import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


_SCREAMING_SNAKE_CASE:						List[str]    	=       logging.get_logger(__name__)

@dataclass
class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		A_						 =      42
		A_						 =      42
		A_						 =      None
		A_						 =      None



		def 				_a				(			self    :				Union[str, Any]   ):






					"""simple docstring"""
					return json.dumps(dataclasses.asdict(self   )  ,					indent=2   ) + "\n"

@dataclass(frozen=_lowercase							)
class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		A_						 =      42
		A_						 =      None
		A_						 =      None
		A_						 =      None



		def 				_a				(			self    :				List[Any]   ):






					"""simple docstring"""
					return json.dumps(dataclasses.asdict(self   )   ) + "\n"

class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		def 				_a				(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				Tuple   ):






					"""simple docstring"""
					raise NotImplementedError()



		def 				_a				(			self    :				Any  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]   ):






					"""simple docstring"""
					raise NotImplementedError()



		def 				_a				(			self    :				str  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]   ):






					"""simple docstring"""
					raise NotImplementedError()



		def 				_a				(			self    :				str  ,					__SCREAMING_SNAKE_CASE    :				Tuple   ):






					"""simple docstring"""
					raise NotImplementedError()



		def 				_a				(			self    :				Dict   ):






					"""simple docstring"""
					raise NotImplementedError()



		def 				_a				(			self    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				str   ):






					"""simple docstring"""
					if len(self.get_labels()   ) > 1:
								_snake_case              =      self.get_labels()[int(example.label   )]
					return example







		@classmethod
		def 				_a				(			cls    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				Tuple=None   ):






					"""simple docstring"""
					with open(__SCREAMING_SNAKE_CASE  ,					"r"  ,					encoding="utf-8-sig"   ) as f:
								return list(csv.reader(__SCREAMING_SNAKE_CASE  ,					delimiter="\t"  ,					quotechar=__SCREAMING_SNAKE_CASE   )   )

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		def __init__(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				List[str]=None  ,					__SCREAMING_SNAKE_CASE    :				Any=None  ,					__SCREAMING_SNAKE_CASE    :				List[str]="classification"  ,					__SCREAMING_SNAKE_CASE    :				Any=False   ):






					"""simple docstring"""
					_snake_case              =      [] if labels is None else labels
					_snake_case              =      [] if examples is None else examples
					_snake_case              =      mode
					_snake_case              =      verbose



		def __len__(			self    :				List[str]   ):






					"""simple docstring"""
					return len(self.examples   )



		def __getitem__(			self    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				Tuple   ):






					"""simple docstring"""
					if isinstance(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   ):
								return SingleSentenceClassificationProcessor(labels=self.labels  ,					examples=self.examples[idx]   )
					return self.examples[idx]



		@classmethod
		def 				_a				(			cls    :				int  ,					__SCREAMING_SNAKE_CASE    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]=""  ,					__SCREAMING_SNAKE_CASE    :				Tuple=0  ,					__SCREAMING_SNAKE_CASE    :				Dict=1  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=None  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]=False  ,					**__SCREAMING_SNAKE_CASE    :				str   ):






					"""simple docstring"""
					_snake_case              =      cls(**__SCREAMING_SNAKE_CASE   )
					processor.add_examples_from_csv(
					    __SCREAMING_SNAKE_CASE  ,					split_name=__SCREAMING_SNAKE_CASE  ,					column_label=__SCREAMING_SNAKE_CASE  ,					column_text=__SCREAMING_SNAKE_CASE  ,					column_id=__SCREAMING_SNAKE_CASE  ,					skip_first_row=__SCREAMING_SNAKE_CASE  ,					overwrite_labels=__SCREAMING_SNAKE_CASE  ,					overwrite_examples=__SCREAMING_SNAKE_CASE  ,					)
					return processor



		@classmethod
		def 				_a				(			cls    :				Any  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				List[str]=None  ,					**__SCREAMING_SNAKE_CASE    :				List[str]   ):






					"""simple docstring"""
					_snake_case              =      cls(**__SCREAMING_SNAKE_CASE   )
					processor.add_examples(__SCREAMING_SNAKE_CASE  ,					labels=__SCREAMING_SNAKE_CASE   )
					return processor



		def 				_a				(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				Dict=""  ,					__SCREAMING_SNAKE_CASE    :				str=0  ,					__SCREAMING_SNAKE_CASE    :				List[str]=1  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]=None  ,					__SCREAMING_SNAKE_CASE    :				List[str]=False  ,					__SCREAMING_SNAKE_CASE    :				Any=False  ,					__SCREAMING_SNAKE_CASE    :				str=False  ,					):






					"""simple docstring"""
					_snake_case              =      self._read_tsv(__SCREAMING_SNAKE_CASE   )
					if skip_first_row:
								_snake_case              =      lines[1:]
					_snake_case              =      []
					_snake_case              =      []
					_snake_case              =      []
					for i, line in enumerate(__SCREAMING_SNAKE_CASE   ):
								texts.append(line[column_text]   )
								labels.append(line[column_label]   )
								if column_id is not None:
											ids.append(line[column_id]   )
								else:
											_snake_case              =      F'{split_name}-{i}' if split_name else str(__SCREAMING_SNAKE_CASE   )
											ids.append(__SCREAMING_SNAKE_CASE   )

					return self.add_examples(
					    __SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					overwrite_labels=__SCREAMING_SNAKE_CASE  ,					overwrite_examples=__SCREAMING_SNAKE_CASE   )



		def 				_a				(			self    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				int=None  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=None  ,					__SCREAMING_SNAKE_CASE    :				Tuple=False  ,					__SCREAMING_SNAKE_CASE    :				str=False   ):






					"""simple docstring"""
					if labels is not None and len(__SCREAMING_SNAKE_CASE   ) != len(__SCREAMING_SNAKE_CASE   ):
								raise ValueError(
								    F'Text and labels have mismatched lengths {len(__SCREAMING_SNAKE_CASE   )} and {len(__SCREAMING_SNAKE_CASE   )}'   )
					if ids is not None and len(__SCREAMING_SNAKE_CASE   ) != len(__SCREAMING_SNAKE_CASE   ):
								raise ValueError(F'Text and ids have mismatched lengths {len(__SCREAMING_SNAKE_CASE   )} and {len(__SCREAMING_SNAKE_CASE   )}'   )
					if ids is None:
								_snake_case              =      [None] * len(__SCREAMING_SNAKE_CASE   )
					if labels is None:
								_snake_case              =      [None] * len(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      []
					_snake_case              =      set()
					for text_or_text_and_label, label, guid in zip(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   ):
								if isinstance(__SCREAMING_SNAKE_CASE  ,					(tuple, list)   ) and label is None:
											_snake_case       , _snake_case              =      text_or_text_and_label
								else:
											_snake_case              =      text_or_text_and_label
								added_labels.add(__SCREAMING_SNAKE_CASE   )
								examples.append(InputExample(guid=__SCREAMING_SNAKE_CASE  ,					text_a=__SCREAMING_SNAKE_CASE  ,					text_b=__SCREAMING_SNAKE_CASE  ,					label=__SCREAMING_SNAKE_CASE   )   )

					# Update examples
					if overwrite_examples:
								_snake_case              =      examples
					else:
								self.examples.extend(__SCREAMING_SNAKE_CASE   )

					# Update labels
					if overwrite_labels:
								_snake_case              =      list(__SCREAMING_SNAKE_CASE   )
					else:
								_snake_case              =      list(set(self.labels   ).union(__SCREAMING_SNAKE_CASE   )   )

					return self.examples







		def 				_a				(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]=None  ,					__SCREAMING_SNAKE_CASE    :				Any=False  ,					__SCREAMING_SNAKE_CASE    :				Any=0  ,					__SCREAMING_SNAKE_CASE    :				Tuple=True  ,					__SCREAMING_SNAKE_CASE    :				List[str]=None  ,					):






					"""simple docstring"""
					if max_length is None:
								_snake_case              =      tokenizer.max_len

					_snake_case              =      {label: i for i, label in enumerate(self.labels   )}

					_snake_case              =      []
					for ex_index, example in enumerate(self.examples   ):
								if ex_index % 10_000 == 0:
											logger.info(F'Tokenizing example {ex_index}'   )

								_snake_case              =      tokenizer.encode(
								    example.text_a  ,					add_special_tokens=__SCREAMING_SNAKE_CASE  ,					max_length=min(__SCREAMING_SNAKE_CASE  ,					tokenizer.max_len   )  ,					)
								all_input_ids.append(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      max(len(__SCREAMING_SNAKE_CASE   ) for input_ids in all_input_ids   )

					_snake_case              =      []
					for ex_index, (input_ids, example) in enumerate(zip(__SCREAMING_SNAKE_CASE  ,					self.examples   )   ):
								if ex_index % 10_000 == 0:
											logger.info(F'Writing example {ex_index}/{len(self.examples   )}'   )
								# The mask has 1 for real tokens and 0 for padding tokens. Only real
								# tokens are attended to.
								_snake_case              =      [1 if mask_padding_with_zero else 0] * len(__SCREAMING_SNAKE_CASE   )

								# Zero-pad up to the sequence length.
								_snake_case              =      batch_length - len(__SCREAMING_SNAKE_CASE   )
								if pad_on_left:
											_snake_case              =      ([pad_token] * padding_length) + input_ids
											_snake_case              =      ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
								else:
											_snake_case              =      input_ids + ([pad_token] * padding_length)
											_snake_case              =      attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

								if len(__SCREAMING_SNAKE_CASE   ) != batch_length:
											raise ValueError(F'Error with input length {len(__SCREAMING_SNAKE_CASE   )} vs {batch_length}'   )
								if len(__SCREAMING_SNAKE_CASE   ) != batch_length:
											raise ValueError(F'Error with input length {len(__SCREAMING_SNAKE_CASE   )} vs {batch_length}'   )

								if self.mode == "classification":
											_snake_case              =      label_map[example.label]
								elif self.mode == "regression":
											_snake_case              =      float(example.label   )
								else:
											raise ValueError(self.mode   )

								if ex_index < 5 and self.verbose:
											logger.info("*** Example ***"   )
											logger.info(F'guid: {example.guid}'   )
											logger.info(F'input_ids: {" ".join([str(__SCREAMING_SNAKE_CASE   ) for x in input_ids]   )}'   )
											logger.info(F'attention_mask: {" ".join([str(__SCREAMING_SNAKE_CASE   ) for x in attention_mask]   )}'   )
											logger.info(F'label: {example.label} (id = {label})'   )

								features.append(InputFeatures(input_ids=__SCREAMING_SNAKE_CASE  ,					attention_mask=__SCREAMING_SNAKE_CASE  ,					label=__SCREAMING_SNAKE_CASE   )   )

					if return_tensors is None:
								return features
					elif return_tensors == "tf":
								if not is_tf_available():
											raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported"   )
								import tensorflow as tf

								def gen():
											for ex in features:
														yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

								_snake_case              =      tf.data.Dataset.from_generator(
								    __SCREAMING_SNAKE_CASE  ,					({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)  ,					({"input_ids": tf.TensorShape([None]   ), "attention_mask": tf.TensorShape([None]   )}, tf.TensorShape([]   ))  ,					)
								return dataset
					elif return_tensors == "pt":
								if not is_torch_available():
											raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported"   )
								import torch
								from torch.utils.data import TensorDataset

								_snake_case              =      torch.tensor([f.input_ids for f in features]  ,					dtype=torch.long   )
								_snake_case              =      torch.tensor([f.attention_mask for f in features]  ,					dtype=torch.long   )
								if self.mode == "classification":
											_snake_case              =      torch.tensor([f.label for f in features]  ,					dtype=torch.long   )
								elif self.mode == "regression":
											_snake_case              =      torch.tensor([f.label for f in features]  ,					dtype=torch.float   )

								_snake_case              =      TensorDataset(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
								return dataset
					else:
								raise ValueError("return_tensors should be one of 'tf' or 'pt'"   )


import functools
import math

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






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_ = 4096  ):



			"""simple docstring"""





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

			@functools.partial(jax.checkpoint							,	prevent_cse=lowerCamelCase_  )
			def summarize_chunk(lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  ):
						_snake_case              =      jnp.einsum("...qhd,...khd->...qhk"							,	lowerCamelCase_							,	lowerCamelCase_							,	precision=lowerCamelCase_  )

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

						_snake_case              =      jnp.einsum("...vhf,...qhv->...qhf"							,	lowerCamelCase_							,	lowerCamelCase_							,	precision=lowerCamelCase_  )
						_snake_case              =      jnp.einsum("...qhk->...qh"							,	lowerCamelCase_  )

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

			def chunk_scanner(lowerCamelCase_  ):
						# julienne key array
						_snake_case              =      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
						_snake_case              =      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_  )

			_snake_case       , _snake_case       , _snake_case              =      jax.lax.map(f=lowerCamelCase_							,	xs=jnp.arange(0							,	lowerCamelCase_							,	lowerCamelCase_  )  )

			_snake_case              =      jnp.max(lowerCamelCase_							,	axis=0							,	keepdims=lowerCamelCase_  )
			_snake_case              =      jnp.exp(chunk_max - global_max  )

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

			_snake_case              =      chunk_values.sum(axis=0  )
			_snake_case              =      jnp.expand_dims(lowerCamelCase_							,	-1  ).sum(axis=0  )

			return all_values / all_weights






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_=jax.lax.Precision.HIGHEST							,	lowerCamelCase_ = 1024							,	lowerCamelCase_ = 4096  ):



			"""simple docstring"""





			_snake_case       , _snake_case       , _snake_case              =      query.shape[-3:]

			def chunk_scanner(lowerCamelCase_							,	lowerCamelCase_  ):
						# julienne query array
						_snake_case              =      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_  ),
						)

			_snake_case       , _snake_case              =      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							):






		"""simple docstring"""







		A_						 =      42
		A_						 =      8
		A_						 =      64
		A_						 =      0.0
		A_						 =      False
		A_						 =      jnp.floataa



		def 				_a				(			self    :				Union[str, Any]   ):






					"""simple docstring"""
					_snake_case              =      self.dim_head * self.heads
					_snake_case              =      self.dim_head**-0.5

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

					_snake_case              =      nn.Dense(self.query_dim  ,					dtype=self.dtype  ,					name="to_out_0"   )
					_snake_case              =      nn.Dropout(rate=self.dropout   )



		def 				_a				(			self    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				Any   ):






					"""simple docstring"""
					_snake_case       , _snake_case       , _snake_case              =      tensor.shape
					_snake_case              =      self.heads
					_snake_case              =      tensor.reshape(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					dim // head_size   )
					_snake_case              =      jnp.transpose(__SCREAMING_SNAKE_CASE  ,					(0, 2, 1, 3)   )
					_snake_case              =      tensor.reshape(batch_size * head_size  ,					__SCREAMING_SNAKE_CASE  ,					dim // head_size   )
					return tensor



		def 				_a				(			self    :				str  ,					__SCREAMING_SNAKE_CASE    :				Tuple   ):






					"""simple docstring"""
					_snake_case       , _snake_case       , _snake_case              =      tensor.shape
					_snake_case              =      self.heads
					_snake_case              =      tensor.reshape(batch_size // head_size  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
					_snake_case              =      jnp.transpose(__SCREAMING_SNAKE_CASE  ,					(0, 2, 1, 3)   )
					_snake_case              =      tensor.reshape(batch_size // head_size  ,					__SCREAMING_SNAKE_CASE  ,					dim * head_size   )
					return tensor







		def __call__(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				Tuple=None  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=True   ):






					"""simple docstring"""
					_snake_case              =      hidden_states if context is None else context

					_snake_case              =      self.query(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      self.key(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      self.value(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      self.reshape_heads_to_batch_dim(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      self.reshape_heads_to_batch_dim(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      self.reshape_heads_to_batch_dim(__SCREAMING_SNAKE_CASE   )

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

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

								_snake_case              =      jax_memory_efficient_attention(
								    __SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					query_chunk_size=__SCREAMING_SNAKE_CASE  ,					key_chunk_size=4_096 * 4   )

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

								# attend to values
								_snake_case              =      jnp.einsum("b i j, b j d -> b i d"  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )

					_snake_case              =      self.reshape_batch_dim_to_heads(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      self.proj_attn(__SCREAMING_SNAKE_CASE   )
					return self.dropout_layer(__SCREAMING_SNAKE_CASE  ,					deterministic=__SCREAMING_SNAKE_CASE   )

class 	_SCREAMING_SNAKE_CASE							(     nn.Module							):






		"""simple docstring"""







		A_						 =      42
		A_						 =      42
		A_						 =      42
		A_						 =      0.0
		A_						 =      False
		A_						 =      jnp.floataa
		A_						 =      False



		def 				_a				(			self    :				Any   ):






					"""simple docstring"""
					# self attention (or cross_attention if only_cross_attention is True)
					_snake_case              =      FlaxAttention(
					    self.dim  ,					self.n_heads  ,					self.d_head  ,					self.dropout  ,					self.use_memory_efficient_attention  ,					dtype=self.dtype   )
					# cross attention
					_snake_case              =      FlaxAttention(
					    self.dim  ,					self.n_heads  ,					self.d_head  ,					self.dropout  ,					self.use_memory_efficient_attention  ,					dtype=self.dtype   )
					_snake_case              =      FlaxFeedForward(dim=self.dim  ,					dropout=self.dropout  ,					dtype=self.dtype   )
					_snake_case              =      nn.LayerNorm(epsilon=1e-5  ,					dtype=self.dtype   )
					_snake_case              =      nn.LayerNorm(epsilon=1e-5  ,					dtype=self.dtype   )
					_snake_case              =      nn.LayerNorm(epsilon=1e-5  ,					dtype=self.dtype   )
					_snake_case              =      nn.Dropout(rate=self.dropout   )







		def __call__(			self    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				Any  ,					__SCREAMING_SNAKE_CASE    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				Any=True   ):






					"""simple docstring"""
					# self attention
					_snake_case              =      hidden_states
					if self.only_cross_attention:
								_snake_case              =      self.attna(self.norma(__SCREAMING_SNAKE_CASE   )  ,					__SCREAMING_SNAKE_CASE  ,					deterministic=__SCREAMING_SNAKE_CASE   )
					else:
								_snake_case              =      self.attna(self.norma(__SCREAMING_SNAKE_CASE   )  ,					deterministic=__SCREAMING_SNAKE_CASE   )
					_snake_case              =      hidden_states + residual

					# cross attention
					_snake_case              =      hidden_states
					_snake_case              =      self.attna(self.norma(__SCREAMING_SNAKE_CASE   )  ,					__SCREAMING_SNAKE_CASE  ,					deterministic=__SCREAMING_SNAKE_CASE   )
					_snake_case              =      hidden_states + residual

					# feed forward
					_snake_case              =      hidden_states
					_snake_case              =      self.ff(self.norma(__SCREAMING_SNAKE_CASE   )  ,					deterministic=__SCREAMING_SNAKE_CASE   )
					_snake_case              =      hidden_states + residual

					return self.dropout_layer(__SCREAMING_SNAKE_CASE  ,					deterministic=__SCREAMING_SNAKE_CASE   )

class 	_SCREAMING_SNAKE_CASE							(     nn.Module							):






		"""simple docstring"""







		A_						 =      42
		A_						 =      42
		A_						 =      42
		A_						 =      1
		A_						 =      0.0
		A_						 =      False
		A_						 =      False
		A_						 =      jnp.floataa
		A_						 =      False



		def 				_a				(			self    :				Union[str, Any]   ):






					"""simple docstring"""
					_snake_case              =      nn.GroupNorm(num_groups=32  ,					epsilon=1e-5   )

					_snake_case              =      self.n_heads * self.d_head
					if self.use_linear_projection:
								_snake_case              =      nn.Dense(__SCREAMING_SNAKE_CASE  ,					dtype=self.dtype   )
					else:
								_snake_case              =      nn.Conv(
								    __SCREAMING_SNAKE_CASE  ,					kernel_size=(1, 1)  ,					strides=(1, 1)  ,					padding="VALID"  ,					dtype=self.dtype  ,					)

					_snake_case              =      [
					    FlaxBasicTransformerBlock(
					        __SCREAMING_SNAKE_CASE  ,					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:
								_snake_case              =      nn.Dense(__SCREAMING_SNAKE_CASE  ,					dtype=self.dtype   )
					else:
								_snake_case              =      nn.Conv(
								    __SCREAMING_SNAKE_CASE  ,					kernel_size=(1, 1)  ,					strides=(1, 1)  ,					padding="VALID"  ,					dtype=self.dtype  ,					)

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







		def __call__(			self    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=True   ):






					"""simple docstring"""
					_snake_case       , _snake_case       , _snake_case       , _snake_case              =      hidden_states.shape
					_snake_case              =      hidden_states
					_snake_case              =      self.norm(__SCREAMING_SNAKE_CASE   )
					if self.use_linear_projection:
								_snake_case              =      hidden_states.reshape(__SCREAMING_SNAKE_CASE  ,					height * width  ,					__SCREAMING_SNAKE_CASE   )
								_snake_case              =      self.proj_in(__SCREAMING_SNAKE_CASE   )
					else:
								_snake_case              =      self.proj_in(__SCREAMING_SNAKE_CASE   )
								_snake_case              =      hidden_states.reshape(__SCREAMING_SNAKE_CASE  ,					height * width  ,					__SCREAMING_SNAKE_CASE   )

					for transformer_block in self.transformer_blocks:
								_snake_case              =      transformer_block(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					deterministic=__SCREAMING_SNAKE_CASE   )

					if self.use_linear_projection:
								_snake_case              =      self.proj_out(__SCREAMING_SNAKE_CASE   )
								_snake_case              =      hidden_states.reshape(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
					else:
								_snake_case              =      hidden_states.reshape(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
								_snake_case              =      self.proj_out(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      hidden_states + residual
					return self.dropout_layer(__SCREAMING_SNAKE_CASE  ,					deterministic=__SCREAMING_SNAKE_CASE   )

class 	_SCREAMING_SNAKE_CASE							(     nn.Module							):






		"""simple docstring"""







		A_						 =      42
		A_						 =      0.0
		A_						 =      jnp.floataa



		def 				_a				(			self    :				Optional[int]   ):






					"""simple docstring"""
					# The second linear layer needs to be called
					# net_2 for now to match the index of the Sequential layer
					_snake_case              =      FlaxGEGLU(self.dim  ,					self.dropout  ,					self.dtype   )
					_snake_case              =      nn.Dense(self.dim  ,					dtype=self.dtype   )







		def __call__(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=True   ):






					"""simple docstring"""
					_snake_case              =      self.net_a(__SCREAMING_SNAKE_CASE  ,					deterministic=__SCREAMING_SNAKE_CASE   )
					_snake_case              =      self.net_a(__SCREAMING_SNAKE_CASE   )
					return hidden_states

class 	_SCREAMING_SNAKE_CASE							(     nn.Module							):






		"""simple docstring"""







		A_						 =      42
		A_						 =      0.0
		A_						 =      jnp.floataa



		def 				_a				(			self    :				Optional[Any]   ):






					"""simple docstring"""
					_snake_case              =      self.dim * 4
					_snake_case              =      nn.Dense(inner_dim * 2  ,					dtype=self.dtype   )
					_snake_case              =      nn.Dropout(rate=self.dropout   )







		def __call__(			self    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]  ,					__SCREAMING_SNAKE_CASE    :				List[str]=True   ):






					"""simple docstring"""
					_snake_case              =      self.proj(__SCREAMING_SNAKE_CASE   )
					_snake_case       , _snake_case              =      jnp.split(__SCREAMING_SNAKE_CASE  ,					2  ,					axis=2   )
					return self.dropout_layer(hidden_linear * nn.gelu(__SCREAMING_SNAKE_CASE   )  ,					deterministic=__SCREAMING_SNAKE_CASE   )

