

import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowerCAmelCase						=		[]
for i in range(6):
 # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
 rename_keys.append(
     (F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
 )
 rename_keys.append(
     (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
 )
 rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
 rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
 rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
 rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
 rename_keys.append(
     (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
 )
 rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
 rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
 rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
 # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
 )
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
 )
 rename_keys.append(
     (
         F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
         F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
     )
 )
 rename_keys.append(
     (
         F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
         F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
     )
 )
 rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
 rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
 rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
 rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')
 )
 rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
 )
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
 )
 rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
 rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ("""input_proj.weight""", """input_projection.weight"""),
        ("""input_proj.bias""", """input_projection.bias"""),
        ("""query_embed.weight""", """query_position_embeddings.weight"""),
        ("""transformer.encoder.norm.weight""", """encoder.layernorm.weight"""),
        ("""transformer.encoder.norm.bias""", """encoder.layernorm.bias"""),
        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
        ("""class_embed.weight""", """class_labels_classifier.weight"""),
        ("""class_embed.bias""", """class_labels_classifier.bias"""),
        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
    ]
)

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[int]  =    state_dict.pop(_lowerCamelCase   )
    _lowerCAmelCase       :   Optional[int]  =    val

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[int]  =    OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            _lowerCAmelCase       :   Any  =    key.replace('backbone.0.body' ,   'backbone.conv_encoder.model'   )
            _lowerCAmelCase       :   Any  =    value
        else:
            _lowerCAmelCase       :   Any  =    value

    return new_state_dict

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[int]  =    ''

    # first: transformer encoder
    for i in range(6   ):
        # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
        _lowerCAmelCase       :   List[str]  =    state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""   )
        _lowerCAmelCase       :   List[Any]  =    state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""   )
        # next, add query, keys and values (in that order) to the state dict
        _lowerCAmelCase       :   Dict  =    in_proj_weight[:256, :]
        _lowerCAmelCase       :   Optional[Any]  =    in_proj_bias[:256]
        _lowerCAmelCase       :   int  =    in_proj_weight[256:512, :]
        _lowerCAmelCase       :   str  =    in_proj_bias[256:512]
        _lowerCAmelCase       :   str  =    in_proj_weight[-256:, :]
        _lowerCAmelCase       :   Optional[Any]  =    in_proj_bias[-256:]
    # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
    for i in range(6   ):
        # read in weights + bias of input projection layer of self-attention
        _lowerCAmelCase       :   str  =    state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""   )
        _lowerCAmelCase       :   Optional[int]  =    state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""   )
        # next, add query, keys and values (in that order) to the state dict
        _lowerCAmelCase       :   Dict  =    in_proj_weight[:256, :]
        _lowerCAmelCase       :   Optional[Any]  =    in_proj_bias[:256]
        _lowerCAmelCase       :   List[str]  =    in_proj_weight[256:512, :]
        _lowerCAmelCase       :   int  =    in_proj_bias[256:512]
        _lowerCAmelCase       :   Any  =    in_proj_weight[-256:, :]
        _lowerCAmelCase       :   Optional[Any]  =    in_proj_bias[-256:]
        # read in weights + bias of input projection layer of cross-attention
        _lowerCAmelCase       :   List[Any]  =    state_dict.pop(
            f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""   )
        _lowerCAmelCase       :   Optional[Any]  =    state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""   )
        # next, add query, keys and values (in that order) of cross-attention to the state dict
        _lowerCAmelCase       :   Union[str, Any]  =    in_proj_weight_cross_attn[:256, :]
        _lowerCAmelCase       :   str  =    in_proj_bias_cross_attn[:256]
        _lowerCAmelCase       :   Tuple  =    in_proj_weight_cross_attn[256:512, :]
        _lowerCAmelCase       :   Union[str, Any]  =    in_proj_bias_cross_attn[256:512]
        _lowerCAmelCase       :   List[str]  =    in_proj_weight_cross_attn[-256:, :]
        _lowerCAmelCase       :   List[Any]  =    in_proj_bias_cross_attn[-256:]

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   str  =    image.size
    _lowerCAmelCase       :   Optional[Any]  =    max(_lowerCamelCase ,   _lowerCamelCase   )
    _lowerCAmelCase       :   Dict  =    800 if 'detection' in checkpoint_url else 1000
    _lowerCAmelCase       :   int  =    target_max_size / current_max_size
    _lowerCAmelCase       :   List[Any]  =    image.resize((int(round(scale * width   )   ), int(round(scale * height   )   ))   )

    return resized_image

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Dict  =    F.to_tensor(_lowerCamelCase   )
    _lowerCAmelCase       :   Dict  =    F.normalize(_lowerCamelCase ,   mean=[0.485, 0.456, 0.406] ,   std=[0.229, 0.224, 0.225]   )
    return image

@torch.no_grad()
def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    logger.info('Converting model...'   )

    # load original state dict
    _lowerCAmelCase       :   Optional[int]  =    torch.hub.load_state_dict_from_url(_lowerCamelCase ,   map_location='cpu'   )
    # rename keys
    for src, dest in rename_keys:
        rename_key(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   )
    _lowerCAmelCase       :   Any  =    rename_backbone_keys(_lowerCamelCase   )
    # query, key and value matrices need special treatment
    read_in_q_k_v(_lowerCamelCase   )
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    _lowerCAmelCase       :   Optional[Any]  =    'model.'
    for key in state_dict.copy().keys():
        if not key.startswith('class_labels_classifier'   ) and not key.startswith('bbox_predictor'   ):
            _lowerCAmelCase       :   List[str]  =    state_dict.pop(_lowerCamelCase   )
            _lowerCAmelCase       :   Tuple  =    val
    # create HuggingFace model and load state dict
    _lowerCAmelCase       :   Union[str, Any]  =    TableTransformerConfig(
        backbone='resnet18' ,   mask_loss_coefficient=1 ,   dice_loss_coefficient=1 ,   ce_loss_coefficient=1 ,   bbox_loss_coefficient=5 ,   giou_loss_coefficient=2 ,   eos_coefficient=0.4 ,   class_cost=1 ,   bbox_cost=5 ,   giou_cost=2 ,   )

    if "detection" in checkpoint_url:
        _lowerCAmelCase       :   int  =    15
        _lowerCAmelCase       :   Dict  =    2
        _lowerCAmelCase       :   Union[str, Any]  =    {0: 'table', 1: 'table rotated'}
        _lowerCAmelCase       :   Optional[Any]  =    idalabel
        _lowerCAmelCase       :   Tuple  =    {v: k for k, v in idalabel.items()}
    else:
        _lowerCAmelCase       :   Any  =    125
        _lowerCAmelCase       :   Any  =    6
        _lowerCAmelCase       :   int  =    {
            0: 'table',
            1: 'table column',
            2: 'table row',
            3: 'table column header',
            4: 'table projected row header',
            5: 'table spanning cell',
        }
        _lowerCAmelCase       :   Any  =    idalabel
        _lowerCAmelCase       :   str  =    {v: k for k, v in idalabel.items()}

    _lowerCAmelCase       :   int  =    DetrImageProcessor(
        format='coco_detection' ,   max_size=800 if 'detection' in checkpoint_url else 1000   )
    _lowerCAmelCase       :   List[Any]  =    TableTransformerForObjectDetection(_lowerCamelCase   )
    model.load_state_dict(_lowerCamelCase   )
    model.eval()

    # verify our conversion
    _lowerCAmelCase       :   Dict  =    'example_pdf.png' if 'detection' in checkpoint_url else 'example_table.png'
    _lowerCAmelCase       :   Optional[Any]  =    hf_hub_download(repo_id='nielsr/example-pdf' ,   repo_type='dataset' ,   filename=_lowerCamelCase   )
    _lowerCAmelCase       :   List[str]  =    Image.open(_lowerCamelCase   ).convert('RGB'   )
    _lowerCAmelCase       :   Tuple  =    normalize(resize(_lowerCamelCase ,   _lowerCamelCase   )   ).unsqueeze(0   )

    _lowerCAmelCase       :   str  =    model(_lowerCamelCase   )

    if "detection" in checkpoint_url:
        _lowerCAmelCase       :   List[Any]  =    (1, 15, 3)
        _lowerCAmelCase       :   Union[str, Any]  =    torch.tensor(
            [[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]]   )
        _lowerCAmelCase       :   Optional[int]  =    torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]]   )

    else:
        _lowerCAmelCase       :   Tuple  =    (1, 125, 7)
        _lowerCAmelCase       :   Union[str, Any]  =    torch.tensor(
            [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]   )
        _lowerCAmelCase       :   List[Any]  =    torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]]   )

    assert outputs.logits.shape == expected_shape
    assert torch.allclose(outputs.logits[0, :3, :3] ,   _lowerCamelCase ,   atol=1e-4   )
    assert torch.allclose(outputs.pred_boxes[0, :3, :3] ,   _lowerCamelCase ,   atol=1e-4   )
    print('Looks ok!'   )

    if pytorch_dump_folder_path is not None:
        # Save model and image processor
        logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""   )
        Path(_lowerCamelCase   ).mkdir(exist_ok=_lowerCamelCase   )
        model.save_pretrained(_lowerCamelCase   )
        image_processor.save_pretrained(_lowerCamelCase   )

    if push_to_hub:
        # Push model to HF hub
        logger.info('Pushing model to the hub...'   )
        _lowerCAmelCase       :   str  =    (
            'microsoft/table-transformer-detection'
            if 'detection' in checkpoint_url
            else 'microsoft/table-transformer-structure-recognition'
        )
        model.push_to_hub(_lowerCamelCase   )
        image_processor.push_to_hub(_lowerCamelCase   )


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

 parser.add_argument(
     """--checkpoint_url""",
     default="""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
     type=str,
     choices=[
         """https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
         """https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth""",
     ],
     help="""URL of the Table Transformer checkpoint you'd like to convert.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, 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."""
 )
 _lowerCAmelCase						=		parser.parse_args()
 convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)








"""simple docstring"""






import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin







class 	__UpperCamelCase    (    a__							,							a__						):

  @register_to_config
  def __init__(		self    ,_A = 128    ,_A = 256    ,_A = 2_0_0_0.0    ,_A = 768    ,_A = 12    ,_A = 12    ,_A = 64    ,_A = 2048    ,_A = 0.1    ,):
      '''simple docstring'''

      super().__init__()

      _lowerCAmelCase       :   int  =    nn.Sequential(
          nn.Linear(_A    ,d_model * 4    ,bias=_A      )    ,nn.SiLU()    ,nn.Linear(d_model * 4    ,d_model * 4    ,bias=_A      )    ,nn.SiLU()    ,)

      _lowerCAmelCase       :   Any  =    nn.Embedding(_A    ,_A      )
      _lowerCAmelCase       :   Tuple  =    False

      _lowerCAmelCase       :   Union[str, Any]  =    nn.Linear(_A    ,_A    ,bias=_A      )

      _lowerCAmelCase       :   int  =    nn.Dropout(p=_A      )

      _lowerCAmelCase       :   int  =    nn.ModuleList()
      for lyr_num in range(_A      ):
          # FiLM conditional T5 decoder
          _lowerCAmelCase       :   Any  =    DecoderLayer(d_model=_A    ,d_kv=_A    ,num_heads=_A    ,d_ff=_A    ,dropout_rate=_A      )
          self.decoders.append(_A      )

      _lowerCAmelCase       :   Optional[Any]  =    TaLayerNorm(_A      )

      _lowerCAmelCase       :   List[str]  =    nn.Dropout(p=_A      )
      _lowerCAmelCase       :   Optional[Any]  =    nn.Linear(_A    ,_A    ,bias=_A      )

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Dict  =    torch.mul(query_input.unsqueeze(-1      )    ,key_input.unsqueeze(-2      )      )
      return mask.unsqueeze(-3      )







  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   Dict  =    decoder_input_tokens.shape
      assert decoder_noise_time.shape == (batch,)

      # decoder_noise_time is in [0, 1), so rescale to expected timing range.
      _lowerCAmelCase       :   Any  =    get_timestep_embedding(
          decoder_noise_time * self.config.max_decoder_noise_time    ,embedding_dim=self.config.d_model    ,max_period=self.config.max_decoder_noise_time    ,).to(dtype=self.dtype      )

      _lowerCAmelCase       :   Union[str, Any]  =    self.conditioning_emb(_A      ).unsqueeze(1      )

      assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

      _lowerCAmelCase       :   str  =    decoder_input_tokens.shape[1]

      # If we want to use relative positions for audio context, we can just offset
      # this sequence by the length of encodings_and_masks.
      _lowerCAmelCase       :   Union[str, Any]  =    torch.broadcast_to(
          torch.arange(_A    ,device=decoder_input_tokens.device      )    ,(batch, seq_length)    ,)

      _lowerCAmelCase       :   Any  =    self.position_encoding(_A      )

      _lowerCAmelCase       :   str  =    self.continuous_inputs_projection(_A      )
      inputs += position_encodings
      _lowerCAmelCase       :   int  =    self.dropout(_A      )

      # decoder: No padding present.
      _lowerCAmelCase       :   Union[str, Any]  =    torch.ones(
          decoder_input_tokens.shape[:2]    ,device=decoder_input_tokens.device    ,dtype=inputs.dtype      )

      # Translate encoding masks to encoder-decoder masks.
      _lowerCAmelCase       :   Optional[Any]  =    [(x, self.encoder_decoder_mask(_A    ,_A      )) for x, y in encodings_and_masks]

      # cross attend style: concat encodings
      _lowerCAmelCase       :   Dict  =    torch.cat([x[0] for x in encodings_and_encdec_masks]    ,dim=1      )
      _lowerCAmelCase       :   Tuple  =    torch.cat([x[1] for x in encodings_and_encdec_masks]    ,dim=-1      )

      for lyr in self.decoders:
          _lowerCAmelCase       :   Tuple  =    lyr(
              _A    ,conditioning_emb=_A    ,encoder_hidden_states=_A    ,encoder_attention_mask=_A    ,)[0]

      _lowerCAmelCase       :   Any  =    self.decoder_norm(_A      )
      _lowerCAmelCase       :   List[Any]  =    self.post_dropout(_A      )

      _lowerCAmelCase       :   int  =    self.spec_out(_A      )
      return spec_out







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A=1E-6      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Optional[Any]  =    nn.ModuleList()

      # cond self attention: layer 0
      self.layer.append(
          TaLayerSelfAttentionCond(d_model=_A    ,d_kv=_A    ,num_heads=_A    ,dropout_rate=_A      )      )

      # cross attention: layer 1
      self.layer.append(
          TaLayerCrossAttention(
              d_model=_A    ,d_kv=_A    ,num_heads=_A    ,dropout_rate=_A    ,layer_norm_epsilon=_A    ,)      )

      # Film Cond MLP + dropout: last layer
      self.layer.append(
          TaLayerFFCond(d_model=_A    ,d_ff=_A    ,dropout_rate=_A    ,layer_norm_epsilon=_A      )      )







  def __lowerCamelCase		(		self    ,_A    ,_A=None    ,_A=None    ,_A=None    ,_A=None    ,_A=None    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    self.layer[0](
          _A    ,conditioning_emb=_A    ,attention_mask=_A    ,)

      if encoder_hidden_states is not None:
          _lowerCAmelCase       :   Any  =    torch.where(encoder_attention_mask > 0    ,0    ,-1E10      ).to(
              encoder_hidden_states.dtype      )

          _lowerCAmelCase       :   str  =    self.layer[1](
              _A    ,key_value_states=_A    ,attention_mask=_A    ,)

      # Apply Film Conditional Feed Forward layer
      _lowerCAmelCase       :   Optional[Any]  =    self.layer[-1](_A    ,_A      )

      return (hidden_states,)







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Union[str, Any]  =    TaLayerNorm(_A      )
      _lowerCAmelCase       :   Any  =    TaFiLMLayer(in_features=d_model * 4    ,out_features=_A      )
      _lowerCAmelCase       :   Dict  =    Attention(query_dim=_A    ,heads=_A    ,dim_head=_A    ,out_bias=_A    ,scale_qk=_A      )
      _lowerCAmelCase       :   Tuple  =    nn.Dropout(_A      )







  def __lowerCamelCase		(		self    ,_A    ,_A=None    ,_A=None    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    self.layer_norm(_A      )

      if conditioning_emb is not None:
          _lowerCAmelCase       :   Union[str, Any]  =    self.FiLMLayer(_A    ,_A      )

      # Self-attention block
      _lowerCAmelCase       :   Union[str, Any]  =    self.attention(_A      )

      _lowerCAmelCase       :   Optional[Any]  =    hidden_states + self.dropout(_A      )

      return hidden_states







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   List[str]  =    Attention(query_dim=_A    ,heads=_A    ,dim_head=_A    ,out_bias=_A    ,scale_qk=_A      )
      _lowerCAmelCase       :   Optional[int]  =    TaLayerNorm(_A    ,eps=_A      )
      _lowerCAmelCase       :   Tuple  =    nn.Dropout(_A      )







  def __lowerCamelCase		(		self    ,_A    ,_A=None    ,_A=None    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    self.layer_norm(_A      )
      _lowerCAmelCase       :   str  =    self.attention(
          _A    ,encoder_hidden_states=_A    ,attention_mask=attention_mask.squeeze(1      )    ,)
      _lowerCAmelCase       :   Any  =    hidden_states + self.dropout(_A      )
      return layer_output







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Optional[int]  =    TaDenseGatedActDense(d_model=_A    ,d_ff=_A    ,dropout_rate=_A      )
      _lowerCAmelCase       :   Tuple  =    TaFiLMLayer(in_features=d_model * 4    ,out_features=_A      )
      _lowerCAmelCase       :   Any  =    TaLayerNorm(_A    ,eps=_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Dropout(_A      )







  def __lowerCamelCase		(		self    ,_A    ,_A=None      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    self.layer_norm(_A      )
      if conditioning_emb is not None:
          _lowerCAmelCase       :   Union[str, Any]  =    self.film(_A    ,_A      )

      _lowerCAmelCase       :   str  =    self.DenseReluDense(_A      )
      _lowerCAmelCase       :   Tuple  =    hidden_states + self.dropout(_A      )
      return hidden_states







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Linear(_A    ,_A    ,bias=_A      )
      _lowerCAmelCase       :   Any  =    nn.Linear(_A    ,_A    ,bias=_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Linear(_A    ,_A    ,bias=_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Dropout(_A      )
      _lowerCAmelCase       :   int  =    NewGELUActivation()







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    self.act(self.wi_a(_A      )      )
      _lowerCAmelCase       :   Optional[int]  =    self.wi_a(_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    hidden_gelu * hidden_linear
      _lowerCAmelCase       :   Dict  =    self.dropout(_A      )

      _lowerCAmelCase       :   Dict  =    self.wo(_A      )
      return hidden_states







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A=1E-6      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Parameter(torch.ones(_A      )      )
      _lowerCAmelCase       :   Optional[int]  =    eps







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    hidden_states.to(torch.floataa      ).pow(2      ).mean(-1    ,keepdim=_A      )
      _lowerCAmelCase       :   List[Any]  =    hidden_states * torch.rsqrt(variance + self.variance_epsilon      )

      # convert into half-precision if necessary
      if self.weight.dtype in [torch.floataa, torch.bfloataa]:
          _lowerCAmelCase       :   Optional[int]  =    hidden_states.to(self.weight.dtype      )

      return self.weight * hidden_states







class 	__UpperCamelCase    (    nn.Module						):

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi      ) * (input + 0.0_4_4_7_1_5 * torch.pow(_A    ,3.0      ))      ))







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   List[str]  =    nn.Linear(_A    ,out_features * 2    ,bias=_A      )







  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    self.scale_bias(_A      )
      _lowerCAmelCase,					_lowerCAmelCase       :   List[Any]  =    torch.chunk(_A    ,2    ,-1      )
      _lowerCAmelCase       :   List[Any]  =    x * (1 + scale) + shift
      return x





