

"""simple docstring"""





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_a				   =							logging.get_logger(__name__)




def 						_A						(						UpperCamelCase_  : List[str])			->		Union[str, Any]:
   '''simple docstring'''


   if "backbone.0.body" in orig_key:
      __lowercase			=		orig_key.replace("backbone.0.body",    "backbone.conv_encoder.model")
   if "transformer" in orig_key:
      __lowercase			=		orig_key.replace("transformer.",    "")
   if "norm1" in orig_key:
      if "encoder" in orig_key:
         __lowercase			=		orig_key.replace("norm1",    "self_attn_layer_norm")
      else:
         __lowercase			=		orig_key.replace("norm1",    "encoder_attn_layer_norm")
   if "norm2" in orig_key:
      if "encoder" in orig_key:
         __lowercase			=		orig_key.replace("norm2",    "final_layer_norm")
      else:
         __lowercase			=		orig_key.replace("norm2",    "self_attn_layer_norm")
   if "norm3" in orig_key:
      __lowercase			=		orig_key.replace("norm3",    "final_layer_norm")
   if "linear1" in orig_key:
      __lowercase			=		orig_key.replace("linear1",    "fc1")
   if "linear2" in orig_key:
      __lowercase			=		orig_key.replace("linear2",    "fc2")
   if "query_embed" in orig_key:
      __lowercase			=		orig_key.replace("query_embed",    "query_position_embeddings")
   if "cross_attn" in orig_key:
      __lowercase			=		orig_key.replace("cross_attn",    "encoder_attn")

   return orig_key




def 						_A						(						UpperCamelCase_  : Any)			->		Optional[Any]:
   '''simple docstring'''


   for i in range(6):
      # read in weights + bias of input projection layer of self-attention
      __lowercase			=		state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_weight""")
      __lowercase			=		state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_bias""")
      # next, add query, keys and values (in that order) to the state dict
      __lowercase			=		in_proj_weight[:256, :]
      __lowercase			=		in_proj_bias[:256]
      __lowercase			=		in_proj_weight[256:512, :]
      __lowercase			=		in_proj_bias[256:512]
      __lowercase			=		in_proj_weight[-256:, :]
      __lowercase			=		in_proj_bias[-256:]




def 						_A						(						)			->		List[str]:
   '''simple docstring'''


   __lowercase			=		"http://images.cocodataset.org/val2017/000000039769.jpg"
   __lowercase			=		Image.open(requests.get(UpperCamelCase_,    stream=UpperCamelCase_).raw)

   return im




@torch.no_grad()
def 						_A						(						UpperCamelCase_  : Optional[int],    UpperCamelCase_  : int,    UpperCamelCase_  : int,    UpperCamelCase_  : int,    UpperCamelCase_  : str,    UpperCamelCase_  : int,    UpperCamelCase_  : List[Any],    )			->		List[str]:
   '''simple docstring'''


   __lowercase			=		DeformableDetrConfig()
   # set config attributes
   if single_scale:
      __lowercase			=		1
   __lowercase			=		dilation
   __lowercase			=		with_box_refine
   __lowercase			=		two_stage
   # set labels
   __lowercase			=		91
   __lowercase			=		"huggingface/label-files"
   __lowercase			=		"coco-detection-id2label.json"
   __lowercase			=		json.load(open(cached_download(hf_hub_url(UpperCamelCase_,    UpperCamelCase_,    repo_type="dataset")),    "r"))
   __lowercase			=		{int(UpperCamelCase_): v for k, v in idalabel.items()}
   __lowercase			=		idalabel
   __lowercase			=		{v: k for k, v in idalabel.items()}

   # load image processor
   __lowercase			=		DeformableDetrImageProcessor(format="coco_detection")

   # prepare image
   __lowercase			=		prepare_img()
   __lowercase			=		image_processor(images=UpperCamelCase_,    return_tensors="pt")
   __lowercase			=		encoding["pixel_values"]

   logger.info("Converting model...")

   # load original state dict
   __lowercase			=		torch.load(UpperCamelCase_,    map_location="cpu")["model"]
   # rename keys
   for key in state_dict.copy().keys():
      __lowercase			=		state_dict.pop(UpperCamelCase_)
      __lowercase			=		val
   # query, key and value matrices need special treatment
   read_in_q_k_v(UpperCamelCase_)
   # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
   __lowercase			=		"model."
   for key in state_dict.copy().keys():
      if not key.startswith("class_embed") and not key.startswith("bbox_embed"):
         __lowercase			=		state_dict.pop(UpperCamelCase_)
         __lowercase			=		val
    # finally, create HuggingFace model and load state dict
   __lowercase			=		DeformableDetrForObjectDetection(UpperCamelCase_)
   model.load_state_dict(UpperCamelCase_)
   model.eval()

   __lowercase			=		"cuda" if torch.cuda.is_available() else "cpu"
   model.to(UpperCamelCase_)
   # verify our conversion
   __lowercase			=		model(pixel_values.to(UpperCamelCase_))

   __lowercase			=		torch.tensor(
       [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5_634, -5.3_379, -7.5_116]])
   __lowercase			=		torch.tensor([[0.8_693, 0.2_289, 0.2_492], [0.3_150, 0.5_489, 0.5_845], [0.5_563, 0.7_580, 0.8_518]])

   if single_scale:
      __lowercase			=		torch.tensor(
          [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0_665, -5.8_470, -7.7_003]])
      __lowercase			=		torch.tensor([[0.7_292, 0.4_991, 0.5_532], [0.7_959, 0.2_426, 0.4_236], [0.7_582, 0.3_518, 0.4_451]])

   if single_scale and dilation:
      __lowercase			=		torch.tensor(
          [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1_178, -4.5_275, -6.2_671]])
      __lowercase			=		torch.tensor([[0.7_665, 0.4_130, 0.4_769], [0.8_364, 0.1_841, 0.3_391], [0.6_261, 0.3_895, 0.7_978]])

   if with_box_refine:
      __lowercase			=		torch.tensor(
          [[-8.8_895, -5.4_187, -6.8_153], [-8.4_706, -6.1_668, -7.6_184], [-9.0_042, -5.5_359, -6.9_141]])
      __lowercase			=		torch.tensor([[0.7_828, 0.2_208, 0.4_323], [0.0_892, 0.5_996, 0.1_319], [0.5_524, 0.6_389, 0.8_914]])

   if with_box_refine and two_stage:
      __lowercase			=		torch.tensor(
          [[-6.7_108, -4.3_213, -6.3_777], [-8.9_014, -6.1_799, -6.7_240], [-6.9_315, -4.4_735, -6.2_298]])
      __lowercase			=		torch.tensor([[0.2_583, 0.5_499, 0.4_683], [0.7_652, 0.9_068, 0.4_882], [0.5_490, 0.2_763, 0.0_564]])

   print("Logits:",    outputs.logits[0, :3, :3])

   assert torch.allclose(outputs.logits[0, :3, :3],    expected_logits.to(UpperCamelCase_),    atol=1E-4)
   assert torch.allclose(outputs.pred_boxes[0, :3, :3],    expected_boxes.to(UpperCamelCase_),    atol=1E-4)

   print("Everything ok!")

   # Save model and image processor
   logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""")
   Path(UpperCamelCase_).mkdir(exist_ok=UpperCamelCase_)
   model.save_pretrained(UpperCamelCase_)
   image_processor.save_pretrained(UpperCamelCase_)

   # Push to hub
   if push_to_hub:
      __lowercase			=		"deformable-detr"
      model_name += "-single-scale" if single_scale else ""
      model_name += "-dc5" if dilation else ""
      model_name += "-with-box-refine" if with_box_refine else ""
      model_name += "-two-stage" if two_stage else ""
      print("Pushing model to hub...")
      model.push_to_hub(repo_path_or_name=UpperCamelCase_,    organization="nielsr",    commit_message="Add model")


if __name__ == "__main__":
   _a				   =							argparse.ArgumentParser()

   parser.add_argument(
       '--checkpoint_path',
       type=str,
       default='/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth',
       help='Path to Pytorch checkpoint (.pth file) you\'d like to convert.',
   )
   parser.add_argument('--single_scale', action='store_true', help='Whether to set config.num_features_levels = 1.')
   parser.add_argument('--dilation', action='store_true', help='Whether to set config.dilation=True.')
   parser.add_argument('--with_box_refine', action='store_true', help='Whether to set config.with_box_refine=True.')
   parser.add_argument('--two_stage', action='store_true', help='Whether to set config.two_stage=True.')
   parser.add_argument(
       '--pytorch_dump_folder_path',
       default=None,
       type=str,
       required=True,
       help='Path to the folder to output PyTorch model.',
   )
   parser.add_argument(
       '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
   )
   _a				   =							parser.parse_args()
   convert_deformable_detr_checkpoint(
       args.checkpoint_path,
       args.single_scale,
       args.dilation,
       args.with_box_refine,
       args.two_stage,
       args.pytorch_dump_folder_path,
       args.push_to_hub,
   )



"""simple docstring"""





import functools
import math

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




def 						_A						(						UpperCamelCase_  : int,    UpperCamelCase_  : Union[str, Any],    UpperCamelCase_  : List[Any],    UpperCamelCase_  : Dict,    UpperCamelCase_  : int = 4096)			->		Optional[Any]:
   '''simple docstring'''


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

   @functools.partial(jax.checkpoint,    prevent_cse=UpperCamelCase_)
   def summarize_chunk(UpperCamelCase_  : int,    UpperCamelCase_  : Union[str, Any],    UpperCamelCase_  : Any):
      __lowercase			=		jnp.einsum("...qhd,...khd->...qhk",    UpperCamelCase_,    UpperCamelCase_,    precision=UpperCamelCase_)

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

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

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

   def chunk_scanner(UpperCamelCase_  : int):
      # julienne key array
      __lowercase			=		jax.lax.dynamic_slice(
          operand=UpperCamelCase_,    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
      __lowercase			=		jax.lax.dynamic_slice(
          operand=UpperCamelCase_,    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(UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_)

   __lowercase	,__lowercase	,__lowercase			=		jax.lax.map(f=UpperCamelCase_,    xs=jnp.arange(0,    UpperCamelCase_,    UpperCamelCase_))

   __lowercase			=		jnp.max(UpperCamelCase_,    axis=0,    keepdims=UpperCamelCase_)
   __lowercase			=		jnp.exp(chunk_max - global_max)

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

   __lowercase			=		chunk_values.sum(axis=0)
   __lowercase			=		jnp.expand_dims(UpperCamelCase_,    -1).sum(axis=0)

   return all_values / all_weights




def 						_A						(						UpperCamelCase_  : Optional[Any],    UpperCamelCase_  : Optional[int],    UpperCamelCase_  : List[str],    UpperCamelCase_  : Union[str, Any]=jax.lax.Precision.HIGHEST,    UpperCamelCase_  : int = 1024,    UpperCamelCase_  : int = 4096)			->		str:
   '''simple docstring'''


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

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

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

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

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




class       _lowerCAmelCase						(			nn.Module     ):







 """simple docstring"""







 __UpperCAmelCase			:      int
 __UpperCAmelCase			:      int     	=			8
 __UpperCAmelCase			:      int     	=			6_4
 __UpperCAmelCase			:      float     	=			0.0
 __UpperCAmelCase			:      bool     	=			False
 __UpperCAmelCase			:      jnp.dtype     	=			jnp.floataa


 def       _lowercase				(    self							:    List[Any]			):
    __lowercase			=		self.dim_head * self.heads
    __lowercase			=		self.dim_head**-0.5

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

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


 def       _lowercase				(    self							:    Any,      UpperCAmelCase__							:    Union[str, Any]			):
    __lowercase	,__lowercase	,__lowercase			=		tensor.shape
    __lowercase			=		self.heads
    __lowercase			=		tensor.reshape(UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__,      dim // head_size			)
    __lowercase			=		jnp.transpose(UpperCAmelCase__,      (0, 2, 1, 3)			)
    __lowercase			=		tensor.reshape(batch_size * head_size,      UpperCAmelCase__,      dim // head_size			)
    return tensor


 def       _lowercase				(    self							:    Union[str, Any],      UpperCAmelCase__							:    int			):
    __lowercase	,__lowercase	,__lowercase			=		tensor.shape
    __lowercase			=		self.heads
    __lowercase			=		tensor.reshape(batch_size // head_size,      UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__			)
    __lowercase			=		jnp.transpose(UpperCAmelCase__,      (0, 2, 1, 3)			)
    __lowercase			=		tensor.reshape(batch_size // head_size,      UpperCAmelCase__,      dim * head_size			)
    return tensor



 def __call__(    self							:    List[Any],      UpperCAmelCase__							:    str,      UpperCAmelCase__							:    Union[str, Any]=None,      UpperCAmelCase__							:    Optional[Any]=True			):
    __lowercase			=		hidden_states if context is None else context

    __lowercase			=		self.query(UpperCAmelCase__			)
    __lowercase			=		self.key(UpperCAmelCase__			)
    __lowercase			=		self.value(UpperCAmelCase__			)

    __lowercase			=		self.reshape_heads_to_batch_dim(UpperCAmelCase__			)
    __lowercase			=		self.reshape_heads_to_batch_dim(UpperCAmelCase__			)
    __lowercase			=		self.reshape_heads_to_batch_dim(UpperCAmelCase__			)

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

       __lowercase			=		query_states.shape[-3]
       if flatten_latent_dim % 6_4 == 0:
          __lowercase			=		int(flatten_latent_dim / 6_4			)
       elif flatten_latent_dim % 1_6 == 0:
          __lowercase			=		int(flatten_latent_dim / 1_6			)
       elif flatten_latent_dim % 4 == 0:
          __lowercase			=		int(flatten_latent_dim / 4			)
       else:
          __lowercase			=		int(UpperCAmelCase__			)

       __lowercase			=		jax_memory_efficient_attention(
           UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__,      query_chunk_size=UpperCAmelCase__,      key_chunk_size=4_0_9_6 * 4			)

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

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

    __lowercase			=		self.reshape_batch_dim_to_heads(UpperCAmelCase__			)
    __lowercase			=		self.proj_attn(UpperCAmelCase__			)
    return self.dropout_layer(UpperCAmelCase__,      deterministic=UpperCAmelCase__			)







class       _lowerCAmelCase						(			nn.Module     ):







 """simple docstring"""







 __UpperCAmelCase			:      int
 __UpperCAmelCase			:      int
 __UpperCAmelCase			:      int
 __UpperCAmelCase			:      float     	=			0.0
 __UpperCAmelCase			:      bool     	=			False
 __UpperCAmelCase			:      jnp.dtype     	=			jnp.floataa
 __UpperCAmelCase			:      bool     	=			False


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



 def __call__(    self							:    str,      UpperCAmelCase__							:    Union[str, Any],      UpperCAmelCase__							:    Optional[Any],      UpperCAmelCase__							:    Dict=True			):
    # self attention
    __lowercase			=		hidden_states
    if self.only_cross_attention:
       __lowercase			=		self.attna(self.norma(UpperCAmelCase__			),      UpperCAmelCase__,      deterministic=UpperCAmelCase__			)
    else:
       __lowercase			=		self.attna(self.norma(UpperCAmelCase__			),      deterministic=UpperCAmelCase__			)
    __lowercase			=		hidden_states + residual

    # cross attention
    __lowercase			=		hidden_states
    __lowercase			=		self.attna(self.norma(UpperCAmelCase__			),      UpperCAmelCase__,      deterministic=UpperCAmelCase__			)
    __lowercase			=		hidden_states + residual

    # feed forward
    __lowercase			=		hidden_states
    __lowercase			=		self.ff(self.norma(UpperCAmelCase__			),      deterministic=UpperCAmelCase__			)
    __lowercase			=		hidden_states + residual

    return self.dropout_layer(UpperCAmelCase__,      deterministic=UpperCAmelCase__			)







class       _lowerCAmelCase						(			nn.Module     ):







 """simple docstring"""







 __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							:    Dict			):
    __lowercase			=		nn.GroupNorm(num_groups=3_2,      epsilon=1E-5			)

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

    __lowercase			=		[
        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:
       __lowercase			=		nn.Dense(UpperCAmelCase__,      dtype=self.dtype			)
    else:
       __lowercase			=		nn.Conv(
           UpperCAmelCase__,      kernel_size=(1, 1),      strides=(1, 1),      padding="VALID",      dtype=self.dtype,      )

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



 def __call__(    self							:    Dict,      UpperCAmelCase__							:    Union[str, Any],      UpperCAmelCase__							:    Tuple,      UpperCAmelCase__							:    List[str]=True			):
    __lowercase	,__lowercase	,__lowercase	,__lowercase			=		hidden_states.shape
    __lowercase			=		hidden_states
    __lowercase			=		self.norm(UpperCAmelCase__			)
    if self.use_linear_projection:
       __lowercase			=		hidden_states.reshape(UpperCAmelCase__,      height * width,      UpperCAmelCase__			)
       __lowercase			=		self.proj_in(UpperCAmelCase__			)
    else:
       __lowercase			=		self.proj_in(UpperCAmelCase__			)
       __lowercase			=		hidden_states.reshape(UpperCAmelCase__,      height * width,      UpperCAmelCase__			)

    for transformer_block in self.transformer_blocks:
       __lowercase			=		transformer_block(UpperCAmelCase__,      UpperCAmelCase__,      deterministic=UpperCAmelCase__			)

    if self.use_linear_projection:
       __lowercase			=		self.proj_out(UpperCAmelCase__			)
       __lowercase			=		hidden_states.reshape(UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__			)
    else:
       __lowercase			=		hidden_states.reshape(UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__			)
       __lowercase			=		self.proj_out(UpperCAmelCase__			)

    __lowercase			=		hidden_states + residual
    return self.dropout_layer(UpperCAmelCase__,      deterministic=UpperCAmelCase__			)







class       _lowerCAmelCase						(			nn.Module     ):







 """simple docstring"""







 __UpperCAmelCase			:      int
 __UpperCAmelCase			:      float     	=			0.0
 __UpperCAmelCase			:      jnp.dtype     	=			jnp.floataa


 def       _lowercase				(    self							:    List[str]			):
    # The second linear layer needs to be called
    # net_2 for now to match the index of the Sequential layer
    __lowercase			=		FlaxGEGLU(self.dim,      self.dropout,      self.dtype			)
    __lowercase			=		nn.Dense(self.dim,      dtype=self.dtype			)



 def __call__(    self							:    int,      UpperCAmelCase__							:    List[str],      UpperCAmelCase__							:    Union[str, Any]=True			):
    __lowercase			=		self.net_a(UpperCAmelCase__,      deterministic=UpperCAmelCase__			)
    __lowercase			=		self.net_a(UpperCAmelCase__			)
    return hidden_states







class       _lowerCAmelCase						(			nn.Module     ):







 """simple docstring"""







 __UpperCAmelCase			:      int
 __UpperCAmelCase			:      float     	=			0.0
 __UpperCAmelCase			:      jnp.dtype     	=			jnp.floataa


 def       _lowercase				(    self							:    int			):
    __lowercase			=		self.dim * 4
    __lowercase			=		nn.Dense(inner_dim * 2,      dtype=self.dtype			)
    __lowercase			=		nn.Dropout(rate=self.dropout			)



 def __call__(    self							:    Optional[Any],      UpperCAmelCase__							:    str,      UpperCAmelCase__							:    int=True			):
    __lowercase			=		self.proj(UpperCAmelCase__			)
    __lowercase	,__lowercase			=		jnp.split(UpperCAmelCase__,      2,      axis=2			)
    return self.dropout_layer(hidden_linear * nn.gelu(UpperCAmelCase__			),      deterministic=UpperCAmelCase__			)




