

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()
__A			:					Tuple						=			logging.get_logger(__name__)





def 	__a     (      A__			:  Optional[int]       ):
    SCREAMING_SNAKE_CASE										=   SwinConfig(
        embed_dim=192			,  depths=(2, 2, 18, 2)			,  num_heads=(6, 12, 24, 48)			,  window_size=12			,  out_features=["stage2", "stage3", "stage4"]			,  )

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

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

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

    return config





def 	__a     (      A__			:  Any       ):
    SCREAMING_SNAKE_CASE										=   []

    # 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     (      A__			:  Optional[Any]			,  A__			:  Union[str, Any]			,  A__			:  Any       ):
    SCREAMING_SNAKE_CASE										=   dct.pop(A__       )
    SCREAMING_SNAKE_CASE										=   val





def 	__a     (      A__			:  str			,  A__			:  int       ):
    SCREAMING_SNAKE_CASE										=   [int(backbone_config.embed_dim * 2**i       ) for i in range(len(backbone_config.depths       )       )]
    for i in range(len(backbone_config.depths       )       ):
        SCREAMING_SNAKE_CASE										=   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)
            SCREAMING_SNAKE_CASE										=   state_dict.pop(F"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"       )
            SCREAMING_SNAKE_CASE										=   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
            SCREAMING_SNAKE_CASE										=   in_proj_weight[:dim, :]
            SCREAMING_SNAKE_CASE										=   in_proj_bias[: dim]
            SCREAMING_SNAKE_CASE										=   in_proj_weight[
                dim : dim * 2, :
            ]
            SCREAMING_SNAKE_CASE										=   in_proj_bias[
                dim : dim * 2
            ]
            SCREAMING_SNAKE_CASE										=   in_proj_weight[
                -dim :, :
            ]
            SCREAMING_SNAKE_CASE										=   in_proj_bias[-dim :]
            # fmt: on





def 	__a     (      A__			:  Any			,  A__			:  Tuple       ):
    # transformer decoder self-attention layers
    SCREAMING_SNAKE_CASE										=   config.d_model
    for i in range(config.decoder_layers       ):
        # read in weights + bias of input projection layer of self-attention
        SCREAMING_SNAKE_CASE										=   state_dict.pop(F"transformer.decoder.layers.{i}.self_attn.in_proj_weight"       )
        SCREAMING_SNAKE_CASE										=   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
        SCREAMING_SNAKE_CASE										=   in_proj_weight[:hidden_size, :]
        SCREAMING_SNAKE_CASE										=   in_proj_bias[:hidden_size]
        SCREAMING_SNAKE_CASE										=   in_proj_weight[
            hidden_size : hidden_size * 2, :
        ]
        SCREAMING_SNAKE_CASE										=   in_proj_bias[hidden_size : hidden_size * 2]
        SCREAMING_SNAKE_CASE										=   in_proj_weight[-hidden_size:, :]
        SCREAMING_SNAKE_CASE										=   in_proj_bias[-hidden_size:]





def 	__a     (      ):
    SCREAMING_SNAKE_CASE										=   "http://images.cocodataset.org/val2017/000000039769.jpg"
    SCREAMING_SNAKE_CASE										=   Image.open(requests.get(A__			,  stream=A__       ).raw       )

    return im





@torch.no_grad()
def 	__a     (      A__			:  List[str]			,  A__			:  Union[str, Any]			,  A__			:  str       ):
    SCREAMING_SNAKE_CASE										=   get_deta_config(A__       )

    # load original state dict
    if model_name == "deta-swin-large":
        SCREAMING_SNAKE_CASE										=   hf_hub_download(repo_id="nielsr/deta-checkpoints"			,  filename="adet_swin_ft.pth"       )
    elif model_name == "deta-swin-large-o365":
        SCREAMING_SNAKE_CASE										=   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"       )

    SCREAMING_SNAKE_CASE										=   torch.load(A__			,  map_location="cpu"       )["model"]

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

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

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

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

    SCREAMING_SNAKE_CASE										=   "cuda" if torch.cuda.is_available() else "cpu"
    model.to(A__       )

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

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

    # verify logits
    print("Logits:"			,  outputs.logits[0, :3, :3]       )
    print("Boxes:"			,  outputs.pred_boxes[0, :3, :3]       )
    if model_name == "deta-swin-large":
        SCREAMING_SNAKE_CASE										=   torch.tensor(
            [[-7.6_3_0_8, -2.8_4_8_5, -5.3_7_3_7], [-7.2_0_3_7, -4.5_5_0_5, -4.8_0_2_7], [-7.2_9_4_3, -4.2_6_1_1, -4.6_6_1_7]]       )
        SCREAMING_SNAKE_CASE										=   torch.tensor([[0.4_9_8_7, 0.4_9_6_9, 0.9_9_9_9], [0.2_5_4_9, 0.5_4_9_8, 0.4_8_0_5], [0.5_4_9_8, 0.2_7_5_7, 0.0_5_6_9]]       )
    elif model_name == "deta-swin-large-o365":
        SCREAMING_SNAKE_CASE										=   torch.tensor(
            [[-8.0_1_2_2, -3.5_7_2_0, -4.9_7_1_7], [-8.1_5_4_7, -3.6_8_8_6, -4.6_3_8_9], [-7.6_6_1_0, -3.6_1_9_4, -5.0_1_3_4]]       )
        SCREAMING_SNAKE_CASE										=   torch.tensor([[0.2_5_2_3, 0.5_5_4_9, 0.4_8_8_1], [0.7_7_1_5, 0.4_1_4_9, 0.4_6_0_1], [0.5_5_0_3, 0.2_7_5_3, 0.0_5_7_5]]       )
    assert torch.allclose(outputs.logits[0, :3, :3]			,  expected_logits.to(A__       )			,  atol=1E-4       )
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]			,  expected_boxes.to(A__       )			,  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(A__       ).mkdir(exist_ok=A__       )
        model.save_pretrained(A__       )
        processor.save_pretrained(A__       )

    # 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__":
    __A			:					str						=			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.'
    )
    __A			:					str						=			parser.parse_args()
    convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


__A			:					int						=			logging.get_logger(__name__)


__A			:					List[str]						=			OrderedDict(
    [
        # Base model mapping
        ('albert', 'FlaxAlbertModel'),
        ('bart', 'FlaxBartModel'),
        ('beit', 'FlaxBeitModel'),
        ('bert', 'FlaxBertModel'),
        ('big_bird', 'FlaxBigBirdModel'),
        ('blenderbot', 'FlaxBlenderbotModel'),
        ('blenderbot-small', 'FlaxBlenderbotSmallModel'),
        ('clip', 'FlaxCLIPModel'),
        ('distilbert', 'FlaxDistilBertModel'),
        ('electra', 'FlaxElectraModel'),
        ('gpt-sw3', 'FlaxGPT2Model'),
        ('gpt2', 'FlaxGPT2Model'),
        ('gpt_neo', 'FlaxGPTNeoModel'),
        ('gptj', 'FlaxGPTJModel'),
        ('longt5', 'FlaxLongT5Model'),
        ('marian', 'FlaxMarianModel'),
        ('mbart', 'FlaxMBartModel'),
        ('mt5', 'FlaxMT5Model'),
        ('opt', 'FlaxOPTModel'),
        ('pegasus', 'FlaxPegasusModel'),
        ('regnet', 'FlaxRegNetModel'),
        ('resnet', 'FlaxResNetModel'),
        ('roberta', 'FlaxRobertaModel'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormModel'),
        ('roformer', 'FlaxRoFormerModel'),
        ('t5', 'FlaxT5Model'),
        ('vision-text-dual-encoder', 'FlaxVisionTextDualEncoderModel'),
        ('vit', 'FlaxViTModel'),
        ('wav2vec2', 'FlaxWav2Vec2Model'),
        ('whisper', 'FlaxWhisperModel'),
        ('xglm', 'FlaxXGLMModel'),
        ('xlm-roberta', 'FlaxXLMRobertaModel'),
    ]
)

__A			:					Optional[Any]						=			OrderedDict(
    [
        # Model for pre-training mapping
        ('albert', 'FlaxAlbertForPreTraining'),
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('bert', 'FlaxBertForPreTraining'),
        ('big_bird', 'FlaxBigBirdForPreTraining'),
        ('electra', 'FlaxElectraForPreTraining'),
        ('longt5', 'FlaxLongT5ForConditionalGeneration'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('mt5', 'FlaxMT5ForConditionalGeneration'),
        ('roberta', 'FlaxRobertaForMaskedLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
        ('roformer', 'FlaxRoFormerForMaskedLM'),
        ('t5', 'FlaxT5ForConditionalGeneration'),
        ('wav2vec2', 'FlaxWav2Vec2ForPreTraining'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
        ('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
    ]
)

__A			:					Tuple						=			OrderedDict(
    [
        # Model for Masked LM mapping
        ('albert', 'FlaxAlbertForMaskedLM'),
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('bert', 'FlaxBertForMaskedLM'),
        ('big_bird', 'FlaxBigBirdForMaskedLM'),
        ('distilbert', 'FlaxDistilBertForMaskedLM'),
        ('electra', 'FlaxElectraForMaskedLM'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('roberta', 'FlaxRobertaForMaskedLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
        ('roformer', 'FlaxRoFormerForMaskedLM'),
        ('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
    ]
)

__A			:					Dict						=			OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('blenderbot', 'FlaxBlenderbotForConditionalGeneration'),
        ('blenderbot-small', 'FlaxBlenderbotSmallForConditionalGeneration'),
        ('encoder-decoder', 'FlaxEncoderDecoderModel'),
        ('longt5', 'FlaxLongT5ForConditionalGeneration'),
        ('marian', 'FlaxMarianMTModel'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('mt5', 'FlaxMT5ForConditionalGeneration'),
        ('pegasus', 'FlaxPegasusForConditionalGeneration'),
        ('t5', 'FlaxT5ForConditionalGeneration'),
    ]
)

__A			:					Any						=			OrderedDict(
    [
        # Model for Image-classsification
        ('beit', 'FlaxBeitForImageClassification'),
        ('regnet', 'FlaxRegNetForImageClassification'),
        ('resnet', 'FlaxResNetForImageClassification'),
        ('vit', 'FlaxViTForImageClassification'),
    ]
)

__A			:					Optional[int]						=			OrderedDict(
    [
        ('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
    ]
)

__A			:					Union[str, Any]						=			OrderedDict(
    [
        # Model for Causal LM mapping
        ('bart', 'FlaxBartForCausalLM'),
        ('bert', 'FlaxBertForCausalLM'),
        ('big_bird', 'FlaxBigBirdForCausalLM'),
        ('electra', 'FlaxElectraForCausalLM'),
        ('gpt-sw3', 'FlaxGPT2LMHeadModel'),
        ('gpt2', 'FlaxGPT2LMHeadModel'),
        ('gpt_neo', 'FlaxGPTNeoForCausalLM'),
        ('gptj', 'FlaxGPTJForCausalLM'),
        ('opt', 'FlaxOPTForCausalLM'),
        ('roberta', 'FlaxRobertaForCausalLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForCausalLM'),
        ('xglm', 'FlaxXGLMForCausalLM'),
        ('xlm-roberta', 'FlaxXLMRobertaForCausalLM'),
    ]
)

__A			:					Optional[int]						=			OrderedDict(
    [
        # Model for Sequence Classification mapping
        ('albert', 'FlaxAlbertForSequenceClassification'),
        ('bart', 'FlaxBartForSequenceClassification'),
        ('bert', 'FlaxBertForSequenceClassification'),
        ('big_bird', 'FlaxBigBirdForSequenceClassification'),
        ('distilbert', 'FlaxDistilBertForSequenceClassification'),
        ('electra', 'FlaxElectraForSequenceClassification'),
        ('mbart', 'FlaxMBartForSequenceClassification'),
        ('roberta', 'FlaxRobertaForSequenceClassification'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForSequenceClassification'),
        ('roformer', 'FlaxRoFormerForSequenceClassification'),
        ('xlm-roberta', 'FlaxXLMRobertaForSequenceClassification'),
    ]
)

__A			:					str						=			OrderedDict(
    [
        # Model for Question Answering mapping
        ('albert', 'FlaxAlbertForQuestionAnswering'),
        ('bart', 'FlaxBartForQuestionAnswering'),
        ('bert', 'FlaxBertForQuestionAnswering'),
        ('big_bird', 'FlaxBigBirdForQuestionAnswering'),
        ('distilbert', 'FlaxDistilBertForQuestionAnswering'),
        ('electra', 'FlaxElectraForQuestionAnswering'),
        ('mbart', 'FlaxMBartForQuestionAnswering'),
        ('roberta', 'FlaxRobertaForQuestionAnswering'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForQuestionAnswering'),
        ('roformer', 'FlaxRoFormerForQuestionAnswering'),
        ('xlm-roberta', 'FlaxXLMRobertaForQuestionAnswering'),
    ]
)

__A			:					Dict						=			OrderedDict(
    [
        # Model for Token Classification mapping
        ('albert', 'FlaxAlbertForTokenClassification'),
        ('bert', 'FlaxBertForTokenClassification'),
        ('big_bird', 'FlaxBigBirdForTokenClassification'),
        ('distilbert', 'FlaxDistilBertForTokenClassification'),
        ('electra', 'FlaxElectraForTokenClassification'),
        ('roberta', 'FlaxRobertaForTokenClassification'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForTokenClassification'),
        ('roformer', 'FlaxRoFormerForTokenClassification'),
        ('xlm-roberta', 'FlaxXLMRobertaForTokenClassification'),
    ]
)

__A			:					Dict						=			OrderedDict(
    [
        # Model for Multiple Choice mapping
        ('albert', 'FlaxAlbertForMultipleChoice'),
        ('bert', 'FlaxBertForMultipleChoice'),
        ('big_bird', 'FlaxBigBirdForMultipleChoice'),
        ('distilbert', 'FlaxDistilBertForMultipleChoice'),
        ('electra', 'FlaxElectraForMultipleChoice'),
        ('roberta', 'FlaxRobertaForMultipleChoice'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMultipleChoice'),
        ('roformer', 'FlaxRoFormerForMultipleChoice'),
        ('xlm-roberta', 'FlaxXLMRobertaForMultipleChoice'),
    ]
)

__A			:					Any						=			OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

__A			:					Optional[int]						=			OrderedDict(
    [
        ('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
    ]
)

__A			:					List[str]						=			OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

__A			:					Optional[int]						=			_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
__A			:					str						=			_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
__A			:					List[Any]						=			_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
__A			:					Tuple						=			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
__A			:					Any						=			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
__A			:					str						=			_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
__A			:					Tuple						=			_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
__A			:					Optional[int]						=			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
__A			:					List[str]						=			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
__A			:					Union[str, Any]						=			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
__A			:					List[Any]						=			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
__A			:					Optional[int]						=			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
__A			:					List[Any]						=			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
__A			:					Tuple						=			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_MAPPING




__A			:					Optional[int]						=			auto_class_update(FlaxAutoModel)





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_PRETRAINING_MAPPING




__A			:					Optional[Any]						=			auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_CAUSAL_LM_MAPPING




__A			:					Tuple						=			auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_MASKED_LM_MAPPING




__A			:					List[Any]						=			auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




__A			:					int						=			auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING




__A			:					Optional[int]						=			auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING




__A			:					int						=			auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING




__A			:					List[Any]						=			auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING




__A			:					Any						=			auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING




__A			:					Union[str, Any]						=			auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING




__A			:					Optional[Any]						=			auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING




__A			:					int						=			auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING




__A			:					Optional[int]						=			auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)