import functools
import math

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







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_ = 4_0_9_6       )	->	int:
     a			,							a			,							a  :    Dict				=						key.shape[-3:]
     a  :    Tuple				=						value.shape[-1]
     a  :    int				=						min(lowerCAmelCase_						,      lowerCAmelCase_       )
     a  :    Optional[int]				=						query / jnp.sqrt(lowerCAmelCase_       )

     @functools.partial(jax.checkpoint						,      prevent_cse=lowerCAmelCase_       )
     def summarize_chunk(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       ):
          a  :    Dict				=						jnp.einsum('''...qhd,...khd->...qhk'''						,      lowerCAmelCase_						,      lowerCAmelCase_						,      precision=lowerCAmelCase_       )

          a  :    Tuple				=						jnp.max(lowerCAmelCase_						,      axis=-1						,      keepdims=lowerCAmelCase_       )
          a  :    Optional[int]				=						jax.lax.stop_gradient(lowerCAmelCase_       )
          a  :    int				=						jnp.exp(attn_weights - max_score       )

          a  :    Optional[Any]				=						jnp.einsum('''...vhf,...qhv->...qhf'''						,      lowerCAmelCase_						,      lowerCAmelCase_						,      precision=lowerCAmelCase_       )
          a  :    Union[str, Any]				=						jnp.einsum('''...qhk->...qh'''						,      lowerCAmelCase_       )

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

     def chunk_scanner(lowerCAmelCase_       ):
          # julienne key array
          a  :    Optional[Any]				=						jax.lax.dynamic_slice(
              operand=lowerCAmelCase_						,      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
          a  :    Optional[int]				=						jax.lax.dynamic_slice(
              operand=lowerCAmelCase_						,      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(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )

     a			,							a			,							a  :    Optional[Any]				=						jax.lax.map(f=lowerCAmelCase_						,      xs=jnp.arange(0						,      lowerCAmelCase_						,      lowerCAmelCase_       )       )

     a  :    List[Any]				=						jnp.max(lowerCAmelCase_						,      axis=0						,      keepdims=lowerCAmelCase_       )
     a  :    Optional[int]				=						jnp.exp(chunk_max - global_max       )

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

     a  :    Any				=						chunk_values.sum(axis=0       )
     a  :    Optional[int]				=						jnp.expand_dims(lowerCAmelCase_						,      -1       ).sum(axis=0       )

     return all_values / all_weights







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=jax.lax.Precision.HIGHEST						,      lowerCAmelCase_ = 1_0_2_4						,      lowerCAmelCase_ = 4_0_9_6       )	->	Dict:
     a			,							a			,							a  :    str				=						query.shape[-3:]

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

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

     a			,							a  :    Any				=						jax.lax.scan(
         f=lowerCAmelCase_						,      init=0						,      xs=lowerCAmelCase_						,      length=math.ceil(num_q / query_chunk_size       )       )

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






class 							_SCREAMING_SNAKE_CASE(   nn.Module	):
   UpperCamelCase_				:						int
   UpperCamelCase_				:						int 			=   8
   UpperCamelCase_				:						int 			=   64
   UpperCamelCase_				:						float 			=   0.0
   UpperCamelCase_				:						bool 			=   False
   UpperCamelCase_				:						jnp.dtype 			=   jnp.floataa
   def _lowercase(    self    )	->Any:
        a  :    Union[str, Any]				=						self.dim_head * self.heads
        a  :    Dict				=						self.dim_head**-0.5

        # Weights were exported with old names {to_q, to_k, to_v, to_out}
        a  :    Optional[int]				=						nn.Dense(lowerCAmelCase_							,     use_bias=lowerCAmelCase_							,     dtype=self.dtype							,     name='''to_q'''    )
        a  :    Tuple				=						nn.Dense(lowerCAmelCase_							,     use_bias=lowerCAmelCase_							,     dtype=self.dtype							,     name='''to_k'''    )
        a  :    str				=						nn.Dense(lowerCAmelCase_							,     use_bias=lowerCAmelCase_							,     dtype=self.dtype							,     name='''to_v'''    )

        a  :    Any				=						nn.Dense(self.query_dim							,     dtype=self.dtype							,     name='''to_out_0'''    )
        a  :    List[Any]				=						nn.Dropout(rate=self.dropout    )
   def _lowercase(    self							,     lowerCAmelCase_    )	->Tuple:
        a			,							a			,							a  :    Tuple				=						tensor.shape
        a  :    List[str]				=						self.heads
        a  :    Any				=						tensor.reshape(lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     dim // head_size    )
        a  :    Tuple				=						jnp.transpose(lowerCAmelCase_							,     (0, 2, 1, 3)    )
        a  :    List[Any]				=						tensor.reshape(batch_size * head_size							,     lowerCAmelCase_							,     dim // head_size    )
        return tensor
   def _lowercase(    self							,     lowerCAmelCase_    )	->Dict:
        a			,							a			,							a  :    Any				=						tensor.shape
        a  :    List[Any]				=						self.heads
        a  :    Optional[Any]				=						tensor.reshape(batch_size // head_size							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )
        a  :    int				=						jnp.transpose(lowerCAmelCase_							,     (0, 2, 1, 3)    )
        a  :    Union[str, Any]				=						tensor.reshape(batch_size // head_size							,     lowerCAmelCase_							,     dim * head_size    )
        return tensor


   def __call__(    self							,     lowerCAmelCase_							,     lowerCAmelCase_=None							,     lowerCAmelCase_=True    )	->Tuple:
        a  :    str				=						hidden_states if context is None else context

        a  :    Optional[Any]				=						self.query(lowerCAmelCase_    )
        a  :    Dict				=						self.key(lowerCAmelCase_    )
        a  :    List[str]				=						self.value(lowerCAmelCase_    )

        a  :    str				=						self.reshape_heads_to_batch_dim(lowerCAmelCase_    )
        a  :    Optional[Any]				=						self.reshape_heads_to_batch_dim(lowerCAmelCase_    )
        a  :    List[str]				=						self.reshape_heads_to_batch_dim(lowerCAmelCase_    )

        if self.use_memory_efficient_attention:
             a  :    Tuple				=						query_states.transpose(1							,     0							,     2    )
             a  :    Union[str, Any]				=						key_states.transpose(1							,     0							,     2    )
             a  :    List[str]				=						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

             a  :    Optional[Any]				=						query_states.shape[-3]
             if flatten_latent_dim % 64 == 0:
                  a  :    str				=						int(flatten_latent_dim / 64    )
             elif flatten_latent_dim % 16 == 0:
                  a  :    str				=						int(flatten_latent_dim / 16    )
             elif flatten_latent_dim % 4 == 0:
                  a  :    Optional[Any]				=						int(flatten_latent_dim / 4    )
             else:
                  a  :    Tuple				=						int(lowerCAmelCase_    )

             a  :    Union[str, Any]				=						jax_memory_efficient_attention(
                 lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     query_chunk_size=lowerCAmelCase_							,     key_chunk_size=40_96 * 4    )

             a  :    List[str]				=						hidden_states.transpose(1							,     0							,     2    )
        else:
             # compute attentions
             a  :    Union[str, Any]				=						jnp.einsum('''b i d, b j d->b i j'''							,     lowerCAmelCase_							,     lowerCAmelCase_    )
             a  :    List[Any]				=						attention_scores * self.scale
             a  :    List[str]				=						nn.softmax(lowerCAmelCase_							,     axis=2    )

             # attend to values
             a  :    List[str]				=						jnp.einsum('''b i j, b j d -> b i d'''							,     lowerCAmelCase_							,     lowerCAmelCase_    )

        a  :    List[Any]				=						self.reshape_batch_dim_to_heads(lowerCAmelCase_    )
        a  :    List[Any]				=						self.proj_attn(lowerCAmelCase_    )
        return self.dropout_layer(lowerCAmelCase_							,     deterministic=lowerCAmelCase_    )






class 							_SCREAMING_SNAKE_CASE(   nn.Module	):
   UpperCamelCase_				:						int
   UpperCamelCase_				:						int
   UpperCamelCase_				:						int
   UpperCamelCase_				:						float 			=   0.0
   UpperCamelCase_				:						bool 			=   False
   UpperCamelCase_				:						jnp.dtype 			=   jnp.floataa
   UpperCamelCase_				:						bool 			=   False
   def _lowercase(    self    )	->Union[str, Any]:
        # self attention (or cross_attention if only_cross_attention is True)
        a  :    List[Any]				=						FlaxAttention(
            self.dim							,     self.n_heads							,     self.d_head							,     self.dropout							,     self.use_memory_efficient_attention							,     dtype=self.dtype    )
        # cross attention
        a  :    str				=						FlaxAttention(
            self.dim							,     self.n_heads							,     self.d_head							,     self.dropout							,     self.use_memory_efficient_attention							,     dtype=self.dtype    )
        a  :    List[Any]				=						FlaxFeedForward(dim=self.dim							,     dropout=self.dropout							,     dtype=self.dtype    )
        a  :    int				=						nn.LayerNorm(epsilon=1e-5							,     dtype=self.dtype    )
        a  :    Optional[Any]				=						nn.LayerNorm(epsilon=1e-5							,     dtype=self.dtype    )
        a  :    int				=						nn.LayerNorm(epsilon=1e-5							,     dtype=self.dtype    )
        a  :    str				=						nn.Dropout(rate=self.dropout    )


   def __call__(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_=True    )	->Dict:
        # self attention
        a  :    int				=						hidden_states
        if self.only_cross_attention:
             a  :    Optional[Any]				=						self.attna(self.norma(lowerCAmelCase_    )							,     lowerCAmelCase_							,     deterministic=lowerCAmelCase_    )
        else:
             a  :    str				=						self.attna(self.norma(lowerCAmelCase_    )							,     deterministic=lowerCAmelCase_    )
        a  :    List[str]				=						hidden_states + residual

        # cross attention
        a  :    int				=						hidden_states
        a  :    Union[str, Any]				=						self.attna(self.norma(lowerCAmelCase_    )							,     lowerCAmelCase_							,     deterministic=lowerCAmelCase_    )
        a  :    Dict				=						hidden_states + residual

        # feed forward
        a  :    int				=						hidden_states
        a  :    Tuple				=						self.ff(self.norma(lowerCAmelCase_    )							,     deterministic=lowerCAmelCase_    )
        a  :    Tuple				=						hidden_states + residual

        return self.dropout_layer(lowerCAmelCase_							,     deterministic=lowerCAmelCase_    )






class 							_SCREAMING_SNAKE_CASE(   nn.Module	):
   UpperCamelCase_				:						int
   UpperCamelCase_				:						int
   UpperCamelCase_				:						int
   UpperCamelCase_				:						int 			=   1
   UpperCamelCase_				:						float 			=   0.0
   UpperCamelCase_				:						bool 			=   False
   UpperCamelCase_				:						bool 			=   False
   UpperCamelCase_				:						jnp.dtype 			=   jnp.floataa
   UpperCamelCase_				:						bool 			=   False
   def _lowercase(    self    )	->Tuple:
        a  :    str				=						nn.GroupNorm(num_groups=32							,     epsilon=1e-5    )

        a  :    Union[str, Any]				=						self.n_heads * self.d_head
        if self.use_linear_projection:
             a  :    str				=						nn.Dense(lowerCAmelCase_							,     dtype=self.dtype    )
        else:
             a  :    List[Any]				=						nn.Conv(
                 lowerCAmelCase_							,     kernel_size=(1, 1)							,     strides=(1, 1)							,     padding='''VALID'''							,     dtype=self.dtype							,     )

        a  :    Optional[int]				=						[
            FlaxBasicTransformerBlock(
                lowerCAmelCase_							,     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:
             a  :    Any				=						nn.Dense(lowerCAmelCase_							,     dtype=self.dtype    )
        else:
             a  :    Optional[int]				=						nn.Conv(
                 lowerCAmelCase_							,     kernel_size=(1, 1)							,     strides=(1, 1)							,     padding='''VALID'''							,     dtype=self.dtype							,     )

        a  :    Tuple				=						nn.Dropout(rate=self.dropout    )


   def __call__(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_=True    )	->List[str]:
        a			,							a			,							a			,							a  :    Optional[Any]				=						hidden_states.shape
        a  :    Dict				=						hidden_states
        a  :    int				=						self.norm(lowerCAmelCase_    )
        if self.use_linear_projection:
             a  :    Tuple				=						hidden_states.reshape(lowerCAmelCase_							,     height * width							,     lowerCAmelCase_    )
             a  :    List[Any]				=						self.proj_in(lowerCAmelCase_    )
        else:
             a  :    Tuple				=						self.proj_in(lowerCAmelCase_    )
             a  :    int				=						hidden_states.reshape(lowerCAmelCase_							,     height * width							,     lowerCAmelCase_    )

        for transformer_block in self.transformer_blocks:
             a  :    List[str]				=						transformer_block(lowerCAmelCase_							,     lowerCAmelCase_							,     deterministic=lowerCAmelCase_    )

        if self.use_linear_projection:
             a  :    List[str]				=						self.proj_out(lowerCAmelCase_    )
             a  :    List[str]				=						hidden_states.reshape(lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )
        else:
             a  :    Dict				=						hidden_states.reshape(lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )
             a  :    int				=						self.proj_out(lowerCAmelCase_    )

        a  :    List[str]				=						hidden_states + residual
        return self.dropout_layer(lowerCAmelCase_							,     deterministic=lowerCAmelCase_    )






class 							_SCREAMING_SNAKE_CASE(   nn.Module	):
   UpperCamelCase_				:						int
   UpperCamelCase_				:						float 			=   0.0
   UpperCamelCase_				:						jnp.dtype 			=   jnp.floataa
   def _lowercase(    self    )	->Union[str, Any]:
        # The second linear layer needs to be called
        # net_2 for now to match the index of the Sequential layer
        a  :    Tuple				=						FlaxGEGLU(self.dim							,     self.dropout							,     self.dtype    )
        a  :    Optional[Any]				=						nn.Dense(self.dim							,     dtype=self.dtype    )


   def __call__(    self							,     lowerCAmelCase_							,     lowerCAmelCase_=True    )	->List[Any]:
        a  :    Union[str, Any]				=						self.net_a(lowerCAmelCase_							,     deterministic=lowerCAmelCase_    )
        a  :    Tuple				=						self.net_a(lowerCAmelCase_    )
        return hidden_states






class 							_SCREAMING_SNAKE_CASE(   nn.Module	):
   UpperCamelCase_				:						int
   UpperCamelCase_				:						float 			=   0.0
   UpperCamelCase_				:						jnp.dtype 			=   jnp.floataa
   def _lowercase(    self    )	->List[str]:
        a  :    int				=						self.dim * 4
        a  :    int				=						nn.Dense(inner_dim * 2							,     dtype=self.dtype    )
        a  :    int				=						nn.Dropout(rate=self.dropout    )


   def __call__(    self							,     lowerCAmelCase_							,     lowerCAmelCase_=True    )	->Dict:
        a  :    Union[str, Any]				=						self.proj(lowerCAmelCase_    )
        a			,							a  :    int				=						jnp.split(lowerCAmelCase_							,     2							,     axis=2    )
        return self.dropout_layer(hidden_linear * nn.gelu(lowerCAmelCase_    )							,     deterministic=lowerCAmelCase_    )
import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property






class 							_SCREAMING_SNAKE_CASE(   unittest.TestCase	):
   @cached_property
   def _lowercase(    self    )	->List[str]:
        return BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''    )
   @cached_property
   def _lowercase(    self    )	->str:
        return BlenderbotTokenizerFast.from_pretrained('''facebook/blenderbot-3B'''    )
   def _lowercase(    self    )	->str:
        a  :    Optional[int]				=						self.tokenizer_ab
        a  :    List[Any]				=						''' I am a small frog.'''
        a  :    Optional[Any]				=						tok([src_text]							,     padding=lowerCAmelCase_							,     truncation=lowerCAmelCase_    )['''input_ids''']
        a  :    int				=						tok.batch_decode(lowerCAmelCase_							,     skip_special_tokens=lowerCAmelCase_							,     clean_up_tokenization_spaces=lowerCAmelCase_    )[0]
        assert src_text == decoded
   def _lowercase(    self    )	->Dict:
        a  :    List[Any]				=						self.rust_tokenizer_ab
        a  :    str				=						''' I am a small frog.'''
        a  :    Tuple				=						tok([src_text]							,     padding=lowerCAmelCase_							,     truncation=lowerCAmelCase_    )['''input_ids''']
        a  :    Optional[Any]				=						tok.batch_decode(lowerCAmelCase_							,     skip_special_tokens=lowerCAmelCase_							,     clean_up_tokenization_spaces=lowerCAmelCase_    )[0]
        assert src_text == decoded
   def _lowercase(    self    )	->Tuple:
        assert self.tokenizer_ab.add_prefix_space
        assert self.tokenizer_ab([''' Sam''', '''Sam''']    ).input_ids == [[55_02, 2], [55_02, 2]]


   def _lowercase(    self    )	->Optional[Any]:
        assert self.rust_tokenizer_ab.add_prefix_space
        assert self.rust_tokenizer_ab([''' Sam''', '''Sam''']    ).input_ids == [[55_02, 2], [55_02, 2]]