






"""simple docstring"""




import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__a :    List[Any]								=				logging.get_logger(__name__)

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

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



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Union[str, Any]     ,			__lowercase				:     List[Any]     ,			__lowercase				:     Any      )  -> int:





    """simple docstring"""

    __A             =		state_dict.pop(__UpperCamelCase      )
    __A             =		val



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Optional[Any]      )  -> int:





    """simple docstring"""

    __A             =		OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            __A             =		key.replace("""backbone.0.body"""     ,			"""backbone.conv_encoder.model"""      )
            __A             =		value
        else:
            __A             =		value

    return new_state_dict



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Optional[Any]     ,			__lowercase				:     Optional[Any]=False      )  -> int:





    """simple docstring"""

    __A             =		""""""
    if is_panoptic:
        __A             =		"""detr."""

    # first: transformer encoder
    for i in range(6      ):
        # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
        __A             =		state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"      )
        __A             =		state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"      )
        # next, add query, keys and values (in that order) to the state dict
        __A             =		in_proj_weight[:2_5_6, :]
        __A             =		in_proj_bias[:2_5_6]
        __A             =		in_proj_weight[2_5_6:5_1_2, :]
        __A             =		in_proj_bias[2_5_6:5_1_2]
        __A             =		in_proj_weight[-2_5_6:, :]
        __A             =		in_proj_bias[-2_5_6:]
    # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
    for i in range(6      ):
        # read in weights + bias of input projection layer of self-attention
        __A             =		state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"      )
        __A             =		state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"      )
        # next, add query, keys and values (in that order) to the state dict
        __A             =		in_proj_weight[:2_5_6, :]
        __A             =		in_proj_bias[:2_5_6]
        __A             =		in_proj_weight[2_5_6:5_1_2, :]
        __A             =		in_proj_bias[2_5_6:5_1_2]
        __A             =		in_proj_weight[-2_5_6:, :]
        __A             =		in_proj_bias[-2_5_6:]
        # read in weights + bias of input projection layer of cross-attention
        __A             =		state_dict.pop(
            f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"      )
        __A             =		state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"      )
        # next, add query, keys and values (in that order) of cross-attention to the state dict
        __A             =		in_proj_weight_cross_attn[:2_5_6, :]
        __A             =		in_proj_bias_cross_attn[:2_5_6]
        __A             =		in_proj_weight_cross_attn[2_5_6:5_1_2, :]
        __A             =		in_proj_bias_cross_attn[2_5_6:5_1_2]
        __A             =		in_proj_weight_cross_attn[-2_5_6:, :]
        __A             =		in_proj_bias_cross_attn[-2_5_6:]



def      _SCREAMING_SNAKE_CASE  (	)  -> int:





    """simple docstring"""

    __A             =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    __A             =		Image.open(requests.get(__UpperCamelCase     ,			stream=__UpperCamelCase      ).raw      )

    return im



@torch.no_grad()
def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Optional[int]     ,			__lowercase				:     Any      )  -> Any:





    """simple docstring"""

    __A             =		DetrConfig()
    # set backbone and dilation attributes
    if "resnet101" in model_name:
        __A             =		"""resnet101"""
    if "dc5" in model_name:
        __A             =		True
    __A             =		"""panoptic""" in model_name
    if is_panoptic:
        __A             =		2_5_0
    else:
        __A             =		9_1
        __A             =		"""huggingface/label-files"""
        __A             =		"""coco-detection-id2label.json"""
        __A             =		json.load(open(hf_hub_download(__UpperCamelCase     ,			__UpperCamelCase     ,			repo_type="""dataset"""      )     ,			"""r"""      )      )
        __A             =		{int(__UpperCamelCase      ): v for k, v in idalabel.items()}
        __A             =		idalabel
        __A             =		{v: k for k, v in idalabel.items()}

    # load image processor
    __A             =		"""coco_panoptic""" if is_panoptic else """coco_detection"""
    __A             =		DetrImageProcessor(format=__UpperCamelCase      )

    # prepare image
    __A             =		prepare_img()
    __A             =		image_processor(images=__UpperCamelCase     ,			return_tensors="""pt"""      )
    __A             =		encoding["""pixel_values"""]

    logger.info(f"Converting model {model_name}..."      )

    # load original model from torch hub
    __A             =		torch.hub.load("""facebookresearch/detr"""     ,			__UpperCamelCase     ,			pretrained=__UpperCamelCase      ).eval()
    __A             =		detr.state_dict()
    # rename keys
    for src, dest in rename_keys:
        if is_panoptic:
            __A             =		"""detr.""" + src
        rename_key(__UpperCamelCase     ,			__UpperCamelCase     ,			__UpperCamelCase      )
    __A             =		rename_backbone_keys(__UpperCamelCase      )
    # query, key and value matrices need special treatment
    read_in_q_k_v(__UpperCamelCase     ,			is_panoptic=__UpperCamelCase      )
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    __A             =		"""detr.model.""" if is_panoptic else """model."""
    for key in state_dict.copy().keys():
        if is_panoptic:
            if (
                key.startswith("""detr"""      )
                and not key.startswith("""class_labels_classifier"""      )
                and not key.startswith("""bbox_predictor"""      )
            ):
                __A             =		state_dict.pop(__UpperCamelCase      )
                __A             =		val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                __A             =		state_dict.pop(__UpperCamelCase      )
                __A             =		val
            elif key.startswith("""bbox_attention"""      ) or key.startswith("""mask_head"""      ):
                continue
            else:
                __A             =		state_dict.pop(__UpperCamelCase      )
                __A             =		val
        else:
            if not key.startswith("""class_labels_classifier"""      ) and not key.startswith("""bbox_predictor"""      ):
                __A             =		state_dict.pop(__UpperCamelCase      )
                __A             =		val
    # finally, create HuggingFace model and load state dict
    __A             =		DetrForSegmentation(__UpperCamelCase      ) if is_panoptic else DetrForObjectDetection(__UpperCamelCase      )
    model.load_state_dict(__UpperCamelCase      )
    model.eval()
    # verify our conversion
    __A             =		detr(__UpperCamelCase      )
    __A             =		model(__UpperCamelCase      )
    assert torch.allclose(outputs.logits     ,			original_outputs["""pred_logits"""]     ,			atol=1E-4      )
    assert torch.allclose(outputs.pred_boxes     ,			original_outputs["""pred_boxes"""]     ,			atol=1E-4      )
    if is_panoptic:
        assert torch.allclose(outputs.pred_masks     ,			original_outputs["""pred_masks"""]     ,			atol=1E-4      )

    # Save model and image processor
    logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."      )
    Path(__UpperCamelCase      ).mkdir(exist_ok=__UpperCamelCase      )
    model.save_pretrained(__UpperCamelCase      )
    image_processor.save_pretrained(__UpperCamelCase      )


if __name__ == "__main__":
  __a :    Any								=				argparse.ArgumentParser()

  parser.add_argument(
      "--model_name", default="detr_resnet50", type=str, help="Name of the DETR 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."
  )
  __a :    List[Any]								=				parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)












import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


__a :    str								=				logging.get_logger(__name__)


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"semantic"




     def __init__(			self    :     Optional[int]	,		UpperCamelCase_    :     List[Any]=10_000	,		UpperCamelCase_    :     int=True	,		UpperCamelCase_    :     Dict=768	,		UpperCamelCase_    :     str=False	,		UpperCamelCase_    :     Dict=False	,		UpperCamelCase_    :     List[Any]=False	,		UpperCamelCase_    :     Any=False	,		UpperCamelCase_    :     Any=0.7	,		UpperCamelCase_    :     List[Any]=True	,		UpperCamelCase_    :     Dict=10_048	,		UpperCamelCase_    :     Tuple=129_595	,		UpperCamelCase_    :     List[str]=129_599	,		UpperCamelCase_    :     Optional[int]=10_000	,		UpperCamelCase_    :     Dict=256	,		UpperCamelCase_    :     int=49.9	,		**UpperCamelCase_    :     Any	,		):


         """simple docstring"""



         super().__init__(
             temperature=UpperCamelCase_	,		do_sample=UpperCamelCase_	,		eos_token_id=UpperCamelCase_	,		renormalize_logits=UpperCamelCase_	,		max_new_tokens=UpperCamelCase_	,		output_scores=UpperCamelCase_	,		return_dict_in_generate=UpperCamelCase_	,		output_hidden_states=UpperCamelCase_	,		output_attentions=UpperCamelCase_	,		**UpperCamelCase_	,		)

         __A             =		text_encoding_offset
         __A             =		text_pad_token
         __A             =		eos_token_id
         __A             =		semantic_infer_token
         __A             =		semantic_vocab_size
         __A             =		max_input_semantic_length
         __A             =		semantic_rate_hz


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"coarse_acoustics"




     def __init__(			self    :     int	,		UpperCamelCase_    :     List[Any]=True	,		UpperCamelCase_    :     int=False	,		UpperCamelCase_    :     Dict=False	,		UpperCamelCase_    :     Dict=False	,		UpperCamelCase_    :     Union[str, Any]=False	,		UpperCamelCase_    :     Any=0.7	,		UpperCamelCase_    :     Optional[Any]=True	,		UpperCamelCase_    :     str=12_048	,		UpperCamelCase_    :     int=75	,		UpperCamelCase_    :     Dict=2	,		UpperCamelCase_    :     List[str]=12_050	,		UpperCamelCase_    :     str=256	,		UpperCamelCase_    :     int = 630	,		UpperCamelCase_    :     int = 60	,		**UpperCamelCase_    :     Union[str, Any]	,		):


         """simple docstring"""



         super().__init__(
             temperature=UpperCamelCase_	,		do_sample=UpperCamelCase_	,		renormalize_logits=UpperCamelCase_	,		output_scores=UpperCamelCase_	,		return_dict_in_generate=UpperCamelCase_	,		output_hidden_states=UpperCamelCase_	,		output_attentions=UpperCamelCase_	,		**UpperCamelCase_	,		)

         __A             =		coarse_semantic_pad_token
         __A             =		coarse_rate_hz
         __A             =		n_coarse_codebooks
         __A             =		coarse_infer_token
         __A             =		max_coarse_input_length
         __A             =		max_coarse_history
         __A             =		sliding_window_len


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"fine_acoustics"




     def __init__(			self    :     Any	,		UpperCamelCase_    :     Dict=0.5	,		UpperCamelCase_    :     List[Any]=512	,		UpperCamelCase_    :     int=1_024	,		UpperCamelCase_    :     Union[str, Any]=8	,		**UpperCamelCase_    :     int	,		):


         """simple docstring"""



         super().__init__(temperature=UpperCamelCase_			)

         __A             =		max_fine_history_length
         __A             =		max_fine_input_length
         __A             =		n_fine_codebooks


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"bark"
     SCREAMING_SNAKE_CASE				  =			True




     def __init__(			self    :     Dict	,		UpperCamelCase_    :     Dict = None	,		UpperCamelCase_    :     Dict = None	,		UpperCamelCase_    :     Dict = None	,		UpperCamelCase_    :     List[str]=24_000	,		UpperCamelCase_    :     Union[str, Any]=1_024	,		**UpperCamelCase_    :     List[str]	,		):


         """simple docstring"""



         if semantic_config is None:
             __A             =		{}
             logger.info("""semantic_config is None. initializing the semantic model with default values."""			)

         if coarse_acoustics_config is None:
             __A             =		{}
             logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""			)

         if fine_acoustics_config is None:
             __A             =		{}
             logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""			)

         __A             =		BarkSemanticGenerationConfig(**UpperCamelCase_			)
         __A             =		BarkCoarseGenerationConfig(**UpperCamelCase_			)
         __A             =		BarkFineGenerationConfig(**UpperCamelCase_			)

         __A             =		sample_rate
         __A             =		codebook_size




     @classmethod
     def 						lowerCAmelCase_       (			cls    :     int	,		UpperCamelCase_    :     BarkSemanticGenerationConfig	,		UpperCamelCase_    :     BarkCoarseGenerationConfig	,		UpperCamelCase_    :     BarkFineGenerationConfig	,		**UpperCamelCase_    :     Tuple	,		):


         """simple docstring"""



         return cls(
             semantic_config=semantic_config.to_dict()	,		coarse_acoustics_config=coarse_acoustics_config.to_dict()	,		fine_acoustics_config=fine_acoustics_config.to_dict()	,		**UpperCamelCase_	,		)





     def 						lowerCAmelCase_       (			self    :     List[str]			):


         """simple docstring"""



         __A             =		copy.deepcopy(self.__dict__			)

         __A             =		self.semantic_config.to_dict()
         __A             =		self.coarse_acoustics_config.to_dict()
         __A             =		self.fine_acoustics_config.to_dict()

         __A             =		self.__class__.model_type
         return output




