






import argparse
import json
from pathlib import Path

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

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_	:    Optional[Any]            =      logging.get_logger(__name__)






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			SwinConfig(
           embed_dim=192	,      depths=(2, 2, 18, 2)	,      num_heads=(6, 12, 24, 48)	,      window_size=12	,      out_features=["""stage2""", """stage3""", """stage4"""]	,      )

       UpperCAmelCase	    =			DetaConfig(
           backbone_config=lowerCAmelCase	,      num_queries=900	,      encoder_ffn_dim=2048	,      decoder_ffn_dim=2048	,      num_feature_levels=5	,      assign_first_stage=lowerCAmelCase	,      with_box_refine=lowerCAmelCase	,      two_stage=lowerCAmelCase	,      )

       # set labels
       UpperCAmelCase	    =			"""huggingface/label-files"""
       if "o365" in model_name:
              UpperCAmelCase	    =			366
              UpperCAmelCase	    =			"""object365-id2label.json"""
       else:
              UpperCAmelCase	    =			91
              UpperCAmelCase	    =			"""coco-detection-id2label.json"""

       UpperCAmelCase	    =			num_labels
       UpperCAmelCase	    =			json.load(open(cached_download(hf_hub_url(lowerCAmelCase	,      lowerCAmelCase	,      repo_type="""dataset"""		)		)	,      """r"""		)		)
       UpperCAmelCase	    =			{int(lowerCAmelCase		): v for k, v in idalabel.items()}
       UpperCAmelCase	    =			idalabel
       UpperCAmelCase	    =			{v: k for k, v in idalabel.items()}

       return config






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			[]

       # stem
       # fmt: off
       rename_keys.append(("""backbone.0.body.patch_embed.proj.weight""", """model.backbone.model.embeddings.patch_embeddings.projection.weight""")		)
       rename_keys.append(("""backbone.0.body.patch_embed.proj.bias""", """model.backbone.model.embeddings.patch_embeddings.projection.bias""")		)
       rename_keys.append(("""backbone.0.body.patch_embed.norm.weight""", """model.backbone.model.embeddings.norm.weight""")		)
       rename_keys.append(("""backbone.0.body.patch_embed.norm.bias""", """model.backbone.model.embeddings.norm.bias""")		)
       # stages
       for i in range(len(config.backbone_config.depths		)		):
              for j in range(config.backbone_config.depths[i]		):
                     rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm1.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')		)
                     rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm1.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')		)
                     rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')		)
                     rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')		)
                     rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')		)
                     rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')		)
                     rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm2.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')		)
                     rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm2.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')		)
                     rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')		)
                     rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')		)
                     rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')		)
                     rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')		)

              if i < 3:
                     rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.reduction.weight''', F'''model.backbone.model.encoder.layers.{i}.downsample.reduction.weight''')		)
                     rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.norm.weight''', F'''model.backbone.model.encoder.layers.{i}.downsample.norm.weight''')		)
                     rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.norm.bias''', F'''model.backbone.model.encoder.layers.{i}.downsample.norm.bias''')		)

       rename_keys.append(("""backbone.0.body.norm1.weight""", """model.backbone.model.hidden_states_norms.stage2.weight""")		)
       rename_keys.append(("""backbone.0.body.norm1.bias""", """model.backbone.model.hidden_states_norms.stage2.bias""")		)
       rename_keys.append(("""backbone.0.body.norm2.weight""", """model.backbone.model.hidden_states_norms.stage3.weight""")		)
       rename_keys.append(("""backbone.0.body.norm2.bias""", """model.backbone.model.hidden_states_norms.stage3.bias""")		)
       rename_keys.append(("""backbone.0.body.norm3.weight""", """model.backbone.model.hidden_states_norms.stage4.weight""")		)
       rename_keys.append(("""backbone.0.body.norm3.bias""", """model.backbone.model.hidden_states_norms.stage4.bias""")		)

       # transformer encoder
       for i in range(config.encoder_layers		):
              rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', F'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''')		)
              rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', F'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''')		)
              rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', F'''model.encoder.layers.{i}.self_attn.attention_weights.weight''')		)
              rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', F'''model.encoder.layers.{i}.self_attn.attention_weights.bias''')		)
              rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', F'''model.encoder.layers.{i}.self_attn.value_proj.weight''')		)
              rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', F'''model.encoder.layers.{i}.self_attn.value_proj.bias''')		)
              rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', F'''model.encoder.layers.{i}.self_attn.output_proj.weight''')		)
              rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', F'''model.encoder.layers.{i}.self_attn.output_proj.bias''')		)
              rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.weight''', F'''model.encoder.layers.{i}.self_attn_layer_norm.weight''')		)
              rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''model.encoder.layers.{i}.self_attn_layer_norm.bias''')		)
              rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''model.encoder.layers.{i}.fc1.weight''')		)
              rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''model.encoder.layers.{i}.fc1.bias''')		)
              rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''model.encoder.layers.{i}.fc2.weight''')		)
              rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''model.encoder.layers.{i}.fc2.bias''')		)
              rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''model.encoder.layers.{i}.final_layer_norm.weight''')		)
              rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''model.encoder.layers.{i}.final_layer_norm.bias''')		)

       # transformer decoder
       for i in range(config.decoder_layers		):
              rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', F'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''')		)
              rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', F'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''')		)
              rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', F'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''')		)
              rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', F'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''')		)
              rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', F'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''')		)
              rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', F'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''')		)
              rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', F'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''')		)
              rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', F'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''')		)
              rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.weight''', F'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''')		)
              rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''')		)
              rename_keys.append((F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''model.decoder.layers.{i}.self_attn.out_proj.weight''')		)
              rename_keys.append((F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''model.decoder.layers.{i}.self_attn.out_proj.bias''')		)
              rename_keys.append((F'''transformer.decoder.layers.{i}.norm2.weight''', F'''model.decoder.layers.{i}.self_attn_layer_norm.weight''')		)
              rename_keys.append((F'''transformer.decoder.layers.{i}.norm2.bias''', F'''model.decoder.layers.{i}.self_attn_layer_norm.bias''')		)
              rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''model.decoder.layers.{i}.fc1.weight''')		)
              rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''model.decoder.layers.{i}.fc1.bias''')		)
              rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''model.decoder.layers.{i}.fc2.weight''')		)
              rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''model.decoder.layers.{i}.fc2.bias''')		)
              rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''model.decoder.layers.{i}.final_layer_norm.weight''')		)
              rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''model.decoder.layers.{i}.final_layer_norm.bias''')		)

       # fmt: on

       return rename_keys






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			dct.pop(lowerCAmelCase		)
       UpperCAmelCase	    =			val






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			[int(backbone_config.embed_dim * 2**i		) for i in range(len(backbone_config.depths		)		)]
       for i in range(len(backbone_config.depths		)		):
              UpperCAmelCase	    =			num_features[i]
              for j in range(backbone_config.depths[i]		):
                     # fmt: off
                     # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
                     UpperCAmelCase	    =			state_dict.pop(F'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'''		)
                     UpperCAmelCase	    =			state_dict.pop(F'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'''		)
                     # next, add query, keys and values (in that order) to the state dict
                     UpperCAmelCase	    =			in_proj_weight[:dim, :]
                     UpperCAmelCase	    =			in_proj_bias[: dim]
                     UpperCAmelCase	    =			in_proj_weight[
                         dim : dim * 2, :
                     ]
                     UpperCAmelCase	    =			in_proj_bias[
                         dim : dim * 2
                     ]
                     UpperCAmelCase	    =			in_proj_weight[
                         -dim :, :
                     ]
                     UpperCAmelCase	    =			in_proj_bias[-dim :]
                     # fmt: on






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			config.d_model
       for i in range(config.decoder_layers		):
              # read in weights + bias of input projection layer of self-attention
              UpperCAmelCase	    =			state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''		)
              UpperCAmelCase	    =			state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_bias'''		)
              # next, add query, keys and values (in that order) to the state dict
              UpperCAmelCase	    =			in_proj_weight[:hidden_size, :]
              UpperCAmelCase	    =			in_proj_bias[:hidden_size]
              UpperCAmelCase	    =			in_proj_weight[
                  hidden_size : hidden_size * 2, :
              ]
              UpperCAmelCase	    =			in_proj_bias[hidden_size : hidden_size * 2]
              UpperCAmelCase	    =			in_proj_weight[-hidden_size:, :]
              UpperCAmelCase	    =			in_proj_bias[-hidden_size:]






def       _lowerCAmelCase       (      ):







       '''simple docstring'''
       UpperCAmelCase	    =			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
       UpperCAmelCase	    =			Image.open(requests.get(lowerCAmelCase	,      stream=lowerCAmelCase		).raw		)

       return im






@torch.no_grad()
def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			get_deta_config(lowerCAmelCase		)

       # load original state dict
       if model_name == "deta-swin-large":
              UpperCAmelCase	    =			hf_hub_download(repo_id="""nielsr/deta-checkpoints"""	,      filename="""adet_swin_ft.pth"""		)
       elif model_name == "deta-swin-large-o365":
              UpperCAmelCase	    =			hf_hub_download(repo_id="""jozhang97/deta-swin-l-o365"""	,      filename="""deta_swin_pt_o365.pth"""		)
       else:
              raise ValueError(F'''Model name {model_name} not supported'''		)

       UpperCAmelCase	    =			torch.load(lowerCAmelCase	,      map_location="""cpu"""		)["""model"""]

       # original state dict
       for name, param in state_dict.items():
              print(lowerCAmelCase	,      param.shape		)

       # rename keys
       UpperCAmelCase	    =			create_rename_keys(lowerCAmelCase		)
       for src, dest in rename_keys:
              rename_key(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)
       read_in_swin_q_k_v(lowerCAmelCase	,      config.backbone_config		)
       read_in_decoder_q_k_v(lowerCAmelCase	,      lowerCAmelCase		)

       # fix some prefixes
       for key in state_dict.copy().keys():
              if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
                     UpperCAmelCase	    =			state_dict.pop(lowerCAmelCase		)
                     UpperCAmelCase	    =			val
              if "input_proj" in key:
                     UpperCAmelCase	    =			state_dict.pop(lowerCAmelCase		)
                     UpperCAmelCase	    =			val
              if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
                     UpperCAmelCase	    =			state_dict.pop(lowerCAmelCase		)
                     UpperCAmelCase	    =			val

    # finally, create HuggingFace model and load state dict
       UpperCAmelCase	    =			DetaForObjectDetection(lowerCAmelCase		)
       model.load_state_dict(lowerCAmelCase		)
       model.eval()

       UpperCAmelCase	    =			"""cuda""" if torch.cuda.is_available() else """cpu"""
       model.to(lowerCAmelCase		)

       # load image processor
       UpperCAmelCase	    =			DetaImageProcessor(format="""coco_detection"""		)

       # verify our conversion on image
       UpperCAmelCase	    =			prepare_img()
       UpperCAmelCase	    =			processor(images=lowerCAmelCase	,      return_tensors="""pt"""		)
       UpperCAmelCase	    =			encoding["""pixel_values"""]
       UpperCAmelCase	    =			model(pixel_values.to(lowerCAmelCase		)		)

       # verify logits
       print("""Logits:"""	,      outputs.logits[0, :3, :3]		)
       print("""Boxes:"""	,      outputs.pred_boxes[0, :3, :3]		)
       if model_name == "deta-swin-large":
              UpperCAmelCase	    =			torch.tensor(
                  [[-7.63_08, -2.84_85, -5.37_37], [-7.20_37, -4.55_05, -4.80_27], [-7.29_43, -4.26_11, -4.66_17]]		)
              UpperCAmelCase	    =			torch.tensor([[0.49_87, 0.49_69, 0.99_99], [0.25_49, 0.54_98, 0.48_05], [0.54_98, 0.27_57, 0.05_69]]		)
       elif model_name == "deta-swin-large-o365":
              UpperCAmelCase	    =			torch.tensor(
                  [[-8.01_22, -3.57_20, -4.97_17], [-8.15_47, -3.68_86, -4.63_89], [-7.66_10, -3.61_94, -5.01_34]]		)
              UpperCAmelCase	    =			torch.tensor([[0.25_23, 0.55_49, 0.48_81], [0.77_15, 0.41_49, 0.46_01], [0.55_03, 0.27_53, 0.05_75]]		)
       assert torch.allclose(outputs.logits[0, :3, :3]	,      expected_logits.to(lowerCAmelCase		)	,      atol=1e-4		)
       assert torch.allclose(outputs.pred_boxes[0, :3, :3]	,      expected_boxes.to(lowerCAmelCase		)	,      atol=1e-4		)
       print("""Everything ok!"""		)

       if pytorch_dump_folder_path:
              # Save model and processor
              logger.info(F'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...'''		)
              Path(lowerCAmelCase		).mkdir(exist_ok=lowerCAmelCase		)
              model.save_pretrained(lowerCAmelCase		)
              processor.save_pretrained(lowerCAmelCase		)

       # Push to hub
       if push_to_hub:
              print("""Pushing model and processor to hub..."""		)
              model.push_to_hub(F'''jozhang97/{model_name}'''		)
              processor.push_to_hub(F'''jozhang97/{model_name}'''		)


if __name__ == "__main__":
 lowerCAmelCase_	:    Optional[int]            =      argparse.ArgumentParser()

 parser.add_argument(
     '''--model_name''',
     type=str,
     default='''deta-swin-large''',
     choices=['''deta-swin-large''', '''deta-swin-large-o365'''],
     help='''Name of the model 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_	:    List[str]            =      parser.parse_args()
 convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








"""simple docstring"""







from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_	:    str            =      logging.get_logger(__name__)

lowerCAmelCase_	:    Optional[int]            =      {
    '''huggingface/time-series-transformer-tourism-monthly''': (
        '''https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'''
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}





class   UpperCamelCase_ (       a_  ):
  _A       :					int				=	'time_series_transformer'
  _A       :					List[Any]				=	{
      'hidden_size': 'd_model',
      'num_attention_heads': 'encoder_attention_heads',
      'num_hidden_layers': 'encoder_layers',
  }



  def __init__(      self ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = "student_t" ,		snake_case__ = "nll" ,		snake_case__ = 1 ,		snake_case__ = [1, 2, 3, 4, 5, 6, 7] ,		snake_case__ = "mean" ,		snake_case__ = 0 ,		snake_case__ = 0 ,		snake_case__ = 0 ,		snake_case__ = 0 ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = 32 ,		snake_case__ = 32 ,		snake_case__ = 2 ,		snake_case__ = 2 ,		snake_case__ = 2 ,		snake_case__ = 2 ,		snake_case__ = True ,		snake_case__ = "gelu" ,		snake_case__ = 64 ,		snake_case__ = 0.1 ,		snake_case__ = 0.1 ,		snake_case__ = 0.1 ,		snake_case__ = 0.1 ,		snake_case__ = 0.1 ,		snake_case__ = 1_00 ,		snake_case__ = 0.02 ,		snake_case__=True ,		**snake_case__ ,		)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			prediction_length
         UpperCAmelCase	    =			context_length or prediction_length
         UpperCAmelCase	    =			distribution_output
         UpperCAmelCase	    =			loss
         UpperCAmelCase	    =			input_size
         UpperCAmelCase	    =			num_time_features
         UpperCAmelCase	    =			lags_sequence
         UpperCAmelCase	    =			scaling
         UpperCAmelCase	    =			num_dynamic_real_features
         UpperCAmelCase	    =			num_static_real_features
         UpperCAmelCase	    =			num_static_categorical_features
         if cardinality and num_static_categorical_features > 0:
                if len(snake_case__				) != num_static_categorical_features:
                       raise ValueError(
                           """The cardinality should be a list of the same length as `num_static_categorical_features`"""				)
                UpperCAmelCase	    =			cardinality
         else:
                UpperCAmelCase	    =			[0]
         if embedding_dimension and num_static_categorical_features > 0:
                if len(snake_case__				) != num_static_categorical_features:
                       raise ValueError(
                           """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""				)
                UpperCAmelCase	    =			embedding_dimension
         else:
                UpperCAmelCase	    =			[min(50 ,		(cat + 1) // 2				) for cat in self.cardinality]
         UpperCAmelCase	    =			num_parallel_samples

         # Transformer architecture configuration
         UpperCAmelCase	    =			input_size * len(snake_case__				) + self._number_of_features
         UpperCAmelCase	    =			d_model
         UpperCAmelCase	    =			encoder_attention_heads
         UpperCAmelCase	    =			decoder_attention_heads
         UpperCAmelCase	    =			encoder_ffn_dim
         UpperCAmelCase	    =			decoder_ffn_dim
         UpperCAmelCase	    =			encoder_layers
         UpperCAmelCase	    =			decoder_layers

         UpperCAmelCase	    =			dropout
         UpperCAmelCase	    =			attention_dropout
         UpperCAmelCase	    =			activation_dropout
         UpperCAmelCase	    =			encoder_layerdrop
         UpperCAmelCase	    =			decoder_layerdrop

         UpperCAmelCase	    =			activation_function
         UpperCAmelCase	    =			init_std

         UpperCAmelCase	    =			use_cache

         super().__init__(is_encoder_decoder=snake_case__ ,		**snake_case__				)



  @property
  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         return (
             sum(self.embedding_dimension				)
             + self.num_dynamic_real_features
             + self.num_time_features
             + self.num_static_real_features
             + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
         )
