


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											=       logging.get_logger(__name__)




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Optional[Any]     ):
       '''simple docstring'''





       _lowerCamelCase       :		List[str]		=SwinConfig(
           embed_dim=192		,			depths=(2, 2, 18, 2)		,			num_heads=(6, 12, 24, 48)		,			window_size=12		,			out_features=['stage2', 'stage3', 'stage4']		,			)

       _lowerCamelCase       :		Any		=DetaConfig(
           backbone_config=_UpperCamelCase		,			num_queries=900		,			encoder_ffn_dim=2_048		,			decoder_ffn_dim=2_048		,			num_feature_levels=5		,			assign_first_stage=_UpperCamelCase		,			with_box_refine=_UpperCamelCase		,			two_stage=_UpperCamelCase		,			)

       # set labels
       _lowerCamelCase       :		int		='huggingface/label-files'
       if "o365" in model_name:
              _lowerCamelCase       :		int		=366
              _lowerCamelCase       :		List[str]		='object365-id2label.json'
       else:
              _lowerCamelCase       :		List[str]		=91
              _lowerCamelCase       :		Union[str, Any]		='coco-detection-id2label.json'

       _lowerCamelCase       :		int		=num_labels
       _lowerCamelCase       :		Optional[Any]		=json.load(open(cached_download(hf_hub_url(_UpperCamelCase		,			_UpperCamelCase		,			repo_type='dataset'     )     )		,			'r'     )     )
       _lowerCamelCase       :		Any		={int(_UpperCamelCase     ): v for k, v in idalabel.items()}
       _lowerCamelCase       :		List[str]		=idalabel
       _lowerCamelCase       :		Any		={v: k for k, v in idalabel.items()}

       return config




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Optional[Any]     ):
       '''simple docstring'''





       _lowerCamelCase       :		str		=[]

       # 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 	a_      (    SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      Optional[int]		,			SCREAMING_SNAKE_CASE__					:      List[str]     ):
       '''simple docstring'''





       _lowerCamelCase       :		int		=dct.pop(_UpperCamelCase     )
       _lowerCamelCase       :		List[Any]		=val




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Optional[int]		,			SCREAMING_SNAKE_CASE__					:      int     ):
       '''simple docstring'''





       _lowerCamelCase       :		List[Any]		=[int(backbone_config.embed_dim * 2**i     ) for i in range(len(backbone_config.depths     )     )]
       for i in range(len(backbone_config.depths     )     ):
              _lowerCamelCase       :		List[str]		=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)
                     _lowerCamelCase       :		List[Any]		=state_dict.pop(F'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'''     )
                     _lowerCamelCase       :		str		=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
                     _lowerCamelCase       :		Optional[Any]		=in_proj_weight[:dim, :]
                     _lowerCamelCase       :		int		=in_proj_bias[: dim]
                     _lowerCamelCase       :		Dict		=in_proj_weight[
                         dim : dim * 2, :
                     ]
                     _lowerCamelCase       :		Any		=in_proj_bias[
                         dim : dim * 2
                     ]
                     _lowerCamelCase       :		Optional[Any]		=in_proj_weight[
                         -dim :, :
                     ]
                     _lowerCamelCase       :		List[str]		=in_proj_bias[-dim :]
                     # fmt: on




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Dict		,			SCREAMING_SNAKE_CASE__					:      str     ):
       '''simple docstring'''





       _lowerCamelCase       :		Any		=config.d_model
       for i in range(config.decoder_layers     ):
              # read in weights + bias of input projection layer of self-attention
              _lowerCamelCase       :		Union[str, Any]		=state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''     )
              _lowerCamelCase       :		Tuple		=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
              _lowerCamelCase       :		Tuple		=in_proj_weight[:hidden_size, :]
              _lowerCamelCase       :		int		=in_proj_bias[:hidden_size]
              _lowerCamelCase       :		int		=in_proj_weight[
                  hidden_size : hidden_size * 2, :
              ]
              _lowerCamelCase       :		Union[str, Any]		=in_proj_bias[hidden_size : hidden_size * 2]
              _lowerCamelCase       :		List[Any]		=in_proj_weight[-hidden_size:, :]
              _lowerCamelCase       :		Optional[int]		=in_proj_bias[-hidden_size:]




def 	a_      (    ):
       '''simple docstring'''





       _lowerCamelCase       :		str		='http://images.cocodataset.org/val2017/000000039769.jpg'
       _lowerCamelCase       :		Union[str, Any]		=Image.open(requests.get(_UpperCamelCase		,			stream=_UpperCamelCase     ).raw     )

       return im




@torch.no_grad()
def 	a_      (    SCREAMING_SNAKE_CASE__					:      int		,			SCREAMING_SNAKE_CASE__					:      Union[str, Any]		,			SCREAMING_SNAKE_CASE__					:      Optional[Any]     ):
       '''simple docstring'''





       _lowerCamelCase       :		Optional[int]		=get_deta_config(_UpperCamelCase     )

       # load original state dict
       if model_name == "deta-swin-large":
              _lowerCamelCase       :		Union[str, Any]		=hf_hub_download(repo_id='nielsr/deta-checkpoints'		,			filename='adet_swin_ft.pth'     )
       elif model_name == "deta-swin-large-o365":
              _lowerCamelCase       :		Optional[Any]		=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'''     )

       _lowerCamelCase       :		Union[str, Any]		=torch.load(_UpperCamelCase		,			map_location='cpu'     )['model']

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

       # rename keys
       _lowerCamelCase       :		Optional[int]		=create_rename_keys(_UpperCamelCase     )
       for src, dest in rename_keys:
              rename_key(_UpperCamelCase		,			_UpperCamelCase		,			_UpperCamelCase     )
       read_in_swin_q_k_v(_UpperCamelCase		,			config.backbone_config     )
       read_in_decoder_q_k_v(_UpperCamelCase		,			_UpperCamelCase     )

       # fix some prefixes
       for key in state_dict.copy().keys():
              if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
                     _lowerCamelCase       :		Optional[int]		=state_dict.pop(_UpperCamelCase     )
                     _lowerCamelCase       :		Dict		=val
              if "input_proj" in key:
                     _lowerCamelCase       :		Union[str, Any]		=state_dict.pop(_UpperCamelCase     )
                     _lowerCamelCase       :		List[str]		=val
              if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
                     _lowerCamelCase       :		List[str]		=state_dict.pop(_UpperCamelCase     )
                     _lowerCamelCase       :		Optional[int]		=val

    # finally, create HuggingFace model and load state dict
       _lowerCamelCase       :		Optional[Any]		=DetaForObjectDetection(_UpperCamelCase     )
       model.load_state_dict(_UpperCamelCase     )
       model.eval()

       _lowerCamelCase       :		Optional[int]		='cuda' if torch.cuda.is_available() else 'cpu'
       model.to(_UpperCamelCase     )

       # load image processor
       _lowerCamelCase       :		Optional[int]		=DetaImageProcessor(format='coco_detection'     )

       # verify our conversion on image
       _lowerCamelCase       :		Dict		=prepare_img()
       _lowerCamelCase       :		Union[str, Any]		=processor(images=_UpperCamelCase		,			return_tensors='pt'     )
       _lowerCamelCase       :		Optional[int]		=encoding['pixel_values']
       _lowerCamelCase       :		Optional[int]		=model(pixel_values.to(_UpperCamelCase     )     )

       # verify logits
       print('Logits:'		,			outputs.logits[0, :3, :3]     )
       print('Boxes:'		,			outputs.pred_boxes[0, :3, :3]     )
       if model_name == "deta-swin-large":
              _lowerCamelCase       :		List[Any]		=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]]     )
              _lowerCamelCase       :		List[Any]		=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":
              _lowerCamelCase       :		Dict		=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]]     )
              _lowerCamelCase       :		Tuple		=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(_UpperCamelCase     )		,			atol=1e-4     )
       assert torch.allclose(outputs.pred_boxes[0, :3, :3]		,			expected_boxes.to(_UpperCamelCase     )		,			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(_UpperCamelCase     ).mkdir(exist_ok=_UpperCamelCase     )
              model.save_pretrained(_UpperCamelCase     )
              processor.save_pretrained(_UpperCamelCase     )

       # 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											=       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											=       parser.parse_args()
 convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A     :   Tuple			    =			{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   Dict			    =			["BlenderbotTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   str			    =			[
	    "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "BlenderbotForCausalLM",
	    "BlenderbotForConditionalGeneration",
	    "BlenderbotModel",
	    "BlenderbotPreTrainedModel",
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   List[str]			    =			[
	    "TFBlenderbotForConditionalGeneration",
	    "TFBlenderbotModel",
	    "TFBlenderbotPreTrainedModel",
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   List[str]			    =			[
	    "FlaxBlenderbotForConditionalGeneration",
	    "FlaxBlenderbotModel",
	    "FlaxBlenderbotPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_blenderbot import (
	    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BlenderbotConfig,
	    BlenderbotOnnxConfig,
	)
	from .tokenization_blenderbot import BlenderbotTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blenderbot import (
		    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlenderbotForCausalLM,
		    BlenderbotForConditionalGeneration,
		    BlenderbotModel,
		    BlenderbotPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blenderbot import (
		    TFBlenderbotForConditionalGeneration,
		    TFBlenderbotModel,
		    TFBlenderbotPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    FlaxBlenderbotPreTrainedModel,
		)

else:
	import sys

	A     :   Optional[Any]			    =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




