
"""simple docstring"""


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()
SCREAMING_SNAKE_CASE      =     logging.get_logger(__name__)



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							List[str]:







       """simple docstring"""
       UpperCamelCase					 =		SwinConfig(
           embed_dim=1_92		,      depths=(2, 2, 18, 2)		,      num_heads=(6, 12, 24, 48)		,      window_size=12		,      out_features=["stage2", "stage3", "stage4"]		,      )

       UpperCamelCase					 =		DetaConfig(
           backbone_config=UpperCAmelCase_		,      num_queries=9_00		,      encoder_ffn_dim=20_48		,      decoder_ffn_dim=20_48		,      num_feature_levels=5		,      assign_first_stage=UpperCAmelCase_		,      with_box_refine=UpperCAmelCase_		,      two_stage=UpperCAmelCase_		,      )

       # set labels
       UpperCamelCase					 =		"huggingface/label-files"
       if "o365" in model_name:
              UpperCamelCase					 =		3_66
              UpperCamelCase					 =		"object365-id2label.json"
       else:
              UpperCamelCase					 =		91
              UpperCamelCase					 =		"coco-detection-id2label.json"

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

       return config



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							str:







       """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__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_				)->							List[Any]:







       """simple docstring"""
       UpperCamelCase					 =		dct.pop(UpperCAmelCase_				)
       UpperCamelCase					 =		val



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_				)->							List[str]:







       """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__			(							UpperCAmelCase_		,      UpperCAmelCase_				)->							Tuple:







       """simple docstring"""
       # transformer decoder self-attention layers
       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__			(							)->							Optional[int]:







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

       return im



@torch.no_grad()
def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_				)->							Optional[Any]:







       """simple docstring"""
       UpperCamelCase					 =		get_deta_config(UpperCAmelCase_				)

       # 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(UpperCAmelCase_		,      map_location="cpu"				)["model"]

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

       # rename keys
       UpperCamelCase					 =		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:
                     UpperCamelCase					 =		state_dict.pop(UpperCAmelCase_				)
                     UpperCamelCase					 =		val
              if "input_proj" in key:
                     UpperCamelCase					 =		state_dict.pop(UpperCAmelCase_				)
                     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(UpperCAmelCase_				)
                     UpperCamelCase					 =		val

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

       UpperCamelCase					 =		"cuda" if torch.cuda.is_available() else "cpu"
       model.to(UpperCAmelCase_				)

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

       # verify our conversion on image
       UpperCamelCase					 =		prepare_img()
       UpperCamelCase					 =		processor(images=UpperCAmelCase_		,      return_tensors="pt"				)
       UpperCamelCase					 =		encoding["pixel_values"]
       UpperCamelCase					 =		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":
              UpperCamelCase					 =		torch.tensor(
                  [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]				)
              UpperCamelCase					 =		torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]				)
       elif model_name == "deta-swin-large-o365":
              UpperCamelCase					 =		torch.tensor(
                  [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]				)
              UpperCamelCase					 =		torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]				)
       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__":
      SCREAMING_SNAKE_CASE      =     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."""
      )
      SCREAMING_SNAKE_CASE      =     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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE      =     {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     ["""MBartTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     ["""MBartTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MBartForCausalLM""",
          """MBartForConditionalGeneration""",
          """MBartForQuestionAnswering""",
          """MBartForSequenceClassification""",
          """MBartModel""",
          """MBartPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """TFMBartForConditionalGeneration""",
          """TFMBartModel""",
          """TFMBartPreTrainedModel""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """FlaxMBartForConditionalGeneration""",
          """FlaxMBartForQuestionAnswering""",
          """FlaxMBartForSequenceClassification""",
          """FlaxMBartModel""",
          """FlaxMBartPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mbart import MBartTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mbart_fast import MBartTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mbart import (
                MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                MBartForCausalLM,
                MBartForConditionalGeneration,
                MBartForQuestionAnswering,
                MBartForSequenceClassification,
                MBartModel,
                MBartPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_mbart import (
                FlaxMBartForConditionalGeneration,
                FlaxMBartForQuestionAnswering,
                FlaxMBartForSequenceClassification,
                FlaxMBartModel,
                FlaxMBartPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE      =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





