result back



class  __snake_case					(      nn.Module    ):
  __lowerCAmelCase       :		int
  __lowerCAmelCase       :		int  	=					8
  __lowerCAmelCase       :		int  	=					64
  __lowerCAmelCase       :		float  	=					0.0
  __lowerCAmelCase       :		bool  	=					False
  __lowerCAmelCase       :		jnp.dtype  	=					jnp.floataa

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.dim_head * self.heads
      SCREAMING_SNAKE_CASE_													=  self.dim_head**-0.5

      # Weights were exported with old names {to_q, to_k, to_v, to_out}
      SCREAMING_SNAKE_CASE_													=  nn.Dense(UpperCamelCase__  ,						use_bias=UpperCamelCase__  ,						dtype=self.dtype  ,						name='to_q')
      SCREAMING_SNAKE_CASE_													=  nn.Dense(UpperCamelCase__  ,						use_bias=UpperCamelCase__  ,						dtype=self.dtype  ,						name='to_k')
      SCREAMING_SNAKE_CASE_													=  nn.Dense(UpperCamelCase__  ,						use_bias=UpperCamelCase__  ,						dtype=self.dtype  ,						name='to_v')

      SCREAMING_SNAKE_CASE_													=  nn.Dense(self.query_dim  ,						dtype=self.dtype  ,						name='to_out_0')
      SCREAMING_SNAKE_CASE_													=  nn.Dropout(rate=self.dropout)

  def   lowerCAmelCase__       (   self  ,						_A):
      SCREAMING_SNAKE_CASE_													=  tensor.shape
      SCREAMING_SNAKE_CASE_													=  self.heads
      SCREAMING_SNAKE_CASE_													=  tensor.reshape(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						dim // head_size)
      SCREAMING_SNAKE_CASE_													=  jnp.transpose(UpperCamelCase__  ,						(0, 2, 1, 3))
      SCREAMING_SNAKE_CASE_													=  tensor.reshape(batch_size * head_size  ,						UpperCamelCase__  ,						dim // head_size)
      return tensor

  def   lowerCAmelCase__       (   self  ,						_A):
      SCREAMING_SNAKE_CASE_													=  tensor.shape
      SCREAMING_SNAKE_CASE_													=  self.heads
      SCREAMING_SNAKE_CASE_													=  tensor.reshape(batch_size // head_size  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__)
      SCREAMING_SNAKE_CASE_													=  jnp.transpose(UpperCamelCase__  ,						(0, 2, 1, 3))
      SCREAMING_SNAKE_CASE_													=  tensor.reshape(batch_size // head_size  ,						UpperCamelCase__  ,						dim * head_size)
      return tensor


  def __call__(   self  ,						_A  ,						_A=None  ,						_A=True):
      SCREAMING_SNAKE_CASE_													=  hidden_states if context is None else context

      SCREAMING_SNAKE_CASE_													=  self.query(UpperCamelCase__)
      SCREAMING_SNAKE_CASE_													=  self.key(UpperCamelCase__)
      SCREAMING_SNAKE_CASE_													=  self.value(UpperCamelCase__)

      SCREAMING_SNAKE_CASE_													=  self.reshape_heads_to_batch_dim(UpperCamelCase__)
      SCREAMING_SNAKE_CASE_													=  self.reshape_heads_to_batch_dim(UpperCamelCase__)
      SCREAMING_SNAKE_CASE_													=  self.reshape_heads_to_batch_dim(UpperCamelCase__)

      if self.use_memory_efficient_attention:
          SCREAMING_SNAKE_CASE_													=  query_states.transpose(1  ,						0  ,						2)
          SCREAMING_SNAKE_CASE_													=  key_states.transpose(1  ,						0  ,						2)
          SCREAMING_SNAKE_CASE_													=  value_states.transpose(1  ,						0  ,						2)

          # this if statement create a chunk size for each layer of the unet
          # the chunk size is equal to the query_length dimension of the deepest layer of the unet

          SCREAMING_SNAKE_CASE_													=  query_states.shape[-3]
          if flatten_latent_dim % 64 == 0:
              SCREAMING_SNAKE_CASE_													=  int(flatten_latent_dim / 64)
          elif flatten_latent_dim % 16 == 0:
              SCREAMING_SNAKE_CASE_													=  int(flatten_latent_dim / 16)
          elif flatten_latent_dim % 4 == 0:
              SCREAMING_SNAKE_CASE_													=  int(flatten_latent_dim / 4)
          else:
              SCREAMING_SNAKE_CASE_													=  int(UpperCamelCase__)

          SCREAMING_SNAKE_CASE_													=  jax_memory_efficient_attention(
              UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						query_chunk_size=UpperCamelCase__  ,						key_chunk_size=4096 * 4)

          SCREAMING_SNAKE_CASE_													=  hidden_states.transpose(1  ,						0  ,						2)
      else:
          # compute attentions
          SCREAMING_SNAKE_CASE_													=  jnp.einsum('b i d, b j d->b i j'  ,						UpperCamelCase__  ,						UpperCamelCase__)
          SCREAMING_SNAKE_CASE_													=  attention_scores * self.scale
          SCREAMING_SNAKE_CASE_													=  nn.softmax(UpperCamelCase__  ,						axis=2)

          # attend to values
          SCREAMING_SNAKE_CASE_													=  jnp.einsum('b i j, b j d -> b i d'  ,						UpperCamelCase__  ,						UpperCamelCase__)

      SCREAMING_SNAKE_CASE_													=  self.reshape_batch_dim_to_heads(UpperCamelCase__)
      SCREAMING_SNAKE_CASE_													=  self.proj_attn(UpperCamelCase__)
      return self.dropout_layer(UpperCamelCase__  ,						deterministic=UpperCamelCase__)






class  __snake_case					(      nn.Module    ):
  __lowerCAmelCase       :		int
  __lowerCAmelCase       :		int
  __lowerCAmelCase       :		int
  __lowerCAmelCase       :		float  	=					0.0
  __lowerCAmelCase       :		bool  	=					False
  __lowerCAmelCase       :		jnp.dtype  	=					jnp.floataa
  __lowerCAmelCase       :		bool  	=					False

  def   lowerCAmelCase__       (   self):
      # self attention (or cross_attention if only_cross_attention is True)
      SCREAMING_SNAKE_CASE_													=  FlaxAttention(
          self.dim  ,						self.n_heads  ,						self.d_head  ,						self.dropout  ,						self.use_memory_efficient_attention  ,						dtype=self.dtype)
      # cross attention
      SCREAMING_SNAKE_CASE_													=  FlaxAttention(
          self.dim  ,						self.n_heads  ,						self.d_head  ,						self.dropout  ,						self.use_memory_efficient_attention  ,						dtype=self.dtype)
      SCREAMING_SNAKE_CASE_													=  FlaxFeedForward(dim=self.dim  ,						dropout=self.dropout  ,						dtype=self.dtype)
      SCREAMING_SNAKE_CASE_													=  nn.LayerNorm(epsilon=1E-5  ,						dtype=self.dtype)
      SCREAMING_SNAKE_CASE_													=  nn.LayerNorm(epsilon=1E-5  ,						dtype=self.dtype)
      SCREAMING_SNAKE_CASE_													=  nn.LayerNorm(epsilon=1E-5  ,						dtype=self.dtype)
      SCREAMING_SNAKE_CASE_													=  nn.Dropout(rate=self.dropout)


  def __call__(   self  ,						_A  ,						_A  ,						_A=True):
      # self attention
      SCREAMING_SNAKE_CASE_													=  hidden_states
      if self.only_cross_attention:
          SCREAMING_SNAKE_CASE_													=  self.attna(self.norma(UpperCamelCase__)  ,						UpperCamelCase__  ,						deterministic=UpperCamelCase__)
      else:
          SCREAMING_SNAKE_CASE_													=  self.attna(self.norma(UpperCamelCase__)  ,						deterministic=UpperCamelCase__)
      SCREAMING_SNAKE_CASE_													=  hidden_states + residual

      # cross attention
      SCREAMING_SNAKE_CASE_													=  hidden_states
      SCREAMING_SNAKE_CASE_													=  self.attna(self.norma(UpperCamelCase__)  ,						UpperCamelCase__  ,						deterministic=UpperCamelCase__)
      SCREAMING_SNAKE_CASE_													=  hidden_states + residual

      # feed forward
      SCREAMING_SNAKE_CASE_													=  hidden_states
      SCREAMING_SNAKE_CASE_													=  self.ff(self.norma(UpperCamelCase__)  ,						deterministic=UpperCamelCase__)
      SCREAMING_SNAKE_CASE_													=  hidden_states + residual

      return self.dropout_layer(UpperCamelCase__  ,						deterministic=UpperCamelCase__)






class  __snake_case					(      nn.Module    ):
  __lowerCAmelCase       :		int
  __lowerCAmelCase       :		int
  __lowerCAmelCase       :		int
  __lowerCAmelCase       :		int  	=					1
  __lowerCAmelCase       :		float  	=					0.0
  __lowerCAmelCase       :		bool  	=					False
  __lowerCAmelCase       :		bool  	=					False
  __lowerCAmelCase       :		jnp.dtype  	=					jnp.floataa
  __lowerCAmelCase       :		bool  	=					False

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  nn.GroupNorm(num_groups=32  ,						epsilon=1E-5)

      SCREAMING_SNAKE_CASE_													=  self.n_heads * self.d_head
      if self.use_linear_projection:
          SCREAMING_SNAKE_CASE_													=  nn.Dense(UpperCamelCase__  ,						dtype=self.dtype)
      else:
          SCREAMING_SNAKE_CASE_													=  nn.Conv(
              UpperCamelCase__  ,						kernel_size=(1, 1)  ,						strides=(1, 1)  ,						padding='VALID'  ,						dtype=self.dtype  ,						)

      SCREAMING_SNAKE_CASE_													=  [
          FlaxBasicTransformerBlock(
              UpperCamelCase__  ,						self.n_heads  ,						self.d_head  ,						dropout=self.dropout  ,						only_cross_attention=self.only_cross_attention  ,						dtype=self.dtype  ,						use_memory_efficient_attention=self.use_memory_efficient_attention  ,						)
          for _ in range(self.depth)
      ]

      if self.use_linear_projection:
          SCREAMING_SNAKE_CASE_													=  nn.Dense(UpperCamelCase__  ,						dtype=self.dtype)
      else:
          SCREAMING_SNAKE_CASE_													=  nn.Conv(
              UpperCamelCase__  ,						kernel_size=(1, 1)  ,						strides=(1, 1)  ,						padding='VALID'  ,						dtype=self.dtype  ,						)

      SCREAMING_SNAKE_CASE_													=  nn.Dropout(rate=self.dropout)


  def __call__(   self  ,						_A  ,						_A  ,						_A=True):
      SCREAMING_SNAKE_CASE_													=  hidden_states.shape
      SCREAMING_SNAKE_CASE_													=  hidden_states
      SCREAMING_SNAKE_CASE_													=  self.norm(UpperCamelCase__)
      if self.use_linear_projection:
          SCREAMING_SNAKE_CASE_													=  hidden_states.reshape(UpperCamelCase__  ,						height * width  ,						UpperCamelCase__)
          SCREAMING_SNAKE_CASE_													=  self.proj_in(UpperCamelCase__)
      else:
          SCREAMING_SNAKE_CASE_													=  self.proj_in(UpperCamelCase__)
          SCREAMING_SNAKE_CASE_													=  hidden_states.reshape(UpperCamelCase__  ,						height * width  ,						UpperCamelCase__)

      for transformer_block in self.transformer_blocks:
          SCREAMING_SNAKE_CASE_													=  transformer_block(UpperCamelCase__  ,						UpperCamelCase__  ,						deterministic=UpperCamelCase__)

      if self.use_linear_projection:
          SCREAMING_SNAKE_CASE_													=  self.proj_out(UpperCamelCase__)
          SCREAMING_SNAKE_CASE_													=  hidden_states.reshape(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__)
      else:
          SCREAMING_SNAKE_CASE_													=  hidden_states.reshape(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__)
          SCREAMING_SNAKE_CASE_													=  self.proj_out(UpperCamelCase__)

      SCREAMING_SNAKE_CASE_													=  hidden_states + residual
      return self.dropout_layer(UpperCamelCase__  ,						deterministic=UpperCamelCase__)






class  __snake_case					(      nn.Module    ):
  __lowerCAmelCase       :		int
  __lowerCAmelCase       :		float  	=					0.0
  __lowerCAmelCase       :		jnp.dtype  	=					jnp.floataa

  def   lowerCAmelCase__       (   self):
      # The second linear layer needs to be called
      # net_2 for now to match the index of the Sequential layer
      SCREAMING_SNAKE_CASE_													=  FlaxGEGLU(self.dim  ,						self.dropout  ,						self.dtype)
      SCREAMING_SNAKE_CASE_													=  nn.Dense(self.dim  ,						dtype=self.dtype)


  def __call__(   self  ,						_A  ,						_A=True):
      SCREAMING_SNAKE_CASE_													=  self.net_a(UpperCamelCase__  ,						deterministic=UpperCamelCase__)
      SCREAMING_SNAKE_CASE_													=  self.net_a(UpperCamelCase__)
      return hidden_states






class  __snake_case					(      nn.Module    ):
  __lowerCAmelCase       :		int
  __lowerCAmelCase       :		float  	=					0.0
  __lowerCAmelCase       :		jnp.dtype  	=					jnp.floataa

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.dim * 4
      SCREAMING_SNAKE_CASE_													=  nn.Dense(inner_dim * 2  ,						dtype=self.dtype)
      SCREAMING_SNAKE_CASE_													=  nn.Dropout(rate=self.dropout)


  def __call__(   self  ,						_A  ,						_A=True):
      SCREAMING_SNAKE_CASE_													=  self.proj(UpperCamelCase__)
      SCREAMING_SNAKE_CASE_													=  jnp.split(UpperCamelCase__  ,						2  ,						axis=2)
      return self.dropout_layer(hidden_linear * nn.gelu(UpperCamelCase__)  ,						deterministic=UpperCamelCase__)





import cva
import numpy as np


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						np.ndarray	,				_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int    ):





    """simple docstring"""


    try:
        return int(image[x_coordinate][y_coordinate] >= center    )
    except (IndexError, TypeError):
        return 0


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						np.ndarray	,				_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int    ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  image[x_coordinate][y_coordinate]
    SCREAMING_SNAKE_CASE_													=  [1, 2, 4, 8, 16, 32, 64, 128]

    # skip get_neighbors_pixel if center is null
    if center is None:
        return 0

    # Starting from the top right, assigning value to pixels clockwise
    SCREAMING_SNAKE_CASE_													=  [
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate - 1	,				y_coordinate + 1	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				y_coordinate + 1	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate - 1	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate + 1	,				y_coordinate + 1	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate + 1	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate + 1	,				y_coordinate - 1	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				y_coordinate - 1	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate - 1	,				y_coordinate - 1	,				_SCREAMING_SNAKE_CASE    ),
    ]

    # Converting the binary value to decimal.
    return sum(
        binary_value * power for binary_value, power in zip(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    )    )


if __name__ == "__main__":
       # Reading the image and converting it to grayscale.
       UpperCamelCase__					: Optional[int]								=      cva.imread(
           "digital_image_processing/image_data/lena.jpg", cva.IMREAD_GRAYSCALE
       )

       # Create a numpy array as the same height and width of read image
       UpperCamelCase__					: Dict								=      np.zeros((image.shape[0], image.shape[1]))

       # Iterating through the image and calculating the
       # local binary pattern value for each pixel.
       for i in range(0, image.shape[0]):
              for j in range(0, image.shape[1]):
                     UpperCamelCase__					: Any								=      local_binary_value(image, i, j)

       cva.imshow("local binary pattern", lbp_image)
       cva.waitKey(0)
       cva.destroyAllWindows()


