"""simple docstring"""







import functools
import math

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



def 			A__						(  lowercase_    :  Optional[Any]			,   lowercase_    :  Union[str, Any]			,   lowercase_    :  int			,   lowercase_    :  str			,   lowercase_    :  int = 4096	)     ->       str:
   __lowerCamelCase								=				key.shape[-3:]
   __lowerCamelCase								=				value.shape[-1]
   __lowerCamelCase								=				min(_SCREAMING_SNAKE_CASE			,   _SCREAMING_SNAKE_CASE	)
   __lowerCamelCase								=				query / jnp.sqrt(_SCREAMING_SNAKE_CASE	)

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

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

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

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

   def chunk_scanner(lowercase_    :  List[Any]	):
      # julienne key array
      __lowerCamelCase								=				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
      __lowerCamelCase								=				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	)

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

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

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

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

   return all_values / all_weights



def 			A__						(  lowercase_    :  int			,   lowercase_    :  str			,   lowercase_    :  Union[str, Any]			,   lowercase_    :  Optional[Any]=jax.lax.Precision.HIGHEST			,   lowercase_    :  int = 1024			,   lowercase_    :  int = 4096	)     ->       Dict:
   __lowerCamelCase								=				query.shape[-3:]

   def chunk_scanner(lowercase_    :  int			,   lowercase_    :  Dict	):
      # julienne query array
      __lowerCamelCase								=				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	),
      )

   __lowerCamelCase								=				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     _SCREAMING_SNAKE_CASE       ( nn.Module							):



   '''simple docstring'''



   __UpperCAmelCase  :       int						=						42
   __UpperCAmelCase  :       Tuple						=						8
   __UpperCAmelCase  :       List[Any]						=						64
   __UpperCAmelCase  :       Optional[int]						=						0.0
   __UpperCAmelCase  :       int						=						False
   __UpperCAmelCase  :       Union[str, Any]						=						jnp.floataa







   def       _A							(  self  ):


      '''simple docstring'''

      __lowerCamelCase								=				self.dim_head * self.heads
      __lowerCamelCase								=				self.dim_head**-0.5

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

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







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      __lowerCamelCase								=				tensor.shape
      __lowerCamelCase								=				self.heads
      __lowerCamelCase								=				tensor.reshape(__a   ,							__a   ,							__a   ,							dim // head_size  )
      __lowerCamelCase								=				jnp.transpose(__a   ,							(0, 2, 1, 3)  )
      __lowerCamelCase								=				tensor.reshape(batch_size * head_size   ,							__a   ,							dim // head_size  )
      return tensor







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      __lowerCamelCase								=				tensor.shape
      __lowerCamelCase								=				self.heads
      __lowerCamelCase								=				tensor.reshape(batch_size // head_size   ,							__a   ,							__a   ,							__a  )
      __lowerCamelCase								=				jnp.transpose(__a   ,							(0, 2, 1, 3)  )
      __lowerCamelCase								=				tensor.reshape(batch_size // head_size   ,							__a   ,							dim * head_size  )
      return tensor







   def __call__(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE=None   ,							_SCREAMING_SNAKE_CASE=True  ):


      '''simple docstring'''

      __lowerCamelCase								=				hidden_states if context is None else context

      __lowerCamelCase								=				self.query(__a  )
      __lowerCamelCase								=				self.key(__a  )
      __lowerCamelCase								=				self.value(__a  )

      __lowerCamelCase								=				self.reshape_heads_to_batch_dim(__a  )
      __lowerCamelCase								=				self.reshape_heads_to_batch_dim(__a  )
      __lowerCamelCase								=				self.reshape_heads_to_batch_dim(__a  )

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

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

         __lowerCamelCase								=				jax_memory_efficient_attention(
             __a   ,							__a   ,							__a   ,							query_chunk_size=__a   ,							key_chunk_size=4096 * 4  )

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

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

      __lowerCamelCase								=				self.reshape_batch_dim_to_heads(__a  )
      __lowerCamelCase								=				self.proj_attn(__a  )
      return self.dropout_layer(__a   ,							deterministic=__a  )






class     _SCREAMING_SNAKE_CASE       ( nn.Module							):



   '''simple docstring'''



   __UpperCAmelCase  :       Tuple						=						42
   __UpperCAmelCase  :       Optional[Any]						=						42
   __UpperCAmelCase  :       Optional[Any]						=						42
   __UpperCAmelCase  :       int						=						0.0
   __UpperCAmelCase  :       Optional[int]						=						False
   __UpperCAmelCase  :       Any						=						jnp.floataa
   __UpperCAmelCase  :       Any						=						False







   def       _A							(  self  ):


      '''simple docstring'''

      __lowerCamelCase								=				FlaxAttention(
          self.dim   ,							self.n_heads   ,							self.d_head   ,							self.dropout   ,							self.use_memory_efficient_attention   ,							dtype=self.dtype  )
      # cross attention
      __lowerCamelCase								=				FlaxAttention(
          self.dim   ,							self.n_heads   ,							self.d_head   ,							self.dropout   ,							self.use_memory_efficient_attention   ,							dtype=self.dtype  )
      __lowerCamelCase								=				FlaxFeedForward(dim=self.dim   ,							dropout=self.dropout   ,							dtype=self.dtype  )
      __lowerCamelCase								=				nn.LayerNorm(epsilon=1E-5   ,							dtype=self.dtype  )
      __lowerCamelCase								=				nn.LayerNorm(epsilon=1E-5   ,							dtype=self.dtype  )
      __lowerCamelCase								=				nn.LayerNorm(epsilon=1E-5   ,							dtype=self.dtype  )
      __lowerCamelCase								=				nn.Dropout(rate=self.dropout  )







   def __call__(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE=True  ):


      '''simple docstring'''

      __lowerCamelCase								=				hidden_states
      if self.only_cross_attention:
         __lowerCamelCase								=				self.attna(self.norma(__a  )   ,							__a   ,							deterministic=__a  )
      else:
         __lowerCamelCase								=				self.attna(self.norma(__a  )   ,							deterministic=__a  )
      __lowerCamelCase								=				hidden_states + residual

      # cross attention
      __lowerCamelCase								=				hidden_states
      __lowerCamelCase								=				self.attna(self.norma(__a  )   ,							__a   ,							deterministic=__a  )
      __lowerCamelCase								=				hidden_states + residual

      # feed forward
      __lowerCamelCase								=				hidden_states
      __lowerCamelCase								=				self.ff(self.norma(__a  )   ,							deterministic=__a  )
      __lowerCamelCase								=				hidden_states + residual

      return self.dropout_layer(__a   ,							deterministic=__a  )






class     _SCREAMING_SNAKE_CASE       ( nn.Module							):



   '''simple docstring'''



   __UpperCAmelCase  :       Tuple						=						42
   __UpperCAmelCase  :       List[str]						=						42
   __UpperCAmelCase  :       int						=						42
   __UpperCAmelCase  :       Tuple						=						1
   __UpperCAmelCase  :       Optional[Any]						=						0.0
   __UpperCAmelCase  :       Union[str, Any]						=						False
   __UpperCAmelCase  :       List[str]						=						False
   __UpperCAmelCase  :       List[str]						=						jnp.floataa
   __UpperCAmelCase  :       List[Any]						=						False







   def       _A							(  self  ):


      '''simple docstring'''

      __lowerCamelCase								=				nn.GroupNorm(num_groups=32   ,							epsilon=1E-5  )

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

      __lowerCamelCase								=				[
          FlaxBasicTransformerBlock(
              __a   ,							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:
         __lowerCamelCase								=				nn.Dense(__a   ,							dtype=self.dtype  )
      else:
         __lowerCamelCase								=				nn.Conv(
             __a   ,							kernel_size=(1, 1)   ,							strides=(1, 1)   ,							padding='VALID'   ,							dtype=self.dtype   ,							)

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







   def __call__(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE=True  ):


      '''simple docstring'''

      __lowerCamelCase								=				hidden_states.shape
      __lowerCamelCase								=				hidden_states
      __lowerCamelCase								=				self.norm(__a  )
      if self.use_linear_projection:
         __lowerCamelCase								=				hidden_states.reshape(__a   ,							height * width   ,							__a  )
         __lowerCamelCase								=				self.proj_in(__a  )
      else:
         __lowerCamelCase								=				self.proj_in(__a  )
         __lowerCamelCase								=				hidden_states.reshape(__a   ,							height * width   ,							__a  )

      for transformer_block in self.transformer_blocks:
         __lowerCamelCase								=				transformer_block(__a   ,							__a   ,							deterministic=__a  )

      if self.use_linear_projection:
         __lowerCamelCase								=				self.proj_out(__a  )
         __lowerCamelCase								=				hidden_states.reshape(__a   ,							__a   ,							__a   ,							__a  )
      else:
         __lowerCamelCase								=				hidden_states.reshape(__a   ,							__a   ,							__a   ,							__a  )
         __lowerCamelCase								=				self.proj_out(__a  )

      __lowerCamelCase								=				hidden_states + residual
      return self.dropout_layer(__a   ,							deterministic=__a  )






class     _SCREAMING_SNAKE_CASE       ( nn.Module							):



   '''simple docstring'''



   __UpperCAmelCase  :       List[str]						=						42
   __UpperCAmelCase  :       Tuple						=						0.0
   __UpperCAmelCase  :       List[Any]						=						jnp.floataa







   def       _A							(  self  ):


      '''simple docstring'''

      __lowerCamelCase								=				FlaxGEGLU(self.dim   ,							self.dropout   ,							self.dtype  )
      __lowerCamelCase								=				nn.Dense(self.dim   ,							dtype=self.dtype  )







   def __call__(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE=True  ):


      '''simple docstring'''

      __lowerCamelCase								=				self.net_a(__a   ,							deterministic=__a  )
      __lowerCamelCase								=				self.net_a(__a  )
      return hidden_states






class     _SCREAMING_SNAKE_CASE       ( nn.Module							):



   '''simple docstring'''



   __UpperCAmelCase  :       Any						=						42
   __UpperCAmelCase  :       Optional[Any]						=						0.0
   __UpperCAmelCase  :       Optional[Any]						=						jnp.floataa







   def       _A							(  self  ):


      '''simple docstring'''

      __lowerCamelCase								=				self.dim * 4
      __lowerCamelCase								=				nn.Dense(inner_dim * 2   ,							dtype=self.dtype  )
      __lowerCamelCase								=				nn.Dropout(rate=self.dropout  )







   def __call__(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE=True  ):


      '''simple docstring'''

      __lowerCamelCase								=				self.proj(__a  )
      __lowerCamelCase								=				jnp.split(__a   ,							2   ,							axis=2  )
      return self.dropout_layer(hidden_linear * nn.gelu(__a  )   ,							deterministic=__a  )







"""simple docstring"""







# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    '''--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000'''
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/






